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The Award database is continually updated throughout the year. As a result, data for FY24 is not expected to be complete until March, 2025.

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.

The SBIR.gov award data files now contain the required fields to calculate award timeliness for individual awards or for an agency or branch. Additional information on calculating award timeliness is available on the Data Resource Page.

  1. Neurofeedback Training and Hyperscanning for Mission Readiness and Return-to-Duty via Functional Near-Infrared Spectrometry (fNIRS)

    SBC: SOAR TECHNOLOGY INC            Topic: DHA19B001

    Until now,much of theresearch usingfunctional near-infrared spectroscopy (fNIRS) has focused on tailoringasystem to detect onlyafew cognitivestatesand theapplication of theseapproaches outsidethe laboratory is not well tested. This solution provides severely limited coverage of thespacethat this technology could beapplied to,and is notarealistic path for developing neuroimagingasan operational ass ...

    STTR Phase I 2020 Department of DefenseDefense Health Agency
  2. Novel Circulating RNA-based Markers as Diagnostic Biomarkers of Infectious Diseases

    SBC: CFD RESEARCH CORPORATION            Topic: CBD18A001

    In resource limited settings, rapid and accurate diagnosis of infections is critical for managing potential exposures to highly virulent pathogens, whether occurring from an act of bioterrorism or a natural event. This is especially important for hard to detect intracellular bacterial and alphavirus infections, that overlap symptomatically and often treated empirically due to a lack of reliable an ...

    STTR Phase II 2020 Department of DefenseOffice for Chemical and Biological Defense
  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. Model for Surface Finish Prediction and Optimization of Metal Additively Manufactured Parts

    SBC: Global Engineering and Materials, Inc.            Topic: N19BT034

    Global Engineering and Materials, Inc. (GEM) along with its team members, Northwestern University (NWU) and National Institute for Aviation Research (NIAR) at Wichita State University, propose developing a multiscale multi-physics modeling tool for rapid prediction of the surface finish of additively manufactured (AM) parts as a function of AM process parameters. A high-fidelity multi-physics tool ...

    STTR Phase I 2020 Department of DefenseNavy
  5. Machine Learning Tools to Optimize Metal Additive Manufacturing Process Parameters to Enhance Fatigue Performance of Aircraft Components

    SBC: Global Engineering and Materials, Inc.            Topic: N20AT002

    Global Engineering and Materials, Inc. (GEM) along with team members, Northwestern University (NU) and National Institute for Aviation Research (NIAR) at Wichita State University, propose a coupled tool based on machine learning and integrated computational materials engineering (ICME) to optimize metal additive manufacturing (AM) process parameters to improve fatigue performance of aircraft compo ...

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

    SBC: Global Engineering and Materials, Inc.            Topic: N20AT004

    Global Engineering and Materials, Inc. (GEM) along with its team members Carnegie Mellon University (CMU), and HexSpline3D propose to develop a novel hybrid modeling approach for automatic and interactive mesh generation using predominately hexahedral finite elements for naval aviation structures. The hybrid approach consists of a global geometric decomposition, an initial hex-dominant mesh genera ...

    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. Highly Efficient Low Noise Propellers and Rotors for Unmanned Systems

    SBC: CONTINUUM DYNAMICS INC            Topic: N20AT006

    Small Unmanned Air Systems (UAS) are critical to deploying sensors, communications and other mission payloads, however, UAS noise, most notably propeller noise, can jeopardize mission success because vehicles can be detected beyond the range of payload effectiveness.  Continuum Dynamics, Inc. and Georgia Institute of Technology propose to develop a new class of highly efficient ultra-quiet propel ...

    STTR Phase I 2020 Department of DefenseNavy
  9. Hybrid Integration of Photonics and Cryogenic Electronics with Magnetic Shielding

    SBC: SYSTEMS VISIONS, LLC            Topic: N20AT021

    The "Hybrid Integration of Photonics and Cryogenic Electronics with Magnetic Shielding (HIPCEMS)” effort will develop a scalable heterogeneous packaging plan which results in extreme energy efficiency information transfer at high clock rates and low bit error rate of digital data between superconducting and photonic technologies, each at 4K. HIPCEMS will feature a mechanically robust package tha ...

    STTR Phase I 2020 Department of DefenseNavy
  10. Reduction of Predictable Spurs in the ADC outputs using AI

    SBC: VIRTUAL EM INC.            Topic: N20AT025

    An AI-based algorithm is being proposed to increase ADC linearity by 10dB. Neural Nets will be investigated in conjunction with models of spurs to accomplish the task.

    STTR Phase I 2020 Department of DefenseNavy
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