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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. An Integrated Materials Informatics/Sequential Learning Framework to Predict the Effects of Defects in Metals Additive Manufacturing

    SBC: Citrine Informatics, Inc.            Topic: N18AT013

    In this project, Citrine Informatics and the ADAPT Center at the Colorado School of Mines propose to build an informatics-driven system to understand the effects of defects in additive manufactured parts. The entire history of each sample will be captured on this system; from specific printing parameters and details of precursor materials through to part characterizations and performance measureme ...

    STTR Phase I 2018 Department of DefenseNavy
  2. Analysis and Application of Treatments to Mitigate Exfoliation Corrosion (Delamination) of 5XXX Series Aluminum

    SBC: OCEANIT LABORATORIES INC            Topic: N18AT016

    Oceanit proposes to research and develop chemical or non-chemical methods and processes to impart surface morphology modifications to aluminum-magnesium (Al-Mg) alloys to mitigate and increase the exfoliation corrosion resistance.

    STTR Phase I 2018 Department of DefenseNavy
  3. Optimization of Fatigue Test Signal Compression Using the Wavelet Transform

    SBC: ATA ENGINEERING, INC.            Topic: N18BT029

    Traditional approaches to accelerated fatigue testing rely on heuristic methods with thresholds based mostly on experience and engineering judgment. These methods generally do not apply to the multiaxial dynamic loading situations characteristic of most aerospace applications and often result in uncharacteristic fatigue damage and failure modes during testing. To overcome the limitations of tradit ...

    STTR Phase I 2018 Department of DefenseNavy
  4. Ignition Modeling for Present and Future Combustors and Augmentors

    SBC: COMBUSTION SCIENCE & ENGINEERING, INC.            Topic: N17AT003

    The ability to predict the ignitibility potential of a combustor at various operating conditions is not practical at this time due to the complexity of this process. Ignition within a gas turbine combustor is dependent on various parameters; including spark (or plasma) energy, flow conditions, fuel/air ratio, and fuel spray density. All these parameters must be properly predicted in order to effec ...

    STTR Phase II 2018 Department of DefenseNavy
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