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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. Framework for Modeling Turbulent Flow with Conjugated Heat Transfer

    SBC: ADVANCED COOLING TECHNOLOGIES INC            Topic: N19BT027

    With gas turbines becoming smaller and more powerful the need to accurately predict the temperature distribution of the turbine blades become crucial. The high Reynolds number (Re), poorly behave flow encountered in gas turbines makes Reynolds Averaged Navier Stokes (RANS) inaccurate and Direct Numerical Simulations (DNS) too expensive to be used as analysis tools. Large Eddy Simulation (LES) appr ...

    STTR Phase I 2019 Department of DefenseNavy
  2. Conjugate heat transfer for LES of gas turbine engines

    SBC: CASCADE TECHNOLOGIES INC            Topic: N19BT027

    Current design tools for gas turbine engines invoke a variety of simplifying assumptions to estimate heat transfer to solid/metal engine components (e.g., isothermal boundary conditions). These approximations are often not valid, result in inaccurate predictions of heat transfer, and ultimately compromise the thermal integrity of propulsion and power systems. Wall-modeled large eddy simulation (WM ...

    STTR Phase I 2019 Department of DefenseNavy
  3. Large Eddy Simulation (LES) Flow Solver Suitable for Modeling Conjugate Heat Transfer

    SBC: Kord Technologies, Inc.            Topic: N19BT027

    Accurate prediction heat transfer in gas turbine components subject to cooling requires high fidelity modeling of heat transfer in the presence of high Reynolds number turbulent flow. The cooling internal to the blades results in sustained temperature gradients within the structural parts, from low temperature in the interior of the structure to increasingly higher temperature closer to the surfac ...

    STTR Phase I 2019 Department of DefenseNavy
  4. A Hierarchical and Extendable, Component-Based Simulation Tool for Aircraft Thermal Management Systems

    SBC: CFD RESEARCH CORPORATION            Topic: N19BT025

    The requirements for thermal management on tactical aircraft systems have reached a level at which integrated system design must be considered early in the aircraft design process. An integrated propulsion, power and thermal modeling and simulation design approach is necessary for reduced size, weight and power requirements. At the same time, there is an urgent need for capabilities that enable an ...

    STTR Phase I 2019 Department of DefenseNavy
  5. A Unified System-of-Systems Design and Analysis Toolset for Aircraft Thermal Management Systems

    SBC: PC KRAUSE & ASSOCIATES INC            Topic: N19BT025

    Modern and next generation military aircraft face increasing challenges as thermal demands grow while available heat sinks reduce. Legacy platforms upgraded with advanced electrical systems are also encountering similar thermal constraints. Modeling and simulation (M&S) tools provide a cost-effective solution to the design, analysis, and optimization of growing thermal management challenges, but t ...

    STTR Phase I 2019 Department of DefenseNavy
  6. Aircraft Thermal System Evaluation and Analysis Tools

    SBC: CU AEROSPACE L.L.C.            Topic: N19BT025

    This STTR project proposed by CU Aerospace (CUA) and research partner the University of Illinois at Urbana-Champaign (UIUC) will provide innovative thermal management systems-level analysis tools to the Navy, enabling detailed investigations of air platform thermal management issues associated with electronics loads (e.g., avionics/radar upgrades for military aircraft). The Phase I effort focuses ...

    STTR Phase I 2019 Department of DefenseNavy
  7. Joint User-centered Planning artificial Intelligence Tools for Effective mission Reasoning (JUPITER)

    SBC: CHARLES RIVER ANALYTICS, INC.            Topic: N19BT029

    Effective mission planning is critical for military strategy and execution. This process is complex as human operators must consider many variables (e.g., resource limitations, threats, risks) when formulating a plan to accomplish mission goals. Although powerful tools, such as the Navy’s Joint Mission Planning System (JMPS), provide advanced functionality, mission planning remains a hybrid acti ...

    STTR Phase I 2019 Department of DefenseNavy
  8. Data Science Techniques for Various Mission Planning Processes and Performance Validation

    SBC: Perceptronics Solutions, Inc.            Topic: N19BT029

    Mission and planning is a difficult and time-consuming process that places a heavy burden on manpower and critical thinking and is performed under significant pressure. Existing and emerging artificial intelligence (AI) and machine learning (ML) techniques are well-suited to assisting humans with these challenges. While the promise of AI/ML is great, there are significant obstacles to operationali ...

    STTR Phase I 2019 Department of DefenseNavy
  9. AI-Driven, Secure Navy Mission Planning via Deep Reinforcement Learning and Attribute-Based Multi-Level Security

    SBC: EH GROUP INC            Topic: N19BT029

    Current mission planning systems allow strike planners and operations centers to perform time-sensitive strike planning, execution monitoring, and validate mission effects using XML-based tools that visualize time critical attack plan and track plan status vs. execution. In this proposed STTR Phase I design for the Next Generation Navy Mission Planning (NGNMPS) system, we will identify expanded op ...

    STTR Phase I 2019 Department of DefenseNavy
  10. REVAMP: REcommendation, Verification, and Analysis for Mission Planning

    SBC: Intelligent Automation, Inc.            Topic: N19BT029

    Effective and efficient data-driven mission support is crucial for achieving readiness and superiority in warfighting enterprises. Leveraging machine learning (ML) and artificial intelligence (AI) in mission planning would not only minimize the human-error factors and increase accuracy, but also improve speed in planning, execution, and evaluation of a mission. REVAMP will improve the next generat ...

    STTR Phase I 2019 Department of DefenseNavy
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