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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Exploring Atomic-Scale Interactions at the Interface of Reducible Oxide and Ruthenium Nanocatalyst for Ammonia Decomposition

Restructuring atomic-scale interfaces between noble metal nanoparticles and metal oxides provides a promising approach to enhancing catalytic properties. In this study, Ru nanoparticles were supported on CeO 2 , ZrO 2 , and HfO 2 via pyrolysis of MOFs under oxygen-suppressed high-temperature conditions. Despite its nearly ideal particle size (∼2.3 nm), the Ru–CeO 2 /C catalyst exhibited the lowest ammonia decomposition activity. X-ray absorption spectroscopy and DFT calculations revealed that electron transfer from Ru to CeO 2 formed positively charged Ru and partially reduced Ce 4+ , weakening the catalytic performance. In contrast, oxygen-deficient ZrO 2 and HfO 2 support donated electrons to Ru, preserving its metallic state. These findings demonstrate that the reducibility of oxide supports governs the direction and magnitude of interfacial charge transfer, directly tuning catalytic behavior. This study provides insights into the design of oxide-supported Ru catalysts for ammonia decomposition.

Catalysts↗
DOE OSTI2025

Dynamics of nanoscale phase decomposition in laser ablation

Abstract Laser ablation is a process that bears both fundamental physics interest and has wide industrial applications. For decades, the lack of probes on the relevant time and length scales has prevented access to the highly nonequilibrium phase decomposition processes triggered by laser excitation. In this study, a close integration of time-resolved probing by intense femtosecond X-ray pulses with large-scale atomistic modeling has yielded unique insights into the ablation dynamics of thin gold films irradiated by femtosecond laser pulses. The emergence and growth of nanoscale density heterogeneities in the expanding ablation plume, predicted in the simulations, are mapped to the rapid evolution of distinct small angle diffraction features. This mapping enables identification of the characteristic signatures of different phase decomposition processes occurring simultaneously in the plume, which are driven by photomechanical and thermodynamic driving forces. Beyond the specific insights into the ablation phenomenon, this study demonstrates the power of joint X-ray probing and atomistic modeling of material dynamics under extreme conditions of thermal and mechanical nonequilibrium.

Sun, Yanwen (ORCID:0000000159266565)↗
DOE OSTI2025

Direct measurements for the kinetics of C–C bond fission in the high temperature decomposition of isopropanol

The thermal decomposition of isopropanol was studied experimentally and theoretically with a view to isolate and directly measure rate coefficients for the dominant radical channel in this multi-channel process. Two complementary shock tube methods, laser schlieren densitomtery and H-atom atomic resonance absorption spectroscopy, were used to obtain rate coefficents for the C–C bond fission channel. The experimental ranges span temperatures from 1200–2100 K and pressures between 30–690 torr. These are the most direct measurements of this rate coefficent in the fall-off regime of relevance to high-temperature reacting systems. Theoretical studies also performed in this work confirm that the title reaction and a molecular elimination involving dehydration were the sole unimolecular processes at high temperatures. A master equation analysis yielded k ( T, P ) for the C–C fission and these are shown to be in good agreement with the present experimental measurements. Comparisons with prior experimental and theoretical studies in the literature indicate branching ratios as well as absolute k ( T, P ) for C–C bond-fission have been under-predicted for this decomposition reaction. Consequently, results from the present studies place an increased emphasis on radical-driven secondary processes in high-temperature pyrolysis of this simplest secondary alcohol.

Kim, John H. [Univ. of Michigan, Ann Arbor, MI (Un↗
DOE OSTI2025

ZTF SN Ia DR2: An environmental study of Type Ia supernovae using host galaxy image decomposition

The second data release of Type Ia supernovae (SNe Ia) observed by the Zwicky Transient Facility has provided a homogeneous sample of 3628 SNe Ia with photometric and spectral information. This unprecedented sample size enables us to better explore our currently tentative understanding of the dependence of the host environment on SN Ia properties. In this paper, we make use of two-dimensional image decomposition to model the host galaxies of SNe Ia. We model elliptical galaxies as well as disc and spiral galaxies with or without central bulges and bars. This allows for the categorisation of SN Ia based on their morphological host environment, as well as the extraction of intrinsic galaxy properties corrected for both cosmological and atmospheric effects, through point-spread-function (PSF) convolution. We find that although this image decomposition technique leads to a significant bias towards elliptical galaxies in our final sample of processed galaxies, the overall results are still robust. By successfully modelling 728 host galaxies, we find that the photometric properties of SNe Ia found in discs and in elliptical galaxies correlate fundamentally differently with their host environment. We identified strong linear relations between light-curve stretch and our model-derived galaxy colour for both the elliptical (16.8σ) and disc (5.1σ) subpopulations of SNe Ia. Lower-stretch SNe Ia are found in redder environments, which we identify as an age and/or metallicity effect. Within the subpopulation of SNe Ia found in disc-containing galaxies, we find a significant linear trend (6.1σ) between light-curve stretch and model-derived local r-band surface brightness, which we link to the age and metallicity gradients found in disc galaxies. SN Ia colour shows little correlation with the host environment, as is seen in the literature. We do identify a possible dust effect in our model-derived surface brightness (3.3σ) for SNe Ia in disc galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Extended dynamic mode decomposition for model reduction in fluid dynamics simulations

High computational cost and storage/memory requirements of fluid dynamics simulations constrain their usefulness as a predictive tool. Reduced-order models (ROMs) provide a viable solution to this challenge by extracting the key underlying dynamics of a complex system directly from data. We investigate the efficacy and robustness of an extended dynamic mode decomposition (xDMD) algorithm in constructing ROMs of three-dimensional cardiovascular computations. Focusing on the ROMs' accuracy in representation and interpolation, we relate these metrics to the truncation rank of singular value decomposition, which underpins xDMD and other approaches to ROM construction. Our key innovation is to relate the truncation rank to the singular values of the original flow problem. This result establishes a priori guidelines for the xDMD deployment and its likely success as a means of data compression and reconstruction of the system's dynamics from dominant spatiotemporal structures present in the data.

Mechanics↗
DOE OSTI2025

Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data

In this paper, we propose a personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. We introduce an order orthogonality assumption and develop a proximal gradient regularized block coordinate descent algorithm guaranteed to converge to a stationary point. The unique and common representations learned by perTucker reveal intrinsic statistical patterns in data and provide valuable information for a wide range of downstream analytics, including anomaly detection, source classification, and clustering. We demonstrate perTucker’s effectiveness through a simulation study and two case studies on solar flare detection and tonnage signal classification.

14 SOLAR ENERGY↗
DOE OSTI2024

Detecting damage in composites using volume decomposition analysis of tomographic data

Detection of damage in a single tow ceramic matrix composite specimen has been achieved using orthogonal decomposition of volumetric tomographic datasets collected at four tensile loads. This decomposition approach has been applied at two different length scales: (i) individual fibres and (ii) bulk volumes containing fibres and matrix material. Volumes were first decomposed to feature vectors, orders of magnitude smaller than the original volume they describe, and then comparisons between datasets at different load levels were made in feature vector space. The results show quantitative measurements of damage location, damage morphology and the relative growth of this damage with increased load when compared with a dataset with less or no damage. No prior knowledge of the dataset or training of algorithms is required for damage to be detected, it is only necessary that at least two datasets are available for comparison, e.g. from in situ or repeated scanning measurements. Results are generated on significantly shorter timescales when compared with previous automated approaches to tomography data processing. This approach has the potential to be applied to damage detection in a range of materials through comparisons of volumetric datasets from a range of measurement or computational techniques.

Middleton, Ceri A.↗
DOE OSTI2025

Zero curvature is a necessary and sufficient condition for a spin-orbital decomposition

There has been an extended debate regarding the existence of a spin-orbital decomposition of the angular momentum of photons and other massless particles. It was recently shown that there are both geometric and topological obstructions preventing any such decomposition. Here we show that any geometric connection on a particle’s state space induces a splitting of the angular momentum into two operators. These operators are well-defined angular momentum operators if and only if the connection has zero curvature. Massive particles have two canonical curved connections corresponding to boosts and rotations, respectively. Furthermore, these can be uniquely combined to produce a flat connection, and this gives a novel derivation of the Newton-Wigner position operator and the corresponding spin and orbital angular momenta for relativistic massive particles. When the mass is taken to zero, transverse boosts and rotations degenerate, leaving only a single connection for massless particles. This connection produces a commonly proposed splitting of the massless angular momentum into two operators. However, the connection is not flat, explaining why these operators do not satisfy the angular momentum commutation relations and are thus not true spin and orbital angular momentum operators.

Angular momentum↗
DOE OSTI2026

Temporal Convolutional Network Using Empirical Mode Decomposition to Detect Faults in Grid Connected Systems

Grid-connected power electronic systems require timely and reliable fault detection to prevent equipment damage and reduce downtime. This paper presents a forecasting-based anomaly detection pipeline that decomposes voltage and current measurements into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD), then trains a causal temporal convolutional network (TCN) on normal-operation IMF data to predict short-horizon future dynamics. Deviations between forecasts and observations are summarized as reliability-weighted residual scores and thresholded per sensor using robust statistics with temporal persistence constraints to suppress false positives. To reduce runtime, EMD is performed on downsampled signals for detection, while raw-rate EMD is applied only within a short region of interest for high-frequency interpretability near detected events. Results on a simulated grid-connected converter system demonstrate that IMF-domain forecasting improves anomaly separability relative to raw-signal forecasting and provides interpretable evidence of faults across decomposition channels.

Sutton, Elizabeth [ORNL] (ORCID:0009000078885935)↗
DOE OSTI2024

Genomic insights into redox-driven microbial processes for carbon decomposition in thawing Arctic soils and permafrost

Climate change is rapidly transforming Arctic landscapes where increasing soil temperatures speed up permafrost thaw. This exposes large carbon stocks to microbial decomposition, possibly worsening climate change by releasing more greenhouse gases. Understanding how microbes break down soil carbon, especially under the anaerobic conditions of thawing permafrost, is important to determine future changes. Here, we studied the microbial community dynamics and soil carbon decomposition potential in permafrost and active layer soils under anaerobic laboratory conditions that simulated an Arctic summer thaw. The microbial and viral compositions in the samples were analyzed based on metagenomes, metagenome-assembled genomes, and metagenomic viral contigs (mVCs). Following the thawing of permafrost, there was a notable shift in microbial community structure, with fermentative Firmicutes and Bacteroidota taking over from Actinobacteria and Proteobacteria over the 60-day incubation period. The increase in iron and sulfate-reducing microbes had a significant role in limiting methane production from thawed permafrost, underscoring the competition within microbial communities. We explored the growth strategies of microbial communities and found that slow growth was the major strategy in both the active layer and permafrost. Our findings challenge the assumption that fast-growing microbes mainly respond to environmental changes like permafrost thaw. Instead, they indicate a common strategy of slow growth among microbial communities, likely due to the thermodynamic constraints of soil substrates and electron acceptors, and the need for microbes to adjust to post-thaw conditions. The mVCs harbored a wide range of auxiliary metabolic genes that may support cell protection from ice formation in virus-infected cells.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Hierarchical Gaussian Random Field Sampling for Multilevel Markov Chain Monte Carlo: Coupling Stochastic Partial Differential Equation and the Karhunen–Loève Decomposition

This work introduces structure preserving hierarchical decompositions for sampling Gaussian random fields (GRFs) within the context of multilevel Bayesian inference in high-dimensional space. Existing scalable hierarchical sampling methods, such as those based on stochastic partial differential equations (SPDEs), often reduce the dimensionality of the sample space at the cost of accuracy of inference. Other approaches, such that those based on Karhunen-Loève (KL) expansions, offer sample space dimensionality reduction but sacrifice GRF representation accuracy and ergodicity of the Markov chain Monte Carlo (MCMC) sampler and are computationally expensive for high-dimensional problems. The proposed method integrates the dimensionality reduction capabilities of KL expansions with the scalability of SPDE-based sampling, thereby providing a robust, unified framework for high-dimensional uncertainty quantification (UQ) that is scalable and accurate, preserves ergodicity, and offers dimensionality reduction of the sample space. The hierarchy in our multilevel algorithm is derived from the geometric multigrid hierarchy. By constructing a hierarchical decomposition that maintains the covariance structure across the levels in the hierarchy, the approach enables efficient coarse-to-fine sampling while ensuring that all samples are drawn from the desired distribution. The effectiveness of the proposed method is demonstrated on a benchmark subsurface flow problem, demonstrating its effectiveness in improving computational efficiency and statistical accuracy. Furthermore, our proposed technique is more efficient and accurate and displays better convergence properties than existing methods for high-dimensional Bayesian inference problems.

Gaussian random fields↗
DOE OSTI2025

MJO CP Tensor Decompositions

SAND2025-14273O MJO CP Tensor Decompositions is a Python tool for computing canonical polyadic (CP) decompositions of MERRA2 observational data to understand the onset and evolution of the Madden-Julian Oscillation (MJO). Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Bull, Diana [Sandia National Lab. (SNL-CA), Liverm↗
DOE OSTI2026

Goated: goal-oriented tensor decompositions in python

SAND2026-20464O Goated performs goal-oriented tensor decompositions in Python, enabling efficient compression of multi-dimensional simulation data. It extends common tensor decomposition methods by incorporating domain-specific knowledge, such as conservation laws in physics, through a penalty term in the optimization process. This approach improves data compression and modeling accuracy across various applications, including physics simulations, by using specialized algorithms and structure-aware subroutines to accelerate solver performance. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2025

Heat Transfer in Void Generating Foam Decomposition: Further Development

Continued development of the additive conductivity material model, used to simulate changes in heat transfer that occurs in void generating foam decomposition, has resulted in an improved model and new features. The previous version of the model was calibrated against the Aria Bulk Fluid Element (BFE) solution and proposed a third-order polynomial correction term best captured the increased heat transfer due to voids in the foam. An investigation of the Fuego Conjugate Heat Transfer (CHT) and Aria BFE solutions at several geometries revealed the CHT solution and BFE solution had differing behavior across length scales, especially at smaller scales. Five calibration studies, using the Fuego CHT as the calibration data, were carried out with polynomial functions of 4-th, 3-rd, 2-nd, 1-st and 0-th orders to determine the best correction function that generalized well across length scales. Each polynomial function was calibrated/trained on six different sized geometries and then tested on three uniquely sized geometries. This study revealed that the 1-st order additive conductivity model performed the best. A new feature of void formation scaling was implemented to more realistically capture the heat transfer as voids are created. A scaling term was added to the model to activate the conductivity correction as decomposition progresses.

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

In Situ Data Analysis Through Physics-informed Tensor Decompositions (LDRD Final Report)

We introduce a new low-dimensional model of high-dimensional numerical simulation data based on low-rank tensor decompositions. Our new model aims to minimize differences between the model data and simulation data as well as functions of the model data and functions of the simulation data. This novel approach to dimensionality reduction of simulation data provides a means of directly incorporating quantities of interests and invariants associated with conservation principles associated with the simulation data into the low-dimensional model, thus enabling more accurate analysis of the simulation without requiring access to the full set of high-dimensional data. Computational results of applying this approach to two standard low-rank tensor decompositions of data arising from simulation of combustion and plasma physics are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Quantum-classical tradeoffs and multi-controlled quantum gate decompositions in variational algorithms

The computational capabilities of near-term quantum computers are limited by the noisy execution of gate operations and a limited number of physical qubits. Hybrid variational algorithms are well-suited to near-term quantum devices because they allow for a wide range of tradeoffs between the amount of quantum and classical resources used to solve a problem. This paper investigates tradeoffs available at both the algorithmic and hardware levels by studying a specific case – applying the Quantum Approximate Optimization Algorithm (QAOA) to instances of the Maximum Independent Set (MIS) problem. We consider three variants of the QAOA which offer different tradeoffs at the algorithmic level in terms of their required number of classical parameters, quantum gates, and iterations of classical optimization needed. Since MIS is a constrained combinatorial optimization problem, the QAOA must respect the problem constraints. This can be accomplished by using many multi-controlled gate operations which must be decomposed into gates executable by the target hardware. We study the tradeoffs available at this hardware level, combining the gate fidelities and decomposition efficiencies of different native gate sets into a single metric called the gate decomposition cost .

Tomesh, Teague↗