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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. Acoustic Intercept Receiver for Naval Special Warfare Undersea Vehicles

    SBC: INFORMATION SYSTEMS LABORATORIES INC            Topic: N09T012

    Information Systems Laboratories (ISL) and Florida Atlantic University (FAU) propose to develop and test a system that uses existing signal processing algorithms coupled with innovative construction technology developed ISL under our E-Field sensor programs and FAU under UUV programs. The Challenge is to develop a small system package with the capability to intercept active threat emissions early ...

    STTR Phase II 2010 Department of DefenseNavy
  2. Active Combustion Control (ACC) of Augmentor Dynamics

    SBC: Knite Inc            Topic: N14AT004

    The proposed research for Phase II of topic N14A-T004, Active Combustion Control of Augmentor Dynamics, has the objective of maturing the Augmentor Screech Active Suppression technology demonstrated successfully during Phase I by Knite, Inc. and the University of Cincinnati Gas Dynamics and Propulsion Laboratory. The work plan is intended to provide continued progress focusing on increasing knowle ...

    STTR Phase II 2016 Department of DefenseNavy
  3. Adaptive Fleet Synthetic Scenario Research

    SBC: KAB LABORATORIES INC.            Topic: N10AT044

    Synthetic scenario-based training of Navy personnel in the use of Navy SIGINT/IO systems has helped to reduce training costs, and it has enabled the personnel to be trained in an environment that sufficiently approximates real-world situations that could not otherwise be accomplished within the class room. However, scenario development is highly complex and involves a great deal of human effo ...

    STTR Phase I 2010 Department of DefenseNavy
  4. Adaptive Learning for Stall Pre-cursor Identification and General Impending Failure Prediction

    SBC: Frontier Technology Inc.            Topic: N10AT008

    Frontier Technology, Inc. (FTI) and Northeastern University propose to investigate and develop an innovative approach to predict stall events of aircraft engines prior to occurrence and in sufficient time to allow the FADEC controller to adjust engine variables. The team will utilize vector quantization and neural network techniques to develop accurate models of engine behavior that will be used t ...

    STTR Phase I 2010 Department of DefenseNavy
  5. 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
  6. Additive Manufacture of Inorganic Freeform Gradient-Index Optics

    SBC: VOXTEL, INC.            Topic: N19BT028

    An additive-manufacturing process will be developed for printing high-quality freeform inorganic gradient-index (GRIN) optical elements. Candidate nanocrystal materials including ZnS, ZrO, TiO2, PbSe, and rare-earth-doped metal oxides, will be synthesized and characterized. A process for inkjet-print depositing high-volume-percent loading density of binary, ternary, quaternary, etc., nanoparticle ...

    STTR Phase I 2020 Department of DefenseNavy
  7. Additive Manufacturing for Microwave Vacuum Electron Device Cost Reduction

    SBC: RADIABEAM TECHNOLOGIES, LLC            Topic: N16AT010

    The Department of the Navy has a need for the development of an additive manufacturing (AM) process for key vacuum electronic device components to meet on-demand, flexible, and affordable manufacturing requirements. The developed manufacturing method has a potential to reduce cost of vacuum electronics by as much as 70% as well as simplify and hence expedite production process of these devices by ...

    STTR Phase I 2016 Department of DefenseNavy
  8. Additive Manufacturing of Inorganic Transparent Materials for Advanced Optics

    SBC: IRFLEX CORP            Topic: N19BT028

    Additive manufacturing (AM) technology offers the capability to use multiple glass materials and to print complex freeform shape designs and gradient index (GRIN) optics. Although AM is widely used to print commercial 3-dimension metal and plastic/polymer parts, there are no viable commercial solutions for AM of inorganic transparent glasses for high-quality optical components.The proposed work wi ...

    STTR Phase I 2020 Department of DefenseNavy
  9. Additive Manufacturing of 17-4 PH Stainless Steel Metal Matrix Composites using Nickel functionalized Carbon Nanotubes

    SBC: SHEPRA, INC.            Topic: N16AT007

    Additive Manufacturing (AM) has a potential to significantly reduce the cost and lead time associated with the maintenance and sustainment issues faced by the US Navy. However, current materials such as 17-4 PH Stainless Steel typically achieve half the required mechanical properties when additively manufactured, thus limiting the use of AM in critical parts. Recent advancements in carbon nanotube ...

    STTR Phase I 2016 Department of DefenseNavy
  10. Advanced Command and Control Architectures for Autonomous Sensing

    SBC: TOYON RESEARCH CORPORATION            Topic: N18BT030

    We propose to develop an innovative open architecture for the semi-autonomous command and control (C2) of teaming Unmanned Aircraft Systems (UAS). The proposed architecture, based upon Toyon’s Decentralized Asset Management system, supports both centralized and decentralized fusion and control autonomy solutions as well as hybrids approaches. Leveraging STANAG-4586, TCP/IP, UPD, Google™ protob ...

    STTR Phase I 2019 Department of DefenseNavy
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