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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Diverse ecophysiological adaptations of subsurface Thaumarchaeota in floodplain sediments revealed through genome-resolved metagenomics

Abstract The terrestrial subsurface microbiome contains vastly underexplored phylogenetic diversity and metabolic novelty, with critical implications for global biogeochemical cycling. Among the key microbial inhabitants of subsurface soils and sediments are Thaumarchaeota, an archaeal phylum that encompasses ammonia-oxidizing archaea (AOA) as well as non-ammonia-oxidizing basal lineages. Thaumarchaeal ecology in terrestrial systems has been extensively characterized, particularly in the case of AOA. However, there is little knowledge on the diversity and ecophysiology of Thaumarchaeota in deeper soils, as most lineages, particularly basal groups, remain uncultivated and underexplored. Here we use genome-resolved metagenomics to examine the phylogenetic and metabolic diversity of Thaumarchaeota along a 234 cm depth profile of hydrologically variable riparian floodplain sediments in the Wind River Basin near Riverton, Wyoming. Phylogenomic analysis of the metagenome-assembled genomes (MAGs) indicates a shift in AOA population structure from the dominance of the terrestrial Nitrososphaerales lineage in the well-drained top ~100 cm of the profile to the typically marine Nitrosopumilales in deeper, moister, more energy-limited sediment layers. We also describe two deeply rooting non-AOA MAGs with numerous unexpected metabolic features, including the reductive acetyl-CoA (Wood-Ljungdahl) pathway, tetrathionate respiration, a form III RuBisCO, and the potential for extracellular electron transfer. These MAGs also harbor tungsten-containing aldehyde:ferredoxin oxidoreductase, group 4f [NiFe]-hydrogenases and a canonical heme catalase, typically not found in Thaumarchaeota. Our results suggest that hydrological variables, particularly proximity to the water table, impart a strong control on the ecophysiology of Thaumarchaeota in alluvial sediments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Improving galaxy clustering measurements with deep learning: analysis of the DECaLS DR7 data

ABSTRACT Robust measurements of cosmological parameters from galaxy surveys rely on our understanding of systematic effects that impact the observed galaxy density field. In this paper, we present, validate, and implement the idea of adopting the systematics mitigation method of artificial neural networks for modelling the relationship between the target galaxy density field and various observational realities including but not limited to Galactic extinction, seeing, and stellar density. Our method by construction allows a wide class of models and alleviates overtraining by performing k-fold cross-validation and dimensionality reduction via backward feature elimination. By permuting the choice of the training, validation, and test sets, we construct a selection mask for the entire footprint. We apply our method on the extended Baryon Oscillation Spectroscopic Survey (eBOSS) Emission Line Galaxies (ELGs) selection from the Dark Energy Camera Legacy Survey (DECaLS) Data Release 7 and show that the spurious large-scale contamination due to imaging systematics can be significantly reduced by up-weighting the observed galaxy density using the selection mask from the neural network and that our method is more effective than the conventional linear and quadratic polynomial functions. We perform extensive analyses on simulated mock data sets with and without systematic effects. Our analyses indicate that our methodology is more robust to overfitting compared to the conventional methods. This method can be utilized in the catalogue generation of future spectroscopic galaxy surveys such as eBOSS and Dark Energy Spectroscopic Instrument (DESI) to better mitigate observational systematics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Design Advancement of an Indoor Soil Deposition Chamber: A Road to Standardization

An indoor soil deposition chamber has been designed and constructed at the Arizona State University Photovoltaic Reliability Lab (ASU-PRL) to simulate photovoltaic (PV) module soiling in the field. This paper discusses the next set of revisions to the design, which are organized into three categories: design improvements, cost reduction, and new features. The design process was carried out with the intention of working toward the development of a national or international soiling standard. The improvements made to the chamber are described in detail to provide a guide for other laboratories or research facilities who wish to study photovoltaic (PV) soiling.

14 SOLAR ENERGY↗
DOE OSTI2025

Kernel Manifolds: Nonlinear‐Augmentation Dimensionality Reduction Using Reproducing Kernel Hilbert Spaces

This paper generalizes recent advances on quadratic manifold (QM) dimensionality reduction by developing kernel methods-based nonlinear-augmentation dimensionality reduction. QMs, and more generally feature map-based nonlinear corrections, augment linear dimensionality reduction with a nonlinear correction term in the reconstruction map to overcome approximation accuracy limitations of purely linear approaches. While feature map-based approaches typically learn a least squares optimal polynomial correction term, we generalize this approach by learning an optimal nonlinear correction from a user-defined reproducing kernel Hilbert space. Our approach allows one to impose arbitrary nonlinear structure on the correction term, including polynomial structure, and includes feature map and radial basis function-based corrections as special cases. Furthermore, our method has relatively low training cost and has monotonically decreasing error as the latent space dimension increases. In conclusion, we compare our approach to proper orthogonal decomposition and several recent QM approaches on data from several example problems.

kernel methods↗
DOE OSTI2025

Ranking Biological Features in Soil-Based Microbial Multi-Omics Data with Integration Modeling

Distinguishing the most important features (e.g. proteins, metabolites, etc.) per group (e.g. control and treatment) is a critical challenge in feature-rich multi-omics experiments, especially in soil data. Traditional feature identification and ranking approaches, such as differential expression, are based on single omics and thus not directly translatable to multi-omics experiments. Here, 5 multi-omics integration models (DIABLO, JACA, MOFA, MultiMLP, and SLIDE) that were not explicitly built for soil data applications were tested using a soil-based multi-omics experiment. The data were obtained from an experimental setup of an autoclaved soil system inoculated with 8 bacteria and using chitin as the carbon source and including samples collected at 0- (control), 4-, 8-, and 12-weeks post-inoculation. The omics data included metaproteomics, 16S rRNA sequencing, and LC-MS/MS metabolomics (in positive and negative mode). Each multi-omics integration model was implemented, and top features were compared to differential univariate statistics per omic type, demonstrating that integration approaches cut the potential number of top features from 2957 identified by differential statistics to 13-224 (a 99.6% to 92.4% reduction). Interestingly, most top features across integration models were not shared; though, scaling and averaging ranks across models shared similar patterns. This work highlights the usefulness of multi-omics integration models in soil-based microbial studies and the power of using multiple integration models together to interpret results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Advanced Modeling and Simulation to Characterize Advanced Boiling Water Reactor Source Terms to Support a Regulatory Approval Pathway for Right-Sized Emergency Planning Zone. Advanced Boiling Water Reactor (A-BWR) Project Phase 2

The United States Department of Energy (US DOE) is currently supporting the development of various small modular reactor (SMR), microreactor and advanced reactor designs. These reactors have improved safety features as compared to conventional Large Light Water Reactors (LLWRs), which include features that improve potential reduction of radiological source terms in the event of design and beyond-design basis accidents. Specifically, some reactors feature a relatively smaller containment volume with respect to the available surface area for fission product deposition; these include Integrated Pressurized Water Reactors (iPWRs), such as the NuScale SMR and SMR-type Boiling Water Reactors (BWRs) such as General Electric-Hitachi’s BWRX-300 design.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Adapting an Electron Microscopy Microheater for Correlated and Time-Resolved Ambient Pressure X-ray Photoelectron Spectroscopy

Environmental transmission electron microscopy (ETEM) can follow structural and chemical changes in nanomaterials under reaction conditions, including at temperatures up to 1300 °C and pressures up to ~20 mbar. However, ETEM studies are limited to localized areas of a sample and can benefit from correlative studies of a similar sample on larger length scales. Here, in this work, we describe the customization of a commercial microheater holder used in ETEM to anode-based ambient pressure X-ray photoelectron spectroscopy (APXPS), where chemical and electronic structure information is averaged over several hundred square micrometers. To benchmark heating capabilities in the APXPS instrument, core level binding energies were followed during surface chemical reactions of a Pd film, including CO adsorption–desorption, Pd oxidation, and PdO reduction. The fast heating feature of the microheaters advanced XPS experimental capabilities, including the collection of subsecond time-resolved spectra of the Pd 3d 5/2 peak during the reduction of an oxidized Pd film. The potential to use these heaters in a closed gas cell was also demonstrated by oxidizing Pd in a partial pressure of air locally dosed to the sample surface. General tips and considerations for the application of commercial ETEM heaters to any XPS system with a focused X-ray beam are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Reduction of baseplate distortion during directed energy deposition using compliant features

Distortion in additive manufacturing (AM) remains a barrier to its adoption in precision industries. Baseplate warpage is one such issue which compromises the feasibility of post-process precision machining. The restriction to thermal contraction of the deposited part, imposed by the baseplate, generates bending moments, that in turn causes warpage. A novel distortion mitigation strategy using baseplates with integrated compliant features, which enables thermal contraction of the build is presented. Six unique design concepts are evaluated, including a solid reference, using two deposition geometries. A laser, hot-wire, directed energy deposition (DED) process is used to deposit a symmetric cylindrical part (C-part) and a T-shaped asymmetric part (T-part). Flatness deviation of baseplates is measured using structured light 3D scanning. Measurements reveal a reduction in net flatness deviation of 58.8% for the C-part and 40.9% for the T-part, compared to the solid reference. While most designs yielded reductions exceeding 30% and 20% for the C- and T-parts, respectively, one configuration resulted in increased deviation. Finite element (FE) simulations are performed to elucidate the underlying mechanisms affecting distortion of compliant baseplates during DED. Despite variations between predictions and measurements, agreement in the general trend is observed. It also revealed that initial flatness errors in baseplates significantly affect its deviation during deposition. Predictions indicate that compliant features significantly affect the thermal distribution as well as the evolution of flatness deviation in the baseplate during deposition. Notably, one design exhibited a reduction in distortion during cooling, following its initial increase during deposition. FE predictions show a maximum reduction of 60.9% and 38.8% in net flatness deviation for the C- and T-parts, respectively. The performance of compliant baseplates is found to be governed by both its the thermal and mechanical characteristics, which are crucial factors to be considered during design.

Mathews, Ritin [ORNL] (ORCID:0000000301440828)↗
DOE OSTI2025

Reduced Order Modeling conditioned on monitored features for response and error bounds estimation in engineered systems

Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, schemes that allow to retain a portion of the physics tend to enhance the interpretability and generalization of ROMs. However, physics-based techniques can adversely scale when dealing with nonlinear systems that feature parametric dependencies. This study introduces a generative physics-based ROM that is suited for nonlinear systems with parametric dependencies and is additionally able to provide numerical error bounds associated with the respective estimates. A main contribution of this work is the conditioning of these parametric ROMs to features that can be derived from monitoring measurements, feasibly in an online fashion. This is contrary to most existing ROM schemes, which remain restricted to the prescription of the physics-based, and usually a priori unknown, system parameters. Our work utilizes conditional Variational Autoencoders to continuously map the required reduction bases to a feature vector extracted from limited output measurements, while additionally allowing for a probabilistic assessment of the ROM-estimated Quantities of Interest. An auxiliary task using a neural network-based parametrization of suitable probability distributions is introduced to re-establish the link with physical model parameters. We verify the proposed scheme on a series of simulated case studies incorporating effects of geometric and material nonlinearity under parametric dependencies related to system properties and input load characteristics.

Conditional VAEs↗
DOE OSTI2026

Solid-state prealkylation of electrode architectures to tune solid electrolyte interphase composition

Efficient electrochemical cycling of certain Si anodes is limited by irreversible Li consumption to form and continually reform the solid electrolyte interface (SEI) due to Si expansion/contraction and fracture. Prelithiation can compensate for these losses; however, the starting open circuit potential (VOC) becomes highly reducing and, therefore, the electrolyte reduction chemistry that influences the SEI composition can change. Herein, we compare SEI formation for electrodes prelithiated using Solid State Prealkylation of Electrode Architectures (SPEAR) versus traditional electrochemically lithiated architectures (ECLAR), focusing on SEI compositional changes as a function of stoichiometry (0.28 ≤ x ≤ 1.38 in LixSi). Increasing SPEAR prelithiation decreased the initial VOC of Si anodes vs. Li/Li+ from ∼3 V (Li0.28Si) to < 0.5 V for Li1.38Si, enabling simultaneous competitive reduction of EC, EMC, and LiPF6 at low potentials. Ex situ7Li and 29Si cross-polarization NMR and XPS reveal that SPEAR drives thicker SEI formation with substantially increased P/F contributions and a predominantly inorganic insoluble SEI (71.4% inorganic for Li1.38Si), consistent with accelerated LiPF6-derived POx/LiPFx/LiF formation relative to ECLAR analogs which exhibit carbonate-rich organic SEI compositions. Symmetric-cell EIS further indicates SPEAR-specific impedance features consistent with pore reduction (filling) during LixSi formation. In full cells, SPEAR prelithiation increases the initial coulombic efficiency (ICE) and accelerates SEI formation and stabilization with Li1.38Si reaching 99.4% coulombic efficiency (CE) by cycle 2.

Musgrove, Amanda [ORNL] (ORCID:0009000220910389)↗
DOE OSTI2024

Parametric reduced-order modeling for component-oriented treatment and localized nonlinear feature inclusion

Abstract We propose coupling a physics-based reduction framework with a suited response decomposition technique to derive a component-oriented reduction (COR) approach, which is suitable for assembly systems featuring localized nonlinearities. Dependencies on influencing parameters are injected into the reduced-order model (ROM), thus ensuring robustness and validity over a domain of parametric inputs, while capturing nonlinear effects. The implemented approach employs individual component modes to capture localized features while additionally relying on reduced modes of a global nature to approximate the system’s dynamics accurately. The global modes are derived from a linear monolithic system, defined as a result of a coordinate separation scheme, which permits the proposed COR-ROM to naturally couple the response between linear and nonlinear subdomains. The derived low-order representation utilizes a proper orthogonal decomposition projection and is additionally reinforced with the inclusion of a hyper-reduction technique to capture the underlying high-fidelity model response while providing accelerated computations. The resulting approach is exemplified in the synthetic case studies of a four-story shear frame with multiple nonlinear regions driven by hysteresis and a large-scale kingpin connection featuring plasticity.

Vlachas, Konstantinos (ORCID:000000029124219X)↗
DOE OSTI2025

Modeling the impact of smoke from prescribed fire on road visibility

Prescribed fires are planned to achieve conservation and fuel reduction objectives while minimizing smoke ground concentration to limit health impacts and road visibility impairment. Prescribed burns cannot indeed be conducted if those hazards are not within predefined limits. This paper proposes a new framework to evaluate road visibility that overcomes the limitation of the state of the art model, VSMOKE. The framework leverages the fast-running framework QUIC-Fire/QUIC-SMOKE to capture fire and smoke dynamics, and the timing and duration of hazardous conditions on the road network close to the burn unit (within 50–100 km). The paper presents parametric study using a real burn plot at Fort Stewart (GA, USA), under hypothetical wind conditions to understand the interplay between buoyancy and smoke dilution. Here, results showed that faster winds caused fire escape while slower winds did not achieve a complete burn. Furthermore, faster winds featured brief road visibility reduction below braking distance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Rotor blade imbalance fault detection for variable-speed marine current turbines via generator power signal analysis

Marine hydrokinetic (MHK) turbines extract renewable energy from oceanic environments. However, due to the harsh conditions that these turbines operate in, system performance naturally degrades over time. Thus, ensuring efficient condition-based maintenance is imperative towards guaranteeing reliable operation and reduced costs for marine hydrokinetic power. This work proposes a novel framework aimed at identifying and classifying the severity of rotor blade pitch imbalance faults experienced by marine current turbines (MCTs). In the framework, a Continuous Morlet Wavelet Transform (CMWT) is first utilized to acquire the wavelet coefficients encompassed within the 1P frequency range of the turbine's rotor shaft. From these coefficients, several statistical indices are tabulated into a six-dimensional feature space. Next, Principle Component Analysis (PCA) is employed on the resulting feature space for dimensionality reduction, and then the application of a K-Nearest Neighbor (KNN) machine learning algorithm is utilized for fault detection and severity classification. The effectiveness of the proposed framework is validated using a high-fidelity MCT numerical simulation platform, where results demonstrate that the presence of a pitch imbalance fault can be accurately detected 100% of the time and correctly classified based upon severity more than 97% of the time.

42 ENGINEERING↗
DOE OSTI2025

Bayesian inference of anisotropic 2D small-angle scattering from sparse measurement

Here, we present a Bayesian inference framework for reconstructing anisotropic two-dimensional small-angle scattering (2D SAS) patterns from sparse, noisy, or partially missing data. The method combines a symmetry-aware angular basis with radial Gaussian process priors to enable accurate, training-free interpolation and denoising. Computational benchmarks demonstrate reliable recovery of both isotropic and high-order anisotropic features under severe data reduction. Experimental validations on stretched polymers, sheared wormlike micelles, and carbon fibers show improved fidelity and resolution compared to raw measurements, achieving comparable accuracy with up to 50-fold fewer detected neutrons. This approach enables quantitative structural analysis under low-flux, time-limited, or single-shot conditions, extending the applicability of 2D SAS techniques to compact neutron sources and mechanically driven soft matter systems undergoing transient structural changes.

Tung, Chi-Huan [Oak Ridge National Laboratory (ORN↗
DOE OSTI2023

Experimental Verification of An Electro-Hydraulic Actuation System Driven by An Integrated Electro-Hydraulic Unit

This paper proposes an electro-hydraulic actuator (EHA) system, and a novel-designed electro-hydraulic unit (EHU) consisting of a fixed-displacement hydraulic gear machine and a variable-speed electric machine. The novel EHU design integrates an electric machine and a hydraulic machine in a single housing, targeting optimal compactness, power density, and component reduction. The EHA system features an open circuit design, where hydraulic hoses connect the EHU with a tank, a valve manifold, and the hydraulic cylinder. In this way, the proposed EHA technology can be used to implement distributed hydraulic actuation in a vehicle without requiring changes to the hydraulic actuators or at the overall layout of the hydraulic components with respect to the original vehicle design. A dedicated test rig is developed to verify the performance of the proposed EHA system. Here, the efficiency of the EHA is measured in a steady state and under a realistic duty cycle of a commercial compact loader. The efficiency of the EHA system based on the measurements on the test rig can reach 54% with up to 20 kN load and 6 kW power level. Furthermore, the EHU is implemented on the reference machine to drive the boom function. Preliminary results on the performance of the EHU show an efficiency of up to 68% under different loading conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Bio-Inspired Catalysts Featuring Earth Abundant Metals and Secondary Coordination Sphere Interactions for the Reduction of Oxyanions

The scientifically challenging problem of catalytically reducing oxyanions was explored and presented great opportunities in terms of the environment, economics and energy self-sufficiency. Oxyanions are pervasive as they are found in many areas of technology, all forms of life, in minerals, and as synthetic materials. Given their ubiquity, the contamination of inorganic oxyanions in drinking water is a national problem as 26 states and Puerto Rico have reported high concentrations of these harmful pollutants. The goals achieved by this research was to catalytically reduce oxyanions utilizing sustainable earth abundant catalysts featuring bio-inspired ligands. This worked allowed us to gain fundamental insights into what dictates the reduction/reactivity for these anions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training) v1

SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training) is a comprehensive data visualization and analysis application focused on working with COLTRIMS (COLd Target Recoil Ion Momentum Spectroscopy) data, which is used in atomic and molecular physics experiments. The application offers several powerful features: - Data uploading and processing capabilities for COLTRIMS files - Multiple visualization methods using UMAP (Uniform Manifold Approximation and Projection) for dimensionality reduction - Interactive selection of data points across multiple views - Feature engineering through various methods: - Manual feature selection from calculated physics parameters - Deep autoencoder for dimension reduction - Genetic programming for discovering meaningful features - Mutual information-based feature selection - Multiple clustering approaches (DBSCAN, KMeans, Agglomerative) - Quality metrics for evaluating clustering results - Export capabilities for selections and generated features

Daoud, Hazem [Lawrence Berkeley National Laborator↗
DOE OSTI2026

A two-stage optical fusion framework for wildfire severity mapping across the conterminous United States

Accurate wildfire severity mapping (WSM) is essential for post-fire recovery planning, erosion risk assessment, ecosystem monitoring, and disaster risk reduction. Although Landsat and Sentinel optical imagery have been widely used for burn severity assessment, the added value of fusing multiple optical sensors has not been sufficiently quantified across diverse fire events, particularly since the launch of Landsat-9. This study evaluates whether multisensor optical fusion improves wildfire severity mapping relative to single-sensor baselines using Sentinel-2, Landsat-8, and Landsat-9 imagery across 40 wildfire events in the conterminous United States. We tested a two-stage fusion framework that combines feature-level fusion with pixel-level dimensionality reduction. First, feature-level fused datasets were created through early fusion by combining standardized post-fire bands from each sensor into a single predictor stack. Both raw reflectance bands and pairwise spectral transforms were retained to capture within- and cross-sensor spectral interactions. Second, Linear Discriminant Analysis was applied to both single-sensor and fused datasets to produce comparable low-dimensional feature spaces. Six machine-learning classifiers were then used to benchmark model performance with repeated spatially buffered train–test splits. Results show that Landsat-9 was the strongest single-sensor baseline. Among the fusion strategies, Sentinel-2 + Landsat-9 produced the most consistent improvement and reduced performance variability. Landscape-condition analysis further showed that this fusion was most beneficial in shrubland-dominated and high-terrain fires, where it achieved the highest overall mean accuracy and the fewest failures. In contrast, its benefits were less reliable in evergreen forests, mixed vegetation, and low- to moderate-elevation terrain. In operational settings, the Sentinel-2 + Landsat-9 configuration offers a practical solution for post-fire recovery planning, erosion-risk assessment, watershed management, and ecological monitoring when field observations are available and timely satellite-based information is needed.

Landsat↗