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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

NASA Johnson Space Center SBIR STTR Program Technology Innovations

The Small Business Innovation Research (SBIR) Program increases opportunities for small businesses to participate in research and development (R&D), increases employment, and improves U.S. competitiveness. Specifically the program stimulates U.S. technological innovation by using small businesses to meet federal R&D needs, increasing private-sector commercialization of innovations derived from federal R&D, and fostering and encouraging the participation of socially disadvantaged businesses. In 2000, the Small Business Technology Transfer (STTR) Program extended and strengthened the SBIR Program, increasing its emphasis on pursuing commercial applications by awarding contracts to small business concerns for cooperative R&D with a nonprofit research institution. Modeled after the SBIR Program, STTR is nevertheless a separately funded activity. Technologies that have resulted from the Johnson Space Center SBIR STTR Program include: a device for regenerating iodinated resin beds; laser-assisted in-situ keratomileusis or LASIK; a miniature physiological monitoring device capable of collecting and analyzing a multitude of real-time signals to transmit medical data from remote locations to medical centers for diagnosis and intervention; a new thermal management system for fibers and fabrics giving rise to new line of garments and thermal-enhancing environments; and a highly electropositive material that attracts and retains electronegative particles in water.

Krishen, Kumar↗
DOE OSTI2023

DOE STTR Phase I Final Report Report: Machine-learning Based Prediction of Thermal Limits for Conjugated Organic Materials

The SCANN-DT Phase I STTR project led by NLM Photonics and partnering with the National Renewable Energy Laboratory (NREL) sought to apply machine learning techniques based on graph neural networks (GNNs) towards the prediction of decomposition temperatures (Td) of organic semiconductors, based on prior work on bond dissociation energy (BDE) prediction as implemented in NREL’s ALFABET prediction tool. Using a curated set of experimental decomposition energies, the project examined GNN-based, classical quantitative structure-property relationship (QSPR) based on DFT calculations, and combinations of both methods to predict Td. While the best MAEs in Td achieved were near 40°C, below project targets, the project led to improvements in the ALFABET model, improvements in cloud-based implementations of NWChem software, and a substantial dataset of calculations on medium-sized conjugated organic molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

A Summary of Recent SBIR and STTR Projects in Support of Cryogenic Fluid Management Modeling

Over the last five years, NASA has invested in development of sub-models to improve the current capabilities of Computational Fluid Dynamics (CFD) codes utilized for cryogenic fluid management (CFM) predictions. The goal is for the submodels to be modular so that they can easily be implemented into NASA’s CFD codes such as ANSYS Fluent, Siemens STAR-CCM+, and Streamline Numerics Loci-Stream. These technologies are being developed through Small Business Innovative Research (SBIR) and Small Business Technology Transfer (STTR) contracts under the CFM subtopic solicitation and funded by the CFM Portfolio Project. All contracts presented include cryogenic experiments to enable model validation. A sub-grid boiling model has been developed by Combustion Research and Flow Technology Inc. and Massachusetts Institute of Technology for cryogenic line chilldown predictions and has completed Phase III. A spray chill-and-fill model for cryogenic tankage is being developed by Combustion Research and Flow Technology Inc. and University of Connecticut and is currently in Phase II. Last, film condensation modeling to support cryogenic liquefaction was progressed through a Phase I contract with Combustion Research and Flow Technology Inc. and University of Connecticut. A summary of accomplishments for each contract is presented.

cryogenics↗
DOE OSTI2021

Value of Information and Data Management Platform Promoting Modern Data Analysis Utilization. STTR Phase I, Final Technical Report

The immense growth in weather and river sensor data availability along with increases in computational power and algorithmic sophistication have enabled the widespread application of physical, machine learning, and artificial intelligence algorithms to river forecasting. The forecasts produced by widely varying model approaches are inherently limited in different ways, such as by the number and quality of training observations, producing biased predictions that achieve optimal skill only over specific ranges of weather and river conditions. Thus, despite the growing availability and new sources of forecasts, hydropower owners cannot fully leverage the information because they lack an effective mechanism to determine the “best” forecast for given conditions or objectives. Tools are needed to consolidate forecast information, demonstrate skill, and extract optimal forecast guidance from the range of possibilities. Such tools will increase uptake and beneficial use of the growing wealth of forecast information, and lead to improved operating decisions, optimal power generation, and enhanced system value. This research proposes to improve the value of forecasts by developing a broadly accessible verification framework and applying machine learning techniques to identify the ‘best’ forecast from an ensemble of sources given the current weather, river, and operating conditions.

13 HYDRO ENERGY↗
DOE OSTI2022

AMTS update for STTR

Advanced nuclear reactor systems currently in development include designs that utilize higher energy neutron spectrums, higher temperatures, and corrosive environments that are quite different from the current fleet of nuclear power plants in use today. As a result, new structural materials must be developed and qualified for use to support these advanced systems. For example, research is currently being conducted on a new class of materials known as multi-principle-element-alloys. These alloys show some promise to be able to meet the structural needs of these challenging reactor systems. However, due to the vast available compositional space, and the limited amount of research conducted thus far, Idaho National Laboratory (INL)sought a way to advanced mechanical testing capability for highly irradiated structural materials to increase both throughput and higher fidelity data analysis through incorporation of digital image correlation. The Automated Mechanical Testing System accomplishes these tasks by automating the testing and incorporating digital image correlation to provide higher fidelity data. INL has now received this system and is currently performing functional testing and training. INL is also performing some modifications to the system to allow for installation into a remote shielded enclosure where testing of irradiated structural materials can be performed. After testing, training, and modifications of the system are complete, INL will install the system into a shielded enclosure and begin using the system to test irradiated structural materials.

36 MATERIALS SCIENCE↗