Unearthing Subsurface Data Using Intelligent Geoinformatics
Presented by NETL at Geologic Society of America Annual Meeting, 11/18
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Presented by NETL at Geologic Society of America Annual Meeting, 11/18
This study explores the viability of using counterfactual reasoning for impact analyses when understanding and responding to “beyond-design-basis” nuclear power plant accidents. Currently, when a severe nuclear power plant accident occurs, plant operators rely on Severe Accident Management Guidelines. However, the current guidelines are limited in scope and depth: for certain types of accidents, plant operators would have to work to mitigate the damage with limited experience and guidance for the particular situation. We aim to fill the need for comprehensive accident support by using a dynamic Bayesian network to aid in the diagnosis of a nuclear reactor’s state and to analyze the impact of possible response measures.
SIEVAS provides an immersive visualization framework for connecting multiple systems in real time for data science. SIEVAS provides the ability to connect multiple COTS and GOTS products in a seamless fashion for data fusion, data analysis, and viewing. It provides this capability by using a combination of micro services, real time messaging, and web service compliant back-end system.
The ILC algorithm prioritizes available loads for curtailment in large and small/medium size commercial buildings.
This code builds machine learning models for transcription and protein data generated for the purpose of classifying innate immune signatures.
Advanced nuclear reactors are a promising option for aiding the world in achieving its net-zero carbon emission goals, however, there are significant challenges to attaining and maintaining economic competitiveness with other sources of electricity. To improve the economic competitiveness of advanced reactor designs, a project was initiated to explore the use of Markov Decision Processes (MDPs) to guide asset-management decision-making during advanced reactor operation. MDPs are a powerful tool for optimizing decision-making in complex environments and their application to advanced reactors can aid in planning maintenance and repair activities to minimize downtime and maximize generation. The described approach expands on previous work regarding the use of MDPs for operational decision-making through the direct incorporation of real-time plant information. The integral MDP analysis includes information from online component diagnostic tools and the plant’s real-time generation risk assessment (GRA) and probabilistic risk assessment (PRA), which evaluate plant risk from both an economic and safety perspective. The result is an asset-management optimization framework that is based on real-time data regarding plant component status and the current best-estimate of plant risk. The paper presents an overview of the theoretical framework to incorporate the different information pathways into an integral MDP analysis, along with example analyses.
The submission includes the labeled datasets, as ESRI Grid files (.gri, .grd) used for training and classification results for our machine leaning model: - brady_som_output.gri, brady_som_output.grd, brady_som_output.* - desert_som_output.gri, desert_som_output.grd, desert_som_output.* The data corresponds to two sites: Brady Hot Springs and Desert Peak, both located near Fallon, NV. Input layers include: - Geothermal: Labeled data (0: Non-geothermal; 1: Geothermal) - Minerals: Hydrothermal mineral alterations, as a result of spectral analysis using Chalcedony, Kaolinite, Gypsum, Hematite and Epsomite - Temperature: Land surface temperature (% of times a pixel was classified as "Hot" by K-Means) - Faults: Fault density with a 300mradius - Subsidence: PSInSAR results showing subsidence displacement of more than 5mm - Uplift: PSInSAR results showing subsidence displacement of more than 5mm Also, the results of the classification using Brady and Desert Peak to build 2 Convolutional Neural Networks. These were applied to the training site as well as the other site, the results are in GeoTiff format. - brady_classification: Results of classification of the Brady-trained model - desert_classification: Results of classification of the Desert Peak-trained model - b2d_classification: Results of classification of Desert Peak using the Brady-trained model - d2b_classification: Results of classification of Brady using the Desert Peak-trained model
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High-Risk Property (HRP) classification is critical at U.S. Department of Energy (DOE) sites, where inventories include sensitive and often dual-use equipment. Compliance must track evolving rules designated by various export control policies to make transparent and auditable decisions. Traditional expert-only workflows are time-consuming, backlog-prone, and struggle to keep pace with shifting regulatory boundaries. We propose ORCHID, a modular agentic framework for HRP classification that pairs retrieval-augmented generation (RAG) with human oversight to produce policy based outputs that can be audited. Small cooperating agents—retrieval, description refiner, classifier, validator, and feedback logger—coordinate via agent-to-agent messaging and invoke tools through the Model Context Protocol (MCP) for model-agnostic on-premise operation. The interface follows an "Item to Evidence to Decision" loop with step-by-step reasoning, on-policy citations, and append-only audit bundles (run-cards, prompts, evidence). In preliminary tests on real HRP cases, ORCHID improves accuracy and traceability over a non-agentic baseline while deferring uncertain items to Subject Matter Experts (SMEs). The demonstration shows single item submission, grounded citations, SME feedback capture, and exportable audit artifacts—illustrating a practical path to trustworthy LLM assistance in sensitive DOE compliance workflows.
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The intent of this research effort is to prove the hypothesis that: Through the employment of controlled processing parameters which are based upon integrated advanced material characterization and multi-physics material modeling, bulk nanolayered composites can be designed to contain high densities of preferred interfaces that can serve as supersinks for the defects responsible for premature damage and failure.
Computational fluid dynamics (CFD) simulations of a Ford Focus Electric demonstrated that a split flow heating, ventilating and air conditioning (HVAC) system with rear recirculation ducts can reduce cabin heating loads by up to 57.4% relative to full fresh air usage under some conditions (steady state, four passengers, ambient temperature of -5 deg C). Simulations also showed that implementing a continuous recirculation fraction control system into the original equipment manufacturer (OEM) HVAC system can reduce cabin heating loads by up to 50.0% relative to full fresh air usage under some conditions (steady state, four passengers, ambient temperature of -5 deg C). Identified that continuous fractional recirculation control of the OEM system can provide significant energy savings for EVs at minimal additional cost, while a split flow HVAC system with rear recirculation ducts only provides minimal additional improvement at significant additional cost.
LANL and CRADA Participant collaborated to develop advanced change detection and feature extraction techniques for satellite, aerial, or ground-based imagery systems, to include still imagery, wide-area motion imagery and FMV-full motion imagery.
The project was conceived and executed with the overarching objective to provide cost effective tools to cooperative utilities that enabled them to quickly detect, characterize and take remediative action against cyber attacks.
The debate continues on the magnitude and validity of climate change caused by human activities. However, there is no debate about the need to make buildings, modes of transportation, factories, and homes as energy efficient as possible. Given that climate change could occur with the wasteful use of fossil fuel and the fact that fossil energy costs could and will swing wildly, it is imperative that every effort be made to utilize energy sources to their fullest. Hybrid energy systems (HES) are two or more separate energy producers used together to produce energy commodities. The HES this report focuses on is the use of nuclear reactor waste heat as a source of further energy utilization. Nuclear reactors use a fluid to cool the core and produce the steam needed for the production of electricity. Traditionally this steam, or coolant, is used to convert the energy then cooled elsewhere. The heat is released into the environment without being used further. By adding technologies to nuclear reactors to use the wasted heat, a system can be developed to make more than just electricity and allow for loading following capabilities.