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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
DOE OSTI2024

Multiport Converter based Auxiliary Power Supply for Heavy Duty Fuel Cell Power Train

This paper presents a multiport converter (MPC) based power supply to charge the 12 V and 24 V auxiliary batteries in heavy duty (HD) fuel cell (FC) electric vehicle (EV) power train. Compared to conventional auxiliary power supply architectures, the proposed architecture shares the power electronic components of the main power train yet preserves the isolation and all other desirable features of auxiliary power supplies. The proposed auxiliary power supply architecture reduces the requirements of the devices, gate drivers and thermal requirements thereby having potential benefits towards improving the power density, cost and weight of the overall power train. The topology, design parameters and control methodology are discussed, and results are presented to validate the proposed architecture.

Mukherjee, Subho↗
DOE OSTI2025

Integrated On-Board Charger for Dual Motor Based Electric Vehicle Power Train

This paper presents an integrated on-board charger for dual motor based electric vehicle (EV) power train with silicon carbide semiconductor switch-based traction inverters. The proposed charger architecture reconfigures the traction inverters to be used in the battery charging mode, thereby eliminating several high frequency switches and their accessories. The motor windings are reused as line inductors for the power factor correction (PFC) rectifier stage in the charging mode thereby reducing the requirements of magnetic components. The architectures reduce the cost, weight, and volume of the EV power train. Simulation results are presented to demonstrate the working principle of the proposed architecture.

Mukherjee, Subho [ORNL] (ORCID:0009000672297925)↗
DOE OSTI2025

Flux-Switching Machine Based All-Electric Power Train for Future Aircraft

This research investigates a flux-switching motor with superconducting and cryogenically cooled windings, aimed at achieving exceptionally high power densities. In addition to the motor’s topology and superconducting windings, it was found that power density could be further enhanced by incorporating superconducting shields on the rotor at the interpole locations. The resulting publications and patented technology outline the design process. A motor power density of 64 kW/kg—including housing materials—was achieved for a 1 MW design, surpassing the performance of existing motors. An electronic drive was also developed, achieving a power density of 47 kW/kg. This includes the mechanical structure of the drivetrain, for which a detailed CAD model was created, resulting in an overall drivetrain power density of 27 kW/kg.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Human Factors Considerations for Hydrogen Rail Refueling and Maintenance

The Federal Railroad Administration (FRA) contracted Sandia National Laboratories to review the risks introduced specifically through human interactions within hydrogen rail refueling and maintenance activities and present recommendations for future system development and implementation. Due to the lack of domestic hydrogen-powered trains at the time of review, observational data was collected on analogous existing systems to consider various ways hydrogen might be implemented within future designs. The team developed 50 human factors design guidelines through analysis of human risk factors. Because hydrogen rail systems are still in the early design stage, the resulting human factors guidelines represent general best practices and do not include specific design features. The research team recommends design and regulatory agencies include a Human Factors Engineer in the design and review process to ensure the guidelines are appropriately implemented and verified.

08 HYDROGEN↗
DOE OSTI2024

Refining Absorber Shroud Geometry to Maximize Power Output and Reduce Power Peaking in ATF Test Train

In the wake of the Fukushima Daiichi nuclear power plant accident in 2011, the accident tolerant fuels (ATF) program was initiated to enhance the safety of light-water reactor fuels, placing significant emphasis on cladding. A crucial step for the broad implementation of ATF in commercial reactors involves irradiation testing of the fuel designs. ATF-2D, the latest experiment in the ATF series, is slated to undergo irradiation in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). ATF-2D is a joint effort of the INL with industry partners General Electric Global Research; Framatome; General Atomics; the Japan Atomic Energy Agency; Hitachi-GE Nuclear Energy, Ltd; Global Nuclear Fuel-Japan Co., Ltd; Nippon Nuclear Fuel Development Co., Ltd; and Mitsubishi Heavy Industries, Ltd. The ATF-2D test train design consists of four tiers, each housing six rodlets. The device will be inserted in Loop 2A within the central flux trap of the ATR, and is anticipated to undergo irradiation throughout three 60-day cycles. Typical pressurized water reactor conditions will be emulated during the irradiation. The objective of the work presented here is to dimension neutron-absorbing hafnium (Hf) components surrounding the fuel rodlets, such that the axial power profile is flattened, while simultaneously ensuring that the total fission power output of the entire test train remains below 200 kW.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Refining Absorber Shroud Geometry to Maximize Power Output and Reduce Power Peaking in ATF Test Train

In the wake of the Fukushima Daiichi nuclear power plant accident in 2011, the accident tolerant fuels (ATF) program was initiated to enhance the safety of light-water reactor fuels, placing significant emphasis on cladding. A crucial step for the broad implementation of ATF in commercial reactors involves irradiation testing of the fuel designs. ATF-2D, the latest experiment in the ATF series, is slated to undergo irradiation in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). ATF-2D is a joint effort of the INL with industry partners General Electric Global Research; Framatome; General Atomics; the Japan Atomic Energy Agency; Hitachi-GE Nuclear Energy, Ltd; Global Nuclear Fuel-Japan Co., Ltd; Nippon Nuclear Fuel Development Co., Ltd; and Mitsubishi Heavy Industries, Ltd. The ATF-2D test train design consists of four tiers, each housing six rodlets. The device will be inserted in Loop 2A within the central flux trap of the ATR, and is anticipated to undergo irradiation throughout three 60-day cycles. Typical pressurized water reactor conditions will be emulated during the irradiation. The objective of the work presented here is to dimension neutron-absorbing hafnium (Hf) components surrounding the fuel rodlets, such that the axial power profile is flattened, while simultaneously ensuring that the total fission power output of the entire test train remains below 200 kW.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

SCORE (Synthesis of COnsists as Rolling Energy microgrids) (Final Technical Report)

The SCORE toolset is an open source, web-based application to assess the impact of new powering technologies on railroad performance, specifically technologies capable of both putting power into the rail (motive force) and taking power from the rail (regeneration). SCORE’s primary goal is generate trade studies to analyze different powering and train make-up options to minimize energy usage and greenhouse gas generation. At the core of generating these trade spaces is calculating the optimal powering policy for the train given the makeup of the train, the route, and time constraints. This paper presents details on the algorithms used in SCORE to compute this powering policies that is fast and accurate, discusses its implementation in an Energy-Longitudinal Train Dynamics (E-LTD) model, compares it to naïve approaches, and demonstrates its use across a variety of train/route pairs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Empirically-calibrated H100 node power models for accurate AI training energy estimation

Accurately quantifying the energy use of artificial intelligence (AI) training is critical for infrastructure planning, carbon accounting, and sustainable data center operation, but few studies have directly measured the power consumption of production workloads on contemporary hardware. By combining empirical measurements from Brookhaven National Laboratory during AI training on 8-graphics-processing-unit H100 systems with open-source benchmarking data, we develop statistical models relating computational intensity to node-level power consumption. We measure the gap between manufacturer-rated thermal design power (TDP) and actual power demand during AI training. Our analysis reveals that even computationally intensive workloads operate at only 76% of the 10.2 kW TDP rating. Our architecture-specific model, calibrated to floating-point operations, predicts energy consumption with 11.4% mean absolute percentage error, significantly outperforming TDP-based approaches (27%–37% error). We identified distinct power signatures between transformer and convolutional neural network architectures, with transformers showing characteristic fluctuations that may impact grid stability. These results provide a measurement-grounded basis for improving AI training energy estimates, enabling more reliable infrastructure sizing, cost projections, and environmental impact assessments.

Newkirk, Alex C↗
DOE OSTI2026

Analysis of Slow Spill Data for the Mu2e Experiment

The execution of the Mu2e experiment requires a stable, low-intensity proton beam from the Delivery Ring to produce clean data and protect equipment. This is done by performing a “slow extraction,” which is the gradual contraction of the stable region within the accelerator’s beam pipe. The Delivery Ring is currently unable to perform slow extraction with the stability required by Mu2e. To resolve this, the FAN-C team is training machine learning models with the purpose of replacing the Delivery Ring’s current PID controllers with AI-powered controllers. Training these models requires clean, processed data from slow spills. Over the course of this project, data from previous slow spills were processed and analyzed, and the clean data, graphs, and insights gained from the process were provided to the FAN-C team to assist them in their efforts.

Osborn, Thomas [Purdue U., West Lafayette]↗
DOE OSTI2024

A Pseudoreversible Normalizing Flow for Stochastic Dynamical Systems with Various Initial Distributions

Here, we present a pseudoreversible normalizing flow method for efficiently generating samples of the state of a stochastic differential equation (SDE) with various initial distributions. The primary objective is to construct an accurate and efficient sampler that can be used as a surrogate model for computationally expensive numerical integration of SDEs, such as those employed in particle simulation. After training, the normalizing flow model can directly generate samples of the SDE’s final state without simulating trajectories. The existing normalizing flow model for SDEs depends on the initial distribution, meaning the model needs to be retrained when the initial distribution changes. The main novelty of our normalizing flow model is that it can learn the conditional distribution of the state, i.e., the distribution of the final state conditional on any initial state, such that the model only needs to be trained once and the trained model can be used to handle various initial distributions. This feature can provide a significant computational saving in studies of how the final state varies with the initial distribution. Additionally, we propose to use a pseudoreversible network architecture to define the normalizing flow model, which has sufficient expressive power and training efficiency for a variety of SDEs in science and engineering, e.g., in particle physics. We provide a rigorous convergence analysis of the pseudoreversible normalizing flow model to the target probability density function in the Kullback–Leibler divergence metric. Numerical experiments are provided to demonstrate the effectiveness of the proposed normalizing flow model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Tailoring coherent beam combined laser pulse trains for high peak and average power applications

Laser systems based on coherent beam combination (CBC) that rely on tiled pupil architecture intrinsically carry digital capabilities independently applicable to all three essential characteristics of a laser pulse: amplitude, phase and polarization. Those capabilities allow the far-field energy distribution to be flexibly tailored in real time. Operation in the femtosecond regime at high repetition rates gives access to a wide range of applications requiring both high peak and average powers. We address the task of independent peak versus average power adjustment needed for applications seeking to decouple nonlinear phenomena associated with GW peak power from the thermal load inherent to kW average power operation. The technical solutions proposed are presented in the framework of the Ecole Polytechnique XCAN CBC laser platform (61 independent channels) with an emphasis on thermal management measures implemented to ensure its nominal operation.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

REDLY

SAND2025-11838O REDLY provides a framework for designing, training, and validating physics-informed neural networks for power system applications. It trains surrogates for Alternating Current Power Flow prediction and general economic dispatch problems, such as Alternating Current Optimal Power Flow and Direct Current Power Flow. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Blakely, Logan↗
DOE OSTI2025

Enhancing Autonomous Control of Microreactors Using Multi-Agent Reinforcement Learning

In order for microreactors to be economically competitive, operation costs will need to be minimized through some degree of autonomous control. Previous work has demonstrated the effectiveness of reinforcement learning (RL) for load-following control in a drum-controlled microreactor. This study extends that work by exploring the potential of RL to independently control each of the reactor’s drums. We compare a single-agent RL approach with a multi-agent RL (MARL) framework, testing them for generalization across different load-following power profiles and control timescales, and for robustness in cases of randomly disabled control drums. Since the point kinetics simulation environment used in this study cannot resolve spatial effects, we assume that in the absence of spatially localized disturbances, optimal drum movements should be symmetrical. We demonstrate that single-agent RL is able to achieve accurate performance only when symmetric actions are ignored; otherwise, it fails to train a useful controller. Meanwhile, the MARL framework performs symmetric actions by design and trains a robust, accurate agent, as evidenced by mean absolute errors in power matching of 0.41% for the training power profile, 0.68% for a profile with half the drums disabled, and 0.21% for a profile on a realistic load-following time horizon.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

An Open-Access Repository of Synchrophasor Data Quality Examples: Curation and Example Applications

Synchrophasor measurements are critical in providing wide-area situational awareness to power system operators. However, data artifacts may be introduced due to various issues such as loss of communication, loss of GPS signal, internal clock error, and vendor-specific implementation of phasor estimation algorithms. Tools designed to provide actionable insights from synchrophasor data, hence, must be designed to be robust to these data quality issues. In this work, two years of synchrophasor data sourced from multiple electric utilities in the United States were analyzed to identify examples of data quality problems. These examples were then labeled and published in the Grid Event Signature Library, a publicly available repository of power system measurements hosted by the Oak Ridge National Laboratory. This paper describes the data curation process, and illustrates two application use cases where the dataset can be valuable to the research community. In the first use case, a random forest classifier is trained to distinguish power system disturbance signatures from data anomalies introduced in synchrophasor measurements due to clock errors. The second use case studies the impact of data quality issues on an example synchrophasor application (specifically, event start time determination). The choice of data quality problems investigated is informed by the examples in the repository curated in this work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Improving Trustworthiness of Data-Driven Power Grid Contingency Analysis With Bayesian Residual Graph Neural Networks

The evolving energy landscape requires novel tools to efficiently perform contingency analysis and reliability assessment of power grids, potentially in real-time. The high computational cost of traditional power flow solvers limits their applicability in practice. Machine learning (ML) surrogates such as deep neural networks (NNs) accelerate power flow solvers computations, enabling high-order contingency analysis and real-time decision-making by learning highly nonlinear functions and integrating grid topology via graph architectures. However, (graph) NNs lack predictive power away from training data and do not provide predictive confidence estimates. Here, we present a Bayesian residual graph NN that integrates knowledge from low-fidelity data via residual training and embeds granular quantification of uncertainties, improving trustworthiness critical for high-consequence decision-making. Applying Bayesian concepts to NNs is challenging due to the high-dimensionality of both the parameter space, complicating derivation of a meaningful prior, and the output space in large grid systems, requiring enhanced techniques to assess the predicted high-dimensional uncertainties. Our contributions include: (1) Deriving a prior for fully connected and graph NNs that leverages low-fidelity data to guide mean predictions and appropriately control prior predictive uncertainty. (2) Integrating this prior within an ensembling with anchoring scheme for efficient approximate posterior inference. (3) Deriving enhanced metrics to assess accuracy of both the mean and uncertainty predictions in high dimensions, appropriately accounting for correlations propagated through graph layers. The resulting Bayesian residual graph NN is tested on a contingency analysis task for 14-bus and 118-bus grids.

24 - POWER TRANSMISSION AND DISTRIBUTION↗