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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. Proactive Contextual Decision Support for Decision Making Under Uncertainty

    SBC: PACIFIC SCIENCE & ENGINEERING GROUP, INC.            Topic: N13AT020

    Current decision tools often omit important situational context. Unfortunately, this can lead to dangerous and costly errors, as context drives decision making. For example, in operational navigation planning tasks, decisions must be made that rely on multiple information sources of different fidelities and uncertainties. Furthermore, after obtaining additional information the necessity for replan ...

    STTR Phase II 2015 Department of DefenseNavy
  2. Non-destructive Webbing Strength Indicator

    SBC: TDA RESEARCH, INC.            Topic: N19BT032

    Webbing is strong, woven material that is used to secure cargo as well as for safety equipment such as seat belts, harnesses, and parachute rigging. Due to its extensive use in Military applications, the strength of the webbing is a key component of equipment design, especially in the case of safety gear that protects soldiers, as lives may be dependent on the strength and proper performance of th ...

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

    SBC: ALPHASTAR TECHNOLOGY SOLUTIONS LLC            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
  4. 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
  5. 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
  6. A High-Speed Digital Holocamera for the 3-D Analysis of Flow Interaction with High Speed Flows

    SBC: METROLASER, INCORPORATED            Topic: N20AT020

    In hypersonic flight, airborne particles such as water or ice can penetrate and alter the bow shock and flow field, enhance erosion mechanisms and alter aerodynamics. Particles break up as they pass through the shock wave, impact the surface, erode and increase surface roughness, increase turbulence and heat transfer, and augment heating that can destroy heat shields prematurely. Many tests and th ...

    STTR Phase I 2020 Department of DefenseNavy
  7. CRISIS: Knowledge Graph Based Cyber Resilience Integrated Security Inspection System

    SBC: INTELLIGENT FUSION TECHNOLOGY, INC.            Topic: N20AT011

    Modern US Navy ships and submarines are configured with an ever-increasing level of automation, including state-of-the-art embedded wireless sensors that monitor vital system functions. However, sensor nodes have the potential to serve as targets for cybersecurity attacks or be susceptible to corruption through accidental or malicious events. To address these shortfalls and minimize vulnerabilitie ...

    STTR Phase I 2020 Department of DefenseNavy
  8. CYANDECA: Cyber Anomaly Detection, Classification, and Analysis for Condition Based Monitoring

    SBC: Intelligent Automation, Inc.            Topic: N20AT011

    Navy is developing the concepts and methods to leverage Machine Learning (ML) techniques for the maintenance decision-making on condition-based maintenance plus (CBM+) platform. Effective health monitoring for condition-based and predictive maintenance requires intelligent sensor selection and placement, and context-aware interpretation of sensor data to detect the many possible fault modes. Moreo ...

    STTR Phase I 2020 Department of DefenseNavy
  9. TIS: Trusted Sensor Integration

    SBC: OBJECTSECURITY LLC            Topic: N20AT011

    Condition-based maintenance plus (CBM+), and cyber-physical systems (CPS) in general, depend on correct sensor data for analysis, decision making and control loops. If the sensor data that arrives at the point of processing is not correct, or more accurate, is outside its accepted error range, then any further processing will be incorrect as well. This could result in, in the case of CBM+, not det ...

    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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