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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.

Download all SBIR.gov award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

  1. Interlaminar Mode I and Mode II Fracture Toughnesses in Ceramic Matrix Composites (CMCs)

    SBC: Thornton Tomasetti, Inc.            Topic: N13AT008

    Susceptibility to delamination is one of the major weaknesses of ceramic matrix composites (CMCs). Knowledge of the resistance of composite to interlaminar fracture is essential for life cycle prediction analyses of structural components. The current test method for Mode I-interlaminar fracture toughness, the double cantilevered beam (DCB), is not satisfactory for thin CMC specimens because the co ...

    STTR Phase I 2013 Department of DefenseNavy
  2. Development of Next-Generation Composite Flywheel Design for Shock and Vibration Tolerant, High Density Rotating Energy Storage

    SBC: Mohawk Innovative Technology, Inc.            Topic: N13AT022

    The overall objective of the Phase I and Phase II proposed effort is to design and demonstrate the ability to develop a high-speed shock tolerant composite flywheel energy storage system (FESS) using a low cost manufacturing process. The Phase I tradeoff design studies will assess the FESS size, operating speeds and material requirements needed to achieve the energy density levels and charge/disch ...

    STTR Phase I 2013 Department of DefenseNavy
  3. Progressive Model Generation for Adaptive Resilient System Software

    SBC: GRAMMATECH INC            Topic: N13AT014

    Software continues to be a weak link in our critical systems. A prudent operator should employ a defense-in-depth strategy whereby"safe"systems are still monitored to detect breaches and respond to them. Unfortunately, such monitoring is challenging in practice, since there is no universal pattern that characterizes misbehaving software. We will capture an application"s intended behavior as it is ...

    STTR Phase I 2013 Department of DefenseNavy
  4. Maneuver Prediction and Avoidance Logic For Unmanned Aircraft System Encounters with Non-Cooperative Air Traffic

    SBC: NUMERICA CORP            Topic: N13AT003

    For Unmanned Aircraft Systems (UAS) to operate seamlessly in both the U.S. National Airspace System (NAS) and abroad, it will be crucial that they possess a sense-and-avoid (SAA) capability that can ensure safe operations among maneuvering, non-cooperative aircraft. Numerica Corporation, in partnership with Johns Hopkins University, proposes to develop a set of algorithms to model the uncertaintie ...

    STTR Phase I 2013 Department of DefenseNavy
  5. Progressive Model Generation for Adaptive Resilient System Software

    SBC: SECURBORATION INC            Topic: N13AT014

    Complex software systems are typically developed by disparate engineering teams working concurrently. At the same time, software requirements are frequently dynamic, evolving even during active development cycles. Discrepancies between how software is defined and how it is implemented at the modular level can cascade into critical system errors when modules are integrated. More troubling is that i ...

    STTR Phase I 2013 Department of DefenseNavy
  6. Compact, cold-atom clock for Navy field use

    SBC: VESCENT PHOTONICS LLC            Topic: N13AT018

    Vescent Photonics proposes to develop a compact laser system and integrate it with a cold-atom micro primary standard developed under the DARPA IMPACT program. In phase I we will investigate performance enhancements resulting from immobilizing the cold-atom sample with an optical lattice formed from an optical field whose wavelength is chosen to minimize the differential light shifts between the s ...

    STTR Phase I 2013 Department of DefenseNavy
  7. Naval Platform Aero-Optic Turbulence and Mitigation Methodology

    SBC: MZA ASSOCIATES CORP            Topic: N13AT001

    MZA partnered with the University of Notre Dame proposes to conduct high-fidelity computational fluid dynamics (CFD) simulations providing volumetric time-resolved aero-optical disturbance modeling for rotary-wing aircraft flow dominated by wing-tip vortices. We will develop detailed wave-optics models of a baseline Navy helicopter beam director including engineering-level simulations of the beam ...

    STTR Phase I 2013 Department of DefenseNavy
  8. Integration of PALACE and Touchdown Planning Methods for Landing CUAS at Unprepared Sites

    SBC: AURORA FLIGHT SCIENCES CORPORATION            Topic: N10AT039

    Aurora Flight Sciences and MIT have been developing tools and techniques that, together with existing 3D environment decision-making and navigation tools developed by AMRDEC in the PALACE program, are well-suited to the problem of autonomous vertical landing on unprepared landing sites. In this program, Aurora will team with MIT researchers and UC Santa Cruz (UCSC licenses PALACE technologies for ...

    STTR Phase I 2010 Department of DefenseNavy
  9. Adaptive Turbine Engine Control for Stall Threat Identification and Avoidance

    SBC: AURORA FLIGHT SCIENCES CORPORATION            Topic: N10AT008

    Aurora Flight Sciences and MIT propose to develop a model-based adaptive health estimation and real-time proactive control to identify gas turbine engine stability risks and avoid them through control action. In this concept, the engine control system actively monitors sensors and actuators, compares them against physical models, and infers which components may be performing poorly and may need to ...

    STTR Phase I 2010 Department of DefenseNavy
  10. Multi-Modal Knowledge Acquisition from Documents

    SBC: ObjectVideo            Topic: N10AT019

    Images with associated text are now available in vast quantities, and provide a rich resource for mining for the relationship between visual information and semantics encoded in language. In particular, the quantity of such data means that sophisticated machine learning approaches can be applied to determine effective models for objects, backgrounds, and scenes. Such understanding can then be used ...

    STTR Phase I 2010 Department of DefenseNavy
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