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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 847 records · Page 47

Western Smart Regional EV Adoption and Infrastructure at Scale- WestSmartEV@Scale (Budget Period 2 – Milestone Report – TNC EV Study)

WestSmartEV@Scale is a comprehensive research project addressing key opportunities to decarbonize transportation throughout the Rocky Mountain West. Building upon the framework established in the original WestSmart project, unique subprojects are scoped targeting transportation emissions across light-, medium-, and heavy-duty vehicles with consideration of equity and access. This report summarizes the progress to date on Subproject 6, TNC EV Study (Urban Mobility). It is comprised of three sections: • Outreach and participant recruitment; • Survey data collection and analysis; and • BEV driver driving and charging behavior data collection and analysis.

33 ADVANCED PROPULSION SYSTEMS↗

Enabling The Next Generation of Smart Sensors in Coal Fired Power Plants using Cellular 5G Technology

Ohio University (OHIO), West Virginia University (WVU), and American Electric Power (AEP) proposed to study and report on the benefits of 5G wireless cellular technologies for coal-fired power plants. The significant advantages, cost savings, and potential of 5G wireless data communications based sensors promised to usher in a new era of reliable, inexpensive, and powerful embedded systems that had not previously been available for coal-fired power plants. The team built upon existing experience with cellular-based systems, power plant water quality sensing, and high temperature sensors developed during past projects. Principal Investigator Wilhelm had been developing cellular-based sensor data systems with a commercial partner for 10 years, pioneering innovative solar-powered devices that began with 2G technology. The lessons and knowledge gained served as a foundation to demonstrate innovations and potential impacts specific to coal fired power plants enabled by 5G technology, along with integration with existing sensors and systems.

20 FOSSIL-FUELED POWER PLANTS↗

Replace Human Intelligence with Fast and Smart Geometric Reasoning and Graph Neural Network to Accelerate Next Gen ModSim Workflows

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

97 MATHEMATICS AND COMPUTING↗

NitroNet: Smart System to Quantify Nitrous Oxide Emissions

Agricultural croplands are the largest anthropogenic source of nitrous oxide (N 2 O), the third most important greenhouse gas. Emissions are characterized by “hot spots” and “hot moments”, meaning emissions are highly heterogeneous in space and time. Emissions are driven by nitrification and denitrification processes which depend upon complex factors such as soil characteristics (type, compaction, pH, moisture, topography), management practices (fertilizer type and application, tillage, crop type, field history, irrigation), biogeochemistry (organic carbon, microbial composition), and meteorology (precipitation, temperature, and wind). For these reasons, quantifying cropland emissions by either measurements or modeling is extremely challenging with large uncertainties.

09 BIOMASS FUELS↗

The Implications of Advanced Manufacturing in a Connected Economy Moving Towards a Smart, Sustainable, and Productive Economy

This report intends to provide a comprehensive overview of information and communications technologies (ICT), which play increasingly vital roles in the United States (U.S.) and global economies and continue to evolve rapidly as both technological capabilities and needs grow in tandem. To underscore the importance of the sector, the U.S. Congress recently passed a $\$$20–$\$$50 billion federal program Creating Helpful Incentives to Produce Semiconductors Act (CHIPS Act) to spur the competitiveness of the U.S. semiconductor industry and encourage U.S. investment, often complemented by state-level incentives (Kessler 2022).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Hydrogen Dispersion Modeling for Development of Smart Distributed Monitoring

Studying hydrogen dispersion is crucial for ensuring the safe and effective deployment of hydrogen as an energy carrier. This study presents a comprehensive CFD modeling framework for simulating hydrogen dispersion at a real-world hydrogen production, storage, and utilization facility. Utilizing the Hydrogen Research Facility under the Advanced Research on Integrated Energy Systems (ARIES) at the National Renewable Energy Laboratory's (NREL) Flatirons campus, controlled hydrogen releases at 27 kg-H2/hr were simulated. The model incorporated site-specific atmospheric conditions, including hourly wind speeds and temperatures recorded between 8 AM and 8 PM from October to December 2023. To reduce computational demands, a statistical reduction technique was applied to condense the dataset to 100 representative scenarios, validated by statistical tests for wind speeds and power law coefficients. Simulations were conducted using the Reynolds-Averaged Navier-Stokes equations. Results demonstrated that wind speed substantially influences hydrogen dispersion, with low wind conditions forming concentrated clouds and higher wind speeds stretching the plume. Additionally, clustering analysis informed optimal sensor placement at various elevations with up to 10 sensor locations on each elevation. This framework offers a robust approach for understanding hydrogen behavior in ambient conditions and informing detection strategies.

08 HYDROGEN↗

NREL Hydrogen Sensor Testing Laboratory and Smart Hydrogen Wide Area Monitoring

The NREL Hydrogen sensor laboratory aims to ensure that hydrogen sensor technology is available to meet end-user needs and to foster the proper use of sensors by advancing next generation sensing and analysis techniques, supporting codes and standards development, and improving component reliability systems.

08 HYDROGEN↗

Smart CNG Dispenser Deployment

This final technical report documents the work performed under DOE Award DE‑EE0008799 by GTI Energy over the course of the project period. The project focused on advanced fueling methods for compressed natural gas (CNG) vehicles which have historically suffered from underfilling.

03 NATURAL GAS↗