DoD 2012.B SBIR Solicitation
NOTE: The Solicitations and topics listed on this site are copies from the various SBIR agency solicitations and are not necessarily the latest and most up-to-date. For this reason, you should use the agency link listed below which will take you directly to the appropriate agency server where you can read the official version of this solicitation and download the appropriate forms and rules.
The official link for this solicitation is: http://www.acq.osd.mil/osbp/sbir/solicitations/index.shtml
Application Due Date:
Available Funding Topics
- MDA12-T004: EOIR Debris Management during ascent phase for C2BMC
- MDA12-T005: Post Intercept Debris Predictions for EO/IR Scene Modeling
- MDA12-T006: Human-in-Control (HIC) Modeling
- MDA12-T007: M & S Uncertainty Quantification
- MDA12-T008: High energy laser analysis tool with experimental verification of DPAL rate constants
EOIR Debris Management during ascent phase for C2BMC
OBJECTIVE: To characterize debris fields and derive a technique which enables system understanding of the debris field and any enclosed objects. DESCRIPTION: During observation from space based electro-optic or infra-red, EOIR, sensors, a missile complex may present debris clouds during boost or mid-course phases of flight. These clouds may appear as various geometric configurations from different sensors and if objects are detected in the clouds, it is important to describe to the system where the objects are in a manner that allows subsequent sensors to acquire them, enabling cross sensor track and correlation. Goals are to minimize the number of objects tracked by the system, while maximizing the positional accuracy as well as scene information content. This may involve development of what the optimal"object"is for transmission and any environmental characterization, or"cloud features"that could be developed. Factors that can be considered are waveband selection, as well as number of bands for information extraction. Debris mitigation occurs at the sensor as well as at the Command, Control, Battle Management and Communications, C2BMC, node. To prevent leakage, the sensor cannot attempt to screen out all debris, so some will likely pass as tracks to C2BMC. However, some tracks may be clusters of objects and the"track"passed to the system may be only a centroid of an extended object or field. This problem is a higher dimensional challenge familiar to astronomers who characterize galaxies with embedded stars. The 2MASS sky survey focused on detection, identification, and characterization of stellar extended sources, including a discussion of the point-spread function (PSF) -a basic component of star-galaxy separation and for us, object-debris separation. Similarly, the WISE data processing pipeline handles galaxies with embedded point sources. However, these methods address, essentially, viewing from a single position. For our purposes, the distance between sensor and observed object is not so great, and additional information can be gleaned from the opportunity to view the scene from multiple (two or three) viewing angles. In traditional image processing, or automatic target recognition, it is assumed that the object represented on the focal plane corresponds to a prototype stored in a database. Thus, the challenge is matching the observed image to the familiar prototype. In our case, the prototype could be as simple as a conic section, an ellipse or parabola, or slightly more complex shapes. Research has shown that a shape vector can be constructed that is a linear combination of prototypes that effectively tells how to warp the average shape of stored prototypes to match the observed image. Furthermore, an image could be represented as a shape vector and a texture vector, meaning, the pixel intensities, grey level or color values, of the image could represent the inhomogeneity of the image. Alternatively, multiple non-resolved point sources could be represented by the convex hull of the detections. For multiple sensors viewing the field, the Minkowski sum of the convex hulls, or support functions, could represent the debris field from various viewing angles. In this case, there may also be M clusters visible to one sensor and N clusters visible to a second, and reconciling the scene could pose difficulties, particularly for range calculations when tracking. Analysis needs to be performed on various ways to represent, and transmit, irregular debris fields with embedded targets, and methods derived to combine this information between sensors, as well as determine where in the field a target of interest could exist, from alternate viewing angles, particularly for C2BMC. The researcher may include multiple EOIR bands, however, targets will be at ranges that will cause them to appear on, at most, one pixel for the EO/IR focal plane. In general, multiple objects, targets and debris, or debris clouds may reach across numerous pixels. There is moderate technical risk as this is a complex, dynamic, time stressing task. But image analysis is a mature field and there are many other applications where similar techniques have been developed and should be available. Expanding the imaging analysis to two or three sensors will require more development. PHASE I: Develop and demonstrate through proof-of-principle tests, debris cloud, or multiple non-resolved object, characterization and transmission methods, such that C2BMC understands the scene adequately to take action. The small business and the research institution need to demonstrate coherent and mutually supporting goals and plans. PHASE II: refine and update concept(s) based on Phase I results and demonstrate the technology in a realistic environment using data from EOIR sensor sources. Demonstrate the technology"s ability real-time in a dense scene, with data from two or three spatially separated sensors. PHASE III: Demonstrate the new technologies via operation as part of a complete system or operation in a system-level test bed to allow for testing and evaluation in realistic scenarios. Market technologies developed under this solicitation to relevant missile defense elements directly, or transition them through electro-optical/infrared sensor vendors. DUAL USE/COMMERCIALIZATION POTENTIAL: The contractor will pursue commercialization of the various technologies and EO/IR components developed in Phase II for potential commercial and military uses in many areas including automated target and threat recognition, battle space surveillance, robotics, medical industry, and in manufacturing processes. REFERENCES: 1. Michael J. Jones, Pawan Sinha, Thomas Vetter and Tomaso Poggio , (1997), Topdown learning of low-level vision tasks, Current Biology, Vol 7 No 12 2. T. H. Jarrett, T. Chester, and R. Cutri, S. Schneider and M. Skrutskie, J. P. Huchra, (2000), 2MASS Extended Source Catalog: Overview And Algorithms, The Astronomical Journal, 119:2498-2531, 2000 May 3. Mark Nixon, Alberto S Aguado, (2008), Feature Extraction & Image Processing for Computer Vision, Academic Press; Second edition (January 22, 2008) 4. Kenneth R. Castleman, (1995), Digital Image Processing, Prentice Hall; 2nd edition (September 2, 1995) 5. J. R. Parker, (2010), Algorithms for Image Processing and Computer Vision, Wiley; 2 edition (December 21, 2010) 6. Jurgen Jost, (2011), Riemannian Geometry and Geometric Analysis, Springer; 6th ed. edition (August 9, 2011) 7. Mark L. G. Althouse, Chein-I Chang, (1991), Chemical Vapor Detection with a Multi-spectral Thermal Imager, Optical Engineering, Vol. 30 No.11, November 1991
Post Intercept Debris Predictions for EO/IR Scene Modeling
OBJECTIVE: Develop an innovative set of physics-based software tools and models to predict both prompt and late time electro-optical/infrared (EO/IR) signatures associated with the debris cloud generated after a missile intercept. The models should be fast-running, should address current and future missile intercept scenarios covering anticipated altitudes and closing velocities, and should be grounded in the critical physics interactions and phenomena. DESCRIPTION: As the Ballistic Missile Defense System (BMDS) continues to mature through the Phased-Adaptive Approach (PAA), a key priority in the development process is the ability of the system to effectively defend against raid scenarios. Successful BMDS operation in raid scenarios relies upon a robust kill assessment capability at both radio and optical/infrared (EO/IR) wavelengths. The wide variety of threats, engagement conditions and sensor viewing geometries dictate the need for a robust modeling and simulation capability in this area. Although many advances have been made in the modeling of radar signatures for post-intercept scenes, the EO/IR scene has presented a greater challenge due to its dynamic intensity range, environmental dependencies and varied phenomenology. Standard first-principle, finite-element modeling approaches experience practical difficulties due to limitations on the element size that can be explicitly modeled in these calculations. Specifically, IR flash signatures are generally dominated by particulates in the micron size regime whereas most finite element models achieve a resolution of order cm or mm. The proposed solution should overcome these obstacles, and remain scalable, massively parallel, and provide rapid turnaround. The debris prediction tool should be driven by first principle numerical modeling techniques and anchored to existing test data. Calculated post-intercept thermal signatures should reflect debris temperature, mass, surface geometry and emissivity. The required tools should calculate the heat generated in target and payload debris due to warhead impact and any subsequent reactions of high explosives that make up the payload. PHASE I: Assess state-of-the-art tools and techniques for simulation of EO/IR emission and temperature profiles of post-impact target debris. Propose new approaches that would address identified deficiencies in existing codes. First-principles simulations that validate the fast running models are encouraged. A critical component of this phase is determining the best general approach to the simulation of heat generated from warhead impact and payload reactions, and the EO/IR radiation emissions that result. The culmination of Phase I would be a work plan for implementing proposed approaches for simulation of late-time thermal signatures of post-impact target and warhead debris. PHASE II: Create numerical tools based on approaches identified in Phase I. This would include the algorithm and code development necessary to simulate temperature profiles and EO/IR signatures for propagating, evolving post-impact debris. Interactions with ambient material and the effects on the EO/IR signatures must be addressed. Demonstrate new algorithms using hypothetical intercept scenarios to show more clearly resolved lethal volumes. Validation will be done using existing MDA flight test data. The numerical tools produced in the stage must meet the requirements of being scalable, massively parallel, and offer quick turn around and analyses of flight test missions. Phase II validation work will be classified. PHASE III: These simulation tools will be used to provide direct guidance to flight test mission execution and BMDS sensor development and performance testing. Specifically, these tools would support pre-test mission planning to include range safety and sensor performance estimates, post-flight reconstruction activities, performance assessments of EO/IR BMDS sensors for realistic threat engagements, and integration with the MDA simulation architectures (e.g. SSF and DSA). DUAL USE/COMMERCIALIZATION POTENTIAL: Space Situational Awareness programs could benefit from this technology. This development effort would provide a fundamental improvement to an array of physics base simulation techniques, including heat generation due to high velocity impact, and coupled multi-body IR emission. Modeling of industrial processes such as welding, spray, sputtering deposition would also benefit from this topic"s simulation development effort. REFERENCES: 1) Jean, B., and Rollins, T. L.,"Radiation from Hypervelocity Impact Generated Plasma,"AIAA J. 8(10), 17421748 (1970) 2) Lawrence, R.J., Reinhart, W.D., Chhabildas, L.C., & Thornhill, T.F.,"Hypervelocity Impact Flash at 6, 11, and 25 km/s", in Shock Compression of Condensed Matter 2005, eds. M.D. Furnish et al. (AIP)
Human-in-Control (HIC) Modeling
OBJECTIVE: Develop and demonstrate an effective, repeatable simulation capability of Human-in-Control (HIC) interactions with other simulated capabilities. Provide capability to represent HIC proficiencies, decisions, decision timeliness, variabilities and outcomes at each interactive system within a system of systems, in order to qualify and quantify impacts on overall system behaviors, capabilities and performance. Provide repeatable simulated HIC performance in order to analyze and isolate any impacts of HIC variabilities on overall system performance and other sources of overall variability. Provide capability to quantify objectively the confidence in simulation-based predictions of HIC behavior metrics and impacts on overall system performance. Provide a delivered solution extensible and applicable to any current and future system capabilities. DESCRIPTION: Human operators are an essential part of BMDS, and their performance significantly shapes the capabilities of the entire BMDS. Warfighters currently participate in Ground Test, Training, Exercises and Wargaming. Simulation of Warfighter performance is additionally essential to Performance Assessment, Element Integration and Future Concept Analysis, but these typically constructive simulations often represent BMDS operators by only their respective"prescribed"Concepts of Operation (CONOPS); Tactics, Techniques and Procedures (TTPs); and Rules of Engagement (ROEs), without real-world variations in operator proficiency, timeliness, creativity, fatigue or morale. To this end, the BMDS Operational Test Agency (OTA) has made operator modeling one of its top priorities stating,"HIC (Human in Control) actions are not accurately modeled in Performance Assessments to assess BMDS performance"resulting in diminished credibility of the simulation. Constructive or synthetic representations of BMDS operators and their performance are desirable in all BMDS M & S Stakeholder Applications. For example, representative simulated BMDS operators in Performance Assessment would address the Director of Test and Evaluation"s specific concern regarding Warfighter performance impact cited in his annual report to Congress. Futuristic Concept Analyses could leverage these same simulated operators in BMDS performance trades analyses of battle management automation vice human operator tasking. Simulated operators can also significantly reduce event costs and lead times by supplementing"missing"human operators in Training, Exercises and Wargames. Simulated operators can extend the scalability of Ground Tests by replacing human operators in digital surrogate representations of BMDS tactical articles that are unavailable for the Ground Test. Simulated operators offer the additional opportunity for full controlled variability of operator performance. For example, Tier 4 operator training could involve a single student with the remaining crew filled out by simulated operators. While the timing and tasks of simulated operator performance is somewhat dependent on the human student"s actions, their variability is known and controllable by the training coordinator, so the coordinator can reliably measure and evaluate the single student"s performance and variability. MDA/DES seeks innovative simulated operator capabilities addressing the following needs and issues: Applicability and"tunability"to operators of any current BMDS Element or Component Representation of operator tasks, timelines, decisions, proficiencies, outcomes, variabilities Ability to adapt to unanticipated operational situations outside training and experience comparably as a human operator Extensibility to futuristic operator performance to support conceptual systems and human roles Validation, verification and accreditation Repeatability and controlled variability Objective quantification of confidence in simulation-based prediction of operator performance Affordability Ability to support both real-time and as-fast-as-possible M & S execution Scalability and portability across BMDS M & S architectures and Stakeholder Applications Usability by event conductors (e.g., training coordinators), including integration and scenario setup Integration with event scenario planning Representation of the effects of multi-mission activities on BMDS operator performance Insertion into existing and anticipated BMDS M & S architectures ("openness") Demonstrable innovation beyond existing human decision-making and performance modeling O & M considerations PHASE I: Develop an innovative HIC Model design for the BMDS context. The HIC Model design should demonstrate the offeror"s understanding of issues and principles of human decision-making and performance simulation in settings comparable to the BMDS context. The HIC Model design should incorporate the offeror"s innovation extending current state-of-the-art and practice. The HIC Model design should also clearly demarcate internally implemented functions from functionality provided by external services, such as time management provided by a BMDS M & S framework or integrated tool. A proof-of-concept demonstration of the HIC Model design or critical functions is highly desirable. Phase I work products should include HIC Model requirements, architecture artifacts and a HIC Model development plan addressing aspects of requirements allocation, design structure, anticipated behavior, functional completeness, limitations/exclusions/deferrals, extensibility, scalability, testing, technical risks, uncertainty quantification, VV & A, scenario planning augmentation, M & S tool insertion, intellectual property (IP) rights and O & M. IP ownership and use arrangements that would facilitate rapid and cost-effective integration and employment of the objective HIC Model capability are highly desirable. Collaboration of Phase I offerors with current M & S tool development and support organizations (DSOs) for both HIC Model requirements validation and risk reduction planning is also desirable. PHASE II: Implement the Phase I HIC Model design in a flexible capability for composition with BMDS M & S. Integrate, test and demonstrate the HIC Model capability with one or more BMDS M & S products. Support excursions or scenarios on a scheduled BMDS M & S-supported Event (e.g., Wargaming, Exercise, Ground Test) to gauge user acceptance of the HIC Model capability. Support DSOs integrating, testing and employing the HIC Model capability, and improve the HIC Model capability on the basis of critical feedback and operating experience from BMDS M & S, Test and Warfighter stakeholders. Develop, demonstrate, and publish a lean process for integration and test of the HIC Model capability with both current and new BMDS element models (e.g. Aegis BMD, THAAD, Patriot, C2BMC). PHASE III: Scale the functional and runtime performance of the HIC Model capability to accommodate stressing operator workloads representative of dense ballistic missile raids in small battle spaces anticipated in future BMD conflicts. Collaborate with COCOM Warfighter stakeholders to gauge the operational realism and military utility of the HIC Model capability in Joint exercises (of which BMD is only a part), and continue improvement responding to critical feedback from Joint Warfighter experience. Demonstrate utility of the HIC Model capability in a mission-critical BMDS M & S-supported activity, such as the DoT & E Performance Assessment. DUAL USE/COMMERCIALIZATION POTENTIAL: The contractor will pursue commercialization opportunities for the HIC Model in diverse related operator-in-the-loop, system-of-systems M & S contexts, such as battlefield, border and maritime ISR; Land, Air and Space control; and law enforcement. REFERENCES: Following is a very limited sample of the large literature on simulation of human decision-making and performance. 1. Modeling and Simulation Coordination Office (formerly Defense Modeling and Simulation Organization), 2001,"Human Behavior Representation (HBR) Literature Review,"http://vva.msco.mil/. 2. Silverman, Barry G., 2004,"Toward Realism in Human Performance Simulation,"Chapter 9 of The Science and Simulation of Human Performance, James W. Ness, Darren R. Ritzer, and Victoria Tepe (ed.), http://repostiroy.upenn.edu/ese.papers/292. 3. Dahn, David, and K. Ronald Laughery, 1997,"The Integrated Performance Modeling EnvironmentSimulating Human-System Performance,"in 1997 Winter Simulation Conference Proceedings, S. Andrak & #8000;ttir, K. J. Healy, D. H. Withers, and B. L. Nelson (ed.), http://www.informs-sim.org/wsc97papers/1141.pdf. 4. Ilar, Torbjrn P. E., 2008,"A Simplified Modeling Approach for Human System Interaction,"in 2008 Winter Simulation Conference Proceedings, S. J. Mason, R. R. Hill, L. Mnch, O. Rose, T. Jefferson, and J. W. Fowler (ed.), http://www.informs-sim.org/wsc08papers/108.pdf 5. Kim, Namhun, Jaekoo Joo, Ling Rothrock and Richard A. Wysk, 2010,"An Affordance-Based Formalism for Modeling Human-Involvement in Complex Systems for Prospective Control,"in 2010 Winter Simulation Conference Proceedings, B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Ycesan (ed.), http://www.informs-sim.org/wsc10papers/073.pdf.
M & S Uncertainty Quantification
OBJECTIVE: Develop and demonstrate Uncertainty Quantification (UQ) capabilities for Ballistic Missile Defense System (BMDS) Modeling and Simulation (M & S). Include methods and tools for efficiently, effectively specifying, representing and analyzing both epistemic (known unknown) and aleatoric (unknown unknown) uncertainties affecting BMDS outcomes. Provide UQ capabilities addressing M & S input uncertainties; intrinsic errors in algorithms, implementation and residual errors; and convolution of input uncertainties and intrinsic errors to uncertainties of M & S outputs. Provide UQ facilities for integration with both legacy and new BMDS M & S, and for leveraging planned or existing M & S tool or framework capabilities to manage or control M & S experimentation across end-to-end runs. Provide UQ capabilities to augment M & S experimentation and scenario planning. Provide means to quantify confidence in M & S predictions of BMDS outcomes. Provide UQ capabilities for M & S to assist BMDS Test planning. DESCRIPTION: M & S has traditionally chased modeling fidelity without a well based understanding of the uncertainty bounds associated with the underlying assumptions. UC facilitates and understanding of the parameters that the modeled system is sensitive to so that a sufficient resolution of input parameters can be quantified. Because M & S explicitly models (abstracts) the real world, M & S introduces intrinsic uncertainties and errors into the calculation of outcomes. Consequently, outputs of M & S contain uncertainties in inputs; intrinsic errors in algorithms; and residual errors in software or data ("bugs"). Recent developments in computational capacity and statistical approaches have facilitated the development of technology that could be used across the DoD, DoE and commercial market place. Significant uncertainties complicate engineering, fielding and employment decisions about BMD. BMDS uncertainties include both aleatoric and epistemic uncertainties (vernacularly distinguished as"known unknowns"and"unknown unknowns"). Aleatoric uncertainties are"irreducible"in the sense that they are always present, while epistemic uncertainties are often"reducible"through investment, time or research. An example of aleatoric BMDS uncertainty is space weather. An example of epistemic BMDS uncertainty is a threat trajectory before launch. UQ is the identification, characterization, propagation, analysis and reduction of all uncertainties in M & S . Motivated by the treaty, legal, hazard, expense and impracticality of testing, the US Department of Energy (DOE) laboratories established a version of UQ to assess quantitatively the reliability and safety of the US nuclear weapons stockpile [1, 2]. Motivated by strongly analogous limitations and aspiring to the Objectives enumerated above, MDA seeks to adapt UQ methodologies to the BMDS M & S domain. To this point, MDA"s BMDS M & S tools and frameworks embody very limited UQ facilities, such as pseudorandom number generators and Monte Carlo sampling from a limited selection of probability distributions. MDA seeks complete UQ processes and facilities enhancing a broad range of BMDS M & S products. BMDS M & S end users and stakeholders also explicitly call for UQ capabilities; for example, the BMDS Operational Test Agency (OTA) identified a need for"Monte Carlo capability"in support of BMDS Performance Assessment . MDA/DES seeks innovative UQ capabilities addressing the following needs and issues: Characterization, propagation and analysis of all uncertainties associated with BMDS M & S Appropriate, effective, efficient and affordable methods for quantification of both aleatoric and epistemic uncertainties Provision of UQ capabilities to both legacy and future BMDS M & S tools and frameworks Support for both real-time and as-fast-as-possible BMDS M & S Support for live, virtual and constructive BMDS M & S Provision of objective, quantified metrics for confidence in predictions from BMDS M & S Support for joint experimentation planning of M & S and Test to improve prediction confidence Usability by both M & S developers and M & S-based event planners PHASE I: Develop a M & S UQ capability architecture and roadmap. The offeror should specify and perform significant trade studies in selecting the best UQ methods for MDA"s BMDS M & S context. The offeror should identify and suggest mitigations to significant technical risks for UQ implementation and use. The M & S UQ capability architecture should incorporate the offeror"s innovation extending current state-of-the-art and -practice. The M & S UQ capability architecture should also clearly demarcate internally implemented functions from functionality provided by external services, such as a BMDS M & S framework or integrated tool. A proof-of-concept demonstration of the M & S UQ capability or a subset of critical functions is highly desirable. Phase I work products should include M & S UQ requirements, architecture artifacts and an architecture roadmap addressing aspects of requirements allocation, design structure, anticipated behavior, functional completeness, limitations/exclusions/deferrals, extensibility, scalability, testing, VV & A, scenario planning augmentation, M & S tool insertion, intellectual property (IP) rights and O & M. IP ownership and use arrangements that would facilitate rapid and cost-effective integration and employment of the objective M & S UQ capability are highly desirable. Collaboration of Phase I offerors with current M & S tool development and support organizations (DSOs) for both UQ requirements validation and risk reduction planning is also desirable. PHASE II: Implement the Phase I M & S UQ architecture in a flexible capability for composition with BMDS M & S. Integrate, test and demonstrate the M & S UQ capability with one or more BMDS M & S products. Support excursions or scenarios on a scheduled BMDS M & S-supported Event (e.g., Future Concept Analysis, Element Integration) to gauge user acceptance of the UQ capability. Support DSOs integrating, testing and employing the UQ capability, and improve the UQ capability on the basis of critical feedback and operating experience from BMDS M & S, Test and Warfighter stakeholders. Develop, demonstrate, and publish a lean process for integration and test of the UQ capability with both current and new BMDS M & S tools (e.g. I-SIM, DSA-P, EADSIM). PHASE III: Scale the functional and runtime performance of the UQ capability to accommodate both epistemic and aleatoric uncertainties characteristic of dense ballistic missile raids in small battle spaces anticipated in future BMD engagements. Collaborate with COCOM Warfighter stakeholders to gauge the operational realism and military utility of the UQ capability in Joint exercises (of which BMD is only a part), and continue improvement responding to critical feedback from Joint Warfighter experience. Demonstrate utility of the UQ capability in a mission-critical BMDS M & S-supported activity, such as the DoT & E Performance Assessment. DUAL USE/COMMERCIALIZATION POTENTIAL: The contractor will pursue commercialization opportunities for the UQ capabilities in diverse domains, such as Military Space, Space Exploration, Air Traffic Control, Homeland Security, Law Enforcement and other contexts in which the impracticality or impossibility of Test necessitates UQ with M & S. REFERENCES: 1. Center for Applied Scientific Computing,"The PSUADE Uncertainty Quantification Project,"Lawrence Livermore National Laboratory, US Department of Energy, https://computation.llnl.gov/casc/uncertainty_quantification. 2. Helton, Jon C., 2009,"Conceptual and Computational Basis for the Quantification of Margins and Uncertainty,"Sandia Report SAND2009-3055, Sandia National Laboratory, US Department of Energy, Albuquerque, NM. 3. Skinner, James, 2010,"BMDS OTA Performance Assessment Requirements,"BMDS Operational Test Agency.
High energy laser analysis tool with experimental verification of DPAL rate constants
OBJECTIVE: Develop, or build upon existing models, a set of physics-based software tools to perform high fidelity modeling for MDA"s high-energy lasers. This tool should allow MDA researchers to perform laser performance and sensitivity analysis tasks (e.g. power, beam quality, efficiency trades, etc.). Development includes university research to assist with model formulation and experimental verification of key rate coefficients relevant to MDA"s laser systems. DESCRIPTION: MDA"s Directed Energy vision includes development of Diode Pumped Alkali Lasers (DPALs), and potentially other lasers. To assist in the development and assessment of these laser technologies, MDA requires a set of physics-based software tools to perform laser performance and sensitivity analysis tasks. Ultimately, a tool or set of tools is desired that canwith minimal future effortbe generalized to model nearly any high-energy laser. Development of these analytical tools should be leveraged with university research in model development and experimental verification of relevant rate coefficients. In the final product, the offeror must assemble modular packages that incorporate but is not limited to the following capabilities: 1. Computational Fluid Dynamics (CFD) 2. Coupled kinetics with the best rate constants extant 3. Wave-optics to study alternative strategies for coupling pump diodes with the gain medium 4. The ability to study alternative strategies for suppression of parasitic modes, viz. amplified stimulated emission (ASE). The goal is essentially to pair small businesses proficient in modeling CFD, optics, and laser resonators to collaborate with university personnel who have the expertise to measure rate constants; the final product should provide substantial insight in to laser efficiency and beam quality. Offerors are also encouraged to present solutions that leverage relevant existing tools / tool sets, and provide a capability that is scalable and massively parallel with reasonable turnaround times. PHASE I: Demonstrate an understanding of the challenges associated with modeling the lasers identified previously, begin developing analytical tools, and identify & catalog relevant experimental coefficients required for the analytical tools. The university component of proposed efforts should identify parameters of interest to the modeling efforts and propose experiments for Phase IIor the end of Phase Ito verify key parameters that are not well known or understood. For example, coefficients for DPALS should focus onbut are not limited torate constants specific to Rubidium and Helium kinetics (and buffer gases for lower operating pressures), a pressure range 0-20 Atm, and a temperature range of: 100 degrees C - 300 degrees C. The Phase I work product should include a clear technology development plan, schedule, and transition risk assessment. These details should be presented in the Phase I final report along with progress on analytical tools development. Offerors are highly encouraged to interact with MDA/DVLD for feedback and input to ensure the final products are developing along a useful path. PHASE II: Implement the selected solutions proposed/started in Phase I. The university component should execute proposed experiments for the key system parameters. The objective is to validate a robust and producible technology approach that MDA users and prime contractors can transition in Phase III for their unique application. The goal is to demonstrate technology viability and the offeror needs to have working relationships with MDA/DVLD to assist with analysis validation and architecture feasibility. PHASE III: Finalize a product that can be used for multiple high-energy laser applicationse.g. the final product may be able to work with other high energy laser technologies with the insertion of additional appropriate laser kinetics subroutines and rate constants. The objective is to demonstrate the developed technology, and transition the technology. A major step in this Phase III will be developing a means to release the tools to appropriate organizations/ groups within the DoD community in a way the complies with ITAR restrictions. Additionally the contractor should demonstrate a secure means of protecting the software from misuse and/or improper release. A partnership with a current or potential supplier of MDA element systems, subsystems or components is highly desirable, as is interaction with the High Energy Laser Joint Technology Office modeling and simulation efforts. COMMERCIALIZATION: The ability to model high-energy laser components has the potential for many commercial applications. High power lasers have numerous commercial and other Government agency applications in metal cutting, material processing, welding, remote sensing (both terrestrial and space), satellite communications, power beaming, and weather sensing. Outside of MDA, numerous other DoD applications of the technology include tracking, designation, directed energy, demilitarization of munitions, and IED destruction. The contractor is also encouraged to identify additional sources. REFERENCES: 1) D. Hostutler, W. Klennert, Power Enhancement of a Rubidium Vapor Laser with a Master Oscillator Power Amplifier (Postprint), AFRL-RD-PS-TP- AFRL-RD-PS-TP-2009-1016, 15 September 2009, http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA506024 & Location=U2 & doc=GetTRDoc.pdf 2) DPALS symposium at the SPIE High Power and Laser Ablation (HPLA) conference, Santa Fe, 2008 (Proc SPIE, 7005). 3) W.F. Krupke, Diode pumped alkali lasers (DPALs) an overview, Proc. SPIE High-Power Laser Ablation, Vol. 7005, pp. 700521-13, (2008). 4) R. Magnusson, Y. Ding, K.J. Lee, D. Shin, P.S. Priambodo, P.P. Young, T.A. Maldonado, Photonic devices enabled by waveguide-mode resonance effects in periodically modulated films, Proc SPIE, Vol. 5225, No.1, pp. 20-34, (2003). 5) B. Zhdanov and R.J. Knize,Diode-pumped 10 W continuous wave cesium laser, Opt. Letters, Vol. 32, No. 15, pp. 2167-2169, (2007). 6) B. Zhdanov, T. Ehrereich, and R.J. Knize, Narrowband external cavity laser diode array, Elec. Letters, Vol. 43, No. 4, pp. 221-222, (2007). 7) Aleksey M. Komashko; Jason Zweiback, Modeling laser performance of scalable side pumped alkali laser (Proceedings Paper) SPIE Proceedings Vol. 7581, High Energy/Average Power Lasers and Intense Beam Applications IV, 17 February 2010. 8) Boris Zhdanov, Thomas Ehrenreich, and Randall Knize, Optically pumped alkali-vapor lasers http://spie.org/documents/Newsroom/Imported/412/2006090412.pdf 9) Zhdanov, B.V. Stooke, A. Boyadjian, G. Voci, A. Knize, R.J., 17 Watts continuous wave Rubidium laser, IEEE Lasers and Electro-Optics, 2008 and 2008 Conference on Quantum Electronics and Laser Science. CLEO/QELS 2008. 4-9 May 2008. 10)"Ballistic Missile Defense Review,"Office of the U. S. Secretary of Defense, February 2010. Available via internet at http://www.defense.gov/bmdr/