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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

April 2025 Semiannual Composite Salt Waste Processing Facility (SWPF) Decontaminated Salt Solution (DSS) Toxicity Characteristic Leaching Procedure (TCLP) Results

The aqueous waste from the Salt Waste Processing Facility is sampled semiannually for transfers to the Saltstone Production Facility. Salt solution is treated at the Saltstone Production Facility and disposed of in the Saltstone Disposal Facility. Per request of customer, X-TTR-Z-00027, Revision 0, one Saltstone Disposal Facility waste form was prepared at Savannah River National Laboratory using the Salt Waste Processing Facility Decontaminated Salt Solution and Z-area premix material for the April 2025 semiannual Toxicity Characteristic Leaching Procedure sample. The sample contained 60:40 (by weight) of slag and fly ash (referred to as the “Cement-Free grout sample”). Results from the technical report support Task 2: ‘Grout Leaching Analyses’ of the Task Technical Request prepared by Savannah River Mission Completion. At 43 days cured, the Saltstone sample was collected and shipped to a certified laboratory for analysis using the Toxicity Characteristic Leaching Procedure. The April 2025 semiannual Cement-Free grout sample met the South Carolina Code of Regulations for Hazardous Waste Management Regulations 61-79.261.24 and 61-79.268.48 requirements for a non-hazardous waste form with respect to the Resource Conservation and Recovery Act metals and Underlying Hazardous Constituents, and met the Saltstone Production Facility Waste Acceptance Criteria that was in effect at the time of the sample collection at the Salt Waste Processing Facility.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Enhancement of HLW glass property-composition models

Since the WTP compositional space of HLW glasses is extremely large, development of HLW property-composition models is a multi-year task consisting of multiple phases. The primary focus of this work was to enhance the WTP HLW models of interest including PCT releases, spinel crystallization (T1%), viscosity, electrical conductivity, and TCLP-Cd response. Model development to predict nepheline formation upon CCC is the subject of a separate task. In particular, the earlier work has produced property-composition models for glass melt viscosity and glass melt electrical conductivity that showed good performance, while models for PCT releases and spinel crystallization (T1%) required improvement. Therefore, more efforts were directed in the present work to collect data and improve model performance for HLW glass PCT releases and spinel crystallization than for other properties. The present work is a continuation of earlier development phases and is responsive to the applicable Test Plan.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

High-Torque Heavy-Rare-Earth-Free Electric Motor Thermal Management

This project is part of a multi-lab Next-Generation Reliable Electric Drive Systems for Medium and Heavy-Duty Vehicles (NEXT-DRIVE) project led by Oak Ridge National Laboratory (ORNL), and including NREL, Sandia National Laboratories (SNL), and Ames Laboratory that leverages research expertise and facilities of these national labs to develop tools and approaches for reducing the design and development time of new electric drive technologies for medium and heavy-duty vehicles (MHDVs) and their associated costs while increasing reliability and asset utilization. The Next-Drive project aligns with the DOE's goals by introducing high-fidelity multi-physics and AI/ML-based modeling to design low-cost, highly reliable, and longer-lifetime drivetrains, aiming to achieve 25 years of progress in 5 years. The efforts of this project will focus on NEXT-DRIVE Task 4 (led by ORNL, NREL, and AMES) - developing high-fidelity modeling framework and identifying technologies enabling heavy-rare-earth-free electric motors for medium- and heavy-duty vehicles to achieve 1 million miles of operation. Contrary to conventional approaches that optimize the motor for power density, the focus will be to identify motor designs that achieve the best trade-off between motor power density and durable operation. NREL tasks include development of high-fidelity motor thermal models incorporating rotor windage losses and identification, evaluation and measurement of motor interface materials in key thermal pathways. The poster summarizes NREL's accomplishments for the first half of FY 2025 and outlines future plans.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Roadmap for Assessing Fuel Reprocessing in Fluoride-Based Salts

This joint report assesses the pyroprocessing of used nuclear fuel from molten fluoride salts being conducted between Idaho National Laboratory and Argonne National Laboratory. The goal of this report is to identify the research and development gaps needed to reduce the technical risks of extending pyroprocessing technologies and unit operations to molten fluoride salts used in molten salt reactors (MSRs). Assessing and performing the tasks outlined in this report will help mitigate the technical risks and design appropriate flowsheets for reprocessing fuel and coolant salts. These suggested tasks will provide necessary data to implement the development of unit operations. Sections are highlighted to identify processes that need to be assessed and unit operations that may be used in fluoride-based chemistries. Research and development needs are listed and discussed including re-fluorination methods, electrowinning for separations, reference electrode development, materials compatibility, salt purification/re-fluorination, and waste disposition. This report will examine the technical challenges associated with pyroprocessing in molten fluoride-based salt and outline approaches to increase the technical readiness level of pyroprocessing in molten fluoride salts.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Corrosion Testing of Refractory in Contact with Molten Glasses Designed for Waste Vitrification – VSL Touchpoint Matrix Glasses (Revision 1)

It is known that the predictive life of the refractory ceramic liner of nuclear waste glass melters is conservative, as demonstrated by performance of these materials such as in the Defense Waste Processing Facility (DWPF).[1] The motivation for this task is to maximize the useful life of the melters that will be operated at the Waste Treatment and Immobilization Plant (WTP), which will in turn minimize procurement and disposal costs and melter outage times, as well as to identify maximum loadings in the waste glass of those species that corrode melter components. This task was initiated jointly with Pacific Northwest National Laboratory (PNNL) with the objective to develop a methodology and model to enable more accurate prediction of refractory service life under prototypic conditions from laboratory-scale material corrosion tests.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Development and Implementation of a New AI-Based Tool to Support Fast Reactor Software Model Generation and Validation

This report summarizes FY26 work to develop Maggie, an artificial intelligence-based assistant designed to support software model generation and validation activities for fast reactor analysis codes. The project established a modular, code-agnostic software architecture that separates reusable agent capabilities from code-specific knowledge and tools, with initial implementation focused on the FRP-supported fast reactor safety analysis code SAS4A/SASSYS1 (SAS). A curated SAS-specific knowledge base was assembled from the code manual, training materials, historical analysis reports, and representative input files, and was integrated through retrieval-augmented generation to ground Maggie’s responses in authoritative sources. Maggie was deployed on the internal Argonne network, where it demonstrated practical user-facing capability as a chatbot for answering natural language questions about SAS and retrieving relevant technical information. Demonstration cases also showed that Maggie can generate useful snippets of SAS input for selected modeling tasks, while highlighting current limitations in reliability and consistency for more complex input generation tasks. Overall, the FY26 effort established the technical foundation for an AI-assisted capability intended to improve the efficiency, consistency, and accessibility of fast reactor software model development at Argonne and, with further improvements, to support eventual use by the broader fast reactor community, including industry users of FRP-supported analysis tools.

Thomas, Rachel [Argonne National Laboratory (ANL),↗
DOE OSTI2026

October 2025 Semiannual Composite Salt Waste Processing Facility Decontaminated Salt Solution Toxicity Characteristic Leaching Procedure Results

The aqueous waste from the Salt Waste Processing Facility is sampled semiannually for transfers to the Saltstone Disposal Facility. Salt solution is treated at the Saltstone Production Facility and disposed of in the Saltstone Disposal Facility. Per request of customer, X-TTR-Z-00027, Revision 0, one Saltstone Processing Facility Decontaminated Salt Solution and Z area premix material for the October 2025 semiannual Toxicity Characteristic Leaching Procedure sample. The sample contained 60:40 (by weight) of slag and fly ash (referred to as the “Cement-Free grout sample”). Results from the technical report support Task 2: ‘Grout Leaching Analyses’ of the Task Technical Request prepared by Savannah River Mission Completion. At 51 days cured, the Saltstone sample was collected and shipped to a certified laboratory for analysis using the Toxicity Characteristic Leaching Procedure. The October 2025 Semiannual Cement-Free grout sample met 61 79.268.48 requirements for a non-hazardous waste form with respect to the Resource Conservation and Recovery Act metals and Underlying Hazardous Constituents and met the Saltstone Production Facility Waste Acceptance Criteria that was in effect at the time of the sample collection at the Salt Waste Processing Facility.

Hsieh, Madison [Savannah River National Laboratory↗
DOE OSTI2025

HydroBio: Hydropower Capacity and Freshwater Biodiversity in Conterminous United States Sub-basins

This dataset summarizes existing and potential hydropower capacity and freshwater biodiversity at the sub-basin level throughout the conterminous United States (CONUS). It contains descriptive information regarding each sub-basin (e.g., 8-digit hydrologic unit code identifier, name, states, and size) along with sub-basin-level summaries of: 1) existing hydropower capacity (MW), 2) potential nominal non-powered dam (NPD) capacity (MW), 3) potential capacity of new stream reach development (NSD) (MW), and 4) freshwater biodiversity, including the total richness of fish, crayfish, and mussels and metrics that account for how rare and threatened those species tend to be. Hydropower data were obtained from Oak Ridge National Laboratory data resources (Existing Hydropower Assets, Non-Powered Dam Technical Potential, and New Stream Reach Development). Freshwater biodiversity data were obtained from NatureServe. Sub-basin characteristic information was obtained from the United States Geological Survey. Additionally, long data that provide lists of unique elements within each sub-basin for each constituent data resource (e.g., NatureServe, Existing Hydropower Assets) are provided to enhance dataset utility for users. The dataset provides, for the first time, a national-level assessment of existing and potential hydropower capacity in the context of freshwater biodiversity and is a valuable resource for stakeholders tasked with providing affordable, reliable energy to the American public while maintaining or enhancing invaluable freshwater resources. The dataset contains six data files in comma separated (*.csv) format that are within a zipped file.

Bozeman, Bryan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2026

Arbitrary Polynomial Separations in Trainable Quantum Machine Learning

Recent theoretical results in quantum machine learning have demonstrated a general trade-off between the expressive power of quantum neural networks (QNNs) and their trainability; as a corollary of these results, practical exponential separations in expressive power over classical machine learning models are believed to be infeasible as such QNNs take a time to train that is exponential in the model size. We here circumvent these negative results by constructing a hierarchy of efficiently trainable QNNs that exhibit unconditionally provable, polynomial memory separations of arbitrary constant degree over classical neural networks—including state-of-the-art models, such as Transformers—in performing a classical sequence modeling task. This construction is also computationally efficient, as each unit cell of the introduced class of QNNs only has constant gate complexity. We show that contextuality—informally, a quantitative notion of semantic ambiguity—is the source of the expressivity separation, suggesting that other learning tasks with this property may be a natural setting for the use of quantum learning algorithms.

Anschuetz, Eric R. [California Institute of Techno↗
DOE OSTI2025

Defining quantum-ready primitives for hybrid HPC-QC supercomputing: a case study in Hamiltonian simulation

As computational demands in scientific applications continue to rise, hybrid high-performance computing (HPC) systems integrating classical and quantum computers (HPC-QC) are emerging as a promising approach to tackling complex computational challenges. One critical area of application is Hamiltonian simulation, a fundamental task in quantum physics and other large-scale scientific domains. This paper investigates strategies for quantum-classical integration to enhance Hamiltonian simulation within hybrid supercomputing environments. By analyzing computational primitives in HPC allocations dedicated to these tasks, we identify key components in Hamiltonian simulation workflows that stand to benefit from quantum acceleration. To this end, we systematically break down the Hamiltonian simulation process into discrete computational phases, highlighting specific primitives that could be effectively offloaded to quantum processors for improved efficiency. Our empirical findings provide insights into system integration, potential offloading techniques, and the challenges of achieving seamless quantum-classical interoperability. We assess the feasibility of quantum-ready primitives within HPC workflows and discuss key barriers such as synchronization, data transfer latency, and algorithmic adaptability. These results contribute to the ongoing development of optimized hybrid solutions, advancing the role of quantum-enhanced computing in scientific research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Learned adaptive properties for mitigation of weight perturbations in embedded spiking networks

Recent years have seen an increased importance of neural network inference in edge-based scenarios, which impose size and power constraints requiring novel computing devices. These same edge scenarios may require operating over long periods of time, or exposure to extreme environments, resulting in a drift of neural network weights that cause degraded performance. In searching for ways to develop neural network approaches that perform robustly under these conditions, we propose a biologically-inspired mechanism for the dynamic adaptation of within-neuron parameters that is guided by a global context signal carrying information about perturbations and variability in incoming stimuli. Specifically, we demonstrate that adaptive voltage thresholds or neuronal time constants, when informed by a global context signal, can enable network-level mechanisms to recover from perturbed synaptic weights. Consistent with prior literature, the context-modulated approach is effective for recurrent, but not feedforward networks, by modulating network level dynamics. We demonstrate this approach successfully recovers performance in image classification tasks and spatiotemporal tracking tasks under idealized and Gaussian noise as well as for realistic perturbations from a memristive device when exposed to ionizing radiation. Finally, we discuss how this approach enables the design of robust and energy-efficient neuromorphic systems that perform well, even in resource-constrained scenarios with extreme environments such as edge processing.

context modulation↗
DOE OSTI2025

Variance Preserving Spectral Subsampling

Generating statistically faithful short-duration gamma-ray spectra from a single long measurement is essential in nuclear safeguards, supporting tasks such as algorithm development and machine-learning applications, especially when list-mode data are unavailable. Existing subsampling methods often distort the statistical characteristics of genuine short-duration measurements, leading to biased or unreliable analytical outcomes and thereby undermining downstream tasks. In this work, we compare five subsampling approaches using a benchmark set of 156 genuine replicate spectra collected with a high-purity germanium detector. We evaluate each method with respect to run-to-run variance, channel-to-channel variance, and preservation of total counts (losslessness). Across a wide range of subsampling ratios, only binomial subsampling without replacement consistently reproduces the statistical properties of genuine short-duration spectra, maintaining proper dispersion even in sparse spectral regions and perfectly preserving total counts. These results provide a mathematically principled and practically validated framework for generating synthetically shortened spectra when true short-duration measurements are unavailable.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Development of a Multi-Robot System for Autonomous Inspection of Nuclear Waste Tank Pits

This paper introduces the overall design plan, development timeline, and preliminary progress of the Autonomous Pit Exploration System project. This project aims to develop an advanced multi-robot system for the efficient inspection of nuclear waste-storage tank pits. The project is structured into three phases: Phase 1 involves data collection and interface definition in collaboration with Hanford Site experts and university partners, focusing on tank riser geometry and hardware solutions. Phase 2 includes the selection of sensors and robot components, detailed mechanical design, and prototyping. Phase 3 integrates all components into a cohesive system managed by a master control package which also incorporates digital twin and surrogate models, and culminates in comprehensive testing and validation at a simulated tank pit at the Idaho National Laboratory. Additionally, the system’s communication design ensures coordinated operation through shared data, power, and control signals. For transportation and deployment, an electric vehicle (EV) is chosen to support the system for a full 10 h shift with better regulatory compliance for field deployment. A telescopic arm design is selected for its simple configuration and superior reach capability and controllability. Preliminary testing utilizes an educational robot to demonstrate the feasibility of splitting computational tasks between edge and cloud computers. Successful simultaneous localization and mapping (SLAM) tasks validate our distributed computing approach. More design considerations are also discussed, including radiation hardness assurance, SLAM performance, software transferability, and digital twinning strategies.

Nuclear waste management↗
DOE OSTI2025

Flood Susceptibility Mapping Using Machine Learning and Geospatial-Sentinel-1 SAR Integration for Enhanced Early Warning Systems

This study presents a comprehensive framework for flood susceptibility mapping by integrating geospatial factors with both statistical and machine learning models. Thirteen Flood-related factors, including DEM, slope, TWI, NDVI, etc., are extracted as features of models, and historical flood data derived from Sentinel-1 SAR from 2018 to 2023 are used as the target variables of the models. These datasets are analyzed using a frequency-based statistical model and three machine learning models, including Random Forest, XGBoost, and CNN, to generate flood susceptibility maps. The performance of each model is evaluated through AUC; and SHAP scores are separately generated for Machine learning (ML) models to explain each feature contribution in the ML model. The generated susceptibility maps are validated by high-flood-risk locations monitored by flood sensors, BLE inundation models, and flood-prone areas suggested by the Local Community Task Force. The results indicate that the XGBoost model outperforms all other models, with an AUC of 0.92 and demonstrates the highest alignment with recommended high-flood-risk locations, while the frequency-based statistical model showed the weakest performance with an AUC of 0.65. SHAP value graphs highlight the elevation, slope, and TWI as the most influential features across all models. The susceptibility maps generated by the machine learning model show strong agreement with the BLE map and high-flood-risk areas identified by the local Community Task Force.

Google Engine↗
DOE OSTI2025

Employing Eye Trackers to Reduce Nuisance Alarms

When process operators anticipate an alarm prior to its annunciation, that alarm loses information value and becomes a nuisance. This study investigated using eye trackers to measure and adjust the salience of alarms with three methods of gaze-based acknowledgement (GBA) of alarms that estimate operator anticipation. When these methods detected possible alarm anticipation, the alarm’s audio and visual salience was reduced. A total of 24 engineering students (male = 14, female = 10) aged between 18 and 45 were recruited to predict alarms and control a process parameter in three scenario types (parameter near threshold, trending, or fluctuating). The study evaluated whether behaviors of the monitored parameter affected how frequently the three GBA methods were utilized and whether reducing alarm salience improved control task performance. The results did not show significant task improvement with any GBA methods (F(3,69) = 1.357, p = 0.263, partial η 2 = 0.056). However, the scenario type affected which GBA method was more utilized (X 2 (2, N = 432) = 30.147, p < 0.001). Alarm prediction hits with gaze-based acknowledgements coincided more frequently than alarm prediction hits without gaze-based acknowledgements (X 2 (1, N = 432) = 23.802, p < 0.001, OR = 3.877, 95% CI 2.25–6.68, p < 0.05). Participant ratings indicated an overall preference for the three GBA methods over a standard alarm design (F(3,63) = 3.745, p = 0.015, partial η 2 = 0.151). This study provides empirical evidence for the potential of eye tracking in alarm management but highlights the need for additional research to increase validity for inferring alarm anticipation.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Producing High-fidelity Synthetic Population Ensembles at Scale

Used within social simulations, synthetic population ensembles enable uncertainty quantification (UQ) methods for obtaining more robust model inference and prediction. A synthetic population ensemble is a series of plausible virtual reconstructions of an area’s population at the granularity of people and residences, generated stochastically to preserve privacy of the source population survey’s respondents. In this paper, we demonstrate the production of large synthetic population ensembles for the US via Oak Ridge National Laboratory’s UrbanPop framework to support modeling of high spatial resolution energy affordability metrics from nationwide social surveys in collaboration with the fusionACS project. Our initial task involves creating ensembles for 17 US metropolitan areas, each consisting of 41 population instances (a base realization and 40 replicates). To accomplish this task at scale, we configured an integrated system comprised of a research cloud, virtual containerization, GPU-enhanced functionality, and a dual API/CLI to interact with UrbanPop’s maturing Likeness Python ecosystem. We observe a reduction in theoretical execution time while maintaining high-fidelity approximations of residential totals by metropolitan area and the demographic characteristics of neighborhoods. We discuss expansion of our approach to produce synthetic population ensembles for the entire US, particularly plans to establish automated workflows for job orchestration to increase computational efficiency, as well as provide outlook for broadening applications of the ensembles.

Gaboardi, James [ORNL] (ORCID:0000000247766826)↗
DOE OSTI2024

Towards in-situ certification of additively manufactured parts: the vital roles of physics-based and data-driven models

Certifying additively manufactured (AM) parts in-situ at the completion of a build is an enticing prospect, as it can help reduce the high costs associated with post-build testing and evaluation. However, achieving this goal presents significant challenges that may keep it aspirational for the foreseeable future. Nonetheless, incremental progress can pave the way forward. A critical aspect of in-situ certification involves continuous quality checking due to the random nature of the AM process and the difficulties in detecting defects or anomalies once layers are built over. While real-time in-situ monitoring strategies assisted by machine learning (ML) play a pivotal role in auditing part quality, they must ideally be supported by real-time (or near real-time) adaptive process control enabled by ML-assisted decision-making. By analyzing in-situ monitoring data in real-time (or near real- time) to dynamically adjust manufacturing parameters, such intervention can ensure AM parts are built to meet stringent certification standards. This can be achieved virtually by using high-fidelity performance models for the physical testing and evaluation tasks. In this short editorial, we discuss the key contributions made by data-driven and physics-based models in providing intelligence to the monitoring and process control tasks underpinning in-situ certification and in the simulation of the build’s performance under test and service conditions. While our focus lies in metal AM, the concepts discussed here are also relevant to other AM processes.

: In-situ monitoring↗
DOE OSTI2024

Numerical Evaluation of Effective Thermal Conductivity of PCM with Metal Foam Incorporating Buoyancy Effects for Thermal Energy Storage

The thermal energy storage (TES) system has the capability to efficiently preserve thermal energy directly derived from the energy source, minimizing any conversion losses. Especially latent heat storage offers distinct advantages, including a substantial increase in energy storage density and minimization of temperature fluctuations within the plants. However, the phase change material (PCM) employed in latent heat storage has low thermal conductivity. Consequently, various studies are being conducted to enhance heat transfer. One approach to enhance heat transfer involves utilizing metal foam to maximize the heat transfer area. However, modeling metal foam with its intricate structure is a challenging task in numerical analysis. For this reason, ongoing research focuses on simplifying the modeling of metal foam. Nevertheless, fully encompassing all the characteristics of actual metal foam proves to be a challenging task for the simplified analytical model. The objective of this paper is to interpret the simple lattice metal foam analysis model from the perspective of behavior induced by buoyancy. When comparing the analysis results of solid PCM and liquid PCM with the same thermal conductivity under changes in porosity and gravity direction, we conducted an analysis to discern the trends in effective thermal conductivity that are overestimated due to convection. In the analysis, a constant heat flux of 10 kW and a constant surface boundary condition of 350 K were applied, and a sensitivity study regarding the mesh was conducted. The results indicate that, from the perspective of gravity in the simple lattice model, the solid analysis yields an effective thermal conductivity 29-47% higher compared to the liquid analysis. Additionally, as porosity increases, there is an observed increase of 24-33% in effective thermal conductivity.

25 ENERGY STORAGE↗