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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Chapter 3: Photovoltaic Module Stability and Reliability

Profits realized from investment in photovoltaic will benefit from decades of reliable operation. Service life prediction through accelerated tests is only possible if indoor tests duplicate power loss and failure modes observed in fielded systems. Therefore, detailing and quantifying power loss and failure modes is imperative. In the first section, we examine recent trends in degradation rates, the gradual power loss observed for different technologies, climates and other significant factors. In the second section, we provide a summary of the most commonly observed failure modes in fielded systems.

14 SOLAR ENERGY↗
DOE OSTI2026

Predicting river turbidity in Pine Island Bayou using machine learning techniques coupled with variational mode decomposition

Elevated turbidity levels pose significant public health risks by facilitating the transport of harmful pollutants, including metals, organic compounds, and pathogenic microorganisms into the surface water. These conditions create serious challenges for public recreational water use and drinking water treatment, leading to economic losses and health risks. This study utilizes water monitoring data in Pine Island Bayou, Texas, and develops a Sequence-to-Sequence (S2S) model to predict turbidity using Attention-based Gated Recurrent Units with Encoder-Decoder (AT-GRU-ED) and Long Short-Term Memory (LSTM), coupled with Variational Mode Decomposition (VMD). Compared to the model without VMD, the model demonstrates satisfactory 72-hour turbidity prediction performance, achieving MAEs of 2.60 and 3.29 NTU (reductions of 53% and 58%), RMSEs of 21.08 and 31.49 NTU (reductions of 82% and 80%), and R² values of 0.96 and 0.84 on the validation and test sets, respectively. Feature importance analysis reveals that water temperature is the dominant factor influencing seasonal turbidity patterns, while real-time hourly rainfall significantly contributes to short-term variability. Turbidity typically peaks within 48 hours after rainfall events due to lagged effects from surface runoff and upstream flow. Findings suggest suspending recreational water use and water supply pumping for three days after heavy rainfall can benefit public health and improve water treatment processes. Discharges above 100 m3/s are found to accelerate sediment dilution and transport, reducing turbidity levels more quickly after the peak. In conclusion, the proposed model demonstrates reliable 72-hour turbidity prediction, supporting decision-making for water treatment plant operations and providing early warning for public recreational water use.

Deep learning↗
DOE OSTI2023

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure–property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.

25 ENERGY STORAGE↗
DOE OSTI2017

Parameter Calibration of GTN Damage Model and Formability Analysis of 22MnB5 in Hot Forming Process

Hot forming of high strength steel at elevated temperatures is an attractive technology to achieve the lightweight of vehicle body. The mechanical behavior of boron steel 22MnB5 strongly depends on the variation of temperature which makes the process design more difficult. In this paper, the Gurson–Tvergaard–Needleman (GTN) model is used to study the formability of 22MnB5 sheet at different temperatures. Firstly, the rheological behavior of 22MnB5 is analyzed through a series of hot tensile tests at a temperature range of 600-800 °C. Then, a detailed process to calibrate the damage parameters is given based on the response surface methodology and genetic algorithm method. The GTN model together with the damage parameters calibrated is then implemented to simulate the deformation and damage evolution of 22MnB5 in the process of high-temperature Nakazima test. The capability of the GTN model as a suitable tool to evaluate the sheet formability is confirmed by comparing experimental and calculated results. Finally, as a practical application, the forming limit diagram of 22MnB5 at 700 °C is constructed using the Nakazima simulation and Marciniak–Kuczynski (M–K) model, respectively. And the simulation integrated GTN model shows a higher reliability by comparing the predicted results of these two approaches with the experimental ones.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Influence of Wear Test Parameters on the Electrical Contact Performance of Brass Alloy/Copper Contactors Under Fretting Wear

Fretting of mated electronic connectors may be due to extreme increase in contact resistance. We present a detailed analysis of contact resistance by fretting experiments using brass alloys/copper contact pairs. The relationship between wear mechanisms and contact resistance was determined. The influence of fundamental factors such as normal load, displacement amplitude, and current on contact resistance change was also evaluated. It was found out that displacement amplitude, normal load, and current are important parameters for infinite lifetime or stable electrical resistance during fretting condition. Influence of displacement amplitude on the electrical contact performance might be affected by wear debris in the contact area. Electric contact performance could be improved by increasing the power current, which could break down the thick oxide film formed. The predicted working lifetime and reliability requirements of the connector were determined to optimize and extend the service life under the working condition.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials

Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recently developed the Latent Ewald Summation (LES) method, which infers electrostatic interactions, polarization, and Born effective charges (BECs), just by learning from energy and force training data. Here, in this study, we present LES as a standalone library, compatible with any short-range MLIP, and demonstrate its integration with methods such as MACE, NequIP, Allegro, CACE, CHGNet, and UMA. We benchmark LES-enhanced models on distinct systems, including bulk water, polar dipeptides, and gold dimer adsorption on defective substrates, and show that LES not only captures correct electrostatics but also improves accuracy. Additionally, we scale LES to large and chemically diverse data by training MACELES-OFF on the SPICE set containing molecules and clusters, making a universal MLIP with electrostatics for organic systems, including biomolecules. MACELES-OFF is more accurate than its short-range counterpart (MACE-OFF) trained on the same data set, predicts dipoles and BECs reliably, and has better descriptions of bulk liquids. By enabling efficient long-range electrostatics without directly training on electrical properties, LES paves the way for electrostatic foundation MLIPs.

Kim, Dongjin [University of California, Berkeley, ↗
DOE OSTI2020

Nano- to microscale three-dimensional morphology relevant to transport properties in reactive porous composite paint films

The quantitative evaluation of the three-dimensional (3D) morphology of porous composite materials is important for understanding mass transport phenomena, which further impact their functionalities and durability. Reactive porous paint materials are composites in nature and widely used in arts and technological applications. In artistic oil paintings, ambient moisture and water and organic solvents used in conservation treatments are known to trigger multiple physical and chemical degradation processes; however, there is no complete physical model that can quantitatively describe their transport in the paint films. In the present study, model oil paints with lead white (2PbCO 3 ·Pb(OH) 2 ) and zinc white (ZnO) pigments, which are frequently found in artistic oil paintings and are associated with the widespread heavy metal soap deterioration, were studied using synchrotron X-ray nano-tomography and unilateral nuclear magnetic resonance. This study aims to establish a relationship among the paints’ compositions, the 3D morphological properties and degradation. This connection is crucial for establishing reliable models that can predict transport properties of solvents used in conservation treatments and of species involved in deterioration reactions, such as soap formation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Impact of in situ nuclear networks and atomic opacities on neutron star merger ejecta dynamics, nucleosynthesis, and kilonovae

Context. Binary neutron star merger (BNSM) ejecta are key sites of rapid neutron capture (r-process) nucleosynthesis and they produce kilonovae powered by the radioactive decay of freshly synthesized nuclei. Modeling their evolution requires multi-physics simulations involving hydrodynamics, nuclear reactions, and radiative processes. The impact of nuclear burning and atomic opacity is poorly understood and often treated with simplified prescriptions. Aims. We systematically investigate different treatments of nuclear heating, particle thermalization, and atomic opacities in radiation-hydrodynamics simulations of BNSM ejecta and kilonova light curves. Methods. Ejecta profiles from long-term numerical-relativity simulations of asymmetric neutron star binaries with a massive neutron star remnant were evolved to ∼30 days using a 2D ray-by-ray approach. We compared simplified heating-rate and thermalization prescriptions with in situ Nuclear reaction Network (NN) calculations that track nuclear energy deposition and include a composition-dependent thermalization scheme. We also contrasted various gray opacity models with a frequency-dependent treatment based on atomic calculations. Results. Coupling NN and hydrodynamics significantly affects nucleosynthesis and kilonova emission. Assuming homologous expansion alters abundance evolution and produces a narrower, less populated second r-process peak and a third peak shifted to higher mass numbers. The back-reaction of nuclear heating affects the temperature evolution enough to delay and redden the early (t∼ hours) kilonova peaks. A constant thermalization efficiency underestimates and reddens the early emission while overestimating the late-time luminosity compared to the composition-dependent treatment. Analytical opacity prescriptions yield a more extended, colder photosphere, resulting in dimmer, redder kilonovae at early times (t≲ hour), while the delayed recession of the photosphere prolongs the red emission at t ≳ 5 days. Conclusions. Coupling hydrodynamics to an in situ NN is crucial for reliable nucleosynthesis and kilonova predictions. Resolving the first several hundred milliseconds of the hydrodynamics is essential for robust nucleosynthesis calculations. Composition-dependent thermalization and frequency-dependent, atomic-physics-based opacities are needed to accurately capture the temperature evolution of the ejecta and the brightness and color evolution of the kilonova. Calibrated analytic nuclear-power fits with simplified thermalization and opacity prescriptions can still reproduce the density and temperature evolution of the ejecta.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

The molecular dipole moment ( μ ) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly—via the ground state electron density—from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). Here, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression framework which assigns a (vector) dipole moment to each atom, while the movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting “MuML” models are fitted together to reproduce molecular μ computed using high-level coupled-cluster theory and density functional theory (DFT) on the QM7b dataset, achieving more accurate results due to the physics-based combination of these complementary terms. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model. The ultimate performance of the models—and the optimal weighting of their combination—depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to μ ; furthermore, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Development of a Validation Approach for an Integrated Waste Glass Melter Model

Integrated models are being developed to represent the physics occurring within the high-level and low-activity waste melters that will be used to vitrify legacy tank waste at the Hanford site. These models couple the melt pool, cold cap, and plenum region within a single computational domain. Validation of the models is essential to ensure the reliability of the numerical predictions of the operational melters. Experimental data from laboratory- and pilot-scale tests are thus being used to inform and validate various aspects of the melter model. This study presents a tiered approach to model validation consisting of a series of progressively more complex test cases designed to model the physics occurring in the full-scale system. A hierarchical methodology has been developed to segregate and simplify the physical phenomena affecting the multiphase flow and heat transfer within a waste glass melter. Finally, four hierarchical levels are defined in a validation pyramid and built up in levels of increasing complexity from unit problems to subsystem cases, to pilot-scale systems, and then to the full-scale system.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Evaluation of calibration efficacy under different levels of uncertainty

This study examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty.We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data with differing levels of detail in building design, usage, and operation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Accurate spectroscopic characterization of ethyl mercaptan and dimethyl sulfide isotopologues: a route toward their astrophysical detection

Using state-of-the-art computational methodologies, we predict a set of reliable rotational and torsional parameters for ethyl mercaptan and dimethyl sulfide monosubstituted isotopologues. This includes rotational, quartic, and sextic centrifugal-distortion constants, torsional levels, and torsional splittings. The accuracy of the present data was assessed from a comparison to the available experimental data. Generally, our computed parameters should help in the characterization and the identification of these organo-sulfur molecules in laboratory settings and in the interstellar medium.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Constraining the Milky Way’s ultraviolet-to-infrared SED with Gaussian process regression

ABSTRACT Improving our knowledge of global Milky Way (MW) properties is critical for connecting the detailed measurements only possible from within our Galaxy to our understanding of the broader galaxy population. We here train Gaussian process regression (GPR) models on SDSS (Sloan Digital Sky Survey) galaxies to map from galaxy properties (stellar mass, apparent axial ratio, star formation rate, bulge-to-total ratio, disc scale length, and bar vote fraction) to ultraviolet (UV; GALEX FUV/NUV), optical (SDSS ugriz), and infrared (IR; 2MASS JHKs and WISE W1/W2/W3/W4) fluxes and uncertainties. With these models, we estimate the photometric properties of the MW, resulting in a full UV-to-IR spectral energy distribution (SED) as it would be measured externally, viewed face-on. We confirm that the MW lies in the green valley in optical diagnostic diagrams, but show for the first time that the MW is in the star-forming region in standard UV and IR diagnostics – characteristic of the population of red spiral galaxies. Although our GPR method predicts one band at a time, the resulting MW UV–IR SED is consistent with SEDs of local spirals with characteristics broadly similar to the MW, suggesting that these independent predictions can be combined reliably. Our UV–IR SED will be invaluable for reconstructing the MW’s star formation history using the same tools employed for external galaxies, allowing comparisons of results from in situ measurements to those from the methods used for extragalactic objects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Resonant inelastic X-ray scattering study of spin-wave excitations in the cuprate parent compound Ca 2 CuO 2 Cl 2

By means of resonant inelastic x-ray scattering at the Cu L 3 edge, we measured the spin wave dispersion along <100> and <110> in the undoped cuprate Ca 2 CuO 2 Cl 2 . The data yields a reliable estimate of the superexchange parameter J = 135 ± 4 meV using a classical spin-1/2 2D Heisenberg model with nearest-neighbor interactions and including quantum fluctuations. Including further exchange interactions increases the estimate to J = 141 meV. The 40 meV dispersion between the magnetic Brillouin zone boundary points (1/2, 0) and (1/4, 1/4) indicates that next-nearest neighbor interactions in this compound are intermediate between the values found in La 2 CuO 4 and Sr 2 CuO 2 Cl 2 . Here by owing to the low- Z elements composing Ca 2 CuOCl 2 , the present results may enable a reliable comparison with the predictions of quantum many-body calculations, which would improve our understanding of the role of magnetic excitations and of electronic correlations in cuprates.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Solid-to-plasma transition of polystyrene induced by a nanosecond laser pulse within the context of inertial confinement fusion

Laser Direct Drive (LDD) Inertial Confinement Fusion (ICF) involves irradiating a spherical target of thermonuclear fuel coated with an ablator, usually made of polystyrene. Laser energy absorption near the target surface leads to matter ablation, hydrodynamic shocks, and ultimately capsule implosion. The conservation of spherical symmetry is crucial for implosion efficiency, yet spatial modulations in laser intensity can induce nonuniformities, causing the laser imprint phenomenon. Understanding laser imprint, especially considering the initial solid state, is essential for advancing LDD ICF. A first microscopic model of solid-to-plasma transition was built in 2019, accounting for laser absorption in the solid state with a band structure based ionization model. This model has been improved to include chemical fragmentation and a more accurate description of electron collision frequency in various matter states. The latest development involves assessing the model reliability by comparing theoretical predictions with experimental observations. Despite the success of this approach, questions remain, leading to further investigations and observations under different irradiation conditions. This work presents an experiment with a nanosecond pulse, taking into account hydrodynamic effects, and measures transmission dynamics over the entire laser beam area to observe two-dimensional effects. As a result, the objective is to adapt the theoretical model, couple it with a hydrodynamic code, and observe additional effects related to the initial solid state.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Efficient Unsteady Flow Visualization with High-Order Access Dependencies

We present a novel high-order access dependencies based model for efficient pathline computation in unsteady flow visualization. By taking longer access sequences into account to model more sophisticated data access patterns in particle tracing, our method greatly improves the accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing uniformly-seeded pathlines in both forward and backward directions in a preprocessing stage. The effectiveness of our proposed approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method achieves higher data locality and hence improves the efficiency of pathline computation.

Zhang, Jiang↗
DOE OSTI2016

Machine learning based job status prediction in scientific clusters

Large high-performance computing systems are built with increasing number of components with more CPU cores, more memory, and more storage space. At the same time, scientific applications have been growing in complexity. Together, they are leading to more frequent unsuccessful job statuses on HPC systems. From measured job statuses, 23.4% of CPU time was spent to the unsuccessful jobs. Here, we set out to study whether these unsuccessful job statuses could be anticipated from known job characteristics. To explore this possibility, we have developed a job status prediction method for the execution of jobs on scientific clusters. The Random Forests algorithm was applied to extract and characterize the patterns of unsuccessful job statuses. Experimental results show that our method can predict the unsuccessful job statuses from the monitored ongoing job executions in 99.8% the cases with 83.6% recall and 94.8% precision. Lastly, this prediction accuracy can be sufficiently high that it can be used to mitigation procedures of predicted failures.

97 MATHEMATICS AND COMPUTING↗