DoD STTR 2021.B

RT&L FOCUS AREA(S): Autonomy;General Warfighting Requirements (GWR);Networked C3 TECHNOLOGY AREA(S): Air Platforms OBJECTIVE: Develop a tunable differential interferometer for wideband phase-to-amplitude conversion to enable wide-dynamic-range radio frequency (RF) photonic links. DESCRIPTION: Many defense applications require the remoting of antennas at a significant distance from the receiver. At high frequencies, coaxial cables losses are consequential for many applications and require the use of distributed low-noise amplifiers to prevent impacts to receiver performance. In certain applications, the antenna aperture is highly size, weight, and power (SWaP)-constrained, and the implementation of any electronics at the antenna aperture is problematic. Recent advances in RF photonic components show promise in realizing high-frequency antenna remoting with low-noise figure and high-dynamic range. However, most broadband link architectures utilize amplitude modulators at the encoding point that require active bias compensation to ensure linear operation, which can be problematic in SWaP-constrained environments. Many attempts to develop a bias-free modulator have met with limited success [Refs 1, 2], particularly in the harsh environments dictated by most military applications. An alternative amplitude modulation link architecture utilizes phase-to-amplitude conversion devices, such as a differential Mach-Zehnder interferometer (DMZI) to convert a phase-modulated link signal to an amplitude-modulated link signal directly prior to photo detection, thereby removing the need for any bias electronics at the RF encoding point [Refs 3, 4]. Unfortunately, this conversion process results in links limited in bandwidth on the order of one octave due to the details of the conversion process, even though the phase modulators can encode much wider bands. This STTR topic seeks the development of tunable phase-to-amplitude conversion elements, which can take advantage of wideband, bias-free modulation at the remote RF encoding point. The goals of this effort are to develop a fiber-pigtailed phase-to-amplitude conversion device with a tunable operating frequency range that is compatible with both single and balanced photodiodes. The device must have sufficiently high-optical power handling (> 300 mW) and low loss (< 3 dB excess optical loss) to ensure the creation of low-noise figure, high-dynamic range RF-over-fiber links. The device should operate over a -40°C to +85°C operational temperature range, and be tunable to cover phase-to-amplitude conversion from 1 GHz on the low end to 45 GHz on the high end, with an instantaneous operational bandwidth of at least one octave [Ref 6]. The device should have dimensions no greater than 1 cm height, 10 cm long, and 3 cm wide. Individual devices should be designed to operate in 1 µm wavelength and 1550 nm wavelength RF over fiber links. Tuning speeds over this range on the order of < 10 ms are desired. It is expected that bias control of the device will be necessary to ensure linear operation, but this bias control is performed at the receiver where SWaP constraints are less burdensome. The proposed techniques must provide for closed-loop bias control. Dual-output devices that would be compatible with differential balanced photodiodes are also desirable. Highly accelerated life testing will provide initial device reliability performance [Refs 5, 6]. PHASE I: Develop and analyze a new design. Demonstrate key performance parameters of the proposed phase-to-amplitude conversion approach and simulate component performance. Develop a fabrication process, packaging approach, and test plan. Demonstrate the feasibility that the wideband differential interferometer can achieve the desired RF performance specifications with a proof of principle bench top experiment or preferably in an initial prototype. The Phase I effort will include prototype plans to be developed under Phase II. PHASE II: Optimize the Phase I design and create a functioning tunable phase-to-amplitude conversion prototype device. Demonstrate prototype operation in an RF photonic link. Show compliance of the prototype with the objective power levels, optical losses, tuning range, tuning speed, and temperature performance reached. Demonstrate a packaged, fiber-pigtailed prototype for direct insertion into single-ended and balanced-photonic links. PHASE III DUAL USE APPLICATIONS: The proposed phase-to-amplitude conversion devices also function for digital-link applications and can be used as quadrature phase-shift keying (QPSK) demodulators for optical communications links. Such a tunable device would enable tunable bit-rate digital demodulators for reconfigurable communications links and would provide a direct dual-use application for telecommunications. REFERENCES: 1. Fu, Y., Zhang, X., Hraimel, B., Liu, T. and Shen, D. “Mach-Zehnder: a review of bias control techniques for Mach-Zehnder modulators in photonic analog links.” IEEE Microwave Magazine, 14(7), 2013, pp. 102-107. https://doi.org/10.1109/MMM.2013.2280332 2. Salvestrini, J. P., Guilbert, L., Fontana, M., Abarkan, M. and Gille, S. “Analysis and control of the DC drift in LiNbO3Based Mach–Zehnder modulators.” Journal of Lightwave Technology, 29(10), May15, 2011, pp1522-1534. https://doi.org/10.1109/JLT.2011.2136322 3. Urick, V. J., Bucholtz, F., Devgan, P. S., McKinney, J. D. and Williams, K. J. “Phase modulation with interferometric detection as an alternative to intensity modulation with direct detection for analog-photonic links.” IEEE transactions on microwave theory and techniques, 55(9), October 2007, pp. 1978-1985. https://doi.org/10.1109/TMTT.2007.904087 4. Urick, V. J., Williams, K. J. and McKinney, J. D. “Fundamentals of microwave photonics.” John Wiley & Sons, 2015. https://doi.org/10.1002/9781119029816 5. AS-3 Fiber Optics and Applied Photonics Committee. “ARP6318 Verification of Discrete and Packaged Photonic Device Technology Readiness.” SAE International, August 20, 2018. https://doi.org/10.4271/ARP6318 6. “MIL-STD-810H, Department of Defense test method standard: Environmental engineering considerations and laboratory tests.” Department of Defense, US Army Test and Evaluation Command, January 31, 2019. http://everyspec.com/MIL-STD/MIL-STD-0800-0899/MIL-STD-810H_55998/

RT&L FOCUS AREA(S): General Warfighting Requirements (GWR) TECHNOLOGY AREA(S): Electronics OBJECTIVE: Improve transformer rectifier (T/R) maintainability via modular, portable design and/or introduction of technologies to significantly decrease footprint, volume, and weight. DESCRIPTION: An existing transformer/rectifier (T/R) is approximately 450 ft³ (12.75 m³) in volume and weighs nearly 40,000 lbs (18,144 kg). The transformer accounts for approximately 25% of the volume and 45% of the weight of the T/R. If the transformer fails, the entire T/R must be removed, which is a complex, expensive, and time-consuming process with a lengthy mean time to repair (MTTR). The Navy requires a transformer/rectifier that receives 13.8 kVAC RMS, three-phase, 60 Hz power, and outputs ±850 VDC nominal. The T/R must be capable of providing output power in the single-digit megawatt (MW) range continuously for tens of minutes. It must also output less than 0.5 MW for greater than one hour. It receives single-digit MW input power. The T/R should be hatchable, that is, T/R components or line replaceable units (LRUs) must be smaller than 26” x 66” x 33” (66 x 167 x 83 cm) in order to fit through hatches. Therefore, solutions should focus on decreasing T/R size and weight and improving supportability by making components removable/replaceable/repairable within the space constraints. A hatchable T/R will improve maintainability and decrease MTTR. LRUs, or other removable subassemblies or parts, should be of reasonable weight so that they can be lifted and carried over moderate distances through passageways, doors, and hatches. For reference, existing LRUs are 31.5” H x 9.5” W x 22” D (80 cm H x 24 cm W x 56 cm D) and weigh approximately 150 lbs (68 kg). Technologies that minimize LRU weight are encouraged and preferred as heavier loads increase injury risk and require additional personnel. MIL-STD-1472G, TABLE XXXIX [Ref 5] and similar tables may be used as a guide for one-person, two-person, and more than two-person lifting/carrying limits. Other military standards should be referenced for shock (MIL-DTL-901E [Grade A]) [Ref 2], vibration (MIL-STD-167-1A [Type 1]) [Ref 3], electromagnetic interference (MIL-STD-461G) [Ref 4], and environmental factors (MIL-STD-810H) [Ref 1] since the system must be rugged to be viable. The ability to regulate T/R temperature (i.e., thermal management) should also be considered. The T/R should remove self-generated heat to maintain acceptable component temperatures. The maximum thermal load from the transformer should be 77.5 kW at 212 °F (100 °C), and the maximum thermal load from the rectifier should be 2.0 kW. At the ambient temperature of 77 °F (25 °C), the operating temperature of control panels and controls should not exceed 120 °F (49 °C). Surface hot spots on accessible equipment exteriors should not exceed 140 °F (60 °C). The temperature of all other exposed surfaces should not be greater than 158 °F (70 °C). Designs that achieve both transformation and rectification in a more reliable, maintainable (modular/portable/hatchable), and compact package are ideal as they will increase operational availability (Ao). However, solutions cannot sacrifice performance as nominal output voltages/currents must meet certain tolerances as defined by requirements in an existing specification. For example, transformer output (rectifier input) shall have a nominal output voltage of hundreds of volts RMS, +/-2%. Further information on this and other requirements will be identified to the Phase I performers. Advances in silicon carbide (SiC) and high-frequency transformer technology, or other related innovations associated with miniaturization of power electronics, may be leveraged to achieve the goals as outlined. PHASE I: Develop a concept for a compact and maintainable transformer/rectifier, which may consist of modular, portable, electronic building blocks, also known as LRUs. Demonstrate feasibility using modeling and power simulation tools, or other applicable design methodologies. Subscale designs are allowable at this preliminary design stage assuming the concepts are scalable. Supporting documentation that shows how a subscale system might be scaled-up to meet full power requirements will help determine if the solution will be effective, suitable, and sustainable for this application. For example, a subscale T/R that meets input/output voltage requirements but not full-scale power requirements may still be practical if it can be shown that multiple subscale T/Rs can be connected together to achieve full-scale power. The same can be said of modules that do not meet full voltage/current requirements but can be connected in series/parallel. Evaluate thermal/cooling requirements to prepare for construction of a physical prototype. The Phase I effort will include prototype plans to be developed under Phase II. PHASE II: Design and build a prototype based on Phase I work. Demonstrate the technology and utilize Hardware-in-the-Loop (HIL) simulations, including Controller Hardware-in-the-Loop (CHIL) and Power Hardware-in-the-Loop (PHIL), to test and characterize performance. Validate and verify operation of the system against electrical, mechanical, and thermal requirements. If the prototype is subscale and intended for partial power, plans for how to achieve scalability and test at full rated power should be well documented. Assuming iterative design is utilized and a larger and more capable system is developed gradually throughout this phase, consideration must be given to packaging, thermal/cooling requirements, communications, controls, and user interfaces as the effort progresses. PHASE III DUAL USE APPLICATIONS: Design and construct a full-scale T/R based on work completed during earlier phases. Perform final testing at full-scale power via T/R test procedures and fault scenarios as defined by existing specifications and test plans. Validate and verify T/R performance. Transition after successful testing. Transformers increase or decrease AC (alternating current) voltage, and rectifiers convert AC to DC (direct current). Transformers and rectifiers are increasingly vital as the energy sector moves towards renewables, such as wind and solar, and the transportation industry moves towards electric vehicles (EVs). This is because T/Rs are useful for energy transmission, storage, and charging applications. For example, to transmit energy over long distances, transformers are used to increase voltage since high-voltage energy transmission decreases energy losses over long cable runs. In addition, more so than fossil fuels, renewables utilize energy storage so that power remains available even if the sun is not shining or the wind is not blowing. Many energy storage technologies, such as batteries, accept DC voltage; however, energy is often generated as AC, so it needs to be converted by a rectifier prior to storage. Conversion from AC to DC is also required to charge everything from cellphones to electric vehicle batteries. Therefore, for those who own an electric vehicle (EV), the AC power available in their houses must be converted to DC to charge their EVs. This functionality is often incorporated into power supplies themselves. For example, the “brick” on a phone or laptop charger converts AC power from a wall outlet to DC to charge/power the device. REFERENCES: 1. Department of Defense. “MIL-STD-810H. Department of Defense test method standard: environmental engineering considerations and laboratory tests.” 2019, January 31. http://everyspec.com/MIL-STD/MIL-STD-0800-0899/MIL-STD-810H_55998/ 2. Department of Defense. “MIL-DTL-901E. Detail specification: shock tests, H. I. (high-impact) shipboard machinery, equipment, and systems, requirements for.” 2017, June 20. http://everyspec.com/MIL-SPECS/MIL-SPECS-MIL-DTL/MIL-DTL-901E_55988/ 3. Department of Defense. “MIL-STD-167/1A. Department of Defense test method standard: mechanical vibrations of shipboard equipment (Type I-environmental and Type II-internally excited).” 2005, November 02. http://everyspec.com/MIL-STD/MIL-STD-0100-0299/MIL-STD-167-1A_22418/ 4. Department of Defense. “MIL-STD-461G. Department of Defense interface standard: requirements for the control of electromagnetic interference characteristics of subsystems and equipment.” 2015, December 11. http://everyspec.com/MIL-STD/MIL-STD-0300-0499/MIL-STD-461G_53571/ 5. Department of Defense. “MIL-STD-1472G. Department of Defense Design Criteria Standard: Human Engineering.” 2012, January 11. http://everyspec.com/MIL-STD/MIL-STD-1400-1499/MIL-STD-1472G_39997/

RT&L FOCUS AREA(S): Artificial Intelligence (AI)/Machine Learning (ML);Autonomy TECHNOLOGY AREA(S): Air Platforms;Battlespace Environments;Information Systems OBJECTIVE: Develop a capability to autonomously generate mission plans for onboard Unmanned Aerial Systems (UAS) in support of Intelligence, Surveillance, and Reconnaissance (ISR) missions by applying artificial intelligence (AI) and machine learning (ML) techniques. DESCRIPTION: With today's advances in software and hardware, autonomous operation is a capability, even if still somewhat disruptive, that is fully realizable as highlighted in references 1–6. In fact, autonomous operation is becoming a critical capability in order to stay ahead of our adversaries. But there are other reasons for autonomous systems [Ref 2], such as "when the world can’t be sufficiently specified a priori" and "when adaptation must occur at machine speed". It also makes a good case for AI, which enables significant autonomy and includes learning, reasoning, introspection, decision making, and much more. Exploiting unmanned systems autonomous mission planning is the next stage in enhancing the capabilities of these systems in the operational environments. This project’s success relies on utilizing sophisticated software solutions including machine intelligence/learning and modern computer hardware or graphics processing units (CPUs/GPUs – a scaled version of a workload-optimized massively parallelized computer). It should be evident that the size of unmanned aerial vehicles (UAVs) (Groups 1-5) and the types of missions will impact the overall mission planning requirements and complexity. The goal is to be entirely autonomous; however, in particular with Group 4-5 systems, embedding trust/risk capabilities and detailed contingency plans in autonomous operation—if unacceptable behavior is detected—is as critical as meeting mission success. Even within autonomous operations, there will still be means to alert the Common Control System operator via the envisioned tool that monitors trust embedded on the platform. With these risk mitigations capabilities, the goal of this project will focus on ISR collection – a more simplistic mission when compared to a strike execution mission, which would in the future add considerable levels of mission complexities. All UAVs will have the necessary sensors and flight control systems to embed the software to generate autonomous missions from takeoff (flight plan and mission plan) to landing, while completing missions including collection and dissemination of ISR data, i.e., when connectivity is available. It is anticipated that activity-based intelligence and/or other relevant information will start the components-based planning process to determine a suitable platform; route planning, types of sensors in support of ISR collection and sensor collection requirements to generate an entire flight plan with associated requirements; and when to disseminate data. Note that many route planning and resource management algorithms exist, thus any solution should include the ability to adaptively change a particular part of the overall planning process. It should also include consideration for automated contingency plans and dynamic replanning capabilities due to various unexpected factors, such as weather, change in mission requirements, etc. These fully autonomous, mission planning service capabilities must be able to be integrated into the Next-Gen Navy Mission Planning System (NGNMPS) and be shared with the Common Control Systems operator with any available communication system with the ability to be modified if necessary, and more importantly, to actually realize the autonomous behavior be embedded on board the platform. Due to the autonomous plan to be initially shared NGNMPS and CCS operator, it will be necessary to define how the plan is presented to the operators. Finally, in order to meet mission requirements, the solution needs to specify CPU/GPU requirements to achieve as close to real-time performance as possible; and to paraphrase the Heilmeier Catechism exams for success [Ref 11], it will be essential to understand “how to eventually test, verify and evaluate the overall accuracy and performance of the autonomous mission planning process” that need to be addressed as part of this development effort. PHASE I: Generate a concept of autonomous mission planning from launch to execution of mission specific requirements (ISR as specified in a tasking order and other data such as activity based intelligence data) to data dissemination, and finally, to return to base. This mission plan may also be an airborne modification (dynamic replanning) to the current mission, applying artificial intelligence techniques. Mission plans will take into consideration threat and friendly disposition, weather, terrain, and any onboard sensor (collection) requirements and limitations. In addition the concept needs to outline required hardware to achieve real-time or near real-time processing capabilities. The Phase I effort will include prototype plans to be developed under Phase II. The overall solution should outline data sources and information that will be required to successfully generate mission plans. It is also required to take into account STANAG processes and procedures to minimize proprietary solutions. PHASE II: Develop a prototype software solution that can be tested in a simulated mission environment. In Phase II, the program office will provide additional details about the platforms and sensors characteristics and other vital data critical in support of a realistic prototype development. PHASE III DUAL USE APPLICATIONS: Finalize the prototype version. Perform final testing and verification in a simulated environment and potentially in a real environment using a surrogate vehicle. Transition to naval platform. Companies such as Amazon, and similar delivery companies that have already started drone-based package delivery, would benefit from this development. FEDEX and UPS would benefit in terms of using large UAVs for package deliveries from large collection centers to smaller distribution centers. REFERENCES: 1. “The role of autonomy in DoD systems.” Defense Science Board, Department of Defense, 2012, July. https://fas.org/irp/agency/dod/dsb/autonomy.pdf 2. Stack, J. “Autonomy & autonomous unmanned systems: Overview, investment approach, and opportunities.” Office of Naval Research Science & Technology, 2019 September 26. https://www.nationalacademies.org/event/09-25-2019/docs/D6731F8D0ABF361CB04E477B57856ED99859C049B008 3. Dyndal, G.L., Berntsen T.A. and Redse-Johansen, S. “Autonomous military drones: No longer science fiction.” NATO Review, 2017 July 28. https://www.nato.int/docu/review/articles/2017/07/28/autonomous-military-drones-nolonger- science-fiction/index.html 4. Cebul, D. “The future of autonomous weapons systems: A domain-specific analysis.” Center for Strategic and International Studies, New Perspectives in Foreign Policy, 14, 2017 December 20. https://www.csis.org/npfp/future-autonomous-weapons-systems-domain-specific-analysis/ 5. Wilson, J.R. “Artificial intelligence (AI) in unmanned vehicles.” Military & Aerospace Electronics, 2019 April 1. https://www.militaryaerospace.com/home/article/16709577/artificial-intelligence-ai-in-unmanned-vehicles 6. Kazior, T. and Lee, D. “Future autonomous systems overview.” Autonomy Working Group, 2016 August 31. https://cra.org/ccc/wp-content/uploads/sites/2/2016/08/Autonomous-Systems-WG-Overview-final.pdf 7. Atyabi, A., MahmoudZadeh, S. and Nefti-Meziani, S. “Current advancements on autonomous mission planning and management systems: An AUV and UAV perspective.” Annual Reviews in Control, 46, 2018, pp.196-215. https://doi.org/10.1016/j.arcontrol.2018.07.002 8. Stenger, A., Fernando, B. and Heni, M. “Autonomous mission planning for UAVs - A cognitive approach.” Paper presentation, Deutscher Luft – und Raumfahrtkongress 2012, Berlin, Germany, 2012 September 10-12. https://www.dglr.de/publikationen/2013/281398.pdf 9. Llinas, J. and Scrofani, J. “Foundational technologies for activity-based intelligence—A review of the literature.” Naval Postgraduate School, 2014 February. https://calhoun.nps.edu/bitstream/handle/10945/40913/NPS-EC-14-001.pdf?sequence=1&isAllowed=y 10. “Robotics and autonomous systems strategy.” U.S. Department of the Army, 2017 March. https://www.tradoc.army.mil/Portals/14/Documents/RAS_Strategy.pdf 11. "The Heilmeier Cathecism." Defense Advanced Research Project Agency. https://www.darpa.mil/work-with-us/heilmeier-catechism

RT&L FOCUS AREA(S): General Warfighting Requirements (GWR);Hypersonics;Space TECHNOLOGY AREA(S): Air Platforms;Materials / Processes;Weapons OBJECTIVE: Develop an Integrated Computational Materials Engineering (ICME) modeling tool to predict the effect of gas flow on metal additive manufacturing processes for improvement in the quality of the parts. DESCRIPTION: Additive manufacturing (AM) processes, such as powder bed fusion (PBF) and directed energy deposition (DED), have the potential to revolutionize the manufacturing and repairing of complex metal components in aerospace, medical, and automotive industries. Current processes are not yet fully matured. There is a great need for the processes to produce parts that are free from defects, such as pores, lack of fusion, metal oxidation, and fusion of splattered condensate. To prevent the parts from oxidizing, AM processes blow inert gases - such as argon and nitrogen - to shield the fusion zone from oxygen. In PBF processes, the shielding gas flow is directed over the build layer to remove metal condensate and spatter from the fusion zone and then is pulled out of the chamber through filters to remove the splattered particle. Improper shielding and removal of spatter particles lead to defects in a PBF process. For example, it has been shown that: a) the condensed metal vapor particles could attenuate the laser beam up to 10%, b) spatter falling back on the powder bed could locally increase the layer thickness, and c) spatter falling onto the consolidated surface could fuse resulting in poor surface finish [Ref 1]. The direction of the flow relative to the laser scanning direction plays a significant role in the quality of the product [Ref 2]. Similarly, the DED processes are also strongly dependent on the flow rates of carrying and shielding gases. Higher flow rates could result in higher cooling rates and reduced heat-affected zone, but could also cause discontinuities and gaps in the deposition. Microhardness could vary with the changes in flow rates [Ref 3]. Current literature surveys show limitations in the modeling efforts. Adam Philo et al. (2017) have developed a computational model of gas-flow effects in the inlet design for the Renishaw AM250 to predict spatter particulate accumulation [Refs 4]. Florian Wirth et al. (2017) have shown the interaction of powder jet and laser beam in a powder-blown machine and cases for laser beam attenuation [Ref 5]. Praveen BidareI et al. (2017) use Schlieren imaging and multiphysics modeling to investigate the inert atmosphere and laser plume in PBF [Ref 6]. References 7 through 14 provide additional experimental and computational efforts. However, a comprehensive modeling tool for gas flow interacting with all major AM process parameters is not available for designing and developing better AM processes. An ICME framework is needed to represent the process-structure-property-performance relationship in metallic AM. The tool sought in this STTR topic will be part of the framework. It should integrate critical fundamental physics, such as mass, fluid and heat transport, phase transition, surface tension, Marangoni stress, recoil pressure, and melt pool fluid dynamics, into one comprehensive framework. With manufacturing parameters and material properties as the inputs, the framework should quantify the effect of gas flow on melt pool dimension, surface morphology, temperature profile, solidification rate, powder spattering, and pore formation/propagation. The framework should provide mitigation strategies for the gas-induced powder spattering and pore formation, which degrade the property of the fabricated metallic part. Overall, the model should enable optimizing the gas flow including improvement in nozzle designs; gas circulation to match the design of the AM machine offering optimum shielding of the fusion area and the melt pool; and the efficient removal of the gas and debris from the chamber. The model should provide ways to set print parameters for optimum part performance for the raw material used and the scan patterns for the part. PHASE I: Demonstrate the feasibility of a multiphysics model gas flow interaction with metal fusion in the PBF or DED additive manufacturing process. Show that the model works efficiently within the ICME framework to enable proper design and control of gas flow for producing defect-free AM products. Carry out experiments for the chosen AM process to validate the simulated results. Evaluate the model based on the AM products, such as surface finish, defects (size, density, and distribution), and/or microhardness. Demonstrate the potential for this prototype to address factors additional to the subset chosen above for a fully developed modeling system in the ICME framework in Phase II. PHASE II: Based on the prototype modeling tool developed in Phase I, fully develop and validate the predictive modeling tool to fine-tune the gas flow and the associated process parameters to improve AM part quality, such as fewer defects, better surface finish, and desirable microhardness. Demonstrate its capability of additive manufacturing of aircraft components with complex geometry and tailored performance. PHASE III DUAL USE APPLICATIONS: Mature the modeling tool further by extending the capability for common airframe metal alloys, such as aluminum, steel, and titanium. Demonstrate the capability to optimize the AM process for multiple metals. Validate the tool in final testing of the capability by printing parts of more than one metal alloy and carrying out component tests demonstrating strength and durability. AM in the commercial sector is progressing with individual companies developing limited capabilities using ICME tools. The commercial sector broadly treats material qualification and part certification for AM as separate processes, one followed by the other. ICME tools integrate them to have a seamless process. Hence, this tool will open the possibilities for the commercial sector to take advantage of developing quality products for their customers. REFERENCES: 1. Schniedenharn, M., Wiedemann, F. and Schleifenbaum, J. H. “Visualization of the shielding gas flow in SLM machines by space-resolved thermal anemometry.” Rapid Prototyping Journal, 24(8), November 12, 2018, pp. 1296-1304. https://doi.org/10.1108/RPJ-07-2017-0149 2. Anwar, A. B. and Pham, Q. C. “Effect of inert gas flow velocity and unidirectional scanning on the formation and accumulation of spattered powder during selective laser melting” [Paper presentation]. Proceedings of the 2nd International Conference on Progress in Additive Manufacturing (Pro-AM 2016), Singapore, May 16-19, 2016. https://hdl.handle.net/10220/41780 3. Koruba, P., Wall, K. and Reiner, J. “Influence of processing gases in laser cladding based on simulation analysis and experimental tests.” 10th CIRP Conference on Photonic Technologies [LANE 2018], 74, pp. 719-723. https://doi.org/10.1016/j.procir.2018.08.025 4. Philo, A. M., Sutcliffe, C. J., Sillars, S. A., Sienz, J., Brown, S. G. R. and Lavery, N. P. “A study into the effects of gas flow inlet design of the Renishaw AM250 laser powder bed fusion machine using computational modelling.” [Paper presentation]. Solid Freeform Fabrication 2017: Proceedings of the 28th Annual International, Austin, TX, United States. https://pdfs.semanticscholar.org/c3d8/2fe33631879d919bc37729f0895d5004dd9c.pdf?ga=2.227959966.248001110.1594670984-1201775627.1589487702 5. Wirth, F., Freihse, S., Eisenbarth, D. and Wegener, K. “Interaction of powder jet and laser beam in blown powder laser deposition processes: Measurement and simulation methods.” [Paper presentation]. Proceedings of Lasers in Manufacturing Conference 2017, Munich, Germany, June 26-29, 2017. http://hdl.handle.net/20.500.11850/211852 6. Bidare, P., Bitharas, I., Ward, R. M., Attallah, M. M. and Moore, A. J. “Fluid and particle dynamics in laser powder bed fusion.” Acta Materialia, 142, January 1, 2018, pp. 107-120. https://doi.org/10.1016/j.actamat.2017.09.051 7. Cunningham, R., Zhao, C., Parab, N., Kantzos, C., Pauza, J., Fezzaa, K., Sun, T. and Rollett, A. D. “Keyhole threshold and morphology in laser melting revealed by ultrahigh-speed x-ray imaging.” Science, 363(6429), February 22, 2019, pp. 849- 852. https://doi.org/10.1126/science.aav4687 8. Guo, Q., Zhao, C., Qu, M., Xiong, L., Hojjatzadeh, S. M. H., Escano, L. I., Parab, N. D., Fezzaa, K., Sun, T. and Chen, L. “In-situ full-field mapping of melt flow dynamics in laser metal additive manufacturing.” Additive Manufacturing, 31, 2020, pp. 100939. https://doi.org/10.1016/j.addma.2019.100939 9. Yan, J., Lin, S., Bazilevs, Y. and Wagner, G. J. “Isogeometric analysis of multi-phase flows with surface tension and with application to dynamics of rising bubbles.” Computers & Fluids, 179, 2019, pp. 777-789. https://doi.org/10.1016/j.compfluid.2018.04.017 10. Yan, J., Yan, W., Lin, S. and Wagner, G. J. “A fully coupled finite element formulation for liquid–solid–gas thermo-fluid flow with melting and solidification.” Computer Methods in Applied Mechanics and Engineering, 336, July 1, 2018, pp. 444- 470. https://doi.org/10.1016/j.cma.2018.03.017 11. Lin, S., Yan, J., Kats, D. and Wagner, G. J. “A volume-conserving balanced-force level set method on unstructured meshes using a control volume finite element formulation.” Journal of Computational Physics, 380, March 1, 2019, pp. 119-142. https://doi.org/10.1016/j.jcp.2018.11.032 12. Zhu, Q., Xu, F., Xu, S., Hsu, M.-C. and Yan, J. “An immersogeometric formulation for free-surface flows with application to marine engineering problems.” Computer Methods in Applied Mechanics and Engineering, 361, April 1, 2020, p. 112748. Https://doi.org/10.1016/j.cma.2019.112748 13. Lin, S., Gan, Z., Yan J. and Wagner, G. J. “A conservative level set method on unstructured meshes for modeling multiphase thermo-fluid flow in additive manufacturing processes.” Computer Methods in Applied Mechanics and Engineering (in review). https://www.sciencedirect.com/science/article/abs/pii/S0045782520305338 14. Yan, W., Lin, S., Kafka, O. L., Lian, Y., Yu, C., Liu, Z., Yan, J., Wolff, S., Wu, H., Ndip-Agbor, E., Mozaffar, M., Ehmann, K., Cao, J., Wagner, G. J. and Liu W. K. “Data-driven multi-scale multi-physics models to derive process–structure–property relationships for additive manufacturing.” Computational Mechanics, 61(5), January 12, 2018, pp. 521- 541. https://doi.org/10.1007/s00466-018-1539-z

RT&L FOCUS AREA(S): General Warfighting Requirements (GWR);Microelectronics;Quantum Science TECHNOLOGY AREA(S): Electronics OBJECTIVE: Develop and demonstrate a novel high-energy (> 600 Whr/kg) rechargeable lithium-ion battery technology to provide high-quality enduring power for Navy hand-held portable electronics and small unmanned aerial system (UAS) applications. DESCRIPTION: Rechargeable Lithium-ion (Li-ion) batteries [Ref 1] are widely used for a wide variety of commercial and naval electronics and electrical applications. The weight of the naval power battery system can be a significant portion of the overall weight of the portable electrical device on board a ground or aerial vehicle. Furthermore, the energy capacity of existing Li-ion batteries is not adequate to support prolonged operating times of current and future naval platforms, such as unmanned aerial systems (UASs) and portable communication and surveillance systems, for extended mission endurance. Moreover, the current batteries necessitate frequent recharging and the times for full recharging are in the range of hours. In order to increase the energy capacity, reduce the weight, and shorten the recharging time of next-generation rechargeable batteries for future naval missions, high-performance rechargeable batteries with higher specific energy and much shorter recharging cycle times are needed. Current state-of-the art Li-ion batteries use graphite as an anode. Research has shown that the use of carbon-based nanomaterials, such as graphene, carbon nanotubes, carbon nanofibers, etc., as potential anode materials for Li-ion batteries enhancements to replace graphite, shows great promise in providing high-galvanometric capacity while also maintaining reasonable cycling stability [Refs 2, 3]. The objective of this STTR topic is to develop and demonstrate a novel rechargeable Li-ion battery enhanced by using carbon-based nanostructures with a specific energy > 600 Whr/kg at 0.5C discharge rate, and specific capacity of > 600 Ahr/kg. The battery is also expected to exhibit an excellent cycle stability and maintain 85% capacity after 1000 cycles and operate over a wide temperature range of -30°C to +55°C. The high-energy cell should have the ability to operate up to a 3C continuous discharge rate at the stated operational conditions, as well as to be stored over a wide temperature range (-40°C to +70°C). Proposed innovative approaches may include improvements to cell components, novel materials or processes, or other innovative ideas. PHASE I: Develop, design, and demonstrate the feasibility of an innovative Li-ion battery using the most promising carbon-based nanomaterials as the anode material. Perform analysis and initial testing to determine the ability of the proposed battery with the chosen anode, cathode, and electrolyte material combination in terms of the performance metrics, including specific energy, specific capacity, reliable charge/discharge capabilities, and cycle life as stated in the Description. Project the overall performance improvements of the proposed battery configuration to be fabricated in Phase II compared to a common lithium ion battery. The Phase I effort will include prototype plans to be developed under Phase II. PHASE II: Fabricate and demonstrate a complete cell, based on the down-selected design in Phase I. Demonstrate and validate the performance of the novel Li-ion battery to meet stated design metrics listed in the Description. Perform laboratory testing to confirm performance. Assess the risks associated with the storage and operation of the battery and propose viable risk mitigation solutions. Deliver a prototype to NAVAIR for further field testing and evaluation. PHASE III DUAL USE APPLICATIONS: Fully develop and transition the Lithium ion Battery based on the final design from Phase II for naval applications in various UAV platforms. The commercial sectors such as electrical vehicles and other commercial electronic devices, would significantly benefit from this research and development in high-performance, lightweight batteries. REFERENCES: 1. Liu, S. F., Wang, X. L., Xie, D., Xia, X. H., Gu, C. D., Wu, J. B. and Tu, J. P. “Recent development in lithium metal anodes of liquid-state rechargeable batteries.” Journal of Alloys and Compounds, 730, January 5, 2018, pp. 135-149. https://doi.org/10.1016/j.jallcom.2017.09.204 2. Uddin, M. J., Alaboina, P. K. and Cho, S. J. “Nanostructured cathode materials synthesis for lithium-ion batteries.” Materials Today Energy, 5, September 2017, pp. 138-157. https://doi.org/10.1016/j.mtener.2017.06.008 3. Manthiram, A., Song, B. and Li, W. “A perspective on nickel-rich layered oxide cathodes for lithium-ion batteries.” Energy Storage Materials, 6, 2017, pp. 125-139. https://doi.org/10.1016/j.ensm.2016.10.007

RT&L FOCUS AREA(S): Artificial Intelligence (AI)/Machine Learning (ML);Autonomy;General Warfighting Requirements (GWR) TECHNOLOGY AREA(S): Air Platforms;Human Systems OBJECTIVE: Research and develop a technology that supports ingesting large and disparate data sets from naval aviation aircraft and uses data science to provide outputs that increase enterprise level knowledge of aviator performance, safety, and effectiveness through data-driven predictive analytics to influence training and operations. DESCRIPTION: The success of military operations significantly depends on the level of quality training, safety, and operational effectiveness demonstrated by its personnel. This is especially true for naval aviation operations. There are a large set of factors that affect the successful employment of naval aircraft during peacetime and wartime. These factors can change with time and with the situation and are articulated in vast and disparate data sets. These data sets, when captured, traditionally provide immediate evaluation and aircrew debrief. Generally, a vast amount of data that affects and describes crew performance is discarded or stored with no long-term data analytics processing conducted that could provide valuable trend and predictive insight. The ability to identify performance trends is a key factor today in the effectiveness of any enterprise. This is especially true in aviation and military operations. The capability to capture large sets of performance/attribute data, and analyze the data to establish baseline and standard performance levels, enables the identification of performance anomalies, trends, and predictive outcomes. This capability has become a standard in commercial aviation and has the same applicability to military operations. The implementation of this capability to the highly complex naval aviation operations would provide great benefit from the comprehensive analysis aircrew performance to gain greater insight into areas including aircraft flight path management, procedural compliance, stores deployment, situational awareness, threat/error management, distraction management, environmental effects, aircraft envelope management, and many other performance areas. However, solutions must address both the opportunities and the challenges associated with data analytic solutions [Ref 1]. The Navy requires a technology that supports ingesting large and disparate data sets from naval aviation aircraft, supporting required parsing, sorting, and fusion to manage relevant data. Development efforts should focus on providing data analytic functionality that results in outputs that increase enterprise-level knowledge of aviator performance, safety, and effectiveness. Further, the technology functionality should extend traditional data science solutions to include capabilities for data-driven predictive analytics to influence training and operations [Ref 2]. The research and development effort should provide focus on the visualization capabilities to increase end user understanding of data analysis processes and outputs, in addition to an underlying data analytic architecture. The technology developed must meet the system DoD accreditation and certification requirements to support processing approvals for use through Risk Management Framework [Refs 4, 5, and 7] and any use of artificial intelligence (AI) as part of defined solutions should understand ethical use recommendations [Ref 6]. The policy cited in Department of Defense Instruction (DoDI) 8510.01, Risk Management Framework (RMF) for DoD Information Technology (IT) [Ref 3] and compliance with appropriate DoDI 8500.01, Cybersecurity [Ref 8] are necessary to support future transition needs. Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by DoD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence Security Agency (DCSA). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this project as set forth by DCSA and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advanced phases of this contract. PHASE I: Develop, design, and demonstrate a strategy, taking into consideration the feasibility, suitability, and acceptability, to leverage all available aircraft and related crew performance data. Identify potential roadblocks likely to be encountered and formulate approaches to overcome them. Design an architecture and implementation plan illustrating the benefits of training analytics through training use cases to demonstrate benefits of predictive analytics. The Phase I effort will include prototype plans to be developed under Phase II, with consideration for options on system architecture (e.g., Navy Marine Corps Intranet (NMCI), standalone system). PHASE II: Develop a working prototype of the selected concept to include high-level requirements, design, initial testing, and demonstration. Demonstrate the prototype in a lab or live environment. Planning and consideration for information assurance compliance and certification for an authority to operate, including updates to support installation on Navy Marine Corps Intranet (NMCI) systems or other DoD hardware. Work in Phase II may become classified. Please see note in the Description section. PHASE III DUAL USE APPLICATIONS: Extend the baseline functionality to include advanced or more robust data analytic techniques, and/or integrate developed capability with existing database and analysis systems. Implement Risk Management Framework guidelines [Refs 3, 4, 5, 6, and 7] to support information assurance compliance and certification for an authority to operate, including updates to support installation on NMCI systems or other DoD hardware. Data analytics are relevant to a range of other domains such as athletics and medical communities. For medical communications, rapidly evolving situations with minimal established information is a critical and timely use case given novel infectious diseases; in addition to traditional data analytics for trends, understanding potential predictive analytics will inform decisions at various levels of leadership based on expected trends. Further, domains with quickly advancing technology due to the rapid pace of innovation and advances will benefit from similar technology solutions as a means to provide unique insights based on data analytics and predictive analyses. REFERENCES: 1. Fan, J., Han, Fang, and Liu, Han. “Challenges of big data analysis.” National Science Review, 1(2), 2014 February 5, pp. 293–314. https://doi.org/10.1093/nsr/nwt032 2. “Top 53 bigdata platforms and bigdata analytics software.” Predictive Analytics Today, 2020. https://www.predictiveanalyticstoday.com/bigdata-platforms-bigdata-analytics-software/ 3. Takai, T.M. “DoDI 8510.01 Risk management framework (RMF) for DoD Information Technology (IT).” Department of Defense, 2012 March 12. https://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/851001p.pdf?ver=2019-02-26-101520-300 4. Ellett, J.M. and Khalfan, S. “The transition begins: DoD risk management framework.” CHIPS, 2014 April-June. https://www.doncio.navy.mil/chips/ArticleDetails.aspx?ID=5015 5. “Information technology. Risk management framework (RMF).” AcqNotes: Defense Acquisitions Made Easy (n. d.). http://acqnotes.com/acqnote/careerfields/risk-management-framework-rmf-dod-information-technology 6. “AI principles: Recommendations on the ethical use of artificial intelligence by the Department of Defense.” Department of Defense, Defense Innovation Board (n. d.). https://media.defense.gov/2019/Oct/31/2002204458/-1/-1/0/DIB_AI_PRINCIPLES_PRIMARY_DOCUMENT.PDF 7. “Information Technology Laboratory. Risk Management Framework (RMF) Overview.” National Institute of Standards and Technology, 2020 October 13. https://csrc.nist.gov/projects/risk-management/rmf-overview 8. Takai, T.M. “DoDI 8500.01 Cybersecurity.” Department of Defense, 2014 March 14. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodi/850001_2014.pdf 9. Defense Counterintelligence and Security Agency. (n.d.). https://www.dcsa.mil/Mission-Centers/Critical-Technology-Protection/NISP-Authorization-Office-NAO-/RMF/ 10. “DoD 5220.22-M National Industrial Security Program Operating Manual (Incorporating Change 2, May 18, 2016).” Department of Defense. https://www.esd.whs.mil/portals/54/documents/dd/issuances/dodm/522022m.pdf

TECH FOCUS AREAS: Autonomy; Artificial Intelligence/Machine Learning TECHNOLOGY AREAS: Information Systems OBJECTIVE: The objective of this topic is to explore the development of a theoretical foundation or model for hierarchical heterogeneous planning and scheduling by which we can reason about autonomous/automated decision-making in multiple different domains while accounting for the hierarchical structure of each domain. This topic will reach companies and universities that can complete research of the foregoing concepts in Phase I schedules. This topic is specifically aimed at the earlier stage basic science and research. DESCRIPTION: The current modus operandi for generating courses of action in military operational scenarios is largely human-derived. An increasingly heterogeneous all-domain (e.g., air, land, sea, cyber, space, electronic warfare) battle space and the resulting warfare complexity presents human decision-makers with an overwhelming amount of data and potential plans. Add to this the inherently hierarchical nature of each domain (e.g. for the air domain, there are wings composed of groups, that are composed of squadrons, that are composed of units) and this gives rise to a unique type of planning and scheduling problem. Indeed, this multi-domain hierarchical planning and scheduling would benefit greatly from automated or autonomous approaches which can model the heterogeneity of the various domains, establish a hierarchical decision-making pipeline within each domain, and explore and optimize over many potential plans and schedules in a short span of time. However, we currently have no means by which to formally reason about such hierarchical heterogeneous planning and scheduling settings. The mathematical modeling of various operational problems lend credence to some theoretical foundation and mathematical model by which to accomplish this. Examples include the Maximum-on-Ground (MOG) parking problem of assigning a set of aircraft to various airfields so as to maximize the packing density of the airfields and how this can be formalized as a Bin Packing problem [1]. This bin packing formulation immediately lets us reason about the complexity of the MOG problem, exact solutions, approximate efficient solutions, heuristics, and interesting extensions to the problem. Similarly, we have seen the problem of air asset scheduling for Air Tasking Orders (ATOs) being modeled using integer programming [2]. Drawing inspiration from such approaches, we seek the development of a theoretical foundation or model for hierarchical heterogeneous planning and scheduling by which we can reason about autonomous/ automated decision-making in multiple different domains while accounting for the hierarchical structure of each domain. Success can be evaluated by comparing the proposed model and solution to the baseline of reasoning over each domain separately and by using naive planning approaches. The heterogeneity of the various domains may be formalized by some abstraction that accounts for domain-specific effects, such as range, mobility, impact, latency, etc. The hierarchical nature of the solution may encapsulate the granularity and delegation of desired effects for a given domain. For example, at the wing level, potential enemy targets may be identified; this information is passed down to the group level, where squadrons are assigned to the different targets; this, in turn, is used to determine the routes and schedules of aircraft at the unit level. The underlying environment within which the agent interacts can take many forms, including purely theoretical models such as Markov Decision Processes (MDPs), performer-developed environments and academic tools like OpenAI Gym and PySC2. The developed concepts need not be specific to military operations. PHASE I: Validate the product-market fit between the proposed solution and the proposed topic and define a clear and immediately actionable plan for running a trial with the proposed solution and the proposed AF customer. This feasibility study should directly address: 1. Clearly identify who the prime (and additional) potential AF end user(s) is and articulate how they would use your solution(s) (i.e., the one who is most likely to be an early adopter, first user, and initial transition partner). 2. Deeply explore the problem or benefit area(s), which are to be addressed by the solution(s) - specifically focusing on how this solution will impact the end user of the solution. 3. Define clear objectives and measurable key results for a potential trial of the proposed solution with the identified Air Force end user(s). 4. Clearly identify any additional specific stakeholders beyond the end user(s) who will be critical to the success of any potential trial. This includes, but is not limited to, program offices, contracting offices, finance offices, information security offices and environmental protection offices. 6. Describe if and how the demonstration can be used by other DoD or governmental customers. 7. Describe technology related development that is required to successfully field the solution. The funds obligated on the resulting Phase I awards are to be used for the sole purpose of conducting a thorough feasibility study using mathematical models, scientific experiments, laboratory studies, commercial research and interviews. PHASE II: Develop, install, integrate and demonstrate a prototype system determined to be the most feasible solution during the Phase I feasibility study. This demonstration should focus specifically on: 1. Evaluating the proposed solution against the objectives and measurable key results as defined in the Phase I feasibility study. 2. Describing in detail how the solution can be scaled to be adopted widely (i.e. how can it be modified for scale). 3. A clear transition path for the proposed solution that takes into account input from all affected stakeholders including but not limited to: end users, engineering, sustainment, contracting, finance, legal, and cyber security. 4. Specific details about how the solution can integrate with other current and potential future solutions. 5. How the solution can be sustainable (i.e. supportability). 6. Clearly identify other specific DoD or governmental customers who want to use the solution. PHASE III DUAL USE APPLICATIONS: The Primary goal of STTR is Phase III. The contractor will pursue commercialization of the various technologies developed in Phase II for transitioning expanded mission capability to a broad range of potential government and civilian users and alternate mission applications. Direct access with end users and government customers will be provided with opportunities to receive Phase III awards for providing the government additional research & development, or direct procurement of products and services developed in coordination with the program. PROPOSAL PREPARATION AND EVALUATION: Please follow the Air Force-specific Phase I instructions under the Department of Defense 21.2 SBIR Broad Agency Announcement and Chart 1 (above) when preparing proposals. Proposals under this topic will have a maximum value of $156,500 SBIR funding and a maximum performance period of five months, including four months technical performance and one month for reporting. Proposals will be evaluated using a two-step process. After proposal receipt, an initial evaluation will be conducted IAW the criteria found in the AF-specific Phase I instructions as previously referenced. Based on the results of that evaluation, Selectable companies will be provided an opportunity to participate in the Air Force Trusted AI Pitch Day, tentatively scheduled for 26-30 July 2021 (possibly virtual). Companies’ pitches will be evaluated using the initial proposal evaluation criteria. Selectees will be notified after the event via email. Companies must participate in the pitch event to be considered for award. REFERENCES: 1. De La Vega, W. Fernandez, and George S. Lueker. "Bin packing can be solved within 1+ ε in linear time." Combinatorica 1.4 (1981): 349-355 2.  Rossillon, Kevin Joseph. Optimized air asset scheduling within a Joint Aerospace Operations Center (JAOC). Diss. Massachusetts Institute of Technology, 2015 3. Paquay, Célia, Michael Schyns, and Sabine Limbourg. "A mixed integer programming formulation for the three‐dimensional bin packing problem deriving from an air cargo application." International Transactions in Operational Research 23.1-2 (2016): 187-213 4. Hoehn, John R. Joint All Domain Command and Control (JADC2). Congressional Research SVC Washington United States, 2020

TECH FOCUS AREAS: Autonomy; Artificial Intelligence/Machine Learning TECHNOLOGY AREAS: Information Systems; Air Platform OBJECTIVE: This is a Phase I Pitch Day. Awards under this topic will include no more than $156,500 in STTR funding. Additionally, the period of performance will cover five months, including four months technical performance and one month for reporting. The objective of this topic is to explore the development of a theoretical foundation or model for hierarchical heterogeneous planning and scheduling by which we can reason about autonomous/automated decision-making in multiple different domains while accounting for the hierarchical structure of each domain. This topic seeks to reach companies and universities able to complete research into the foregoing concepts under a compressed schedule. This topic is specifically aimed at the early-stage basic science and research. DESCRIPTION: Common ground, or the establishment of mutual knowledge, beliefs, and assumptions about a topic or task, is critical for teaming between two or more individuals. Common ground plays an important role in the development of trust, as it helps provide some transparency into processes for acquiring and providing mutually beneficial knowledge, improves communication efficiency through lexical entrainment, and flexibility to accommodate different communication styles or sudden/abrupt changes to a task at hand. The establishment, maintenance, and repair of common ground requires team members to coordinate on the content and the processes for task completion. Typically, this coordination occurs verbally through natural language; however, natural language comprehension/understanding has been an obstacle for the seamless integration of machines within human teams. The point of this topic is to solicit approaches to the establishment of common ground between humans or humans and machines using natural and/or non-natural language (e.g., brevity communication standards, controlled languages, etc.). To achieve this goal, the following are required: • Document human approaches to the establishment of common ground for their codification; • Develop computational models of the codified common ground processes; • Derive or adopt a non-natural language, controlled language, etc., for testing in a human-machine context; • Model and human training for the adopted non-natural language; • Develop and validate objective criteria/metrics for demonstrating the establishment of common ground, with a preference for real-time assessment; • Evaluate performance and identify improvements to the codified processes, language derivation, etc., for further potential development. PHASE I: From a set of alternatives, perform a literature search and feasibility study to demonstrate a path forward for prototype system development, capable of establishing and maintaining common ground with humans while completing a shared task. PHASE II: Develop and demonstrate a prototype system based on the most feasible solution during the Phase I feasibility study. This demonstration should focus specifically on: • Evaluating the proposed solution against the objectives and measurable key results as defined in the Phase I feasibility study. • Describing in detail how the solution can be scaled to be adopted widely (i.e. how can it be modified for scale). • Provide a clear transition path for the proposed solution taking into account input from all affected stakeholders including but not limited to end users, engineering, sustainment, contracting, finance, legal, and cyber security. • Provide specific details about the solution’s integration with other current and potential future solutions. • Explain how the solution can be sustainable, i.e., supportability. • Specifically identify DoD or Governmental customers who want to use the solution. PHASE III DUAL USE APPLICATIONS: The contractor will pursue commercialization of the various technologies developed in Phase II for transitioning expanded mission capability to a broad range of potential government and civilian users and alternate mission applications. Direct access with end users and government customers will be provided with opportunities to receive Phase III awards for providing the government additional research & development, or direct procurement of products and services developed in coordination with the program. PROPOSAL PREPARATION AND EVALUATION: Please follow the Air Force-specific Phase I instructions under the Department of Defense 21.2 SBIR Broad Agency Announcement and Chart 1 (above) when preparing proposals. Proposals under this topic will have a maximum value of $156,500 SBIR funding and a maximum performance period of five months, including four months technical performance and one month for reporting. Proposals will be evaluated using a two-step process. After proposal receipt, an initial evaluation will be conducted IAW the criteria found in the AF-specific Phase I instructions as previously referenced. Based on the results of that evaluation, Selectable companies will be provided an opportunity to participate in the Air Force Trusted AI Pitch Day, tentatively scheduled for 26-30 July 2021 (possibly virtual). Companies’ pitches will be evaluated using the initial proposal evaluation criteria. Selectees will be notified after the event via email. Companies must participate in the pitch event to be considered for award. REFERENCES: 1.  Clark, H. H.; Brennan, S. E. (1991). Perspectives on socially shared cognition. Washington, DC: American Psychological Association. pp. 129–130 2. Clark, H. H, & Wilkes-Gibbs, D. (1986).  Referring as a collaborative process. Cognition 22(1), 1-39 3. Klein, G., Woods, D.D., Bradshaw, J.M., Hoffman, R.R., & Feltovich, P.J. (2004). Ten challenges for making automation a “team player’ in joint human-agent activity. IEEE Intelligent Systems, 91-95

TECH FOCUS AREAS: Biotechnology Space; Autonomy TECHNOLOGY AREAS: Sensors; Chem Bio Defense; Air Platform OBJECTIVE: Develop methodology and hardware to sense ambient wind condition to use as a command control signal for a small autonomous flying platform. The ultimate goal is to perform anemotaxis as a key component of chemotaxis with a single vehicle. DESCRIPTION: Animals are so successful at finding the sources of important chemical plumes by utilizing the direction of the flow around them. Active sensing of the wind direction on a small platform is not currently possible with commercial off the shelf components though some solutions are under development at the basic research level at universities. Two approaches to this problem include: 1. A physical sensor and associated software analysis dedicated to sensing and analyzing wind for control; and 2. Using existing platform commands and sensors to work out the wind for control. Two biotechnology approaches for (1) include using antenna or whisker like structures to physically sense the wind and an observability analysis that demonstrates the theoretical possibility of using this approach. Theoretically the vehicles’ own orientation and motor command responses to wind could be used for approach (2). Neither approach has been demonstrated. Passive control using fins is successful but under very limiting circumstances where the air flow is low velocity and steady. PHASE I: Determine whether there is technical merit to the proposed approach and whether the technology can feasibly detect the wind direction and subsequent reactive command and control of a small autonomous platform. PHASE II: Demonstrate and model in controlled conditions including wind gusts and wind direction changes wind detection and subsequent command and control of a small autonomous platform. PHASE III DUAL USE APPLICATIONS: Demonstrate and model in uncontrolled outdoor conditions including wind gusts and wind direction changes wind detection and subsequent command and control of a small autonomous platform over a long distance upwind. REFERENCES: 1. Anderson, Melanie J., et al. "The “Smellicopter,” a bio-hybrid odor localizing nano air vehicle." 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. 2. Kim, Suhan, et al. "A whisker-inspired fin sensor for multi-directional airflow sensing." 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2020 3. Lopez, Austin P., Ryan Tung, and Floris van Breugel. "Upwind Detection of Ambient Wind Using Biomimetic Antenna Sensors for Aerial Vehicles through Active Sensing." AIAA AVIATION 2020 FORUM. 2020.

TECH FOCUS AREAS: General Warfighting Requirements (GWR) TECHNOLOGY AREAS: Space Platform; Materials OBJECTIVE: The objective of this solicitation is to design insulators through physics-based models, demonstrate fabrication technologies, and validate the predicted response at relevant aero-heating conditions. The insulations should be applicable at temperatures approaching 1700 °C. DESCRIPTION: U.S. Air Force and Space Force are interested in efficient reusable thermal insulations to be used on launch and reentry vehicles. This topic concentrates on efficient reusable insulations that can sustain flight thermal and aerodynamic loads over parts of the vehicles. The reusable insulations can be either a rigid insulation directly subjected to the aerodynamic loads, or a flexible insulation located beneath an aeroshell structure. The insulation must be thermally optimized to provide optimum thermal protection with the lowest possible volume and mass. Thermal optimization can be achieved though minimizing various modes of heat transfer in insulations, such as solid and gas conduction, and radiation transport. The objective of this solicitation is to design insulators through physics-based models, demonstrate fabrication technologies, and validate the predicted response at relevant aero-heating conditions. As previously stated, the insulations should be applicable at temperatures approaching 1700 °C. PHASE I: Phase I should determine feasibility of to-be designed/developed small-scale test articles and preliminary thermal testing to demonstrate proof of concept. PHASE II: Focus of Phase II should be further iterations on design and development that result in functional or manufacturing scale up for larger test articles. PHASE III DUAL USE APPLICATIONS: The fundamental nature of AFOSR programs reflect the broad opportunity to commercialize science to both commercial and defense markets. Awardees will have the opportunity to integrate with prospective follow-on transition partners. The contractor will transition the solution to provide expanded mission capability to a broad range of potential Government and civilian users and alternate mission applications. NOTE: The technology within this topic is restricted under the International Traffic in Arms Regulations (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the proposed tasks intended for accomplishment by the FN(s) in accordance with section 5.4.c.(8) of the Announcement and within the AF Component-specific instructions. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. Please direct questions to the Air Force SBIR/STTR Contracting Officer, Ms. Kris Croake, kristina.croake@us.af.mil. REFERENCES: 1. Lee, SC, and Cunnington, G.R., “Conduction and Radiation Heat Transfer in High-Porosity Fiber Thermal Insulation,” Journal of Thermophysics and Heat Transfer, Vol. 14, No. 2, April-June 2000, pp. 121-136 2. Cunnington, G.R., Lee, SC, and White, S.M. “Radiative Properties of Fiber-Reinforced Aerogel: Theory versus Experiment,” Journal of Thermophysics and Heat Transfer, Jan- March 1998, pp. 17-22 3. Veiseh, S., Hakaki-Fard, A., “Numerical Modeling of Combined Radiation and Conduction Heat Transfer in Mineral Wool Insulations,” Heat Transfer Engineering, Vol. 30, No. 6, 2009, pp. 477-486 4. Carvajal, S.A., Garboczi, E.J., and Zarr, R.R., “Comparison of Models for Heat Transfer in High-Density Fibrous Insulation,” Journal of Research of the National Institute of Standards and Technology, Vol. 124, May 2019. 5. Spagnol, S., Lartigue, B., Trombe, A., Gibiat, V., “Modeling of thermal conduction in granular silica aerogels,” Journal of Sol-Gel Science and Technology, Vol. 48, Nov. 2008, pp. 40-46

TECH FOCUS AREAS: Cybersecurity TECHNOLOGY AREAS: Information Systems OBJECTIVE: Develop and implement a decentralized and distributed security solution on Urban Air Mobility (UAM) networks to enable incorruptible flight data communications and resiliency. DESCRIPTION: The vision to revolutionize air mobility such as agility prime [1] present exciting frontiers in modern aviation. As air traffic grows, there is a need for secure Urban Air Mobility (UAM) for air passenger and cargo transportation in and among commercial, civilian, and military locations. UAM offers the potential to create a faster, cleaner, safer, and more integrated transportation system. However, recent events have shown that modern unmanned aerial vehicles (UAVs) are vulnerable to attack and subversion through buggy or sometimes malicious devices that are present on UAM communication networks, which increase the need for cyber awareness include UAVs in the airspace, development of the Automatic dependent surveillance-broadcast (ADS-B), and the risk of cyber intrusion [2]. The incident of a civilian UAV disrupting a major airport is one example of many incidents raising questions on the future of airspace security. While a civilian hobbyist might be ignorant of the impending harm, the situation could pose a threat to the air operations [3]. Therefore, a seamless trusted communication capability is important in both military and commercial operations for vehicle integrity [4]. The challenge is conventional enabling technologies mainly rely on a centralized system for data aggregation, sharing, and security policy enforcement; and it incurs critical issues related to bottleneck of data analysis, provenance, and consistency. Since air vehicles can be compromised at a single point yet effects can propagate across the entire UAM network, the Department of the Air Force (DAF) is looking for a solution to eliminate the single point of failure through a decentralized and distributed security validation to verify communications with certainty despite there being a valid node on the network acting maliciously. The DAF would like to see this technology applied on a UAV cellular intercommunication network that can perform validation of messages in a form of decentralized security distributed amongst air vehicle controllers as well as provide a sense of resiliency. PHASE I: In the first phase of this effort, the contractor shall design a decentralized and distributed security solution performing validation of communications on UAM networks. Evaluation tradeoffs of the type and source of vulnerabilities to be exploited for a wireless UAV network, considering both accidental and malicious events, should be examined. The technology shall have a low resource consumption, minimal latency, and enhanced security on the air vehicles and networks. The proof of concept should include modeling, simulation, and mathematical description towards a prototype solution in Phase II. PHASE II: Implement and demonstrate the concept developed in Phase I on practical wireless ad-hoc network (WANET) or mobile ad hoc network (MANET) for autonomous UAM network management and aircraft separation service of urban airspace using physical air vehicle controllers. The contractor shall test and evaluate the operation of the technology in a live air vehicle or systems integration lab (SIL) environment. The contractor shall verify the effectiveness of the technology by: (1) Showing other controllers reject valid but malicious messages sent by another controller (2) Performing penetration testing with an independent team to identify other attack vectors against the technology; and (3) Evaluating the solution to refine the initial design prototype to be used in relevant and/or operational environment settings to support all domain mission requirements. Key metrics would be the confidentiality, integrity, and availability of data. PHASE III DUAL USE APPLICATIONS: The fundamental nature of AFOSR programs reflect the broad opportunity to commercialize science to both commercial and defense markets. Awardees will have the opportunity to integrate with prospective follow-on transition partners. The contractor will transition the solution to provide expanded mission capability to a broad range of potential Government and civilian users and alternate mission applications. NOTE: The technology within this topic is restricted under the International Traffic in Arms Regulations (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the proposed tasks intended for accomplishment by the FN(s) in accordance with section 5.4.c.(8) of the Announcement and within the AF Component-specific instructions. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. Please direct questions to the Air Force SBIR/STTR Contracting Officer, Ms. Kris Croake, kristina.croake@us.af.mil. REFERENCES: 1. Flying Cars Could Take Off Soon, if We Let the Military Help | WIRED 2. E. Blasch et al., "Cyber Awareness Trends in Avionics," 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC), San Diego, pp. 1-8, 2019. 3. Flying Cars: Urban Air Mobility Raises Safety Concerns, 2020. Available at: https://www.nationaldefensemagazine.org/articles/2020/7/7/urban-air-mobility-raises-safety-concerns 4. J. A. Maxa, R. Blaize and S. Longuy, "Security Challenges of Vehicle Recovery for Urban Air Mobility Contexts," 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC), 2019.

RT&L FOCUS AREA(S): Artificial Intelligence/ Machine Learning TECHNOLOGY AREA(S): Battlespace; Sensors The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Develop a method to produce synthetic SAR data for augmentation into Artificial Intelligence (AI) Automatic Target Recognition (ATR) algorithms and assess improvement compared to current methods. Leverage existing radiative transfer models (RTMs) within the research community to create phased history as well as radar images from which specific features can be exploited for use in current ATR algorithms. Explore the use of state of the art artificial intelligence (AI) methods such as the Generative Adversarial Network (GAN) in producing realizable synthetic SAR data in conjunction with RTM results to further improve ATR training. DESCRIPTION: Current Standard Operating Procedures (SOPs) for SAR image analysis consists of manual processes that are labor intensive. SAR analysis currently requires a trained analyst with years of experience to accurately classify targets in a scene. Analysts cannot keep up with the amount of captured data that needs to be processed which has spawned attempts to push human capabilities [1]. The sheer volume of data from desperate systems produces a situation in which reviewing all collected imagery becomes an impossibility for the Intelligence Communities (ICs). Specifically for the Counter Weapons of Mass Destruction (CWMD) mission, foreign governments purposely take actions, such as moving locations and the use of remote sites that make it difficult for analysts to identify objects of interest. AI automated solutions have been proposed as a force multiplier with the potential to significantly increase the amount of actionable intelligence an analyst can produce [2]. Despite the promise that AI presents to the SAR analysis problem, training data for ATR algorithms is scarce. AI algorithms must first be trained on existing data in order to process and make classifications on new data. Finding quality data that meets the end goal of the algorithm is often the Achilles heel of ATR systems. Moreover, the training data must incorporate all possible aspects of the target, viewpoint, and scene making the task of creating a training set difficult and cumbersome. Images are often translated, rotated, cropped, and noise added in various ways to capture possibilities. However, creating such a dataset for SAR imagery on desired military targets is even more difficult, cost prohibitive, and impractical with the very limited available data. Instead, the use of RTMs for the creation of synthetic data has shown promise for ATR algorithms on other sensor modalities and can be extended to SAR [3]. A number of RTMs that have SAR capability already exist and should be further developed for the SAR synthetic data augmentation problem. Some of these models include RaySAR [4] CohRas [5], SARViz [6], and DIRSIG [7]. These systems were originally created with engineering studies in mind, for instance, sensor specifications, target characteristics, environmental conditions, platform properties, and so forth. Generally, RTMs are based on statistical ray-tracing techniques into a 3d scene description to predict at sensor radiance contributions from scene components. Scene descriptions can contain detailed information such as surface Bidirectional Reflectance Distribution Functions (BRDFs), textures, and spectral dependencies. Environmental conditions such as atmospheric propagations are also often incorporated with the use of models such as MODTRAN [8]. Sensor and antenna specifications such as power, frequency, and gain pattern are important parameters that are included for robust simulations. With the ability to create physically realizable SAR data, RTM outputs are well suited to solve the lack of training data problem for SAR ATR algorithms. ATR algorithms are aimed at solving the classification problem of objects in a scene. Convolution Neural Networks (CNNs) have become the most common method for difficult classification problems, and have proven to be highly effective due to their ability to hone in on local features in the vector space. CNNs are comprised of layered connections of convolutions with learned filters that enable neighboring semantic meanings, making it an ideal choice for image classification. A number of CNNs have been developed for the SAR classification problem with promising accuracy but often lack sufficient datasets [2] [9] [10]. One of the most recent studies on the creation of synthetic SAR data for augmentation into ATR algorithms looked at processing RTM visible imagery into SAR like imagery by using a GAN [11]. Although the study showed that important features were missing in the GAN produced synthetic imagery required to improve ATR accuracy, the researchers proposed that instead, RTMs should produce the SAR data directly, and a GAN then could be used to improve the realism of the SAR image. PHASE I: An in depth literature review comparing current SAR Radiative Transfer Models, data sets, and ATR algorithms is first required to understand the state of the art. An understanding of the advantages and disadvantages of the different available RTMs as well as their availability for use in this effort will be determined. An RTM will then be chosen, acquired, and used to produce synthetic SAR data, both phased history as well as imagery. SAR datasets will also be researched that contain objects of interest, one example being MSTAR [12]. An ATR algorithm will be chosen based on literature review results and availability. The ATR algorithm will be trained with the “off the shelf” data set and tested for accuracy. Training data will then be augmented from synthetically generated SAR data. Metrics, such as precision and recall will tracked to measure the increase in ATR performance with data augmentation. Deliver model, all software, data, and reports on the effort. PHASE II: Build upon lessons learned from phase I, pursuing efforts that show promise in SAR data augmentation. Research AI methods to enhance synthetic imagery such as usage of GAN algorithms. Implement AI and other synthetic imagery enhancements and test ATR improvements as a result of the enhancements. Produce TRL level 6 system by incorporating models into operational analytical tools and performing a technology demonstration. Metrics, such as precision and recall will be tracked to measure the increase in ATR performance with data augmentation. Deliver the system, model, all software, data, and reports on the effort. PHASE III DUAL USE APPLICATIONS: Finalize and commercialize software for use by customers (e.g. government, satellite companies, etc.). Although additional funding may be provided through DoD sources, the awardee should look to other public or private sector funding sources for assistance with transition and commercialization. REFERENCES: 1. R. A. McKinley, L. McIntire, N. Bridges, C. Goodyear, N. B. Bangera and M. P. Weisend, "Acceleration of image analyst training with trascranial direct current stimulation," Behavioral Neuroscience, vol. 127, no. 6, p. 936, 2013. ; 2. C. Coman, "A deep learning sar target classification experiment on mstar dataset," in International Radar Symposium, Bonn, Germany, 2018. ; 3. R. Kemker, C. Salvaggio and C. Kanan, "Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning," ISPRS journal of photogrammetry and remote sensing, no. 145, pp. 60-77, 2018. ; 4. S. Auer, R. Bamler and P. Reinartz, "RaySAR-3D SAR simulator: Now open source," IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016. ; 5. H. Hammer, K. Hoffmann and K. Schulz, "On the classification of passenger cars in airborne SAR images using simulated training data and a convolutional neural network. Image and Signal Processing for Remote Sensing XXIV," International Society for Optics and Photonics, vol. 10789, 2018. ; 6. M. Gupta, V. Malhotra, B. Shah, S. Prakash, A. Sharma and B. Kartikeyan, "RISAT-1 SAR HRS Mode Data Quality Evaluation," IGARSS IEEE International Geoscience and Remote Sensing Symposium, 2019. ; 7. M. Gartley, A. Goodenough, S. Brown and R. Kauffman, "A comparison of spatial sampling techniques enabling first principles modeling of a synthetic aperture RADAR imaging platform," in SPIE Defense, Security, and Sensing, Orlando, FL, 2010. ; 8. A. Berk, L. Bernstein and D. C., "MODTRAN: A moderate resolution model for LOWTRAN.," Spectral Sciences Inc., Burlington, MA, 1987. ; 9. H. S. Pannu and A. Malhi, "Deep learning-based explainable target classification for synthetic aperture radar images," in 13th International Conference on Huan System Interaction IEEE, Tokyo, Japan, 2020. ; 10. E. G. john, "Convolutional Neural Networks For Feature Extraction and Authomated Target Recognition in Synthetic Aperture Radar Images," Naval Postgraduate School, Monterey, CA, 2020. ; 11. J. Slover, "Synthetic Aperture Radar Simulation by Electro Optical to SAR Transformation Using Generative Adversarial Network," Rochester Institute of Technology, Rochester, NY, 2020. ; 12. T. D. Ross, W. Steven, V. J. Velten, J. C. Mossing and M. L. Bryant, "Standard SAR ATR evaluation experimetns using the MSTAR public release data set," in Algorithms for Sythetic Aperture Radar Imagery V. , Orlando, FL, 1998.

RT&L FOCUS AREA(S): Artificial Intelligence/ Machine Learning TECHNOLOGY AREA(S): Chem Bio Defense; Information Systems OBJECTIVE: DTRA has a need to perform high-fidelity CFD modeling of agent defeat phenomenology and associated test and evaluation activities in order to quantify and increase the accuracy of hazard source predictions for counter weapons of mass destruction (C-WMD) defeat and deny tactics. These simulations are technically and computationally challenging due to the long-time duration of interest (weapon detonation through stabilization of plume), the stochastic nature of fragmentation and turbulent mixing phenomena, the temperature dependency of thermal neutralization mechanisms, and the relatively stiff chemical kinetics models. The objective of this topic is to improve the computational efficiency of the chemical kinetics models for chemical weapon agents and simulants by investigating and developing Numerics-Informed Neural Networks (NINNs). This topic explores the premise that simply using the residual of the PDE as in Physics-Informed Neural Networks (PINNs) is not optimal. One might instead use directly the numerical schemes which are employed to integrate the PDEs in time. This leads naturally to numerics-informed neural nets (NINNs). DESCRIPTION: The last decade has seen a tremendous amount of activity and developments in the field of deep neural networks (DNNs). When trying to apply these to physics governed by partial differential equations (PDEs), traditional DNNs have been ‘supplemented' or ‘informed’ with the underlying physics, leading to physics-informed neural nets (PINNs). This topic explores the premise that simply using the residual of the PDE (as in PINNs) is not optimal. One might instead directly use the numerical schemes which are employed to integrate the PDEs in time. This leads naturally to Numerics-Informed Neural Networks (NINNs). To leverage the ongoing research momentum in Artificial Intelligence and Machine Learning, DTRA seeks innovative ideas for replacing the PDE residuals used for PINNs by the discrete time stepping increments of numerical integrators. Phase I development must demonstrate a NINN approach for local residuals (e.g., chemically reacting flows) and non-local residuals (e.g., PDEs with spatial derivatives). The new techniques should then be compared to PINNs and traditional DNNs. Phase II development will further optimize the NINN approach to extend the range of applicability to other problems. PHASE I: Define and develop NINNs for chemical reactions (CHEM-NINNs). Define and develop NINNs for PDEs with spatial derivatives. Investigate and validate NINNs and CHEM-NINNs by comparison of results with traditional DNNs and PINNs. PHASE II: Further develop, test and optimize the NINN approach to extend the range of applicability. Demonstrate use of NINNs on High Performance Computing (HPC) systems. Perform detailed comparisons with high-fidelity Computational Fluid Dynamics (CFD), Computational Chemistry application codes and observational data, to quantify speed and accuracy of the NINNs and CHEM-NINNs. Generalize and document for pre-commercial release. PHASE III DUAL USE APPLICATIONS: In addition to implementing further improvements that would enhance use of the developed product by the sponsoring office, identify and exploit features that would be attractive for commercial or other private sector HPC applications. The software developed for use in DTRA’s very demanding application codes will be well suited, once refined, for use on more general HPC workloads. Investigate commercialization avenues that could include other government agencies, national labs, research institutes, and defense contractors. Develop a plan to enable successful technology transition at the end of this phase. REFERENCES: [1] Shin, Yeonjong , On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs},Communications in Computational Physics https://arxiv.org/pdf/2004.01806 ; [2] M Raissi, P Perdikaris, GE Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations Journal of Computational Physics, 2019 https://www.osti.gov/servlets/purl/1595805 ; [3] Harbir Antil, Ratna Khatri, Rainald Löhner, Deepanshu Verma, Fractional Deep Neural Network via Constrained Optimization https://arxiv.org/pdf/2004.00719 ; [4] Lars Ruthotto, E. Haber, Journal of Mathematical Imaging and Vision 2019 Deep Neural Networks motivated by Partial Differential Equations https://arxiv.org/pdf/1804.04272

RT&L FOCUS AREA(S): Artificial Intelligence/ Machine Learning TECHNOLOGY AREA(S): Nuclear; Sensors OBJECTIVE: Develop flexible radiation algorithms deployed across battlefield networks to enable the linking of multiple detector variants and fusing of raw detector outputs into usable information. DESCRIPTION: Often, multiple detectors, and multiple detector variants are deployed to characterize a complex scene (i.e. stationary detectors, handheld radioisotope devices, vehicle-mounted detectors, and backpack detectors) within 1 square kilometer. This topic seeks to develop flexible radiation detection algorithms leveraging proven mathematical data models that would sit either at a node for multiple detectors or at a command center that fuses raw detector outputs into useable information. Multiple data types are included in this deployment modality: gross gamma/neutron counts, gamma spectral data, GPS data, etc. Advances in big data theory, machine learning, and artificial intelligence have yielded new mathematical models that could be applied to multiple radiation detection sensors to fuse data in a way that novel algorithms may analyze the overall data input, instead of discrete sensor data. The intent of this topics is to leverage these new mathematical principals and models to decrease time to localize and characterize radiological signature anomalies in a complex scene by leveraging data from all radiation detector types. This would serve to better protect warfighters by reducing mission times and provide commanders better mission radiological characterization for the overall scene. PHASE I: Identification of multi-radiation detector algorithms and demonstrate their potential to improve the identification, characterization, and/or localization of a radioactive source in a complex scene as compared to the singular detector algorithm. Multiple candidate algorithms shall be down selected for further development in Phase II. Demonstrate pathways for meeting the Phase II performance goals through feasibility studies at the end of Phase I. PHASE II: Demonstrate enhanced identification, characterization and/or localization of radioactive sources with the multi-detector algorithm that fuses data (gamma and neutron radiation outputs, and GPS location/time) from disparate ground based and mobile detector types. Demonstrate improved performance of the multi-detector algorithm over single-system algorithms. The algorithm should support the integration of additional new detector types. PHASE III DUAL USE APPLICATIONS: Field demonstration in radiation environment with users deploying multiple and varied radiation detectors linked via communications to a network node in which the algorithm receives detector outputs. The algorithm must conduct scene characterization in real-time as operators move through a complex environment with disparate detector modalities. The multi-system algorithm will be directly compared to legacy single-system algorithms to assess impact on mission. Develop commercialization and transition plan to DoD end users. REFENCES: 1. Information Fusion Volume 57, May 2020, Pages 115-129; https://doi.org/10.1016/j.inffus.2019.12.001 ; 2. Joint Pub 3-11

RT&L FOCUS AREA(S): Microelectronics TECHNOLOGY AREA(S): Sensors; Electronics; Space Platform The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Develop methodologies to evaluate and distinguish between radiation effects from a persistent beta and gamma environment, and determine the circumstances where testing for one environment is sufficient to show survivability in the other, or in a combined environment. DESCRIPTION: This topic seeks innovative, cost effective, solutions for radiation testing of microelectronics. The need for testing of microelectronics in a persistent beta environment vs. a persistent gamma environment is a subject of discussion and debate within the radiation survivability community. Aspects of the discussion include the best way to generate a persistent beta environment in a ground test, the best way to generate a persistent gamma environment in a ground test, whether successful testing in one environment is sufficient to show survivability in the other, and whether combined testing in both persistent beta and gamma environments is required or whether broader combined testing involving the full environment is necessary. PHASE I: In Phase I, show feasibility of a methodology to develop test environments that will demonstrate survivability using partial vs. combined environments. Select representative electronic parts and show survivability results, either using an analytical approach or leveraging existing test data. Consider whether existing methods of generating gamma and beta environments can be used, or whether innovative approaches are needed. PHASE II: In Phase II, implement the Phase I results in a prototype test design. Demonstrate the methodology by conducting an experimental study where electronic parts are tested in partial and combined environments. Consider whether existing methods of generating gamma and beta environments can be used, or whether innovative approaches are needed. PHASE III DUAL USE APPLICATIONS: The offeror should evaluate whether these approaches can be used for commercial space applications where radiation survivability is required, in addition to military system requirements. REFERENCES: 1. Dyal, Palmer, “Particle and field measurements of the Starfish diamagnetic cavity,” Journal of Geophysical Research, Vol. 111, A12211, 2006. 2. Conrad, Gurtman, Kweder, Mandell, and White, “Collateral Damage to Satellites from an EMP Attack,” DTRA-IR-10-22, August 2010. 3. Cladis, Davidson, and Newkirk, eds, “The Trapped Radiation Handbook,” NDA 2534H, Washington, DC, https://apps.dtic.mil/sti/pdfs/ADA020047.pdf. 4. Wang, Y., W. Gekelman, P. Pribyl, B. Van Compernolle, and K. Papadopoulos (2016), Generation of shear Alfvén waves by repetitive electron heating, J. Geophys. Res. Space Physics, 121, 567–577, doi:10.1002/2015JA022078. 5. James R. Schwank, Marty R. Shaneyfelt, and Paul E. Dodd, “Radiation Hardness Assurance Testing of Microelectronic Devices and Integrated Circuits: Radiation Environments, Physical Mechanisms, and Foundations for Hardness Assurance,” IEEE Transactions on Nuclear Science, Vol. 60, No. 3, June 2013. 6. Carleston, Colestock, Cunningham, Delzanno, Dors, Holloway, Jeffrey, Lewellen, Marksteiner, Ngyuen, Reeves, and Shipman, “Radiation-Belt Remediation Using Space-Based Antennas and Electron Beams,” IEEE Transactions on Plasma Science, 2019, Volume 47, Issue 5.

RT&L FOCUS AREA(S): Artificial Intelligence/ Machine Learning; Cybersecurity TECHNOLOGY AREA(S): Information Systems OBJECTIVE: Create a secure environment for collaboration between small businesses and government personnel and provide a central location for SBIR/STTR knowledge management. DESCRIPTION: Industry and the government require access to collaborative data repositories that are tightly controlled with cyber secure protocols, Risk Management Framework (RMF) compliance, and role-based access controls, resulting in the warrant and implementation of DFARS Clause 252.204-7012, Safeguarding Covered Defense Information and Cyber Incident Reporting. This regulation can be costly to small businesses with limited resources, hindering their ability to exchange innovative ideas and research with government entities. Another challenge that faces most small business employees is not being able to send encrypted emails due to the lack of a Common Access Card (CAC) or the required certificates, making secure collaboration difficult. Current processes for collaboration are not only an issue for small businesses lacking the necessary tools for security, but also encourages the unnecessary duplication of data. The Contract Data Requirements List (CDRL) list several directorates to submit SBIR/STTR related deliverables, resulting in the accumulation of costs for storage. The CDRLs also contain a number of deadlines that companies involved in the SBIR/STTR effort must keep track of. This topic seeks solutions for a collaborative repository that: (1) Leverages Artificial Intelligence/Machine Learning (AI/ML) techniques to ensure security. (2) Provides enough flexibility to seamlessly integrate plug-ins and supplemental tools. (3) Employs two-factor authentication methods that do not restrict small businesses to the use of a CAC and username/complex password combination. (4) Encrypts communication while also preserving the integrity and non-repudiation of the message. (5) Implements the Least Privilege principle. (6) Monitors and audits user activity and data movement. (7) Provides authorized users with reminders of upcoming deadlines as established in the CDRLs. PHASE I: Provide proof of concept for the technology. PHASE II: Further develop proof of concept and begin adding technical requirements (1) – (7) (refer to Description). PHASE III DUAL USE APPLICATIONS: Implement collaborative environment within relevant missile defense elements. REFERENCES: 1. https://www.acq.osd.mil/dpap/policy/policyvault/USA002829-17-DPAP.pdf. 2. https://csrc.nist.gov/publications/detail/sp/800-171/rev-2/final. 3. https://www.nist.gov/system/files/documents/2018/10/18/cui18oct2018-104501145-dod_dfars-michetti-thomas.pdf.

RT&L FOCUS AREA(S): Artificial Intelligence/ Machine Learning; Cybersecurity TECHNOLOGY AREA(S): Information Systems OBJECTIVE: Develop methodologies for validating models of phenomenon and processes generated by advanced data fitting methods, such as machine learning techniques. DESCRIPTION: This topic seeks innovative methods for validating data fitted models. Use of collected input and output data from phenomenon/process to generate a data-fitted model has increased with the advent of new techniques in Artificial Intelligence/Machine Learning (AI/ML). AI/ML techniques have made it much easier to create data-fitted models of very complex systems with unknown complex relationships. The standard method for validating data generated models is to withhold a portion (e.g. 20%) of the collected data from the fitting process in order to have an independent validation sample. When data is expensive and/or hard to collect, the bifurcation of the data both limits the available data to fit and to validate the models, therefore limiting the quality of both. The government is in search of methods to mitigate this conundrum. Possibilities include, but are not limited to, use of partial knowledge of the modeled systems (e.g. first order physics models, process flows, etc.), guided sampling/data collection for initial and validation data, cross comparisons of models generated from data-subsets, etc. PHASE I: Provide the following: 1. Method concept descriptions (one or more). 2. Application architecture description, including data management concepts. 3. Proof-of-concept demonstration. 4. Phase II plan, including cyber security approval steps. PHASE II: Complete a detailed prototype design incorporating government performance requirements. PHASE III DUAL USE APPLICATIONS: Develop solution from Phase II into a mature, field-able capability. Work with missile defense integrators to integrate the technology for a missile defense system level test-bed and test in a relevant environment. REFERENCES: 1. https://bdtechtalks.com/2020/06/15/self-explainable-artificial-intelligence. 2. https://arxiv.org/abs/1405.6974. 3. https://www.researchgate.net/profile/Ron_Kohavi/publication/2352264_A_Study_of_Cross-Validation_and_Bootstrap_for_Accuracy_Estimation_and_Model_Selection/links/02e7e51bcc14c5e91c000000.pdf.

RT&L FOCUS AREA(S): Microelectronics TECHNOLOGY AREA(S): Materials; Sensors; Electronics The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Conduct applied research and development of innovative long-wave infrared (LWIR) focal plane arrays (FPAs) in order to approach the state-of-the-art performance of mercury cadmium telluride (MCT). DESCRIPTION: The government seeks to identify and further develop alternatives to MCT for high-performance LWIR FPAs. MCT provides an excellent solution for missile defense applications but it can also be a difficult material to produce, integrate, and maintain (which increases system cost and complexity). For decades, researchers have been working on alternatives to MCT and have made great progress, particularly with superlattice detectors based on III-V material system. Superlattice detectors are easier to use than MCT and, in theory, should also have lower dark current and similar quantum efficiency (QE) when operated at the same cryogenic temperature. However, more work is needed in order to routinely realize all of these benefits for LWIR. In addition to superlattice detectors, there are potentially other detector architectures and other material systems that could, in the future, be viable alternatives to both MCT and superlattice detectors for missile defense applications. Proposed solutions do not need to outperform MCT but should at least have a combination of beneficial properties that would allow it to outcompete MCT in the marketplace for high-performance LWIR FPAs. This topic seeks to invest primarily in LWIR FPA materials, detector design, and growth techniques in order to help close the gap between the performance of MCT and its alternatives. Examples of responsive solutions include innovative superlattice detector designs that enhance QE, suppress dark current, and/or tolerate defects or impurities. Other examples include growth techniques to minimize defects and impurities, or produce features that contribute towards improved performance. Examples also include applied research into new material systems and detector architectures that might outcompete both MCT and superlattice technology. Proposals related to the substrate (e.g. surface preparation), reagents (e.g. purification), growth equipment (e.g. improvements to the MBE (molecular-beam epitaxy) or MOCVD (metal organic chemical vapor deposition) equipment), finishing (e.g. pixel delineation, passivation, coatings), and integration (e.g. with a readout integrated circuit (ROIC)) are allowed but must account for less than half of the total funding and must directly support topic goals. Proposals related to other components of the sensor (e.g. ROIC, optics, image processor, image stabilization) will be considered non-responsive even if the intent is to relax the performance requirements for the LWIR detector (thereby making lower-performing alternatives more acceptable). Currently, we believe that thermal (instead of photon) detectors, as defined in chapter 3 of reference 1, are not sensitive or responsive enough for the intended applications. Therefore, proposals related to thermal detectors would likely be considered non-responsive. The LWIR detector should be sufficiently sensitive and responsive to detect dim fast-moving missile threats at ranges of 100s-1000s of kilometers using narrow field-of-view optics hosted on a dynamic platform. Solutions should be able to approach a QE of 80% anywhere within the 8-12 micrometer waveband and should approach Rule 07 dark current at 77K, or have some other combination of parameters that provides equivalent sensitivity for short (e.g. milliseconds) integration times. The objective FPAs should be 1024x1024 or larger with a 20 micrometer pixel pitch. Integrated FPAs must withstand both a bake-out and a rapid cool-down from room-temperature to cryogenic operating temperatures (e.g. 77K). FPAs might be exposed to natural and manmade radiation during operation. The ability to detect multiple wavebands from a single FPA (e.g. “2-color”) is desirable but not crucial. These are notional specifications that may be negotiated during Phase I. Proposers are highly encouraged to either have an in-house capability to produce test articles or form a major partnership with someone who does. The Research Institute (RI) partner should be a key member of the research team and a source of many of the innovative ideas, rather than a service provider. Reference 1 provides the basis for definitions used in this topic. The remaining references either describe the state-of-the-art for LWIR detectors or provide examples of innovative approaches for improving the performance of MCT alternatives. They should not be misconstrued as describing a preferred approach, organization, or technology, or describing the boundaries within which proposed solutions must fall. PHASE I: Study the scientific and technical feasibility of the proposed approach. Model the expected performance of the proposed solution and compare it to MCT and other emerging alternatives. Identify the disadvantages of the proposed solution and describe how these disadvantages would be overcome or otherwise acceptable. Show, by analysis, the ability to scale up to multi-element arrays meeting the notional specifications described above. Show, by analysis, that the solution is integrate-able and suitable for the intended applications. If possible, grow and characterize single-element detectors to demonstrate proof-of-concept and validate model predictions. Complete a plan for Phase II and contact suppliers to verify that the plan is executable. Seek letters of interest from LWIR sensor suppliers to include in the Phase II proposal. No travel to government facilities would be necessary during Phase I. PHASE II: Study and optimize the growth process in order to steadily improve performance and mitigate challenges. Begin scaling up the size of the FPA to demonstrate its performance and uniformity. Grow and characterize small (e.g. 32x32) detector arrays. Upon request, provide detector samples to the government for an independent assessment. Sample sizes, quantities, and configuration for testing will be coordinated with the government. Complete a plan for Phase III and seek letters of commitment from proposed partners. Seek letters of support from LWIR sensor suppliers to include in the Phase III proposal. PHASE III DUAL USE APPLICATIONS: Grow and characterize moderate-sized (e.g. 256x256) detector arrays. Integrate these arrays with a representative ROIC and Dewar and test performance. Begin early screening tests to verify the ability of the detector to survive and operate in the environments of the intended application. Begin optimizing the growth process to support production and commercialization. Generate plans to scale up the size of the detector to 1024x1024 or larger. Obtain letters of commitment from LWIR sensor suppliers to start transitioning the technology into a product line. REFERENCES: 1. E.L. Dereniak & G.D. Boreman (1996). Infrared Detectors and Systems. Wiley. 2. Proceedings Volume 11180, International Conference on Space Optics — ICSO 2018; 111803T (2019). 3. Relative performance analysis of IR FPA technologies from the perspective of system level performance, Infrared Physics & Technology, Volume 84, August 2017, Pages 7-20. 4. M. D. Goldflam et al., "Next-generation infrared focal plane arrays for high-responsivity low-noise applications," 2017 IEEE Aerospace Conference, Big Sky, MT, 2017, pp. 1-7, doi: 10.1109/AERO.2017.7943984. 5. Brian K. McComas, "The art and science of missile defense sensor design,"Proc. SPIE 9085, Sensors and Systems for Space Applications VII, 90850F (3 June 2014); doi: 10.1117/12.2053504. 6. W. Tennant. 2010. "Rule 07" Revisited: Still a Good Heuristic Predictor of HgCdTe Performance? Journal of Electronic Materials. Vol. 39, Issue 7. 1030-1035.

RT&L FOCUS AREA(S): Hypersonics; Space TECHNOLOGY AREA(S): Space Platform; Weapons The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with section 3.5 of the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Develop novel geometry based solutions to thermal management for future interceptor propulsion systems. DESCRIPTION: Thermal paths for heat from rocket motors are one of the greatest considerations in propulsion system design. Thermal design for components, such as the nozzles, pintles, and motor cases drives the capability of rocket motors. Considerable amounts of insulation are necessary to shield temperature sensitive components, such as electronics and lightweight structures. Newly available manufacturing techniques can enable thermal management designs, such as tortuous paths, that were previously impossible to manufacture. This could enable significant savings in mass and volume, or enable use of higher performing hotter burning propellants. Ultimately, these benefits would manifest as greater reach and containment area for future interceptors. The thermal management design must apply to systems with pressures over 3,000 psi and propellant burn temperatures over 4,000 degrees F. The proposer will be expected to identify specific thermal management techniques, geometry type, materials, and components for development. The proposer may select one of a number of different propulsion components for development, such as pintle, nozzle, motor case, etc. PHASE I: During Phase I, the contractor can develop models and perform simulations to evaluate feasibility and/or down select designs. Coupon fabrication and/or material formulation can be done to provide evaluation of critical properties. The contractor is expected to become familiar with solid propulsion system environments. PHASE II: During Phase II, prototype(s) should be developed in order to validate Phase I models/simulations. The prototype designs can be updated and optimized through experimentation and enhance process/manufacturing techniques. Phase II work should lead the contractor to identify potential applications and insertion into a missile system. PHASE III DUAL USE APPLICATIONS: During Phase III, the contractor will work with a solid propulsion system manufacturer/developer to iteratively design and fabricate prototype thermal management system/techniques for high-fidelity testing in a relevant missile defense environment. The contractor would then provide the necessary technical data to transition the technology into a missile defense application. REFERENCES: 1. Wadleya, Haydn and Douglas Queheillaltb, “Thermal Applications of Cellular Lattice Structures”. University of Virginia https://www2.virginia.edu/ms/research/wadley/Documents/Publications/Thermal_Applications_of_Cellular_Lattice_Structures.pdf. 2. Keicher, David M. and Love, James W. “Manufacturable geometries for thermal management of complex three-dimensional shapes”. 2003. United States Optomec Design Company, United States Patent 6656409, https://www.freepatentsonline.com/6656409.html. 3. Bejan, Adrian and Allan Kraus. Heat Transfer Handbook Volume 1. John Wiley & Sons, 2003. https://books.google.com/books?id=d4cgNG_IUq8C. 4. George P. Sutton. 2010. "Rocket Propulsion Elements." 8th edition, John Wiley & Sons Inc.