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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Post-irradiation examination of AGR-3/4 TRISO fuel compacts using three-dimensional X-ray computed tomography

The AGR-3/4 irradiation tests combined the third and fourth planned irradiation experiments in the US Department of Energy’s Advanced Gas Reactor (AGR) testing campaign of tri-structural isotropic (TRISO) fuel compacts. In this article, we present post-irradiation examination (PIE) using X-ray computed tomography (XCT) of two unirradiated and two irradiated compacts from the AGR-3/4 irradiation tests. The irradiated compacts studied (compact 7–1 and compact 12–4) represent the upper and lower limit of burnup within the AGR-3/4 irradiation experiment. This article presents a detailed quantitative analysis on the post-irradiation structure of TRISO fuel compacts. Various quantitative parameters including shape, size, and packing of kernels, and their spatial distribution, were utilized to gain insights into the structural changes caused by irradiation. The equivalent diameter and sphericity were found to increase and decrease, respectively, in irradiated compact 7–1 due to its higher burnup. Nearest neighbor distance between fuel kernels decreased after irradiation, suggesting irradiation-induced shrinkage of graphitic matrix. Furthermore, each compact in AGR-3/4 irradiation tests contained 20 designed-to-fail (DTF) fuel particles that were meant to act as a source of fission product release to the experiment test train. Furthermore, in the present work, all DTF fuel particles in the four compacts studied were identified, and it was found that they exhibited larger kernel swelling in compact 12–4 and smaller kernel swelling in compact 7–1, compared to the driver particles.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Interpreting the Operando XANES of Surface-Supported Subnanometer Clusters: When Fluxionality, Oxidation State, and Size Effect Fight.

X-ray absorption near edge structure (XANES) spectroscopy is widely used for operando catalyst characterization. We show that, for highly fluxional supported nanoclusters, the customary extraction of the oxidation state of the metal from the XANES data by fitting to the bulk standards is highly questionable. The XANES signatures as well as the apparent oxidation state for such clusters arise from a complex combination of many factors, and not only from the chemical composition in reaction conditions (e.g., oxygen content in oxidizing atmosphere). The thermally accessible isomerization and population of several structurally distinct cluster forms, cluster-support interaction, and intrinsic size effects all impact the metal oxidation state and XANES signal. We demonstrate this on copper oxide clusters with different compositions, Cu4Ox(x = 2-5) and Cu5Oy (y = 3, 5), deposited on amorphous alumina and ultrananocrystalline diamond, for which we computed the XANES spectra and compare the results to the experiment. We show in addition that fitting the experimental spectrum to calculated spectra of supported clusters can, in contrast, provide good agreement and insight into the spectrum-composition-structure relation. Experimental XANES interpreted using the proposed fitting scheme shows the partial reduction of Cu oxide clusters at rising temperatures, and pinpoints the specific stoichiometries that dominate in the ensemble of cluster states as the temperature changes.

Zandkarimi, Borna↗
DOE OSTI2022

A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution

Integrated computational materials engineering (ICME) models have been a crucial building block for modern materials development, relieving heavy reliance on experiments and significantly accelerating the materials design process. However, ICME models are also computationally expensive, particularly with respect to time integration for dynamics, which hinders the ability to study statistical ensembles and thermodynamic properties of large systems for long time scales. To alleviate the computational bottleneck, we propose to model the evolution of statistical microstructure descriptors as a continuous-time stochastic process using a non-linear Langevin equation, where the probability density function (PDF) of the statistical microstructure descriptors, which are also the quantities of interests (QoIs), is modeled by the Fokker–Planck equation. In this work, we discuss how to calibrate the drift and diffusion terms of the Fokker–Planck equation from the theoretical and computational perspectives. The calibrated Fokker–Planck equation can be used as a stochastic reduced-order model to simulate the microstructure evolution of statistical microstructure descriptors PDF. Considering statistical microstructure descriptors in the microstructure evolution as QoIs, we demonstrate our proposed methodology in three integrated computational materials engineering (ICME) models: kinetic Monte Carlo, phase field, and molecular dynamics simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Pore-scale evidence of ion exclusion during methane hydrate growth and evolution of hydrate pore-habit in sandy sediments

Hydrate-bearing sediments constitute complex hydrological systems, within which the pore habit of hydrate significantly affects all physical properties. However, there is scarce pore-scale evidence of the evolution of hydrate growth in porous media and the influence of water salinity in the micro-morphology of hydrate. In this paper, we used X-ray computed micro-tomography (CT) to monitor three experiments of methane hydrate growth in sand partially saturated with either NaBr or KI brine under excess-gas conditions. Gas, brine and hydrate are observed to co-exist at the pore-scale during the evolution of hydrate pore-habit towards three-phase equilibrium. The experiments and data analysis reveal that (1) hydrate forms as a porous medium mixed with inclusions of brine and gas, where hydrate and brine evolve gradually into separate phases as hydrate cages exclude salt ions, (2) hydrate growth mobilizes water over fairly long distances resulting in heterogeneous hydrate distribution, and (3) hydrate can exhibit interconnecting pore-habit at local hydrate saturations higher than ~50%, even if grown under excess-gas conditions. The results imply that hydrate micro-morphology and pore habit evolve during hydrate formation and, we suggest, so do the physical properties of hydrate-bearing sediments. Furthermore, our findings provide new insights into the micro-morphology of hydrate and evolution of the properties of hydrate-bearing sediments synthesized in the laboratory and in natural systems.

Clathrate hydrate↗
DOE OSTI2025

Optimization of distributed compute resources utilization in the CMS Global Pool

The CMS Submission Infrastructure is the primary system for managing computing resources for CMS workflows, including data processing, simulation, and analysis. It integrates geographically distributed resources from Grid, HPC, and cloud providers into federated pools managed by HTCondor and Glidein- WMS, for a total of around 500k CPU cores. This system dynamically manages workloads based on priorities defined by the collaboration. Additionally, CMS scheduling strategies must be flexible to handle multiple concurrent workloads while considering changing processing demands and resource availability from various providers.Efficient utilization of vast amounts of distributed compute resources is a key element for the success of the scientific programs of the LHC experiments. Optimizing the system is essential to maximize resource efficiency and fully utilize the distributed computing power. The CMS Submission Infrastructure team thus systematically investigates sources of inefficiency in workload scheduling to reduce their impact. In addition, a strategy of pilot overloading has been introduced to compensate for other inefficiency sources, thereby optimizing resource utilization and enhancing computational throughput.

Mascheroni, Marco [UC, San Diego (main)]↗
DOE OSTI2024

Measuring the robustness of predictive probability for early stopping in two-group comparisons

We report physical experiments are often expensive and time-consuming. Test engineers must certify the compatibility of aircraft and their weapon systems before they can be deployed in the field, but the testing required is time consuming, expensive, and resource limited. Adopting Bayesian adaptive designs is a promising way to borrow from the successes seen in the clinical trials domain. The use of predictive probability (PP) to stop testing early and make faster decisions is particularly appealing given the aforementioned constraints. Given the high-consequence nature of the tests performed in the national security space, a strong understanding of new methods is required before being deployed. Although PP has been thoroughly studied for binary data, there is less work with continuous data, where many reliability studies are interested in certifying the specification limits of components. A simulation study evaluating the robustness of this approach indicates early stopping based on PP is reasonably robust to minor assumption violations, especially when only a few interim analyses are conducted. The simulation study also compares PP to conditional power, showing its relative strengths and weaknesses. A post-hoc analysis exploring whether release requirements of a weapon system from an aircraft are within specification with desired reliability resulted in stopping the experiment early and saving 33% of the experimental runs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

42 ENGINEERING↗
DOE OSTI2024

Elucidating the formation mechanisms of zeolites using data-driven modelling and in-situ characterization

Zeolites are the main solid catalysts used by the chemical industry. The use of zeolites in separations and as shape selective catalysts requires control of the width and connectivity of their pores. 235 distinct zeolite frameworks have been synthesized to date, of over 2 million that have been proposed. Recent work indicates that the limitation is in large part kinetic: new synthetic pathways are required to access new zeolites. Organic cations are used to direct the synthesis towards specific zeolites. However, the molecular mechanisms by which cations direct the nucleation towards specific zeolites is not known. Elucidating these mechanisms is key to realize new zeolites for catalysis and separations, and is the focus of this project. This project developed and implemented a synergistic, data-driven computational and experimental approach to resolve the molecular pathways of nucleation, growth, and polymorph selection of zeolites and the role of organic cations in directing their formation. The project developed computationally efficient and accurate models for the study of the nucleation and growth of pure silica zeolites in molecular simulations, using machine learning with data from experiments. Simulations with these models were integrated with scanning tunneling electron microscopy, computer vision, and deep learning to unveil the molecular pathways of formation of a zeolite. Of particular interest in this project was to elucidate the role of amorphous precursors in the nucleation of the zeolite. Previous experiments indicate that zeolites are born within non-crystalline aggregates in which the silicates and organic cations have local and medium range order similar to that of the zeolite. The organic cations that direct the formation of zeolites and those that direct the formation of ordered mesoporous silicas are similar. We hypothesized that the frustrated attraction that for large organic cations leads to the formation of stable mesophases that direct the synthesis of mesoporous silicas, could promote the formation of metastable mesophases that can assist in the nucleation and polymorph selection of zeolites. The simulations resolved how structure directing agents build crystalline order and showed that mesoscopic pre-ordering occurs is not required to facilitate the nucleation of zeolites, because the synthesis occurs at high driving forces, where the barriers for nucleation are negligible. This project unveiled that polymorph selection in zeolite synthesis occurs after nucleation, opening a distinct area of control through the kinetics of growth and not through nucleation barriers.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Compressing branch-and-bound trees

A branch-and-bound (BB) tree certifies a dual bound on the value of an integer program. In this work, we introduce the tree compression problem (TCP): Given a BB tree T that certifies a dual bound, can we obtain a smaller tree with the same (or stronger) bound by either (1) applying a different disjunction at some node in T or (2) removing leaves from T? Here we believe such post-hoc analysis of BB trees may assist in identifying helpful general disjunctions in BB algorithms. We initiate our study by considering computational complexity and limitations of TCP. We then conduct experiments to evaluate the compressibility of realistic branch-and-bound trees generated by commonly-used branching strategies, using both an exact and a heuristic compression algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset

Abstract Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic—primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces added complexity to the workflow, leading to longer computation times. This poses a notable challenge to ML applications, as suboptimal hyperparameter selections curtail the potential of ML model performance, ultimately obstructing the full exploitation of ML techniques. In this article, we present a two-step HPO method as a strategic solution to curbing computational demands and wait times, gleaned from practical experiences in applied ML parameterization work. The initial phase involves a preliminary evaluation of hyperparameters on a small subset of the training dataset, followed by a reevaluation of the top-performing candidate models postretraining with the entire training dataset. This two-step HPO method is universally applicable across HPO search algorithms, and we argue it has attractive efficiency gains. As a case study, we present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation. Although our primary use case is a data-rich limit with many millions of samples, we also find that using up to 0.0025% of the data—a few thousand samples—in the initial step is sufficient to find optimal hyperparameter configurations from much more extensive sampling, achieving up to 135× speedup. The benefits of this method materialize through an assessment of hyperparameters and model performance, revealing the minimal model complexity required to achieve the best performance. The assortment of top-performing models harvested from the HPO process allows us to choose a high-performing model with a low inference cost for efficient use in global climate models (GCMs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Factorizations of hyperpower family of iterative methods via least squares approach

A new method for finding proper factorizations of the hyperpower family of iterative methods for computing generalized inverses is proposed. The method is based on numerical optimization of an appropriate least squares problem. An iterative method in a non-normalized form for finding the generalized outer inverses with prescribed range and null space of any given complex matrix is proposed and considered. Computational complexity and convergence of the method are studied. Several numerical experiments are also presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

HDSense: An efficient method for ranking observable sensitivity

Identifying which observables most effectively constrain model parameters can be computationally prohibitive when considering full likelihoods of many correlated observables. This is especially important for, e.g., hadronization models, where high precision is required to interpret the results of collider experiments. We introduce the High-Dimensional Sensitivity (HDSense) score, a computationally efficient metric for ranking observable sets using only one-dimensional histograms. Derived by profiling over unknown correlations in the Fisher information framework, the score balances total information content against redundancy between observables. We apply HDSense to rank a set observables in terms of their constraining power with respect to five parameters of the Lund string model of hadronization implemented in Pythia using simulated leptonic collider events at the $Z$ pole. Validation against machine-learning--based full-likelihood approximations demonstrates that HDSense successfully identifies near-optimal observable subsets. The framework naturally handles data from multiple experiments with different acceptances and incorporates detector effects. While demonstrated on hadronization models, the methodology applies broadly to generic parameter estimation problems where correlations are unknown or difficult to model.

Assi, Benoît [Cincinnati U.] (ORCID:00000003092433↗
DOE OSTI2023

Interface Coating Design for Dynamic Voltage Stability of Solid‐State Batteries

Abstract Intrinsic or interface thermodynamic voltage windows of solid electrolytes are often narrower than the operational voltage range needed by a full battery, thus various interface decomposition reactions can happen in a practical solid‐state battery. Experimentally, it is found that a proper battery design utilizing the reactions can lead to a dynamic evolution from interface instability to stability, giving the so‐called dynamic voltage stability for advanced battery performance. Here, first the state‐of‐the‐art understanding is articulated about how the dynamic voltage stability should be interpreted in physical picture and treated in computation, emphasizing the potential importance of nonequilibrium reaction pathways. The constrained ensemble computational approach is further applied across most types of solid‐state electrolytes to systematically evaluate and compare their dynamic stability voltage windows in response to the mechanical constriction effect. High‐throughput calculations are used to search for coating materials for different interfaces between sulfide, halide, and oxide electrolytes and typical cathode materials with enhanced dynamic voltage stability. A comparison with experiment is given to highlight the value of these computational predictions.

Wang, Yichao↗
DOE OSTI2020

Computationally Efficient Decompositions of Oblique Projection Matrices

Oblique projection matrices arise in problems in weighted least squares, signal processing, and optimization. While these matrices can be potentially very large, their low-rank structure can be exploited for efficient computation. Here, we propose fast and scalable algorithms for computing their eigendecomposition and singular value decomposition (SVD). Numerical experiments that compare our proposed approaches to existing methods, including randomized SVD, are presented. In addition, we test their accuracy on linear systems from equality constrained optimization problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Plasma Science and Innovation Center (PSI-Center) at Washington, Wisconsin, and Utah State, ARRA Supplement

The objective of the Plasma Science and Innovation Center (PSI-Center) is to develop and deploy computational models that simulate conditions in smaller, concept-exploration plasma experiments. The PSIC group at the University of Wisconsin-Madison, led by Prof. Carl Sovinec, uses and enhances the Non-Ideal Magnetohydrodynamics with Rotation, Open Discussion (NIMROD) code, to simulate macroscopic plasma dynamics in a number of magnetic confinement configurations. These numerical simulations provide information on how magnetic fields and plasma flows evolve over all three spatial dimensions, which supplements the limited access of diagnostics in plasma experiments. The information gained from simulation helps explain how plasma evolves. It is also used to engineer more effective plasma confinement systems, reducing the need for building many experiments to cover the physical parameter space. The ultimate benefit is a more cost-effective approach to the development of fusion energy for peaceful power production. The supplemental funds provided by the American Recovery and Reinvestment Act of 2009 were used to purchase computer components that were assembled into a 48-core system with 256 Gb of shared memory. The system was engineered and constructed by the group's system administrator at the time, Anthony Hammond. It was successfully used by then graduate student, Dr. John O'Bryan, for computing magnetic relaxation dynamics that occur during experimental tests of non-inductive startup in the Pegasus Toroidal Experiment (pegasus.ep.wisc.edu). Dr. O'Bryan's simulations provided the first detailed explanation of how the driven helical filament of electrical current evolves into a toroidal tokamak-like plasma configuration.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Development of Non Destructive Assay Approach for Characterization of Spent Fuel Debris at Fukushima - 18543

Fukushima is considering options for decommissioning the damaged reactor. One of the technical challenges is the accurate characterization of fissile isotopes in the spent fuel debris. This is due to the fissile content, high gamma and neutron emission and uncertainties regarding the fuel burnup and unknown, heterogeneous mixture of materials that may be present. These requirements lead to significant challenges for potential measurement approaches with regard to the requirements for shielding and remote operation that result from plant and operational constraints. Mirion Technologies (MTKK) has been working to perform feasibility and concept design studies for a non-destructive assay (NDA) system that could be used to characterize the fissile content of retrieved samples of fuel debris. The main advantage of using an NDA technique is that the entire volume of the sample is interrogated so the method is not reliant upon assumptions of representativeness of samples retrieved and sent for laboratory analysis. The proposed technique comprises a combination of gamma spectroscopy and combined passive / active neutron interrogation with advanced data analysis algorithms in order to determine the maximum available data on the spent fuel properties and obtain a full radionuclide characterization of each sample. This includes quantification of individual fissile nuclides with validated uncertainties. In this paper we describe the NDA techniques and systems that are proposed, their benefits and limitations, and how they may be implemented into a full-scale system for fuel element debris characterization. We describe the studies that were performed based on experience of real systems and research supplemented with extensive computer modelling work to represent the expected fuel element debris properties. The work benefits from Mirion's extensive global physics and engineering expertise in designing, supplying and operating a wide range of NDA systems for diverse applications and in different challenging physical and operational environments. We describe how the advanced and innovative physics techniques that have resulted from Mirion's collective experience in these areas, can be deployed in an NDA system. The final approach that has been developed provides a means for determining the masses of the individual fissile isotopes with robust uncertainties and capable of implementation into an NDA system that meets the expected requirements for an operational plant. We focus on the techniques that have been developed and describe how they are of wider relevance and interest for projects that include challenges for characterisation of fuel residues. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Prediction Uncertainties beyond the Range of Experience: A Case Study in Inertial Confinement Fusion Implosion Experiments

Scientists often predict physical outcomes, e.g., experimental results, with the assistance of computer codes that, at their best, only coarsely approximate reality. Coarse predictions are challenging in critical part due to the multitude of seemingly arbitrary yet consequential decisions that must be made such as choice of relevant data, calibration of code parameters, and construction of empirical discrepancy forms. In this paper, we present a case study in the context of inertial confinement fusion (ICF) implosion experiments where extrapolative predictions are needed with quantified uncertainties. The purpose of this case study is to reflect relevant statistical methods, as applied to ICF model fitting and prediction, to document the numerous decisions that must be made in the prediction pipeline, to extend a complex example in extrapolation to the uncertainty quantification (UQ) community, and to reflect on the challenges we encountered supporting extrapolations with imperfect models and thereby recommend several future research directions. We conclude with a discussion about the UQ community's role in less than ideal predictive scenarios like our ICF exercise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Several Levels of Theory for Description of Isotope Effects in Ozone: Symmetry Effect and Mass Effect

The essential components of theory for the description of isotope effects in recombination reaction that forms ozone are presented, including the introduction of three reaction pathways for symmetric and asymmetric isotopomers, a brief review of relevant experimental data for singly- and doubly substituted isotopologues, the definitions of ζ-effect and η-effect, and the introduction of isotopic enrichment δ. Two levels of theory are developed to elucidate the role of molecular symmetry, atomic masses, vibrational zero-point energies, and rotational excitations in the recombination process. Here, the issue of symmetry is not trivial, since the important factors, such as 1/2 and 2, appear in seven different places in the formalism. It is demonstrated that if all these effects are taken into account properly, then no anomalous isotope effects emerge. At the next level of theory, a model is considered in which one scattering resonance (sitting right at the top of centrifugal barrier) is introduced per ro-vibrational channel. It is found that this approach is equivalent to statistical treatment with partition functions at the transition state. Accurate calculations using hyper-spherical coordinates show that no isotope effects come from difference in the number of states. In contrast, differences in vibrational and rotational energies lead to significant isotope effects. However, those effects appear to be local, found for the rather extreme values of rotational quantum numbers. They largely cancel when rate coefficients are computed for the thermal distribution of rotational excitations. Although large isotope effects (observed in experiments) are not reproduced here, this level of theory can be used as a foundation for more detailed computational treatment, with accurate information about resonance energies and lifetimes computed and included.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗