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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Anomalous magnetic noise in an imperfectly flat landscape in the topological magnet Dy 2 Ti 2 O 7

Noise generated by motion of charge and spin provides a unique window into materials at the atomic scale. From temperature of resistors to electrons breaking into fractional quasiparticles, “listening” to the noise spectrum is a powerful way to decode underlying dynamics. Here, we use ultrasensitive superconducting quantum interference device (SQUIDs) to probe the puzzling noise in a frustrated magnet, the spin-ice compound Dy 2 Ti 2 O 7 (DTO), revealing cooperative and memory effects. DTO is a topological magnet in three dimensions—characterized by emergent magnetostatics and telltale fractionalized magnetic monopole quasiparticles—whose real-time dynamical properties have been an enigma from the very beginning. We show that DTO exhibits highly anomalous noise spectra, differing significantly from the expected Brownian noise of monopole random walks, in three qualitatively different regimes: equilibrium spin ice, a “frozen” regime extending to ultralow temperatures, and a high-temperature “anomalous” paramagnet. We present several distinct mechanisms that give rise to varied colored noise spectra. In addition, we identify the structure of the local spin-flip dynamics as a crucial ingredient for any modeling. Thus, the dynamics of spin ice reflects the interplay of local dynamics with emergent topological degrees of freedom and a frustration-generated imperfectly flat energy landscape, and as such, it points to intriguing cooperative and memory effects for a broad class of magnetic materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Random 3D Interpenetrating Electrode Design for Energy Storage Applications

We introduce the concept of random interpenetrating electrode structures, inspired by spinodal decomposition, as a transformative approach for energy storage applications. While ordered architectures, such as double gyroids and Kelvin cell structures, have long been favored for their structural uniformity and surface area utilization, we demonstrate that random structures offer distinct advantages including shorter diffusion pathways, higher packing density, and enhanced reaction kinetics, particularly at low temperatures. Using a combination of computational and electrochemical analysis, we show that random architecture outperforms their ordered counterparts in scenarios where diffusion dominates. This work represents the first systematic exploration of random structures in energy storage, highlighting their potential to overcome limitations of traditional designs. However, challenges such as structural reproducibility and scalability remain, necessitating further investigation into fabrication techniques and material selection. By establishing a framework to optimize random electrode architectures, we provide critical insights into the interplay between geometry, diffusion, and reaction kinetics. This study not only introduces a new design principle for 3D electrodes but also opens pathways for next-generation energy storage systems that demand both high performance and adaptability to a range of operating conditions.

Materials science↗
DOE OSTI2025

Modeling household-level party composition behavior for multiparty activities: a random parameter nested logit modeling approach

This study presents findings of a household-level party composition model for multiparty activities. It exploits data from a comprehensive Household Travel Survey conducted by Chicago Metropolitan Agency of Planning. The study estimates a random parameter nested logit model to capture households’ unobserved preference heterogeneity and non-proportional substitution patterns in terms of activity party composition for multiparty activities. A wide variety of household demographics, activity attributes and residential neighborhood characteristics are examined in this paper. The magnitude of the impacts of the determinants are tested in this study by analyzing the elasticity of the variables, which suggests that household demographics and attributes of the multiparty activities have significant effects on the household-level activity party composition. Residential neighborhood characteristics, although somewhat less impactful, still play a meaningful role. This model will be implemented within the POLARIS transportation systems simulator to improve the activity generation modeling workflow, and the prediction accuracy of various activity-travel components.

activity party composition↗
DOE OSTI2021

Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

Abstract Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of Mo x Re 1- x S 2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

25 ENERGY STORAGE↗
DOE OSTI2020

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiCf-SiCm) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiCf-SiCm using only acoustic emission (AE) signals generated during the material’s stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiCf-SiCm RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data Driven Correlated Noise Simulation for the ICEBERG LArTPC

Accurate electronic-noise simulation is essential for low-energy physics in liquid-argon TPCs. More realistic noise modeling allows us to better tune reconstruction algorithms and more reliably assess and optimize signal-detection thresholds. We present a data-driven noise simulation framework developed for the ICEBERG test stand for DUNE that generates synthetic noise waveforms that reproduce both (i) the measured per-channel magnitude of the Fast Fourier Transform (FFT) and (ii) frequency-dependent channel-to-channel correlations observed in ICEBERG noise data. Using a dedicated noise-only dataset, we build a compact noise model containing per-channel FFT-magnitude targets together with a small set of band-wise cross-wire color matrices. White noise is generated in the frequency domain by drawing circular-symmetric complex Gaussian coefficients with random phases and scaling them to match the measured FFT-magnitude targets, and cross-wire correlations are subsequently imposed using the stored color matrices. The model and algorithm were integrated into the LArSoft + Wire-Cell Toolkit simulation chain and validated by comparing waveform structure, frequency-domain spectra, and band-limited correlation matrices from simulated noise and ICEBERG data. This approach can be extended to other LArTPC operating conditions.

Ghosh, Avik [Iowa State U.]↗
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2022

Wavelet and Deep-Learning-Based Approach for Generation System Problematic Parameters Identification and Calibration

Accurate models of generation systems are critical for maintaining reliable and secure grid operations. In this paper, a novel and systematic approach is proposed to identify and calibrate the generation system problematic parameters using continuous wavelet transform (CWT) and advanced deep-learning technology. The phasor measurement unit (PMU) data are used through “event playback” to check whether the parameter calibration is required, and if yes, a group of suspicious parameters will be identified as the primary problematic parameter candidates (PPCs). These primary PPCs are randomly perturbed to generate the event playback simulation data, which are used by the CWT and convolutional neural networks (CNNs) to further narrow down the primary PPCs into a smaller set of candidates. Then, the identified candidates are perturbed again to generate massive event playback simulation data for training a parameter calibration neural network. Here, we designed a multi-output neural network structure to find the mappings between the perturbed parameters and the simulation data using both CNN and long short-term memory (LSTM) models. Finally, the well-trained and tested CNN-LSTM model is used to estimate the accurate value of the suspicious parameters with actual PMU measurements. The proposed CNN-LSTM network can accurately and reliably estimate the generation-system problematic parameters, and has better performance when compared to other machine-learning methods, such as the multilayer perceptron network and the conditional variational autoencoder method. The accuracy and effectiveness of the proposed approach have been validated through simulation and real-world data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Multi-octave-spanning supercontinuum generation through high-energy laser filaments in YAG and ZnSe pumped by a 2.4 μm femtosecond Cr:ZnSe laser

Abstract We present experimental and numerical investigations of high-energy mid-infrared filamentation with multi-octave-spanning supercontinuum generation (SCG), pumped by a 2.4 μm, 250 fs Cr:ZnSe chirped-pulse laser amplifier. The SCG is demonstrated in both anomalous and normal dispersion regimes with YAG and polycrystalline ZnSe, respectively. The formation of stable and robust single filaments along with the visible-to-mid-infrared SCG is obtained with a pump energy of up to 100 μJ in a 6-mm-long YAG medium. To the best of the authors’ knowledge, this is the highest-energy multi-octave-spanning SCG from a laser filament in a solid. On the other hand, the SCG and even-harmonic generation based on random quasi-phase matching (RQPM) are simultaneously observed from the single filaments in a 6-mm-long polycrystalline ZnSe medium with a pump energy of up to 15 μJ. The numerical simulations based on unidirectional pulse propagation equation and RQPM show excellent agreement with the measured multi-octave-spanning SCG and even-harmonic generation. They also reveal the temporal structure of mid-infrared filaments, such as soliton-like self-compression in YAG and pulse broadening in ZnSe.

Optics↗
DOE OSTI2020

Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu

Mapping vegetation species is critical to facilitate related quantitative assessment, and mapping invasive plants is important to enhance monitoring and management activities. Integrating high-resolution multispectral remote-sensing (RS) images and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple sources of high-resolution RS data for vegetation mapping on a large spatial scale can be both computationally and sampling intensive. Here, we designed a two-step classification workflow to potentially decrease computational cost and sampling effort and to increase classification accuracy by integrating multispectral and lidar data in order to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1362 km2) of Tennessee (U.S.). Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive, coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the producer’s accuracy, user’s accuracy, and Kappa for the SVM model on kudzu were 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions as well as map other vegetation species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Using automated machine learning for the upscaling of gross primary productivity

Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r 2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

On the Relationship Between Shallow Cumulus Cloud Field Properties and Surface Solar Irradiance

Abstract Shallow cumulus clouds exhibit highly three‐dimensional (3‐D) spatial structure leading to complex variability in the surface solar irradiance (SSI) beneath. This variability is captured by the typically bimodal shape of the SSI probability density function (PDF). Using large eddy simulation to generate well‐resolved cloud fields and Monte Carlo 3‐D radiative transfer to reproduce realistic associated SSI PDFs, we seek direct relationships between the cloud field properties and the SSI PDF shape. Applying both random forest and artificial neural network algorithms, we find variations in the two modes of the SSI PDF are well predicted by just a handful of cloud field properties. The two algorithms utilize cloud properties similarly, with indistinguishable performance despite their different architectures. These results offer a marked improvement in realism relative to one‐dimensional radiative transfer while bypassing computationally expensive 3‐D radiative transfer, with immediate application to renewable energy assessments, and potential for several other geophysical applications.

58 GEOSCIENCES↗
DOE OSTI2021

Meso-scale modelling of compressive fracture in concrete with irregularly shaped aggregates

This paper presents a meso-scale modelling framework to investigate the fracture process in concrete subjected to uniaxial and biaxial compression accounting for its mesostructural characteristics. 3D mesostructure of concrete consisting of coarse aggregates, mortar and interfacial transition zone between them was developed using an in-house code based on the Voronoi tessellation and splining method, which enables to generate the realistic-look aggregates with controllable structural features such as content, location, size and shape. Based on the generated 3D mesostructure, the concrete damage plasticity approach was employed to simulate the compressive fracture behaviour of concrete in terms of crack morphology and stress-strain response against the shape parameters of aggregate. Results indicate that the shape of aggregate has a negligible effect on compressive strength of concrete, which is highly associated with the random location and size distribution of aggregate. The aggregate irregularity has a significant influence on crack initiation and growth of concrete.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Development and testing of coarse-grained models for ultrasonic simulations of cast austenitic stainless steel

Ultrasonic inspection of cast austenitic stainless steel (CASS) in the nuclear industry is particularly challenging because of sound field scatter and attenuation caused by the coarse-grained microstructure. Modeling and simulation are important tools in ultrasonic testing, as they can be used to help address key aspects of inspections, such as developing new probe designs, predicting inspection reliability, and testing phased-array focal laws. However, developing a useful and reliable CASS model is challenging due to the many grain interfaces and crystalline orientations that must be captured. We demonstrate a method of creating a realistic CASS model that is usable in CIVA, a commercially available modeling and simulation software platform. Using polished and chemically etched sections, we generate models of a coarse-grained equiaxed specimen and a columnar specimen. We also test an alternative method of generating a coarse-grained model using Voronoi regions. We qualitatively compare sound field scatter and quantitatively compare sound field attenuation and beam partitioning in simulated sound fields to those of laboratory-measured sound fields. Results show that the Voronoi models perform as well as or better than the models based on actual grain morphology. Here we also show that model-to-model randomness in Voronoi grain structure can impact the magnitude of a simulated echo response by a factor of two or more. Although CASS models are potentially a good depiction of reality for a given scenario, they should not be considered representative since CASS morphology can change significantly from specimen to specimen or within the same specimen.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Comparative Analysis of Radial and Random Microstructures of Mesophase Pitch Carbon Fibers

Carbon fibers (CF) with radial and random microstructures are produced. Here, these fibers are subjected to identical treatment before being mechanically tested and analyzed with Weibull analysis, with the results revealing a statistically significant difference in tensile strengths of 2.23 GPa for random CF and 1.69 GPa for radial CF. Raman mapping probed the crystalline structure perpendicular to the fiber axis and found a uniform structure, while wide‐angle X‐ray diffraction showed a significant difference of 7.5 Å in the crystallites’ basal lengths parallel to the fiber. Small‐angle X‐ray scattering is completed parallel to the fiber for the first time. A cross‐section Guinier plot of the 1D azimuthal integration is generated assuming symmetric scattering, and the parallel scatterers are found to have a similar length scale to the crystallite's length, validating the testing method. Finally, transmission electron microscopy is completed on the longitudinal cross‐section of each fiber. The radial carbon fiber is found to have a core–shell structure, as evidenced further by fast Fourier transform images. Through all studies, it is shown that the structure developed during mesophase pitch spinning altered the microstructure, thus impacting the mechanical properties, confirming a direct relationship between processing, structure, and properties.

Scherschel, Alexander [Univ. of Virginia, Charlott↗
DOE OSTI2026

3D TRISO particle-explicit compact meshing

The TRI-structural ISOtropic (TRISO) layered fuel particle is a robust nuclear fuel form offering enhanced safety and performance for advanced reactor concepts, including high-temperature gas-cooled reactors and other Generation IV designs. These poppy-seed-sized particles are embedded in a graphite matrix to form fuel elements that must withstand elevated temperatures and high burn-up levels. The heterogeneous nature of these fuel elements — comprising thousands of randomly distributed TRISO particles — produces complex stress fields and thermal gradients that one- and two-dimensional models cannot accurately capture. While three-dimensional modeling has improved predictions of dimensional changes, internal pressure buildup, and fission product transport under irradiation, current approaches rely on homogenized material properties that are known to have considerable divergence from experimental observations. This work presents a methodology for optimized random packing of TRISO fuel compacts and full three-dimensional mesh generation within the BISON fuel performance code, with each particle coating layer individually discretized. The resulting mesh was demonstrated through heat conduction simulations under representative in-reactor operating conditions, showing strong agreement with expected behavior. This capability enables detailed analysis of particle-to-particle interactions, matrix cracking mechanisms, and the statistical distribution of coating layer failures — all of which directly govern fuel performance and safety margins.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Scattering of Magnons at Graphene Quantum-Hall-Magnet Junctions

Motivated by recent nonlocal transport studies of quantum-Hall-magnet (QHM) states formed in monolayer graphene’s N = 0 Landau level, here we study the scattering of QHM magnons by gate-controlled junctions between states with different integer filling factors ν . For the ν = 1 | – 1 | 1 geometry we find that magnons are weakly scattered by electric potential variation in the junction region, and that the scattering is chiral when the junction lacks a mirror symmetry. For the ν = 1 | 0 | 1 geometry, we find that kinematic constraints completely block magnon transmission if the incident angle exceeds a critical value. Our results explain the suppressed nonlocal–voltage signals observed in the ν = 1 | 0 | 1 case. We use our theory to propose that valley waves generated at ν = – 1 | 1 junctions and magnons can be used in combination to probe the spin or valley flavor structure of QHM states at integer and fractional filling factors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Deep-learning-guided high-resolution subsurface reflectivity imaging with application to ground-penetrating radar data

Subsurface reflectivity imaging is one of the most important geophysical characterization methods for revealing subsurface structures. In many cases, accurate subsurface reflectivity imaging is challenging because of, for example, random or coherent noise in the data and sparse source-receiver observation geometry. Here, we develop a deep-learning-guided iterative imaging method to improve subsurface structure imaging. Specifically, we train a supervised neural network to infer a noise-free, high-resolution image from a noisy, low-resolution image and use this estimated image as guidance to regularize least-squares imaging. We develop a systematic method to generate high-quality synthetic training data (data-label pairs) to train the guidance neural network. The trained neural network can provide high-fidelity predictions even for field-data images that are not in the training data. We validate our new imaging method using one synthetic and two field ground-penetrating radar data examples, and find that our method can produce clean, high-resolution subsurface reflectivity images where existing single-pass and least-squares imaging methods fail due to noise and insufficient data coverage.

58 GEOSCIENCES↗