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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

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. Accelerated Burn-In Process for High Power Quantum Cascade Lasers to Reduce Total Cost of Ownership

    SBC: ADTECH PHOTONICS, INC            Topic: N20BT029

    Quantum Cascade Lasers (QCLs) are one of the most versatile sources of radiation in the mid-infrared range and have found applications in a variety of fields. Despite their widespread adoption, one of the main hurdles holding QCLs back from large volume manufacturing is the large cost of ownership. While QCLs, like most semiconductor devices based on III-V compounds, can leverage the economies of ...

    STTR Phase I 2021 Department of DefenseNavy
  2. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  3. Multi-scale Physics-based Modeling of Particle-Impact Erosion of CMCs

    SBC: CFD Research Corporation            Topic: N19BT033

    Sand particles ingested into aeroengines can impinge on components made of ceramic-matrix composites (CMCs) and cause structural damage including long-term erosion. Experimental analysis of erosion typically focuses on the damage footprint and mass loss and is limited in the range of operating parameters that can be examined. Hence, high-fidelity modeling of the erosion process is essential to der ...

    STTR Phase I 2020 Department of DefenseNavy
  4. Analysis and Modeling of Erosion in Gas-Turbine Grade Ceramic Matrix Composites (CMCs)

    SBC: Alpha Star Corporation            Topic: N19BT033

    A significant barrier to the insertion of ceramic matrix composite (CMC) materials into advanced aircraft engines is their inherent lack of toughness under erosion and post erosion. Our team will develop and demonstrate a physics-based model for erosion/post erosion of CMC’s at room and elevated temperatures (RT/ET). The ICME (Integrated Computational Material Engineering) Physics based Multi Sc ...

    STTR Phase I 2020 Department of DefenseNavy
  5. Hexahedral Dominant Auto-Mesh Generator

    SBC: Hypercomp, Inc.            Topic: N20AT004

    The objective of our proposed STTR phase-I work is to transition the latest advancements within the academic community to the design of a robust, user-friendly, and application-oriented tool for automatic hex-dominant meshing. Our software will fully couple CAD models to the discretized domain required by finite element software in structural analysis and other simulation and modeling applications ...

    STTR Phase I 2020 Department of DefenseNavy
  6. Hexahedral Dominant Auto-Mesh Generator

    SBC: M4 ENGINEERING, INC.            Topic: N20AT004

    Advances in both software and computer hardware have made the finite element method the preeminent choice for analyzing highly complex systems that are of great value to the Department of Defense.   The US Defense industry, however, continues to spend enormous time and resources in mesh generation, a key step in finite element analysis, despite progress that has been made in automated mesh gener ...

    STTR Phase I 2020 Department of DefenseNavy
  7. Advanced, High-Performance, Low-Noise Propeller Designs for Small UxS

    SBC: CFD Research Corporation            Topic: N20AT006

    Improved propeller designs for Small Unmanned Aerial Systems are needed to improve performance and reduce acoustic emissions. Traditional propeller design methods don’t take advantage of advances in coupled fluid, structure and acoustics computational design methods nor advances in high strength, high modulus materials to extend performance of propellers and reduce noise emissions. In the propos ...

    STTR Phase I 2020 Department of DefenseNavy
  8. High Efficiency Propeller for Small Unmanned X Systems using Advanced Composite Materials

    SBC: CATTO PROPELLERS            Topic: N20AT006

    In the proposed STTR study, Catto Propellers, Inc. (Catto) and the University of North Dakota (UND) will create an efficient new propeller design utilizing advanced composite materials for use on small unmanned x systems.  During Phase I, a comprehensive study will be conducted to develop a new propeller design in order to increase propeller efficiency, reduce aerodynamic noise and utilize innova ...

    STTR Phase I 2020 Department of DefenseNavy
  9. PARTEL: Periscope video Analysis using Reinforcement and TransfEr Learning

    SBC: MAYACHITRA, INC.            Topic: N20AT007

    We propose a suite of video processing algorithms utilizing the machine learning (ML) techniques of artificial intelligence (AI) reinforcement learning, deep learning, and transfer learning to process submarine imagery obtained by means of periscope cameras. Machine learning (ML) can help in addressing the challenge of human failure of assessing the data of periscope imagery. Though pre-tuned blac ...

    STTR Phase I 2020 Department of DefenseNavy
  10. Machine Learning for Transfer Learning for Periscopes

    SBC: Arete Associates            Topic: N20AT007

    Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop and demonstrate new approaches that improve the performance of in situ machine learning (ML) algorithms as they evolve over time, adapt to new environments, and are capable of transferring their learned experiences across platforms.  Technological advances that will be brought t ...

    STTR Phase I 2020 Department of DefenseNavy
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