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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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High-Throughput Optical Mapping for Accelerated Stress Testing of PV Module Materials
The study will provide guidance towards the design and application of larger and/or outdoor-use intended instruments. Measurement capability will be benchmarked against existing specimens, examined in a round-robin study using conventional spectrophotometer instruments. Data acquisition and storage will be integrated into the DuraMAT DataHub network along with data visualization tool, facilitating sharing of information.
Mapping Buried Steel Flowlines Using Geophysical Sensors On Small Drone Aircraft
Development of a rapid method to locate buried pipelines in advance of land development.
Mapping the flow of information during national level radiation emergency response exercises
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AMS5 - Deposition Mapping
NA-84 for FEMA ROSS project support
Prospects of heavy flavor physics with sPHENIX MAPS based vertex detector
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Modelling Proton Interactions with Magnetodynamic Targets for Electromagnetic Field Mapping
This paper does not describe NNSS work on this project. I am a co-author due to our experimental support which will be described in future papers. The principal authors want to include all authors on all papers as a team, so I am included. The primary authors want to ensure NNSS is recognized as part of that team. My work related to this specific paper was limited to intellectual conversations and experimental planning.
Boundary Condition Compensation Map (from Field to Laboratory Response).
Abstract not provided.
Experimental Application of Boundary Condition Compensation Map (from Field to Laboratory Response).
Abstract not provided.
Road Map for Advanced Power System Planning Activities in Indian States with High Renewable Energy
This fact sheet takes the lessons from the previous two and applies it to Indian states to identify areas where states can improve their planning process, including considerations for the level of effort and implementation time for each change.
Mapping SIEM Vulnerabilities in STIG
SIEM (Security Information and Event Management) tools monitor network traffic and allow users to quickly detect problems in their networks. Because of the valuable information processed by SIEM tools, it is important to understand their vulnerabilities. STIG (Structured Threat Intelligence Graph) is an application created at INL used to visualize data related to cyber threats. Using STIG can allow users to understand vulnerabilities related to their SIEM products and how to protect their systems.
GAN-based Data Mapping for Model Adaptation
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Domain knowledge-informed, process-mapping AI graph for designing Fe-based alloys
<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Continuous improvement in efficiency of a power plant relies on designing materials for use at increasingly higher temperature and/or pressure, for 100,000s hours of operation. Due to complexity, non-linearity and high-dimensionality of the problem, traditional Machine Learning (ML) approaches require unreasonably large datasets for the data-driven model development. Science-based material and process engineering complements hard data with, sometimes soft and intuitive, empirical domain knowledge. Artificial Intelligence (AI) was used in this study to incorporate such knowledge into computational graph architecture (process-mimicking artificial neuron design, causal layer and graph structures, ensemble modeling of latent states) and learning procedures (variable transformation, fuzzy physics pre-training and freezing of deep layers, virtual microstructure representation, and adversarial multi-objective optimization). The first alloys design pathways suggested by the AI tool (pyroMind) passed a preliminary engineering review on soundness and transparency.</span>
Designing alloys with process-mapping AI pre-trained on empirical knowledge
<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Accelerated materials design should match the recent trends in the product development cycles. Materials data analytics can be used to significantly shorten development time of specialized alloys needed for next generation energy applications. However, it faces a challenge of scarce data available for training ML models. Incorporation of the domain knowledge into deep-learning graph structure via fuzzy pre-training and causal process imitation presents a viable approach to developing accurate data-driven models and reliable alloy design tools, with limited datasets. Artificial Intelligence (AI) was used in this study to incorporate such knowledge in the domain-specific computational tool, pyroMind. The tool provides not only novel design ideas but also their interpretation via physics and engineering concepts.</span>