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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

The Application of a Statistical Downscaling Process to Derive 21 st Century River Flow Predictions Using a Global Climate Simulation

The ability of water managers to maintain adequate supplies in coming decades depends, in part, on future weather conditions, as climate change has the potential to alter river flows from their current values, possibly rendering them unable to meet demand. Reliable climate projections are therefore critical to predicting the future water supply for the United States. These projections cannot be provided solely by global climate models (GCMs), however, as their resolution is too coarse to resolve the small-scale climate changes that can affect hydrology, and hence water supply, at regional to local scales. A process is needed to ‘downscale’ the GCM results to the smaller scales and feed this into a surface hydrology model to help determine the ability of rivers to provide adequate flow to meet future needs. We apply a statistical downscaling to GCM projections of precipitation and temperature through the use of a scaling method. This technique involves the correction of the cumulative distribution functions (CDFs) of the GCM-derived temperature and precipitation results for the 20 th century, and the application of the same correction to 21 st century GCM projections. This is done for three meteorological stations located within the Coosa River basin in northern Georgia, and is used to calculate future river flow statistics for the upper Coosa River. Results are compared to the historical Coosa River flow upstream from Georgia Power Company’s Hammond coal-fired power plant and to flows calculated with the original, unscaled GCM results to determine the impact of potential changes in meteorology on future flows.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

PreMevE: New Predictive Model for Megaelectron-volt Electrons inside Earth's Outer Radiation Belt

Here, this work designs a new model called PreMevE to predict storm–time distributions of relativistic electrons within Earth's outer radiation belt. This model takes advantage of the cross–energy, –L–shell, and –pitch–angle coherence associated with wave–electron resonant interactions, ingests observations from belt boundaries—mainly by NOAA POES in low–Earth–orbits (LEOs), and provides high–fidelity nowcast (multiple–hour prediction) and forecast (> ~1 day) of MeV electron fluxes over L–shells between 2.8–7 through linear prediction filters. PreMevE can not only reliably anticipate incoming enhancements of MeV electrons during storms with at least 1–day forewarning time, but also accurately specify the evolving event–specific electron spatial distributions afterwards. The performance of PreMevE is assessed against long–term in situ data from one Van Allen Probe and a LANL geosynchronous satellite. Furthermore, this new model enhances our preparedness for severe MeV electron events in the future, and further adds new science utility to existing and next–generation LEO space infrastructure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Identification of pressure points in modern power systems using transfer entropy

Power shortages disrupt daily life, economic activity, and essential services. In modern power systems, weather is an increasingly important driver of reliability: high temperatures raise demand and limit transmission capacity, and calm or cloudy periods reduce wind and solar supply. Using a data-driven analysis, this study identifies grid infrastructure whose operating patterns help predict power shortages. The results show that reliability risks often emerge from interacting stresses across generation, transmission, and demand, rather than from single bottlenecks. By clarifying how system stress propagates through the grid, this diagnostic perspective helps explain why shortages occur under specific conditions and can complement traditional planning and operational tools to support adaptive reliability strategies, targeted monitoring, and coordinated infrastructure investments.

power systems↗
DOE OSTI2025

High-Temperature Gas Sensor Materials with Properties Predicted via First-Principles Calculations with Machine Learning Modeling and Experimental Corroboration

Understanding the temperature dependence of functional properties of sensing materials is vital for their applications in combustion environments. The electron-phonon coupling that derives the electronic structure change with temperatures is a key property of interest as it affects other sensing responses. Herein, we first assess the temperature dependence of band gap renormalization in sensing materials by employing Allen-Heine-Cardona (AHC) theory with density functional theory (DFT) simulations corroborated with experimental observation. As the AHC calculations are impractical for high-throughput screening of materials, we employ data-driven Gaussian process regression to predict the parameters employed in the O’Donnell empirical model from a set of physical features. To mitigate the reliability issues arising from the small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as to quantify the uncertainty associated with theoretical predictions. These models capture well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions and, therefore, the variation of bandgap as a function of temperature for other novel materials. The predicted candidates from machine learning models are further validated by experiments and DFT calculations.

bandgap renormalization↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2024

Establishing performance metrics for quantitative non-targeted analysis: a demonstration using per- and polyfluoroalkyl substances

Abstract Non-targeted analysis (NTA) is an increasingly popular technique for characterizing undefined chemical analytes. Generating quantitative NTA (qNTA) concentration estimates requires the use of training data from calibration “surrogates,” which can yield diminished predictive performance relative to targeted analysis. To evaluate performance differences between targeted and qNTA approaches, we defined new metrics that convey predictive accuracy, uncertainty (using 95% inverse confidence intervals), and reliability (the extent to which confidence intervals contain true values). We calculated and examined these newly defined metrics across five quantitative approaches applied to a mixture of 29 per- and polyfluoroalkyl substances (PFAS). The quantitative approaches spanned a traditional targeted design using chemical-specific calibration curves to a generalizable qNTA design using bootstrap-sampled calibration values from “global” chemical surrogates. As expected, the targeted approaches performed best, with major benefits realized from matched calibration curves and internal standard correction. In comparison to the benchmark targeted approach, the most generalizable qNTA approach (using “global” surrogates) showed a decrease in accuracy by a factor of ~4, an increase in uncertainty by a factor of ~1000, and a decrease in reliability by ~5%, on average. Using “expert-selected” surrogates ( n = 3) instead of “global” surrogates ( n = 25) for qNTA yielded improvements in predictive accuracy (by ~1.5×) and uncertainty (by ~70×) but at the cost of further-reduced reliability (by ~5%). Overall, our results illustrate the utility of qNTA approaches for a subclass of emerging contaminants and present a framework on which to develop new approaches for more complex use cases. Graphical Abstract

Pu, Shirley (ORCID:0000000201223797)↗
DOE OSTI2024

Distance preserving machine learning for uncertainty aware accelerator capacitance predictions

Abstract Accurate uncertainty estimations are essential for producing reliable machine learning models, especially in safety-critical applications such as accelerator systems. Gaussian process models are generally regarded as the gold standard for this task; however, they can struggle with large, high-dimensional datasets. Combining deep neural networks with Gaussian process approximation techniques has shown promising results, but dimensionality reduction through standard deep neural network layers is not guaranteed to maintain the distance information necessary for Gaussian process models. We build on previous work by comparing the use of the singular value decomposition against a spectral-normalized dense layer as a feature extractor for a deep neural Gaussian process approximation model and apply it to a capacitance prediction problem for the High Voltage Converter Modulators in the Oak Ridge Spallation Neutron Source. Our model shows improved distance preservation and predicts in-distribution capacitance values with less than 1% error.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Improving Coal-Fired Plant Performance Through Integrated Predictive and Condition-Based Monitoring Tools

The project demonstrated the ability to improve boiler performance and reliability through the integrated use of condition-based monitoring (CBM) and predictions of the impacts of coal quality on boiler operations at a full-scale coal-fired power plant. The advanced tool developed actively monitors and manages coal quality and overall boiler conditions that maximizes availability and maintains generating capacity while reducing cost. The tool is used to forecast and alert plant operators and engineers about poor boiler conditions which may occur as a result of incoming coal and/or current power plant operating conditions. The Combustion System Performance Indices (CSPI) and CoalTracker (CT) programs predicts fireside performance of the plant including slagging, fouling, erosion, slag flow, strength development (sintering/densification), and slag layer thickness based on the composition of delivered coal. CT program is tailored for each plant and is used to track the coal from the point of delivery to the burner. The most successful application of the CSPI-CT has been at plants where the coal composition is determined through the use of a full stream elemental analyzers based on prompt gamma neutron analysis (PGNAA). The CSPI-CT program information is used by coal procurement, coal mining and plant operations personnel to select and blend coal properties for the best plant performance.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Quantum anomaly detection for collider physics

We explore the use of Quantum Machine Learning (QML) for anomaly detection at the Large Hadron Collider (LHC). In particular, we explore a semi-supervised approach in the four-lepton final state where simulations are reliable enough for a direct background prediction. This is a representative task where classification needs to be performed using small training datasets - a regime that has been suggested for a quantum advantage. We find that Classical Machine Learning (CML) benchmarks outperform standard QML algorithms and are able to automatically identify the presence of anomalous events injected into otherwise background-only datasets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Advanced urothelial cancer: a radiology update

The recent genomic characterization of urothelial carcinoma by the Cancer Genome Atlas Project, made possible by the introduction of high throughput, reduced cost, and sequence analysis, has shed new insights on the biology of advanced disease. In addition, studies on imaging of advanced urothelial carcinoma have widened the knowledge on disease presentation and on pattern of metastatic spread and their correlation with the underlying biology of urothelial carcinoma. The wide range of treatments for advanced urothelial cancer, including combined chemotherapy regimens and immune checkpoint inhibitors, each result in treatment class-specific patterns of response and adverse events. Results of studies point to the need for a reliable biomarker, perhaps with imaging, that predicts prognosis and treatment response to systemic treatment, and can be used to select the most effective treatment while minimizing toxicity. This review of advanced urothelial cancer introduces the latest advances in genetic profiling, the current role of imaging, the radiographic appearance of treatment response and their toxicities, and details potential future areas of imaging research.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Assessment of Outliers in Alloy Datasets Using Unsupervised Techniques

We report advancements in data analytics techniques have enabled complex, disparate datasets to be leveraged for alloy design. Identifying outliers in a dataset can reduce noise, identify erroneous and/or anomalous records, prevent overfitting, and improve model assessment and optimization. In this work, two alloy datasets (9-12% Cr ferritic martensitic steels, and austenitic stainless steels) have been assessed for outliers using unsupervised techniques and supplemented with domain knowledge. Principal component analysis and k-means clustering were applied to the data, and points were assessed as outliers based on their distance away from other points in the cluster and from other points in the dataset. The outlier characteristics were investigated to determine both cluster-specific and overall trends in the properties of the outlier points. The approach demonstrated here is extensible to other alloy datasets for outlier identification and evaluation to improve the reliability of machine learning and modeling predictions for advanced alloy design.

36 MATERIALS SCIENCE↗
DOE OSTI2019

The role of composition in the structure and water-binding in alkali-silica reaction sol and gel

The discrepancy between alkali cation (Na + ) used in accelerated assessments of potential alkali-silica reactivity (ASR) and those predominant in portland cements (larger amounts of K + than Na + ) leads to questions regarding the reliability of standardized test methods to predict concrete field performance. To better understand the role of alkali cation type in ASR, this study investigates the influence of alkali cation type on the structure and water-binding ability of ASR sols and gels of varying composition. Results obtained by small-angle neutron scattering, 1H NMR relaxometry, and rheological measurements indicate the formation of densified agglomerate structures with increasing silica-to-alkali molar mass ratios (S/A). However, Na-based sols exhibit a greater tendency to agglomerate and a higher dynamic viscosity than K-based ones. Furthermore, at high S/A, 1 H NMR relaxometry shows the greater ability of K-based gels to bind water, suggesting the better dispersion of siliceous structures and the development of finer porosities.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Integral nuclear data validation using experimental spent nuclear fuel compositions

Measurements of the isotopic contents of spent nuclear fuel provide experimental data that are a prerequisite for validating computer codes and nuclear data for many spent fuel applications. Under the auspices of the Organisation for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) and guidance of the Expert Group on Assay Data of Spent Nuclear Fuel of the NEA Working Party on Nuclear Criticality Safety, a new database of expanded spent fuel isotopic compositions has been compiled. The database, Spent Fuel Compositions (SFCOMPO) 2.0, includes measured data for more than 750 fuel samples acquired from 44 different reactors and representing eight different reactor technologies. Measurements for more than 90 isotopes are included. This new database provides data essential for establishing the reliability of code systems for inventory predictions, but it also has broader potential application to nuclear data evaluation. Furthermore, the database, together with adjoint based sensitivity and uncertainty tools for transmutation systems developed to quantify the importance of nuclear data on nuclide concentrations, are described.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Interplay of electronic structure, magnetism, strain, and defects in carbide $\mathrm{MX}$enes

We report the two-dimensional (2D) magnetic and semiconducting materials, including carbide MXenes, are in high demand for magneto-electronic devices and applications. Here, we report a first-principles study of electronic structure and magnetism of a Ti 2 C MXene, its derivatives Ti 2 CT 2 (T = -F, -O, -OH, -H), and the effect of single vacancy defects (25% C, 12.5% O, and 12.5% and 5.5% Ti) and transition metal (V, Cr, Mn, and Co) dopings. The MAX phase Ti 2 AlC is a non-magnetic (NM) metal which upon removal of Al layer forms a pristine Ti 2 C MXene with A-type antiferromagnetic (AFM) semiconductor having reliable transition temperature (T N = 41K) predicted by the Heisenberg model. All the functionalized MXenes are stable as NM metal except for Ti 2 CO 2 which remains semiconducting with NM ground state. The significant effect of spin–orbit coupling (SOC) is evident in the band structure forming Dirac-like cones and band inversions near the Fermi level. Depending upon the type of vacancy defects Ti 2 CO 2 is NM metal and ferromagnetic (FM) metal. The transition metal (TM) doped MXenes fulfill FM Stoner criterion. The V- and Co-doped MXenes are metals, whereas Cr- and Mn-doped are half-metal and semiconductor.

2D magnetism↗
DOE OSTI2025

Linking the subseasonal variability of the East Asia winter monsoon and the Madden-Julian Oscillation through wave disturbances along the subtropical jet

Despite an urgent demand for reliable subseasonal-to-seasonal (S2S) predictions to guide disaster preparedness, our current climate models show limited S2S prediction skill, particularly for precipitation, due to an inadequate understanding of the key processes that drive regional S2S variability. Here we demonstrate that the leading subseasonal variability mode of precipitation over the East Asian Winter Monsoon (EAWM) region is not only closely tied to the activity of the Madden-Julian Oscillation (MJO), but also linked to precipitation and temperature extremes worldwide, influenced by a circumglobal Rossby wave-train along the subtropical westerly jet. Despite a close phase-lock relationship between the MJO and subseasonal EAWM precipitation, our findings indicate that the MJO itself may only play a minor role in the subseasonal EAWM variability. Given its significant impact on the S2S variability of global weather extremes, we call for coordinated community efforts to enhance the understanding and prediction of the circumglobal Rossby wave-train.

Atmospheric science↗
DOE OSTI2016

Investigating the Ground-State Rotamers of n-Propylperoxy Radical

The n-propylperoxy radical has been described as a molecule of critical importance to studies of low temperature combustion. Ab initio methods were used to study this three-carbon alkylperoxy radical, normal propylperoxy. Reliable CCSD(T)/ANO0 geometries were predicted for the molecule's five rotamers. For each rotamer, energetic predictions were made using basis sets as large as the cc-pV5Z in conjunction with coupled cluster levels of theory up to CCSDT(Q). Along with the extrapolations, corrections for relativistic effects, zero-point vibrational energies, and diagonal Born--Oppenheimer corrections were used to further refine energies. The results indicate that the lowest conformer is the gauche-gauche (GG) rotamer followed by the gauche-trans (0.12 kcal mol^-1 above GG), trans-gauche (0.44 kcal mol^-1), gauche'-gauche (0.47 kcal mol^-1), and trans-trans (0.57 kcal mol^-1). Fundamental vibrational frequencies were obtained using second-order vibrational perturbation theory (VPT2). This is the first time anharmonic frequencies have been computed for this system. The most intense IR features include all but one of the C-H stretches. The O-O fundamental (1063 cm^-1 for the GG structure) also has a significant IR intensity, 19.6 km mol^-1. The anharmonicity effects on the potential energy surface were also used to compute vibrationally averaged r_g,0 K bond lengths, accounting for zero-point vibrations present within the molecule.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Global gyrokinetic simulations of ASDEX Upgrade up to the transport timescale with GENE–Tango

An accurate description of turbulence up to the transport timescale is essential for predicting core plasma profiles and enabling reliable calculations for designing advanced scenarios and future devices. Here, we exploit the gap separation between turbulence and transport timescales and couple the global gyrokinetic code GENE to the transport-solver Tango, including kinetic electrons, collisions, realistic geometries, toroidal rotation and electromagnetic effects for the first time. This approach overcomes gyrokinetic codes' limitations and enables high-fidelity profile calculations in experimentally relevant plasma conditions, significantly reducing the computational cost. We present numerical results of GENE–Tango for two ASDEX Upgrade discharges, one of which exhibits a pronounced peaking of the ion temperature profile not reproduced by TGLF–ASTRA. We show that GENE–Tango can correctly capture the ion temperature peaking observed in the experiment. By retaining different physical effects in the GENE simulations, e.g., collisions, toroidal rotation and electromagnetic effects, we show that the ion temperature profile's peaking can be linked to electromagnetic effects of submarginal (stable) KBM modes. Furthermore, based on these results, the expected GENE–Tango speedup for the ITER standard scenario is larger than two orders of magnitude compared to a single gyrokinetic simulation up to the transport timescale, possibly making first-principles ITER simulations feasible on current computing resources.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Muon capture on Li 6 , C 12 , and O 16 from ab initio nuclear theory

Muon capture on nuclei is one of the most promising probes of the nuclear electroweak current driving the yet-hypothetical neutrinoless double-beta (0νββ) decay. Both processes involve vector and axial-vector currents at finite momentum transfer, q ~ 100 MeV, as well as the induced pseu doscalar and weak-magnetism currents. Comparing measured muon-capture rates with reliable ab initio nuclear-theory predictions could help us validate these currents. To this end, we compute partial muon-capture rates for 6 Li, 12 C and 16 O, feeding the ground and excited states in 6 He, 12 B and 16 N, using ab initio no-core shell model with two- and three-nucleon chiral interactions. Here, we remove the spurious center-of-mass motion by introducing translationally invariant operators and approximate the effect of hadronic two-body currents by Fermi-gas model. We solve the bound-muon wave function from the Dirac wave equations in the Coulomb field created by a finite nucleus. We find that the computed rates to the low-lying states in the final nuclei are in good agreement with the measured counterparts. We highlight sensitivity of some of the transitions to the sub-leading three-nucleon interaction terms. We also compare summed rates to several tens of final states with the measured total capture rates and note that we slightly underestimate the total rate with this simple approach due to limited range of excitation energies

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗