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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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Focal area: Insights gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics or knowledge-guide AI.
Focal area: Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection)
Focus areas: (1) Data assimilation enabled by machine learning, unsupervised learning (including deep learning), and (2) Predictive modeling through the use of AI techniques and AI-derived model components, comprising a hierarchy of models.
This white paper highlights the potential of using machine learning (ML) to gain new insights from large, high-frequency Earth science observatory network datasets (focal area 3) and better represent complex subsurface processes via ML in Earth System Models (ESMs, focal area 2).
This White Paper focuses on data-driven atmospheric process model emulation and atmospheric process surrogate model development. It proposes leveraging recent AI advances in these approaches to fill in unavoidable observational gaps and enable high-fidelity modeling/predictability of the atmosphere and land-surface interactions in mountainous watersheds. This approach will support studies and predictability of water cycle extremes.
Focal areas include: Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system composed of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Insight gleaned from complex data (both observed and simulated) using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI.
The Waste Isolation Pilot Plant is in Carlsbad, New Mexico. It isolates radioactive waste 2,000 feet underground within the Salado Formation.
Subsurface tile drains act as preferential flow paths that can increase nutrient runoff from agricultural fields. Phosphorus is the limiting nutrient for algal growth in fresh water bodies.
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This report presents the monitoring data collected by the U.S. Department of Energy (DOE) Office of Legacy Management (LM) during the 2022 sampling event at the Rio Blanco, Colorado, Site, where an underground nuclear test was conducted in 1973. The test resulted in residual radionuclide contamination at the detonation depth. The 2022 sampling event included the collection of samples from onsite groundwater wells to monitor for any potential contamination that might be attributed to the Rio Blanco nuclear tests. This report summarizes the laboratory results obtained from the sampling along with the historical results since monitoring began in the 1970s. This annual report and previous reports are available on the LM public website at https://www.energy.gov/lm/rio-blanco-colorado-site. Data collected during this and previous monitoring events are available on the Geospatial Environmental Mapping System (GEMS) website at https://gems.lm.doe.gov/#site=RBL.
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