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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.

Displaying 1 - 10 of 7924 results
  1. Improved Particle Tracking Through Efficient Scale-Resolving Simulations and Advance Physics Models

    SBC: Technology In Blacksburg Inc            Topic: N23AT003

    It is well known that gas-turbine powered aircraft operating in brown-out conditions can ingest a significant amount of particles leading to reduced engine performance, a reduction in time between maintenance and increased safety concerns. As such, it is critical to be able to accurately model the impact of ingested particles in engines for both maintenance purposes and in the design of new, more ...

    STTR Phase I 2023 Department of DefenseNavy
  2. Microwave Radiator for Curing Polymer Composites (MRCPC)

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT006

    The Navy requires a low-cost, industrial microwave system for curing aerospace composite materials. In this Phase I STTR proposal, Physical Sciences Inc outlines the development of a microwave applicator that uses low-cost RF sources and can be installed in heritage autoclaves for curing large aerospace composite parts. This technology has the potential to improve cured mechanical properties, and ...

    STTR Phase I 2023 Department of DefenseNavy
  3. Composite Microwave Curing Hybrid Simulation Model

    SBC: TDA RESEARCH, INC.            Topic: N23AT006

    TDA proposes to develop a multi-physics-based model to simulate and optimize the microwave curing process of thick fiber reinforced composites. The primary objective is to improve the manufacturing speed and cost of high-quality carbon fiber reinforced composites by quickly identifying the right processing parameters for a given part during microwave processing. The model will account for the inte ...

    STTR Phase I 2023 Department of DefenseNavy
  4. Large-scale Meta-optic Optimization

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT008

    Physical Sciences Inc. (PSI), in collaboration with Stanford University, will develop an electromagnetic simulation package used for the development and optimization of large-scale meta-optics, and demonstrate the functionality of the package in the long-wave infrared (LWIR). Our team will combine recent progress in physics-augmented deep learning neural networks with rigorous far-field diffractio ...

    STTR Phase I 2023 Department of DefenseNavy
  5. Tributyltin Oxide Prediction Application

    SBC: LUNA LABS USA LLC            Topic: N23AT010

    Marine fouling has always been a significant problem to manage. All the solutions have associated costs and additional maintenance. If the fouling is left untreated, fuel costs increase, and maneuverability is reduced. Eventually the ship must be docked and cleaned leading to availability and cleaning costs. Alternatively, the hull and other components below the water line can be coated or imbued ...

    STTR Phase I 2023 Department of DefenseNavy
  6. Gradient index for reduced integration costs (GRIN-RICH)

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT011

    Physical Sciences Inc. partnered with Alfred University will develop an F/1, 90 degree full field of view MWIR/SWIR gradient index (GRIN) compound lens for reduced size and lens integration cost. The element-by-element achromatization and athermalization of GRIN provide useful performance improvements to GRIN systems. Element count is reduced (= 2), diversity of optical material needed is fixed, a ...

    STTR Phase I 2023 Department of DefenseNavy
  7. Sensor Modality Translation through Contrastive Deep Learning

    SBC: PHYSICAL SCIENCES INC.            Topic: N23AT013

    Physical Sciences Inc. (PSI), in collaboration with the University of Rhode Island, proposes to develop an advanced algorithm suite for data translation across sensing modalities to support the development of automated target recognition and classification algorithms for Unmanned Underwater Vehicles. The proposed Deep Diffusion Sensor Translation (DDST) leverages recent advancements in generative ...

    STTR Phase I 2023 Department of DefenseNavy
  8. Human Automation Teaming for Efficient Knowledge Extraction and Test Generation

    SBC: BARRON ASSOCIATES, INC.            Topic: N23AT014

    Effective tools for evaluating the proficiency of warfighters at employing complex systems in an operational setting are essential to ensure operational capability within the Navy and other branches of the DoD. Generating exams with answer keys is a time-consuming process, made more difficult by the complexity of both the systems and the operational employment of those systems.  The burden is fur ...

    STTR Phase I 2023 Department of DefenseNavy
  9. Knowledge Extraction for the Evaluation of Learning

    SBC: IN-DEPTH ENGINEERING CORPORATION            Topic: N23AT014

    To maintain undersea dominance in an environment of near peer adversaries the Navy must deploy new capabilities rapidly. In response, development contractors are adopting a DevSecOps culture with agile processes to meet the demand for new technology deployment. To keep pace, the training community needs innovative technology to rapidly update training knowledge bases and automatically generate exa ...

    STTR Phase I 2023 Department of DefenseNavy
  10. Basis Expansion with Transformer and Two-map Spectrum Inference (BETTSI)

    SBC: SHARED SPECTRUM COMPANY            Topic: N23AT017

    Shared Spectrum Company (SSC) and George Mason University (GMU) propose to design Basis Expansion with Transformer and Two-map Spectrum Inference (BETTSI). BETTSI is a distributed, coherent sensing solution to generate a spectrum map of available channels in sparse or dense spectral environments for channel allocation in a decentralized multi-hop network. Sensing-based spectrum allocation is a dif ...

    STTR Phase I 2023 Department of DefenseNavy
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