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Award Data
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.
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Multi-scale Physics-based Modeling of Particle-Impact Erosion of CMCs
SBC: CFD RESEARCH CORPORATION Topic: N19BT033Sand 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 -
Analysis and Modeling of Erosion in Gas-Turbine Grade Ceramic Matrix Composites (CMCs)
SBC: ALPHASTAR TECHNOLOGY SOLUTIONS LLC Topic: N19BT033A 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 -
Hexahedral Dominant Auto-Mesh Generator
SBC: HYPERCOMP INC Topic: N20AT004The 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 -
Hexahedral Dominant Auto-Mesh Generator
SBC: M4 ENGINEERING, INC. Topic: N20AT004Advances 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 -
Advanced, High-Performance, Low-Noise Propeller Designs for Small UxS
SBC: CFD RESEARCH CORPORATION Topic: N20AT006Improved 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 -
High Efficiency Propeller for Small Unmanned X Systems using Advanced Composite Materials
SBC: CATTO PROPELLERS Topic: N20AT006In the proposed STTR study, Catto Propellers, Inc. (Catto) and the University of North Dakota (UND) will create an efficient new propeller design utilizing advanced composite materials for use on small unmanned x systems. During Phase I, a comprehensive study will be conducted to develop a new propeller design in order to increase propeller efficiency, reduce aerodynamic noise and utilize innova ...
STTR Phase I 2020 Department of DefenseNavy -
PARTEL: Periscope video Analysis using Reinforcement and TransfEr Learning
SBC: MAYACHITRA, INC. Topic: N20AT007We propose a suite of video processing algorithms utilizing the machine learning (ML) techniques of artificial intelligence (AI) reinforcement learning, deep learning, and transfer learning to process submarine imagery obtained by means of periscope cameras. Machine learning (ML) can help in addressing the challenge of human failure of assessing the data of periscope imagery. Though pre-tuned blac ...
STTR Phase I 2020 Department of DefenseNavy -
Machine Learning for Transfer Learning for Periscopes
SBC: Arete Associates Topic: N20AT007Areté 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 -
TIS: Trusted Sensor Integration
SBC: Objectsecurity LLC Topic: N20AT011Condition-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 -
Analog Optical Link using Novel Record Performance Laser, Modulator and Photodiode Technology
SBC: FREEDOM PHOTONICS LLC Topic: N20AT012In this program, Freedom Photonics and its research partner institution will demonstrate an analog optical link using novel record performance laser, modulator and photodiode technology. Preliminary designs for a miniature, deployable implementation will be conducted as well in Phase I.
STTR Phase I 2020 Department of DefenseNavy