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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Measurement of the Prompt Fission Neutron Spectrum from 800 keV to 10 MeV for 240 Pu($sf$) and for the 240 Pu($n,f$) Reaction Induced by Neutrons of Energy from 1-20 MeV

The presence of 240 Pu in nuclear fuels for reactors has resulted in high uncertainties in the results of reactor and nuclear transmutation calculations because of deficiencies in 240 Pu-related nuclear data. Specifically for the prompt fission neutron spectrum (PFNS) of 240 Pu, there is only one neutron-induced, ($n,f$), measurement at 0.85 MeV incident neutron energy and only one complete spontaneous fission, ($sf$), measurement. This limited availability of data does not sufficiently guide nuclear data evaluations of these quantities. Here we report on a measurement of both the 240 Pu($sf$) and the 240 Pu($n,f$) PFNS, both over the emitted neutron energy range of 0.79–10.0 MeV, and from incident neutron energies of 1.0–20.0 MeV for the ($n,f$) reaction. Measurements were made with a hemispherical array of liquid scintillators at the high-energy Los Alamos Neutron Science Center white neutron source at the Weapons Neutron Research facility as part of the joint LANL-LLNL Chi-Nu experimental campaign to measure actinide fission neutron spectra. These measurements are the first of their kind, and provide clear experimental evidence for second-chance fission, third-chance fission, and pre-equilibrium neutron emission processes in neutron-induced fission of 240 Pu, and are the first ever measurements above 1 MeV incident neutron energy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Summary Report of the 2nd RCM of the CRP on Updating Fission Yield Data for Applications

The Second Research Coordination Meeting of the IAEA Coordinated Research Project (CRP) on Updating Fission Yield Data for Applications was held in Vienna at the IAEA headquarters from 19 to 23 December 2022, with 23 international experts attending the meeting. The CRP is devoted to evaluation efforts of cumulative and independent fission yields for incident energies from the thermal point up to 14 MeV on actinide targets. Produced fission yield evaluations should include full uncertainty quantification and are expected to combine available experimental data and state-of-the-art model information. The activities undertaken within this CRP were reviewed including the assessment of newly measured data and ongoing evaluation efforts. Technical discussions and the resulting further work plan of this CRP are summarized in this report. The meeting presentations are available at: https://www-nds.iaea.org/index-meeting-crp/2RCM_FY/index.htm.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Direct Observations of Solute Dispersion in Rocks With Distinct Degree of Sub‐Micron Porosity

Abstract The transport of chemical species in rocks is affected by their structural heterogeneity to yield a wide spectrum of local solute concentrations. To quantify such imperfect mixing, advanced methodologies are needed that augment the traditional breakthrough curve analysis by probing solute concentration within the fluids locally. Here, we demonstrate the application of asynchronous, multimodality imaging by X‐ray computed tomography (XCT) and positron emission tomography (PET) to the study of passive tracer experiments in laboratory rock cores. The four‐dimensional concentration maps measured by PET reveal specific signatures of the transport process, which we have quantified using fundamental measures of mixing and spreading. We observe that the extent of solute spreading correlate strongly with the strength of subcore‐scale porosity heterogeneity measured by XCT, while dilution is enhanced in rocks containing substantial sub‐micron porosity. We observe that the analysis of different metrics is necessary, as they can differ in their sensitivity to the strength and forms of heterogeneity. The multimodality imaging approach is uniquely suited to probe the fundamental difference between spreading and mixing in heterogeneous media. We propose that when multi‐dimensional data is available, mixing and spreading can be independently quantified using the same metric. We also demonstrate that one‐dimensional transport models have limited predictive ability toward the internal evolution of the solute concentration, when the model is solely calibrated against the effluent breakthrough curves. The data set generated in this study can be used to build realistic digital rock models and to benchmark transport simulations that account deterministically for rock property heterogeneity.

Kurotori, Takeshi [Department of Chemical Engineer↗
DOE OSTI2025

CO2 Plume Imaging with Accelerated Deep Learning-based Data Assimilation Considering Multiple Realizations: Application to the Illinois Basin-Decatur Carbon Sequestration Project

We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site. Unlike the previous work, this study incorporates the impact of spatial heterogeneity using multiple realizations. In the proposed workflow, a neural network model utilizes available monitoring data such as downhole pressure measurements as input and predicts the propagating pressure ‘front’ using the diffusive time of flight (DTOF) map which is considered as representative reservoir image of the flow field. The DTOF is the arrival time of pressure front propagation, which can be computed by the Fast Marching Method rapidly without flow simulations. Reservoir model calibration can be implemented by selecting the training data samples that describe the predicted DTOF map based on observed data. The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project.

CO2 plume imaging↗
DOE OSTI2025

The Henkin-McGill critical temperature test for explosives revisited: An overview of historical and modern method development

Many overarching standards, regulations, or other requirements necessitate that the critical temperature (T crit ) of an energetic material be known or estimate prior to operations such as heating. However, they rarely, if ever, provide a specific method for doing so. While other methods exist to calculate T crit , such as the Frank-Kamenetskii equation, computer simulation, etc., these cannot be readily utilized for energetics that are physical mixtures (i.e. most pyrotechnics) or materials where detailed material properties required for such calculations are lacking. Here, in this study, a COTS (commercial off-the-shelf) SBAT (Simulated Bulk Auto-ignition Test) apparatus is modified to perform Henkin cookoff tests. This creates a simple, efficient, and cost-effective solution to estimate required T crit values for energetic materials. After a historical overview of the evolution of the Henkin test, several common energetic materials (PETN, RDX, etc.) for which T crit has been readily calculated, and historical data is available, were analyzed for comparison and verification purposes. This was followed by a variety of pyrotechnic mixtures and other materials to where calculation methods cannot be readily used. The modified apparatus, as well as an updated sample shell sealing method, produced results that aligned well with historical data. It also produced reasonable T crit estimates for those materials where the Frank-Kamenetskii equation cannot be applied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Transportation Secure Data Center: Frequently Asked Questions for Data Owners/Contributors

The Transportation Secure Data Center is a centralized repository for detailed transportation data from travel and transit surveys and studies conducted across the nation. It makes vital transportation data broadly available to users while preserving the privacy of survey participants. Hundreds of datasets from surveys and studies of household travel and transit passenger travel are archived in the TSDC, including surveys and studies conducted by state departments of transportation, metropolitan planning organizations, transit agencies, cities, and other public agencies. Detailed data from travel surveys and studies are extremely valuable for research purposes. However, the fine-grained information they contain could potentially be misused to identify individual travelers, so access to these data should only be granted with safeguards in place to protect participant privacy. The TSDC was created to address this challenge and to relieve public agencies from the burden of archiving their data and responding to data requests.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Validated Reactive Force Field Quantifies MXene Interfacial Properties, Mechanics, and Thermal Transport

MXenes combine rich surface chemistry, mechanical strength, and high conductivity for a multitude of emerging applications. Predictive modeling supports accelerated materials designs and has been limited by the absence of validated and transferable force fields. Here, we introduce an interpretable, reactive INTERFACE force field (IFF and IFF-R) for Ti 3 C 2 T x MXenes that is trained based on chemical knowledge and achieves quantitative agreement with experiments across lattice parameters (<0.5%), density (<0.2%), liquid contact angles, Raman spectra, and the in-plane elastic modulus (∼320 GPa). The models cover surface terminations from hydroxyl (−OH) to fluorine (−F) groups and are extensible to other chemistries. We introduce pH-resolved surface chemistry and identify dopamine adsorption mechanisms at MXene–aqueous interfaces supported by QCM-D and UV–Vis experiments. The data reveal coplanar and perpendicular binding modes and concentration-dependent multilayer assembly. We predict previously inaccessible properties, including termination-dependent cleavage energies, interlayer shear moduli and dynamic shear failure, nanoindentation and brittle fracture, anisotropic in-plane and out-of-plane thermal conductivities, including the role of defects. Agreement with available experimental data is consistently close and exceeds DFT accuracy across the benchmark properties examined. The IFF/IFF-R model is compatible with CHARMM, AMBER, OPLS, and CVFF force fields for simulations of MXenes with diverse surface terminations, electrolyte interfaces, biointerfaces, and polymer composites without additional parameters. Parameter sets, 3D models, and analysis scripts are provided for community use. The validated, reactive, and transferable IFF framework facilitates predictive design of MXene-based films, membranes, sensing interfaces, and composites.

MXene↗
DOE OSTI2025

A Performant, Scalable Processing Pipeline for High‐Quality and FAIR Environmental Sensor Data

High-resolution environmental monitoring is necessary to record, understand, and predict biogeochemical and ecological changes particularly in coastal systems but brings significant challenges in processing and making rapidly available the resulting data. The COMPASS-FME project established a network of coastal observational sites across the Chesapeake Bay and western Lake Erie regions extensively instrumented with soil, vegetation, and weather sensors logging data every 15 min. Our data processing framework, written in R and completely open source, prioritizes rapid model-experiment iteration and makes biogeochemical data rapidly available for quality assurance/quality control, analysis, and model ingestion. This pipeline is distinguished by a standardized and modular approach to data curation, extensive metadata and documentation, and its high performance. These attributes combine to make biogeochemical data rapidly accessible across COMPASS-FME and the broader community. Flexible, powerful, and reproducible approaches to handling high-volume environmental data are crucial for accelerating biogeosciences research.

Pennington, Stephanie C. [Pacific Northwest Nation↗
DOE OSTI2024

Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics

Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints and to overcome the challenging and time-consuming task of formulating phenomenological constitutive laws that can accurately capture the observed material response. However, even though neural network-based constitutive laws have been shown to generalize proficiently, the generated representations are not easily interpretable due to their high number of trainable parameters. Sparse regression approaches exist that allow for obtaining interpretable expressions, but the user is tasked with creating a library of model forms which by construction limits their expressiveness to the functional forms provided in the libraries. Here, in this work, we propose to train regularized physics-augmented neural network-based constitutive models utilizing a smoothed version of $L^0$-regularization. This aims to maintain the trustworthiness inherited by the physical constraints, but also enables interpretability which has not been possible thus far on any type of machine learning-based constitutive model where model forms were not assumed a priori but were actually discovered. During the training process, the network simultaneously fits the training data and penalizes the number of active parameters, while also ensuring constitutive constraints such as thermodynamic consistency. We show that the method can reliably obtain interpretable and trustworthy constitutive models for compressible and incompressible hyperelasticity, yield functions, and hardening models for elastoplasticity, using synthetic and experimental data. This work aims to set a new paradigm for interpretable machine learning models in the broad area of solid mechanics where low and limited data is available along with prior knowledge of physical constraints that the learned maps need to obey. This paradigm can potentially be extended to a broader spectrum of scientific exploration.

Data-driven constitutive models↗
DOE OSTI2025

Accelerating Resilience of the Community through Holistic Engagement and use of Renewables (ARCHER) Planning Framework

The primary objective of the Accelerating Resilience of the Community through Holistic Engagement and Use of Renewables (ARCHER) initiative was to identify and incorporate the unique variations in energy burden, social vulnerability, living conditions, and access to essential services that differ across communities. By accounting for these localized factors—down to the neighborhood level—the project supports more targeted and effective investments in community resilience. The framework seeks to establish practical planning guidance, methods, and performance measures for community energy resilience, integrate community-level and electric utility system resilience planning, and assess its effectiveness through comparison with conventional and operational planning approaches. A key component of the project was its data exchange platform, which is used to evaluate and demonstrate the tools, methodologies, and planning approaches developed through ARCHER. This open-source platform enables developers and vendors of distribution and outage management systems to build upon the research by incorporating its concepts into their own tools and workflows. This capability is enabled by the transparent availability of data, functional requirements, and the underlying information model. The project yielded several important insights. First, meaningful engagement with communities is essential to achieving comprehensive resilience outcomes. Second, resilience planning is most effective when electric grid considerations and broader community needs are addressed in a coordinated manner. Third, the use of platforms that allow for real-time input from communities can enhance utility responsiveness during restoration activities. Fourth, a structured and systematic planning approach can successfully translate ARCHER concepts into practice. Fifth, the development of an integrated metric that reflects both grid performance and community impacts provides a more holistic basis for evaluating resilience. Finally, incorporating community engagement and equity considerations into grid operations is critical, particularly during severe weather events that result in extended outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Decision Tree for Variable Selection vs. Impact on Durability for Biomass and Biochar Burial Pathways [Slides]

Quantifying durability for lower-TRL BiCRS pathways has been challenging as limited data are available from real-world projects and long-term experiments, resulting in an overall lack of scientific consensus. We develop a decision tree that aims to summarize the current scientific understanding and state-of-the-art project experience. The decision tree can be used to (1) guide the selection of key variables and evaluate their relative impact on durability, (2) identify data and knowledge gaps for future research.

09 BIOMASS FUELS↗
DOE OSTI2026

Nuclear Structure and Decay Data for A=35 Isobars

Here, this work presents a comprehensive and critical evaluation of experimental nuclear spectroscopic data from reactions and decays for all 11 known nuclides with mass number 35 (Ne, Na, Mg, Al, Si, P, S, Cl, Ar, K, Ca). Recommended values are produced for level energies, spins and parities, half-lives, and radiation properties including energies, branching ratios, and multipolarities of γ rays, as well as characteristics of β radiation decays, based on a rigorous assessment of all available experimental data. Discrepancies among existing results are carefully addressed. This work supersedes earlier full evaluations of A=35 published by 2011Ch48, 1990En08 (also 1998En04 update) and 1978En02.

Sun, Lijie [Michigan State University, East Lansin↗
DOE OSTI2026

Nuclear Structure and Decay Data for A=220 Isobars

Here, this work presents a comprehensive and critical evaluation of experimental nuclear spectroscopic data from reactions and decays for all 11 known nuclides with mass number A=220 (Pb, Bi, Po, At, Rn, Fr, Ra, Ac, Th, Pa, and Np). Recommended values are produced for level energies, spins and parities, half-lives, and radiation properties including energies, branching ratios, and multipolarities of γ rays, as well as characteristics of β and α radiation decays, based on all available experimental data. This work supersedes previous A=220 evaluations: 2011Br05, 1997Ar04, 1986Ma45, 1976El04.

Chen, J. [Michigan State University, East Lansing,↗
DOE OSTI2025

Compressed Liquid (${{p}}$-${{\rho}}$-${{T}}$) Measurements of trans-1,2-Dichloroethene [R-1130(E)]

Pressure-density-temperature (${{p}}$-${{\rho}}$-${{T}}$) data for the refrigerant R-1130(E) (trans-1,2-dichloroethene) were measured in the compressed liquid phase using an automated vibrating-tube densimeter. Overall, the measurements covered temperatures from 270 K to 410 K and pressures from 0.5 MPa to 30 MPa. Relative combined, expanded (95% confidence level) uncertainties ranged from approximately 0.05% to 0.06%. Here, we present measurement results, along with comparisons to available literature data and to a generalized extended corresponding states model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Multi-Modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation

As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly important area of research. Surrogate models that learn from data of multiple auxiliary modalities to support the modeling of a highly expensive quantity of interest have the potential to aid outer loop applications such as optimization, inverse problems, or sensitivity analyses when multi-modal data are available. We develop two multi-modal Bayesian neural network surrogate models and leverage conditionally conjugate distributions in the last layer to estimate model parameters using stochastic variational inference (SVI). We provide a method to perform this conjugate SVI estimation in the presence of partially missing observations. Here, we demonstrate improved prediction accuracy and uncertainty quantification compared to unimodal surrogate models for both scalar and time series data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

State Technical Assistance - New Mexico Energy and Conservation Management Division Report [Slides]

The New Mexico Energy and Conservation Management Division (ECMD) sought technical assistance to enhance their ability to evaluate program impacts using the Low-Income Energy Affordability Data (LEAD) tool. NLR assisted ECMD in leveraging the LEAD tool to calculate and analyze energy burden across electric utility service areas, enabling them to assess program outcomes more effectively. To meet ECMD's goals, NLR developed a customized methodology to calculate utility-specific energy burden metrics using census tract data and available utility service area information from the Energy Information Administration (EIA). While acknowledging some limitations in the EIA dataset, NLR estimated the percentage of households within each service territory and incorporated relevant filters such as income, housing characteristics, and other demographics from the LEAD tool. The analysis provided ECMD with a new capability to evaluate program success based on energy savings, reductions in energy burden, and other performance indicators. The data and methodology also support discussions with utilities to improve the accuracy of service territory datasets. ECMD can use the outputs to track program effectiveness and plan future initiatives. NLR offered the possibility of follow-on work, including capacity-building for ECMD to repeat the analysis independently and the option to refine the analysis with updated service.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Modeling of Stress and Temperature Effects on Creep of Reduced Activation Ferritic-Martensitic Steel Alloy F82H (Tertiary Creep Modeling of RAFM Steel)

A Bayesian optimization procedure is presented for calibrating a multi-mechanism micromechanical model for creep to experimental data of F82H steel. Reduced activation ferritic martensitic (RAFM) steels based on are the most promising candidates for some fusion reactor structures. Although there are indications that RAFM steel could be viable for fusion applications at temperatures up to 600 °C, the maximum operating temperature will be determined by the creep properties of the structural material and the breeder material compatibility with the structural material. Due to the relative paucity of available creep data on F82H steel compared to other alloys such as Grade 91 steel, micromechanical models are sought for simulating creep based on relevant deformation mechanisms. As a point of departure, this work recalibrates a model form that was previously proposed for Grade 91 steel to match creep curves for F82H steel. Due to the large number of parameters (9) and cost of the nonlinear simulations, an automated approach for tuning the parameters is pursued using a recently developed Bayesian optimization for functional output (BOFO) framework [1]. Incorporating extensions such as batch sequencing and weighted experimental load cases into BOFO, a reasonably small error between experimental and simulated creep curves at two load levels is achieved in a reasonable number of iterations. Validation with an additional creep curve provides confidence in the fitted parameters obtained from the automated calibration procedure to describe the creep behavior of F82H steel at 600 °C. The model is further extended using a temperature dependent scaling law approach to simulate creep response between 550 °C and 650 °C. The efficacy of this extension is compared with the previously used scaling law approach for Grade 91 steel.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Calibration of RAFM Micromechanical Model for Creep Using Bayesian Optimization for Functional Output

A Bayesian optimization procedure is presented for calibrating a multimechanism micromechanical model for creep to experimental data of F82H steel. Reduced activation ferritic martensitic (RAFM) steels based on Fe(8–9)%Cr are the most promising candidates for some fusion reactor structures. Although there are indications that RAFM steel could be viable for fusion applications at temperatures up to 600°C, the maximum operating temperature will be determined by the creep properties of the structural material and the breeder material compatibility with the structural material. Due to the relative paucity of available creep data on F82H steel compared to other alloys such as Grade 91 steel, micromechanical models are sought for simulating creep based on relevant deformation mechanisms. As a point of departure, this work recalibrates a model form that was previously proposed for Grade 91 steel to match creep curves for F82H steel. Due to the large number of parameters (9) and cost of the nonlinear simulations, an automated approach for tuning the parameters is pursued using a recently developed Bayesian optimization for functional output (BOFO) framework (Huang et al., 2021, “Bayesian optimization of functional output in inverse problems,” Optim. Eng., 22, pp. 2553–2574). Incorporating extensions such as batch sequencing and weighted experimental load cases into BOFO, a reasonably small error between experimental and simulated creep curves at two load levels is achieved in a reasonable number of iterations. In conclusion, validation with an additional creep curve provides confidence in the fitted parameters obtained from the automated calibration procedure to describe the creep behavior of F82H steel.

42 ENGINEERING↗