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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Evaluating the Impact of Managed EV Charging for Reliable Operation of Bulk Power Systems with High Non-Dispatchable Generation

The growth of electric vehicles (EVs) and variable-generation (VG) sources introduces new challenges for power-system operations. This study introduces a modeling framework and evaluates five EV charging strategies under projected 2040 grid conditions in the Evergy service territory with high non-dispatchable generation. Using realistic EV behavior and generation models, their impacts on system peak demand, ramp rate, and reserve capacity are evaluated. Results show that only the peak-avoidance strategy effectively reduces system peak demand, while decentralized strategies-particularly cost based dynamic charging-can exacerbate peaks due to synchronized user behavior. However, ramp-rate minimization strategy significantly reduce the stress on dispatchable generation achieving the lowest maximum absolute ramp rate (MARR) (56.81 MW) and lowest reserve requirement (2.39 GW). In contrast, unmanaged and TOU random strategies increase the stress on dispatchable generation sources with increased MARR and reserve requirements. These findings highlight the importance of coordinated, system-aware managed charging strategies to ensure reliable and affordable grid operation in the presence of EVs and VG sources.

14 - SOLAR ENERGY↗
DOE OSTI2024

Multi deep learning-based stochastic microstructure reconstruction and high-fidelity micromechanics simulation of time-dependent ceramic matrix composite response

A multi deep learning-based framework is developed for efficient, automated microstructure reconstruction and generation of stochastic representative volume elements (SRVEs) with periodic boundary conditions (PBCs) for accurate modeling of ceramic matrix composite (CMC) response. The methodology comprises a convolutional neural network coupled with regression layers to act as a vanilla regression network for semantic segmentation of the microstructure, allowing accurate characterization of the phases and their distributions at the microscale. Scanning electron microscope and confocal microscope are used to obtain C/SiNC and SiC/SiNC CMCs micrographs for vanilla regression testing. Microstructure variability in terms of fiber volume fraction and porosity are quantified through the output regression layer, ensuring accurate representation of material variability in SRVE construction. Generative adversarial network (GAN) and its variants are designed to produce high-fidelity SRVE, spanning CMCs microstructure variability space. A circular padding algorithm is developed to generate SRVEs with PBCs during training of GANs. The accuracy of the generated SRVEs is established through micromechanics simulations, where an efficient formulation of the high-fidelity generalized methods of cells (HFGMC) approach is used to compute the effective mechanical properties. Furthermore, an iterative algorithm is implemented in the HFGMC solver to simulate time-dependent deformation of SiC/SiNC subjected to creep loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Findings from Large Bench-Scale Testing of Denitration Electrolyzers for the EDCGe Project

This report highlights the key findings and outcomes relevant to the processability of waste supernatant at Hanford using a denitration electrolyzer. The Electrosynthesis Company issued a Phase 1 report to the Savannah River National Laboratory (SRNL), summarizing the evaluation of large bench-scale denitration electrolyzers to support the electrochemical denitration and caustic generation (EDCGe) project. The Electrosynthesis Company’s report (attached as Appendix A) provides insights into the initial steps required to implement an electrolyzer system at Hanford. Phase 1 experiments focused on validating the denitration electrolyzer’s performance, operating parameters, and reaction products. The robustness of the electrochemical denitration process was demonstrated by two electrolyzer flow cell systems (a 100 cm 2 ElectroCell MP and a 150 cm 2 NESI NS01 cell), both of which achieved significant nitrate and nitrite removal (>50%) with a current efficiency of ~95% for both systems. Higher current densities (500 mA cm –2 ) improved nitrate and nitrite removal rates compared to lower current densities (333 mA cm –2 ), while maintaining a current efficiency of ~94%. The NS01 cell achieved a nitrate species removal rate of ~0.41 mol h –1 at 5 kA m –2 (equiv. to 500 mA cm –2 ). The primary reaction product was ammonia (NH 3 ), constituting 78.3–91.6% of the products (excluding OH – formation). NH 3 was predominantly retained in the catholyte liquid phase rather than being off-gassed. Additionally, the NS01 cell reported an NH 3 generation rate of ~0.36 mol h –1 at 5 kA m –2 . Other gas formation included ~7% N 2 , ~7% H 2 , and trace amounts of N 2 O. The estimated power requirement (extrapolated from the 0.015 m 2 cell data) for a full-scale denitration electrolyzer is approximated to be ~1.6 MW (DC-only) to treat 50% of nitrate and nitrite in a waste stream and generates ~2.1 kmol h –1 of NH 3 with an initial concentration of 4 M NO 3 – /NO 2 – at 300 gal h –1 . Simulated waste containing aluminate, carbonate, oxalate, and halogens exhibited no adverse effects on denitration performance. A preliminary experiment comparing alkaline anolyte (5 M NaOH) with a nickel based anode to acidic media (2 M H 2 SO 4 ) with a DSA-O 2 anode showed a lower operating voltage and generated less H 2 than the acid media. Maintaining a stable 5 M OH – concentration in the anolyte through periodic additions of caustic did not significantly impact denitration performance. This operational mode will be required for long-term experiments. All the experiments demonstrated that electrochemical denitration is a promising approach for treating nitrate and nitrite in simulated waste streams, achieving significant conversion and robustness across varying experimental conditions and electrochemical cell configurations. Lastly, the ability to generate a nearly pure NH 3 stream may prove advantageous for processing at other locations within the Hanford site.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

A mechanistic study on environment gas in laser powder bed fusion

A variety of protective or reactive environmental gases have recently gained growing attention in laser-based metal additive manufacturing (AM) technologies due to their unique thermophysical properties and the potential improvements they can bring to the build processes. However, much remains unclear regarding the effects of different gas environments on critical phenomena in laser AM, such as rapid cooling, energy coupling, and defect generation. Through simultaneous high-speed synchrotron x-ray imaging and thermal imaging, we identify distinct effects of two environmental gases in laser AM and gained a deeper understanding of the underlying mechanisms. Compared to the commonly used protective gas, argon, it is found that helium has a negligible effect on cooling the part. However, helium can suppress unstable keyholes by decreasing effective energy absorption, thus mitigating keyhole porosity generation and reducing pore size under certain processing conditions. In conclusion, these observations provide guidelines for the strategic use of environmental gases in laser AM to produce parts with improved quality.

36 MATERIALS SCIENCE↗
DOE OSTI2026

OmicsMLMentor: A Web Application for Guided Machine Learning Analysis of Omics Data

Expression-based omics technologies (e.g. proteomics, metabolomics, transcriptomics, etc.) increasingly rely on supervised and unsupervised machine learning (ML) models to find key biomolecules distinguishing conditions, identify natural groupings in biological data, or generate predictions for outcomes of interest. Fitting ML models to omics data presents several challenges, including handling missing data, selecting a normalization method, choosing a valid model, and optimizing hyperparameters, all requiring statistical programming skills to address these challenges. Thus, the open-source web application SLOPE was designed to lower the barrier to ML modeling for omics data. SLOPE supports the fitting of 15 ML models (10 supervised and 5 unsupervised) tailored to omics datasets, such as proteomics, metabolomics, lipidomics, and transcriptomics. SLOPE offers several omics-specific features, including methods for handling missingness (imputation, conversion, removal), normalization tests, ranking of models based on the structure of a user’s data and user input, and optimal hyperparameter selections using cross-validation splits. By streamlining ML workflows for omics analysis, SLOPE address critical gaps in existing online web tools, facilitating a broader adoption of these models for omics research. Here, SLOPE is applied to data from a lignin exposure study to highlight the workflow for fitting both supervised and unsupervised models to data.

lipidomics↗
DOE OSTI2025

Conformational Dynamics and Catalytic Backups in a Hyper-thermostable Engineered Archaeal Protein Tyrosine Phosphatase

Protein tyrosine phosphatases (PTPs) are a family of enzymes that play important roles in regulating cellular signaling pathways. The activity of these enzymes is regulated by the motion of a catalytic loop that places a critical conserved aspartic acid side chain into the active site for acid–base catalysis upon loop closure. These enzymes also have a conserved phosphate-binding loop that is typically highly rigid and forms a well-defined anion-binding nest. The intimate links between loop dynamics and chemistry in these enzymes make PTPs an excellent model system for understanding the role of loop dynamics in protein function and evolution. In this context, archaeal PTPs, which have often evolved in extremophilic organisms, are highly understudied, despite their unusual biophysical properties. We present here an engineered chimeric PTP (ShufPTP) generated by shuffling the amino acid sequence of five extant hyperthermophilic archaeal PTPs. Despite ShufPTP’s high sequence similarity to its natural counterparts, it presents a suite of unique properties, including high flexibility of the phosphate binding P-loop, facile oxidation of the active-site cysteine, mechanistic promiscuity, and, most notably, hyperthermostability, with a denaturation temperature likely >130 °C (>8 °C higher than the highest recorded growth temperature of any archaeal strain). Our combined structural, biochemical, biophysical, and computational analysis provides insight both into how small steps in evolutionary space can radically modulate the biophysical properties of an enzyme and showcases the tremendous potential of archaeal enzymes for biotechnology, to generate novel enzymes capable of operating under extreme conditions.

archaea↗
DOE OSTI2026

Lithium metal-mediated electrochemical reduction of per- and poly-fluoroalkyl substances

Per- and poly-fluoroalkyl substances (PFAS) have substantial environmental and health hazards. Unfortunately, current degradation routes require high temperatures or corrosive conditions and/or lead to incomplete defluorination and the generation of shorter alkyl chains. Inspired by the lithium-metal battery literature, here we develop an electrochemical degradation process that leverages reactive metals and highly reducing environments. Here, we show that electrodeposited lithium metal can enable 95% degradation and 94% defluorination of perfluorooctanoic acid to LiF without forming any shorter C 2 –C 6 PFAS as end products. Using computational simulations, we find that electron transfer from lithium to perfluorooctanoic acid leads to rapid C–F bond cleavage, fluoride formation and carbon chain fragmentation. We expand the scope to other PFAS compounds and demonstrate substantial degrees of degradation on over 22 different PFAS, plus complete mineralization to inorganic fluorides. Finally, we use the mineralized F − as a fluorine source for the synthesis of fluorinated non-PFAS compounds to complete a circular fluorine loop from waste to valuable product.

Sarkar, Bidushi [Univ. of Chicago, IL (United Stat↗
DOE OSTI2024

Implementation of Detailed Polyethylene Pyrolysis Kinetics into CFD Simulations using Machine Learning

Municipal solid waste (MSW) and waste plastics have received significant attention due to the issues of waste generation and storage, as well as their potential as an energy resource. High-density polyethylene (HDPE) makes up a large portion of plastic waste and has been the subject of several conversion studies. However, the mechanisms associated with converting HDPE through pyrolysis and gasification are extensive and complex making them difficult to implement into high-fidelity computational fluid dynamic (CFD) simulations. For this project, a primary pyrolysis mechanism containing 42 unique species and 737 heterogeneous reactions was used to generate kinetic data over a range of operating conditions. A machine learning (ML) model was developed to replicate the results of the detailed pyrolysis mechanism while significantly increasing the computational efficiency. A deep operator network (DeepONet) architecture was adopted to train the model using time steps relevant to CFD simulations. The ML used physics-based loss functions to ensure mass conservation. The ML model has been deployed in simple MFiX CFD simulations, single particle, and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.

Houston, Ross↗
DOE OSTI2025

MCOR User Guide

User guide for the MCOR software package which is currently publicly hosted on Github (https://github.com/pnnl/MCOR). The Microgrid Component Optimization for Resilience (MCOR) tool simulates the operation of a renewable energy, battery, and back-up generator microgrid under a large range of outage conditions to understand how a potential system can meet the resilience goals of a particular site. It is an open-source, command line, Python-based tool that produces an output Excel spreadsheet as well as several types of plots to enable a user to compare different microgrid system sizes and costs. It is intended for high-level system planning and opportunity identification, and not for detailed electric system modeling and design. The tool includes a range of input parameters that can be adjusted or tuned to provide a more custom analysis as needed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Computational Design of Improved Fast Reactor Cladding

HT9 ferritic-martensitic (FM) steel has served as a leading candidate for sodium-cooled fast reactor (SFR) cladding due to its favorable resistance to irradiation-induced swelling and good thermal and chemical properties. However, its limited creep strength at temperatures above 600 °C and susceptibility to α′ phase embrittlement under specific conditions could limit its application in next-generation SFRs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Diurnal evolution of non-precipitating marine stratocumuli in a large-eddy simulation ensemble

Abstract. We explore the cloud system evolution of non-precipitating marine stratocumuli with a focus on the impacts of the diurnal cycle and free-tropospheric (FT) humidity based on an ensemble of 244 large-eddy simulations generated by perturbing initial thermodynamic profiles and aerosol conditions. Cases are categorized based on their degree of decoupling and the cloud liquid water path (LWPc, based on model columns with cloud optical depths greater than one). A budget analysis method is proposed to analyze the evolution of cloud water in both coupled and decoupled boundary layers. More coupled clouds start with a relatively low LWPc and cloud fraction (fc) but experience the least decrease in LWPc and fc during the daytime. More decoupled clouds undergo greater daytime reduction in LWPc and fc, especially those with higher LWPc at sunrise because they suffer from faster weakening of net radiative cooling. During the nighttime, a positive correlation between FT humidity and the LWPc emerges, consistent with higher FT humidity reducing both radiative cooling and the humidity jump, both of which reduce entrainment and increase LWPc. The LWPc is more likely to decrease during the nighttime for a larger LWPc and greater inversion base height (zi), conditions under which entrainment dominates as turbulence develops. In the morning, the rate of the LWPc reduction depends on the LWPc at sunrise, zi, and the degree of decoupling, with distinct contributions from subsidence and radiation.

Chen, Yao-Sheng (ORCID:0000000208354132)↗
DOE OSTI2025

Fuel Bonding and its Impact on Axial Gas Communication Behavior in Light-Water Reactor Fuel Rods

Axial gas communication concerns the flow along the axial axis of nuclear fuel rods during ramp and loss of coolant accident (LOCA) conditions. During power ramps, the higher linear heat generation rate may cause fuel-to-clad gap closure that may prevent transport of released fission gases to the plenum. Upon reduction in power the gas then can communicate to the plenum. This phenomenon has been experimentally observed by short power dips during ramp experiments completed at the Risø reactor. At higher burnups it is observed that the UO2 fuel and Zircaloy cladding forms a chemical bond. This bond results in complete closure of the gap. When these high burnup rods are subjected to a LOCA, the bond has implications on both the mechanical response (i.e., ballooning) of the cladding and subsequent fuel relocation and axial gas communication. In the LOCA scenario, gas communication is of interest in two different regimes: 1) pre-rupture communication from the plenum towards the lower pressure ballooning area and 2) the post-rupture depressurization of the plenum to the external system pressure. In both regimes the presence of a fuel-to-cladding bond will impact the rate of depressurization. In this work we present a fuel-to-clad bonding model that is coupled to an existing axial gas communication model framework in the BISON fuel performance code. The effect of considering the bond on fuel performance modeling predictions is presented through comparisons to existing experimental data. Experiments considered include several rods from the Halden IFA-650 test series. An evaluation on a full-length rod that explores the combined effect of plenum size and bonding status on axial gas communication behavior is also presented.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

DRDMannTurb: A Python package for scalable, data-driven synthetic turbulence

Synthetic turbulence models (STMs) are used in wind engineering to generate realistic flow fields and are employed as inputs to industrial wind simulations. Examples include prescribing inlet conditions in large eddy simulations that model loads on wind turbines and tall buildings. We are interested in STMs capable of generating fluctuations based on prescribed second-moment statistics since such models can simulate environmental conditions that closely resemble on-site observations. To this end, the widely used Mann model (see Mann, 1994, 1998) is the inspiration for DRDMannTurb. The Mann model is described by three physical parameters: a magnitude parameter influencing the global variance of the wind field and corresponding to the Kolmogorov constant multiplied by the rate of viscous dissipation of the turbulent kinetic energy to the two-thirds, αϵ 2/3 , a turbulence length scale parameter L, and a nondimensional parameter Γ related to the lifetime of the eddies. A number of studies, as well as international standards (e.g., those by the International Electrotechnical Commission (IEC)), include recommended values for these three parameters with the goal of standardizing wind simulations according to observed energy spectra. Yet, having only three parameters, the Mann model faces limitations in accurately representing the diversity of observable spectra. This Python package enables users to extend the Mann model and more accurately fit field measurements through flexible neural network models of the eddy lifetime function. Following Keith et al. (2021), we refer to this class of models as Deep Rapid Distortion (DRD) models. DRDMannTurb also includes a general module implementing an efficient method for synthetic turbulence generation based on a domain decomposition technique. This technique is also described in Keith et al. (2021).

17 WIND ENERGY↗
DOE OSTI2024

Lyophilization of ASFV vaccine candidate ASFV-G-ΔI177L offers long term stability

Abstract For over a century African swine fever (ASF) has been causing outbreaks leading to devastating losses for the swine industry. The current pandemic of ASF has shown no signs of stopping and continues to spread causing outbreaks in additional countries. Currently control relies mostly on culling infected farms, and strict biosecurity procedures. Recently a vaccine, ASFV-G-ΔI177L was approved for use in Vietnam. In this study we evaluate the long-term stability of lyophilized ASFV-G-ΔI177L. Understanding the stability of different formulations of vaccines is information necessary for deployment of vaccines to ASF outbreak areas, particularly those that do not have a reliable well established cold chain to ensure conservation of vaccine quality. In this report, we determined that ASFV-G-ΔI177L, when lyophilized under specific conditions, is stable for up to one year at 4 °C, with similar vaccine titers after storage. Next-generation sequencing analysis also determined that lyophilization and long-term storage under these conditions had no effect on the genome of ASFV as the genome remained genetically identical to the original non-lyophilized form.

Science & Technology - Other Topics↗
DOE OSTI2024

Hydrocoals from waste biomass via catalytic hydrothermal carbonization processing

This study investigates how operating conditions (temperature and residence time) and benzoyl chloride impact hydrochar production from almond shells through hydrothermal carbonization. Carbon content rises while volatile matter decreases with increasing temperature, yielding a hydrochar with an estimated heating value of 28 MJ/kg. Elevated temperatures lower the O/C and H/C atomic ratios of hydrochars. Even at the lowest temperature under scrutiny, catalytic experiments using benzoyl chloride enhance carbonization and fuel ratio across all temperatures. Hydrochars produced through catalytic runs demonstrate elevated fuel ratios relative to those generated through noncatalytic runs, despite operating under the same conditions. These insights reveal the capacity of benzoyl chloride as a catalyst to improve hydrochar properties, suggesting its utility for advancing both the efficiency and effectiveness of hydrothermal carbonization processes.

09 BIOMASS FUELS↗
DOE OSTI2025

A comparison of probabilistic generative frameworks for molecular simulations

Generative artificial intelligence is now a widely used tool in molecular science. Despite the popularity of probabilistic generative models, numerical experiments benchmarking their performance on molecular data are lacking. Here, in this work, we introduce and explain several classes of generative models, broadly sorted into two categories: flow-based models and diffusion models. We select three representative models: neural spline flows, conditional flow matching, and denoising diffusion probabilistic models, and examine their accuracy, computational cost, and generation speed across datasets with tunable dimensionality, complexity, and modal asymmetry. Our findings are varied, with no one framework being the best for all purposes. In a nutshell, (i) neural spline flows do best at capturing mode asymmetry present in low-dimensional data, (ii) conditional flow matching outperforms other models for high-dimensional data with low complexity, and (iii) denoising diffusion probabilistic models appear the best for low-dimensional data with high complexity. Our datasets include a Gaussian mixture model and the dihedral torsion angle distribution of the Aib9 peptide, generated via a molecular dynamics simulation. We hope our taxonomy of probabilistic generative frameworks and numerical results may guide model selection for a wide range of molecular tasks.

Artificial intelligence↗
DOE OSTI2025

MC Formula Protocol for H35HF Fueling (CRADA Final Report)

The National Renewable Energy Lab (NREL), Frontier Energy, and the industry partners worked together to help SAE J2601-5 develop an H35 high-flow (HF) medium-duty (MD) and heavy-duty (HD) fueling protocol. The team upgraded NREL's hydrogen filling simulations (H2FillS) model to accommodate an MC Formula fueling (t-final) table generation capability by leveraging NREL's high-performance computing system. Based on protocol boundary conditions (e.g., allowable maximum flow rate, range of storage system size) set by SAE J2601-5, the team generated the fueling tables and then validated the reliability of those tables by installing them on NREL's HD dispenser and ZBT's H35HF dispenser and then performing H35HF fueling experiments. Through the validation process, this team certified that the fueling tables generated were reliable to install in commercial H35HF dispensers and then performed H35HF fueling of commercial MD/HD vehicles.

08 HYDROGEN↗
DOE OSTI2026

Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction

Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗