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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A novel machine learning based identification of potential adopter of rooftop solar photovoltaics

With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.

14 SOLAR ENERGY↗
DOE OSTI2024

Deep Generative Models in Energy System Applications: Review, Challenges, and Future Directions

In recent years, with the advent of mature machine learning products like ChatGPT, Stable Diffusion, and Sora, the world has witnessed tremendous changes driven by the rapid development of generative artificial intelligence (GAI). Beyond applications in text, speech, image, and video creation, deep generative models (DGMs) underpinning these cutting-edge technologies have also been employed by domain researchers to address scientific and engineering challenges. This paper aims to fill a gap in the research community by providing a systematic review of how DGMs have been utilized in energy system applications. After introducing four most popular DGMs, we review and categorize 196 research articles into five focus areas: data generation, forecasting, situational awareness, modeling, and optimal decision-making. Through this classification, we uncover trends in how DGMs are employed for each type of problem, highlighting GAI techniques that contribute to breakthroughs over traditional methods. We discuss limitations in existing literature, engineering challenges, and propose future directions, all tailored to the unique nature of problems in energy system engineering. Our goal is to offer insights for energy system domain researchers, providing a comprehensive view of existing studies and potential future opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning

Many geological materials, such as shale, mudstone, carbonate rock, limestone and rock salt are multi-porosity porous media in which pores of different scales may co-exist in the host matrix. When fractures propagate in these multi-porosity materials, these pores may enlarge and coalesce and therefore change the magnitude and the principal directions of the effective permeability tensors. The pore-fluid inside the cracks and the pores of host matrix may interact and exchange fluid mass, but the difference in hydraulic properties of these pores often means that a single homogenized effective permeability tensor field is insufficient to characterize the evolving hydraulic properties of these materials at smaller time scale. Furthermore, the complexity of the hydro-mechanical coupling process and the induced mechanical and hydraulic anisotropy originated from the micro-fracture and plasticity at grain scale also makes it difficult to propose, implement and validate separated macroscopic constitutive laws for numerical simulations. Here this article presents a hybrid data-driven method designed to capture the multiscale hydro-mechanical coupling effect of porous media with pores of various different sizes. At each scale, data-driven models generated from supervised machine learning are hybridized with classical constitutive laws in a directed graph that represents the numerical models. By using sub-scale simulations to generate database to train material models, an offline homogenization procedure is used to replace the up-scaling procedure to generate cohesive laws for localized physical discontinuities at both grain and specimen scales. Through a proper homogenization procedure that preserves spatial length scales, the proposed method enables field-scale simulations to gather insights from meso-scale and grain-scale micro-structural attributes. This method is proven to be much more computationally efficient than the classical DEM–FEM or FEM2 approach while at the same time more robust and flexible than the classical surrogate modeling approach. Due to the usage of bridging-scale technique, the proposed model may provide multiple opportunities to incorporate different types of simulations and experimental data across different length scales for machine learning. Numerical issues will also be discussed.

42 ENGINEERING↗
DOE OSTI2017

Spherical indentation of a freestanding circular membrane revisited: Analytical solutions and experiments

Because of the development of novel micro-fabrication techniques to produce ultra-thin materials and increasing interest in thin biological membranes, in recent years, the mechanical characterization of thin films has received a significant amount of attention. To provide a more accurate solution for the relationship among contact radius, load and deflection, the fundamental and widely applicable problem of spherical indentation of a freestanding circular membrane have been revisited. The work presented here significantly extends the previous contributions by providing an exact analytical solution to the governing equations of Föppl–Hecky membrane indented by a frictionless spherical indenter. In this study, experiments of spherical indentation has been performed, and the exact analytical solution presented in this article is compared against experimental data from existing literature as well as our own experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Direct comparison of Fe-Cr unmixing characterization by atom probe tomography and small angle scattering

The fine microstructure obtained by unmixing of a solid solution either by classical precipitation or spinodal decomposition is often characterized either by small angle scattering or atom probe tomography. This article shows that a common data analysis framework can be used to analyze data obtained from these two techniques. An example of the application of this common analysis is given for characterization of the unmixing of the Fe-Cr matrix of a 15-5 PH stainless steel during long-term ageing at 350 °C and 400 °C. A direct comparison of the Cr composition fluctuations amplitudes and characteristic lengths obtained with both techniques is made showing a quantitative agreement for the fluctuation amplitudes. The origin of the discrepancy remaining for the characteristic lengths is discussed. - Highlights: •Common analysis framework for atom probe tomography and small angle scattering •Comparison of same microstructural characteristics obtained using both techniques •Good correlation of Cr composition fluctuations amplitudes from both techniques •Good correlation of Cr composition fluctuations amplitudes with classic V parameter.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A web-based geovisual analytics platform for identifying potential contributors to culvert sedimentation

Sediment accumulation at culverts involves large-scale and interlinked environmental processes that are difficult to address with experimental or physical modeling methods. This article presents an alternative data-driven investigation for shedding insights into these processes. Accordingly, a web-based geovisual analytics application, the IowaDOT platform, was developed, which allows users to explore the complex processes associated with the sediment deposition at culverts. The platform provides systematic procedures for (1) collecting and integrating analytical variables into a single dataset, (2) quantifying the degree of culvert sedimentation using time series of aerial images, (3) identifying drivers that contribute to culvert sedimentation processes from a variety of culvert structural and upstream landscape characteristics using a tree-based feature selection algorithm, and (4) facilitating the understanding of complex spatial and relational patterns of culvert sedimentation processes using multivariate geovisualizations supported by a self-organizing map (SOM). As the outcomes of this study, these patterns identify culvert sedimentation-prone regions in Iowa and quantify empirical relationships between the drivers and culvert sedimentation degrees. Finally, a simple evaluation of the platform was performed to assess the usefulness and user satisfaction of the tool by professional users, and positive feedbacks are received.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

A comprehensive review on experimental and clinical findings in intermediate syndrome caused by organophosphate poisoning

Acute organophosphate (OP) intoxication is important because of its high morbidity and mortality and occurrence of muscular paralysis associated by inhibition of acetylcholinesterase (AChE) activity at the neuromuscular junction. Cholinergic crisis, intermediate syndrome (IMS), and OP-induced delayed neuropathy (OPIDN) are the evidences that can be observed in OP intoxication. The main cause of morbidity due to OP poisoning is IMS that occurs 24–96 h after poisoning. Mechanisms underlying the IMS are not fully known. Although the electrophysiological aspects of delayed neuropathy are best characterized, the IMS remain very little studied. The aim of this study was to revisit current knowledge related to OP and the IMS. For this purpose, a systematic review without date limitation was performed. A total of 599 relevant articles were found and reviewed. Data were categorized according to experimental and clinical studies. Occurrences of persistent AChE inhibition, electromyography changes, muscle cell injury, and oxidative stress are the most important pieces of evidence for involvement of IMS in OP toxicity. Delayed AChE inhibition, muscle necrosis, down regulation or desensitization of postsynaptic ACh receptors, failure of postsynaptic ACh release, and oxidative stress-related myopathy are involved in IMS. Toxicokinetic factors, such as a high lipid-solubility, duration of AChE inhibition and metabolite excretion, evolution of alterations on repetitive nerve stimulation (RNS), type and frequency of muscle lesions can estimate the probability of the IMS. Plasma AChE of less than 200 units is a predictor and the 30 Hz RNS decremental response could be a useful marker for the IMS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Interface learning in fluid dynamics: Statistical inference of closures within micro–macro-coupling models

Many complex multiphysics systems in fluid dynamics involve using solvers with varied levels of approximations in different regions of the computational domain to resolve multiple spatiotemporal scales present in the flow. The accuracy of the solution is governed by how the information is exchanged between these solvers at the interface and several methods have been devised for such coupling problems. In this article, we construct a data-driven model by spatially coupling a microscale lattice Boltzmann method (LBM) solver and macroscale finite difference method (FDM) solver for reaction-diffusion systems. The coupling between the micro-macro solvers has one to many mapping at the interface leading to the interface closure problem, and we propose a statistical inference method based on neural networks to learn this closure relation. The performance of the proposed framework in a bifidelity setting partitioned between the FDM and LBM domain shows its promise for complex systems where analytical relations between micro-macro solvers are not available.

42 ENGINEERING↗
DOE OSTI2026

A Statistician’s Overview of Physics-Informed Neural Networks for Spatio-Temporal Data

The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of mechanistic information in predictive models. Statisticians and others have long been interested in this problem, which has led to several practical and innovative solutions dating back decades. In this overview, we focus on the problem of data-driven prediction and inference of dynamic spatio-temporal processes that include mechanistic information, such as would be available from partial differential equations, with a strong focus on the quantification of uncertainty associated with data, process, and parameters. Here, we give a brief review of several paradigms and focus our attention on Bayesian implementations given they naturally accommodate uncertainty quantification. We then show that it is straight-forward to include the Bayesian PINN (B-PINN) within the Bayesian hierarchical model (BHM) framework that has long been considered for modeling dynamic spatio-temporal processes. Such a BHM-PINN is illustrated via a simulation study in which a latent nonlinear Burgers’ equation PDE governs the dynamics of Poisson distributed spatio-temporal data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Bayesian↗
DOE OSTI2011

DUST HEATING SOURCES IN GALAXIES: THE CASE OF M33 (HERM33ES)

Dust emission is one of the main windows to the physics of galaxies and to star formation as the radiation from young, hot stars is absorbed by the dust and reemitted at longer wavelengths. The recently launched Herschel satellite now provides a view of dust emission in the far-infrared at an unequaled resolution and quality up to 500 {mu}m. In the context of the Herschel HERM33ES open time key project, we are studying the moderately inclined Scd local group galaxy M33 which is located only 840 kpc away. In this article, using Spitzer and Herschel data ranging from 3.6 {mu}m to 500 {mu}m, along with H I, H{alpha} maps, and Galaxy Evolution Explorer ultraviolet data, we have studied the emission of the dust at the high spatial resolution of 150 pc. Combining Spitzer and Herschel bands, we have provided new, inclination-corrected, resolved estimators of the total infrared brightness and of the star formation rate from any combination of these bands. The study of the colors of the warm and cold dust populations shows that the temperature of the former is, at high brightness, dictated by young massive stars but, at lower brightness, heating is taken over by the evolved populations. Conversely, the temperature of the cold dust is tightly driven by the evolved stellar populations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

On the time lags of the LIGO signals

To date, the LIGO collaboration has detected three gravitational wave (GW) events appearing in both its Hanford and Livingston detectors. In this article we reexamine the LIGO data with regard to correlations between the two detectors. With special focus on GW150914, we report correlations in the detector noise which, at the time of the event, happen to be maximized for the same time lag as that found for the event itself. Specifically, we analyze correlations in the calibration lines in the vicinity of 35 Hz as well as the residual noise in the data after subtraction of the best-fit theoretical templates. The residual noise for the other two events, GW151226 and GW170104, exhibits similar behavior. A clear distinction between signal and noise therefore remains to be established in order to determine the contribution of gravitational waves to the detected signals.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Results from a Prototype Chicane-Based Energy Spectrometer for a Linear Collider

The International Linear Collider (ILC) and other proposed high energy e{sup +}e{sup -} machines aim to measure with unprecedented precision Standard Model quantities and new, not yet discovered phenomena. One of the main requirements for achieving this goal is a measurement of the incident beam energy with an uncertainty close to 10{sup -4}. This article presents the analysis of data from a prototype energy spectrometer commissioned in 2006-2007 in SLAC's End Station A beamline. The prototype was a 4-magnet chicane equipped with beam position monitors measuring small changes of the beam orbit through the chicane at different beam energies. A single bunch energy resolution close to 5 {center_dot} 10{sup -4} was measured, which is satisfactory for most scenarios. We also report on the operational experience with the chicane-based spectrometer and suggest ways of improving its performance.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Pulse Shape Discrimination in JSNS 2

JSNS 2 (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment that is searching for sterile neutrinos via the observation of $\overline{ν}$ μ → $\overline{ν}$ e appearance oscillations using neutrinos from muon decay-at-rest. For this search, rejecting cosmic-ray-induced neutron events by Pulse Shape Discrimination (PSD) is essential because the JSNS2 detector is located above ground, on the third floor of the building. We have achieved 94.95% ± 0.15% rejection of neutron events while keeping 92.82% ± 1.77% of signal, electron-like events using a data-driven likelihood method. This article will report the PSD technique using the full fiducial volume of the JSNS2 detector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2011

Physics of Z{sup 0}/{gamma}*-tagged jets at energies available at the CERN Large Hadron Collider

Electroweak bosons produced in conjunction with jets in high-energy collider experiments is one of the principal final-state channels that can be used to test the accuracy of perturbative quantum chromodynamics calculations and to assess the potential to uncover new physics through comparison between data and theory. In this article we present results for the Z{sup 0}/{gamma}*+jet production cross sections at the CERN Large Hadron Collider (LHC) at leading and next-to-leading orders. In proton-proton reactions we elucidate up to O(G{sub F{alpha}s}{sup 2}) the constraints that jet tagging via the Z{sup 0}/{gamma}* decay dileptons provides on the momentum distribution of jets. In nucleus-nucleus reactions we demonstrate that tagged jets can probe important aspects of the dynamics of quark and gluon propagation in hot and dense nuclear matter and characterize the properties of the medium-induced parton showers in ways not possible with more inclusive measurements. Finally, we present specific predictions for the anticipated suppression of the Z{sup 0}/{gamma}{sup *}+jet production cross section in the quark-gluon plasma that is expected to be created in central lead-lead collisions at the LHC relative to the naive superposition of independent nucleon-nucleon scatterings.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

The role of material modeling on strain range estimation for elevated temperature cyclic life evaluation

High temperature nuclear reactors operating in the creep regime are designed to withstand numerous cyclic events. Current ASME code rules provide two basic paths for evaluating creep fatigue and ratcheting under these conditions; one based on full inelastic analysis intended to provide a representative stress and strain history and the other based on elastic material models with adjustments of varying complexity to account for inelastic stress and strain redistribution. More recent developments have used elastic-perfectly plastic analysis to bound the effects of cyclic service. However, these methods still rely on the separate evaluation of fatigue and creep damage utilizing a damage interaction diagram. There is a procedure under current development that uses creep-fatigue data from key feature test articles directly without the use of the damage interaction diagram. However, it requires a reasonable representation of the strain range in a structure as an input. This work develops a simplified procedure based on elastic perfectly-plasticity analysis that can be used to represent the strain range in a structure in the steady state under cyclic loading conditions.

Messner, M. C.↗
NASA NTRS1972

Fe I oscillator strengths determined from anomalous dispersion of shock-heated gases.

Determination of Fe I oscillator strengths by the hook method on shock-heated gases. Within the given errors these values agree with oscillator strengths obtained by other methods, including wall-stabilized arc and shock-tube emission, the beam-foil technique, and delayed-coincidence measurements, except that the data reported in the present article tend to be somewhat higher. If it is assumed that no change occurs in atmospheric models, the authors' f-value scale would suggest a solar abundance at the lower limit of the recently published values - namely, log (A sub Fe/A sub H) + 12 = 7.4. This study demonstrates the effectiveness of the hook method for quantitative spectroscopy on shock-heated gases and points up its usefulness for gas diagnostics on optically thick lines.

Huber, M. C. E.↗