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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Impact of environmental variables on the degradation of photovoltaic components and perspectives for the reliability assessment methodology

Backsheet cracking has been a major issue observed in the field; however, standardized qualification tests, such as IEC61215, are inadequate to reliably identify such failures of PV modules due to the lack of the critical weathering factors applied sequentially or in combination, such as those found in the service environments. To address this problem, in this work we investigated the effects of various environmental variables on the degradation and failure behaviors of the polyamide-based backsheet in PV modules retrieved from five different locations, encompassing a variety of climates, including humid subtropical, hot-summer Mediterranean, tropical savanna climate and hot arid. The correlations between the degradation indicators and the weathering variables were further demonstrated by principle components analysis (PCA). We found strong relationships between: carbonyl formation and reflected solar radiation; hydroxyl formation and module temperature; yellowness and NO2 concentration, while no simple correlation could be found between a specific weathering factor and cracking. By introducing additional stress factors to the aged polyamide-based backsheet films with the novel 'fragmentation test', we successfully reproduced the field cracking behaviour. This study has demonstrated that different degradation modes of PV components respond differently to the environmental stresses encountered in service. Thereby, any accelerated laboratory test based on a single set condition or lacking key environmental variables would be inadequate to assess the long-term performance of PV modules and components. A new reliability-based methodology is proposed to quantitatively link laboratory testing with field results for the service life prediction of PV materials.

14 SOLAR ENERGY↗
DOE OSTI2025

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Spatially resolved nanostructural analysis of disordered phases in carbonated alkali-activated slag

Alkali-activated slag (AAS) is a promising low-CO 2 alternative cement consisting of several disordered phases of similar composition. Although their local atomic arrangements are known to influence macroscopic behavior, determination of structural changes in response to external stimuli remains a challenge. Here, X-ray diffraction-computed tomography (XRD-CT), pair distribution function-CT (PDF-CT), and nanoprobe X-ray fluorescence (nano-XRF) have been used to uncover how an increase of magnesium in AAS affects the atomic structure and spatial arrangement of phases after aggressive carbonation (100% dry CO 2 ), conditions experienced in applications such as oil and gas wells and geological storage of CO 2 . From PDF-CT it is found that a higher magnesium content decreases the average nanoscale crystallite size of disordered calcium carbonate. At the same time, higher magnesium content is correlated with a less decalcified C-(N)-A-S-H gel, as determined via analysis of Ca-Si atom-atom correlations from PDF-CT and Ca/Si ratios from nano-XRF. Finally, nano-XRF reveals that the disordered (i.e., amorphous) calcium carbonate is stabilized by the presence of silicates.

McCaslin, Eric R. [Princeton Univ., NJ (United Sta↗
DOE OSTI2021

Mechanical coupling in the nitrogenase complex

The enzyme nitrogenase reduces dinitrogen to ammonia utilizing electrons, protons, and energy obtained from the hydrolysis of ATP. Mo-dependent nitrogenase is a symmetric dimer, with each half comprising an ATP-dependent reductase, termed the Fe Protein, and a catalytic protein, known as the MoFe protein, which hosts the electron transfer P-cluster and the active-site metal cofactor (FeMo-co). A series of synchronized events for the electron transfer have been characterized experimentally, in which electron delivery is coupled to nucleotide hydrolysis and regulated by an intricate allosteric network. We report a graph theory analysis of the mechanical coupling in the nitrogenase complex as a key step to understanding the dynamics of allosteric regulation of nitrogen reduction. This analysis shows that regions near the active sites undergo large-scale, large-amplitude correlated motions that enable communications within each half and between the two halves of the complex. Computational predictions of mechanically regions were validated against an analysis of the solution phase dynamics of the nitrogenase complex via hydrogen-deuterium exchange. These regions include the P-loops and the switch regions in the Fe proteins, the loop containing the residue β-188Ser adjacent to the P-cluster in the MoFe protein, and the residues near the protein-protein interface. In particular, it is found that: (i) within each Fe protein, the switch regions I and II are coupled to the [4Fe-4S] cluster; (ii) within each half of the complex, the switch regions I and II are coupled to the loop containing β-188Ser; (iii) between the two halves of the complex, the regions near the nucleotide binding pockets of the two Fe proteins (in particular the P-loops, located over 130 Å apart) are also mechanically coupled. Notably, we found that residues next to the P-cluster (in particular the loop containing β-188Ser) are important for communication between the two halves.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A global soil plasmidome resource unveils functional and ecological roles of plasmids in soil microbiomes

Plasmids play significant roles in microbial adaptation to ecosystems, yet their dynamics remain poorly understood due to identification challenges. We present the Global Soil Plasmidome Resource (GSPR), a comprehensive dataset of 98,728 plasmid sequences amassed from 6860 terrestrial microbial communities and isolates. We explore this resource through various computational approaches, including phylogenetic diversity analysis, host prediction, and extensive functional annotation, to understand the contribution of plasmids to the genetic and functional diversity in soil, correlating these findings with sample type, as well as the soil habitat they were retrieved from. Our analysis reveals insights into plasmid-encoded functions such as effector modules, quorum sensing, and stress resistance, which may contribute to their persistence and microbial adaptation in soil. Furthermore, CRISPR analysis suggests a prevalent role of these elements related to intra-plasmid competition. By contrasting plasmids from cultivated and uncultivated organisms, we identify important functions that expand existing knowledge of plasmid roles in these habitats. This study represents a notable step forward in elucidating plasmid diversity and function within soil microbiomes and establishes a foundational framework for exploring their roles in natural environments.

Fiamenghi, Mateus B↗
DOE OSTI2026

- Analysis of a fixed-bottom cylinder in a wave basin

The Offshore Code Comparison, Collaboration, Continued, with Correlation (OC5) is an international research project run under the International Energy Agency (IEA) Wind Task 30. The project is focused on validating the tools used design offshore wind systems. OC5 consists of four phases. • Phase 1b: Analysis of a fixed-bottom cylinder in a wave basin This website provides the data used for validation in these four phases, as well as simulation results from multiple participants.

17 WIND ENERGY↗
DOE OSTI2021

Multimessenger cosmology: Correlating cosmic microwave background and stochastic gravitational wave background measurements

Characterizing the physical properties of the stochastic gravitational wave background (SGWB) is a key step towards identifying the nature of its possible origin. We focus our analysis on SGWB anisotropies. The existence of a nontrivial primordial scalar-tensor-tensor (STT) correlation in the squeezed configuration may be inferred from the effect that a long wavelength scalar mode has on the gravitational wave power spectrum: an anisotropic contribution. Crucially, such a contribution is correlated with temperature anisotropies in the cosmic microwave background (CMB). This paper shows that, for inflationary models that generate suitably large STT non-Gaussianities, cross correlating the CMB with the stochastic background of gravitational waves is a very effective probe of early universe physics. The resulting signal can be a smoking gun for primordial SGWB anisotropies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

A Cryogenic Muon Tagging System Integrated with a Superconducting Qubit Device for Radiation-Induced Error Mitigation

Superconducting qubits are highly sensitive to ionizing radiation, which can induce correlated errors and limit scalable fault-tolerant quantum computing. In particular, cosmic-ray muons can deposit energy in the substrate, generating phonon bursts that break Cooper pairs and produce quasiparticles, leading to correlated decoherence events across multiple qubits. We present the development of a cryogenic muon tagging system based on Kinetic Inductance Detectors (KIDs) and its integration with superconducting quantum hardware. Originally developed within the ACE-SuperQ project and validated as a standalone detector, the system demonstrated a muon tagging efficiency of approximately 90% and excellent agreement with Monte Carlo simulations. Building on this validation, the tagging system has been integrated with a multi-qubit superconducting chip operated in a dilution refrigerator. The detector configuration consists of a multi-layer KID stack arranged above and below the quantum device, enabling time-coincident identification of muon-induced events within the same cryogenic environment. The integrated setup has been successfully commissioned, enabling simultaneous operation of the qubit chip and the muon tagging system. A first measurement campaign has been carried out, and preliminary data show time-correlated events between the muon tagging detectors and the qubit readout. A quantitative analysis of radiation-induced effects on qubit performance is currently ongoing. This work represents a step toward the implementation of event-level radiation tagging as a tool for characterizing and potentially mitigating correlated errors in superconducting quantum processors, while establishing a modular platform for future studies at the interface between particle physics and quantum information science.

Roy, Tanay [Fermilab] (ORCID:000000019442862X)↗
DOE OSTI2020

EVALUATION OF PRESSURE DROP CORRELATIONS FOR THE WIRE-WRAPPED ROD BUNDLES

The accurate prediction of pressure drop in the fuel rod bundle is of importance for the design and thermal-hydraulic analysis of nuclear reactor system. Due to very complex geometry of wire-wrapped fuel rod bundle geometry for the Sodium Fast Reactor (SFR), a simple method using hydraulic (equivalent) diameter is not sufficient to predict the pressure drop of rod bundle accurately. For several decades, many experimental works have been performed to develop an accurate correlation for wire-wrapped fuel bundle friction factor. In these works, the friction factor correlation of wire-wrapped fuel bundle has been developed as a function of Reynolds number of the flow and the design parameters such as fuel pin diameter, wire diameter, rod pitch, and wire lead length. The Cheng and Todreas correlations are well-known and used in many subchannel analysis code, such as CADET, TRIO-U, MATRA-LMW, SE2-ANL, etc. Novendstern model, Rehme model, Baxi and Dalle Donne model are also well-known and widely referred. Computational Fluid Dynamics is another typical approach to predict the pressure drop of wire-wrapped fuel rod bundle, but it requires the model validation because the accuracy of pressure drop prediction by the CFD simulation depends on what turbulence model is employed. The present work aims at evaluating the existing pressure drop correlations for the wire-wrapped fuel rod bundle and evaluating the pressure drop of 217-pin rod bundle by the CFD analysis.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Psychometric property study of the Affective Lability Scale-short form in Chinese patients with mood disorders

Introduction This study aimed to investigate the psychometric properties of the Affective Lability Scale-short form (ALS-SF) among Chinese patients with mood disorders, and to compare ALS-SF subscale scores between patients with major depressive disorder (MDD) and patients with bipolar disorder (BD) depression. Methods A total of 344 patients with mood disorders were included in our study. Participants were measured through a set of questionnaires including the Chinese version of ALS-SF, Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7-item (GAD-7), and NEO-Five Factor Inventory (NEO-FFI). Exploratory factor analysis and confirmatory factor analysis were applied to examine the psychometric properties of ALS-SF. Besides, correlation and regression analyses were performed to explore the relationship between affective lability and depression, anxiety, and neuroticism. Independent samples t -tests were used to compare the subscale scores of ALS-SF between the MDD and BD depression groups. Results Results of factor analysis indicated that the model of ALS-SF was consistent with ALS-SF. The ALS-SF showed a solid validity and high internal consistency (Cronbach’s alpha = 0.861). In addition, each subscale of ALS-SF was significantly correlated with PHQ-9, GAD-7, and NEO-FFI neuroticism subscale, except for the anger subscale showed no significant correlation with PHQ-9. Besides, the depression/elation and anger factor scores in patients with BD depression were higher than in patients with MDD. Conclusion Our study suggests that the Chinese version of ALS-SF has good reliability and validity for measuring affective lability in Chinese patients with mood disorders. Assessing affective lability would assist clinicians to distinguish between MDD and BP depression and may decrease the risks of misdiagnosis.

Ma, Mohan↗
DOE OSTI2023

Investigating the Cuprates as a platform for high-order Van Hove singularities and flat-band physics

Abstract Beyond the two-dimensional saddle-point Van Hove singularities (VHSs) with logarithmic divergences in the density of states, recent studies have identified higher-order VHSs with faster-than-logarithmic divergences that can amplify electron correlation effects. Here we show that the cuprate high-Tc superconductors harbor high-order VHSs in their electronic spectra and unveil a new correlation that the cuprates with high-order VHSs display higher T c ’s. Our analysis indicates that the normal and higher-order VHSs can provide a straightforward new marker for identifying propensity of a material toward the occurrence of correlated phases such as the excitonic insulators and supermetals. Our study suggests cuprates and related high-T c superconductors as materials for exploring the interplay between high-order VHSs, superconducting transition temperatures, and electron correlation effects.

Markiewicz, Robert S. (ORCID:0000000207470713)↗
DOE OSTI2026

Automated TEM Reveals Intercrystalline Correlations of Conjugated Polymers

Transmission electron microscopy (TEM) continues to transform polymer science by revealing key aspects of chain packing, phase separation and nanoscale structure. The development of instrumentation and data analyses tools is driving the field forward and enabling new experiments. Here, we use automated high-resolution TEM (HRTEM) and image processing to identify the structure of a conjugated polymer used in organic electronics. Analysis of more than 600 HRTEM images reveals lattice parameters and orientation correlations between crystals, including the preferred alignment of neighboring crystals along the same crystallographic direction that is likely the result of liquid crystalline order.

Fair, Ryan A. [Pennsylvania State University, Univ↗
DOE OSTI2021

Revisiting Understanding of Electrochemical CO 2 Reduction on Cu(111): Competing Proton-Coupled Electron Transfer Reaction Mechanisms Revealed by Embedded Correlated Wavefunction Theory

Copper (Cu) electrodes, as the most efficacious of CO 2 reduction reaction (CO 2 RR) electrocatalysts, serve as prototypes for determining and validating reaction mechanisms associated with electrochemical CO 2 reduction to hydrocarbons. As in situ electrochemical mechanism determination by experiments is still out of reach, such mechanistic analysis typically is conducted using density functional theory (DFT). The semilocal exchange-correlation (XC) approximations most often used to model such catalysis unfortunately engender a basic error: predicting the wrong adsorption site for CO (a key CO 2 RR intermediate) on the most ubiquitous facet of Cu, namely, Cu(111). This longstanding inconsistency casts lingering doubt on previous DFT predictions of the attendant CO 2 RR kinetics. Here, we apply embedded correlated wavefunction (ECW) theory, which corrects XC functional error, to study the CO 2 RR on Cu(111) via both surface hydride (*H) transfer and proton-coupled electron transfer (PCET). We predict that adsorbed CO (*CO) reduces almost equally to two intermediates, namely, hydroxymethylidyne (*COH) and formyl (*CHO) at –0.9 V vs the RHE. In contrast, semilocal DFT approximations predict a strong preference for *COH. With increasing applied potential, the dominance of *COH (formed via potential-independent surface *H transfer) diminishes, switching to the competitive formation of both *CHO and *COH (both formed via potential-dependent PCET). Furthermore, our results also demonstrate the importance of including explicitly modeled solvent molecules in predicting electron-transfer barriers and reveal the pitfalls of overreliance on simple surface *H transfer models of reduction reactions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Flow condensation heat transfer performance of natural and emerging synthetic refrigerants

There is great interest in predicting flow condensation heat transfer for lower global warming potential (GWP) fluids. This paper analyzes the efficacy of common flow condensation correlations developed for particular fluids in order to identify their suitability to predict heat transfer performance of low GWP fluids. Condensation heat transfer data were extracted from the literature, including 19 papers and 1,473 data points for natural refrigerants [i.e., ammonia (R717), CO 2 (R744), propane (R290), isobutane (R600a)] and 35 papers and 5,030 data points for synthetic refrigerants [i.e., R12, R1234yf, R1234ze(E), R1234ze(Z), R22, R32, R41, R123, R125, R134a, R142b, R152a, R161, R404A, R410A, R448A, R449A, R450A, R452B, R454C, R455A, R513A] encompassing tube diameters of 0.1–11.5 mm, mass fluxes of 55–1200 kg/m2s, and saturation temperatures of -25°C–65°C. Correlations analyzed included Akers et al. (1959), Cavallini et al. (2006, 2011), Kim and Mudawar (2013), Macdonald and Garimella (2016), Shah (1979, 2009, 2013, 2016) and Traviss et al. (1973) for smooth tubes and Chamra et al. (2005) and Kedzierski and Goncalves (1999) for enhanced tubes. Since most studies did not report wall temperature, correlations which relied on wall temperature directly or indirectly were excluded from the analysis. For synthetic refrigerants, mean average error (MAE) ranged from 6%–257%, and Cavallini et al. (2011) and Kim and Mudawar (2013) were the best predictors for emerging synthetic refrigerants. Furthermore, the Kim and Mudawar (2013) correlation was found to best predict the heat transfer performance for propane and R600a data, but most correlations did not accurately predict ammonia and CO 2 flow condensation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Impact Study of Thunderstorms on the US Power Grid Using Publicly Available Datasets

This work analyzes the impact of thunderstorms on the US power grid based on publicly available data. Since thunderstorms can bring lightning, heavy precipitation, and wind storms, analyzing their impact on the power system provides a combined correlation of lightning strikes, floods, and wind storms on power outages. This paper leverages publicly available thunderstorm datasets from the National Weather Service (NWS) and power outage datasets from Oak Ridge National Laboratory’s Environment for Analysis of Geo-Located Energy Information (EAGLE-I) to study the correlation between thunderstorms and power outages. This work is analyzing the patterns of thunderstorms from 2013-2022, which shows that the thunderstorms are not slowing down and will seem to continue their impact on human life in the future. This work also analyzes the monthly and yearly pattern of the impact of thunderstorms on power systems at the national, state, and county level.

Bhusal, Narayan↗
DOE OSTI2021

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Cosmological cross-correlations and nearest neighbour distributions

ABSTRACT Cross-correlations between data sets are used in many different contexts in cosmological analyses. Recently, k-nearest neighbour cumulative distribution functions (kNN-CDF) were shown to be sensitive probes of cosmological (auto) clustering. In this paper, we extend the framework of NN measurements to describe joint distributions of, and correlations between, two data sets. We describe the measurement of joint kNN-CDFs, and show that these measurements are sensitive to all possible connected N-point functions that can be defined in terms of the two data sets. We describe how the cross-correlations can be isolated by combining measurements of the joint kNN-CDFs and those measured from individual data sets. We demonstrate the application of these measurements in the context of Gaussian density fields, as well as for fully non-linear cosmological data sets. Using a Fisher analysis, we show that measurements of the halo-matter cross-correlations, as measured through NN measurements are more sensitive to the underlying cosmological parameters, compared to traditional two-point cross-correlation measurements over the same range of scales. Finally, we demonstrate how the NN cross-correlations can robustly detect cross-correlations between sparse samples – the same regime where the two-point cross-correlation measurements are dominated by noise.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Evaluating the potential of short-term instrument deployment to improve distributed wind resource assessment

Distributed wind projects, which are connected at the distribution level of an electricity system or in off-grid applications to serve specific or local energy needs, often rely solely on wind resource models to establish wind speed and energy generation expectations. Historically, anemometer loan programs have provided an affordable avenue for more accurate onsite wind resource assessment, and the lowering cost of lidar systems has shown similar advantages for more recent assessments. While a full 12 months of onsite wind measurement is the standard for correcting model-based long-term wind speed estimates for utility-scale wind farms, the time and capital investment involved in gathering onsite measurements must be reconciled with the energy needs and funding opportunities that drive expedient deployment of distributed wind projects. Much literature exists to quantify the performance of correcting long-term wind speed estimates with 1 or more years of observational data, but few studies explore the impacts of correcting with months-long observational periods. This study aims to answer the question of how short you can go in terms of the observational time period needed to make impactful improvements to model-based long-term wind speed estimates. Three algorithms, multivariable linear regression, adaptive regression splines, and regression trees, are evaluated for their skill at correcting long-term wind resource estimates from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) using months-long periods of observational data from 66 locations across the US. On average, correction with even 1 month of observations provides significant improvement over the baseline ERA5 wind speed estimates and produces median bias magnitudes and relative errors within 0.22 m s −1 and 4 percentage points of the median bias magnitudes and relative errors achieved using the standard 12 months of data for correction. However, in cases when the shortest observational periods (1 to 2 months) used for correction are not well correlated with the overlapping ERA5 reference, the resultant long-term wind speed errors are worse than those produced using ERA5 without correction. Summer months, which are characterized by weaker relative wind speeds and standard deviations for most of the evaluation sites, tend to produce the worst results for long-term correction using months-long observations. The three tested algorithms perform similarly for long-term wind speed bias; however, regression trees perform notably worse than multivariable linear regression and adaptive regression splines in terms of correlation when using 6 months or less of observational data for correction. Translating the analysis to wind energy, median relative errors in the capacity factor are on average within 10 % using 1 month of training. If the observation period used for correction is not well correlated with the reference data, however, misrepresentation of the observed capacity factor can be substantial. The risk associated with poor correlation between the observed and reference datasets decreases with increasing training period length. In the worst-correlation scenarios, the median capacity factor relative errors from using 1, 3, and 6 months are within 47 %, 26 %, and 16 %, respectively.

17 WIND ENERGY↗