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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Impact of high-fidelity temperature feedback modeling on fusion blanket neutronics

Accurate modeling and simulation of neutron and gamma transport and interactions within fusion tokamaks represents a critical area of research for fusion energy development. This paper presents a method to couple computational fluid dynamics (CFD) results to high-fidelity neutronics analyses, to explore the fusion neutronics’ feedback to blanket temperature and density. This method is demonstrated on a sector model of a tokamak with immersion molten salt blanket, and the relative differences in tritium breeding ratio (TBR) with spatially-resolved temperature modeling are explored. Additionally, the influence of higher fidelity neutronics results on the subsequent CFD analysis is explored. Results show that global TBR changes $<$0.1% due to temperature spatial distribution modeling. However, local TBR in areas with high temperature variability can have up to ∼4% difference.

blanket fusion↗
DOE OSTI2025

LATTE: open-source, high-performance traveltime computation, tomography and source location in acoustic and elastic media

Traveltime-based tomography and source location are fundamental approaches for imaging subsurface structures and understanding the spatiotemporal distribution of seismicity from local to global scales. We present an open-source, high-performance framework integrating eikonal equation solvers and adjoint-state theory for traveltime computation, velocity tomography, source location and joint tomography-location in 2-D/3-D acoustic and elastic media. We introduce novel regularization schemes based on total generalized p-variation, structural similarity and multitask machine learning to enhance the fidelity and interpretability of inverted models and source locations. Key features of our implementation also include the ability to leverage both absolute-difference and double-difference traveltime misfits for high-fidelity velocity tomography and source parameter estimation; support for traveltime computation and inversion in diverse 2-D/3-D scenarios with arbitrary source and receiver distributions; and a perturbation-based optimal step-size estimation method to reduce computational costs. In addition, our implementation employs shared-memory and distributed-memory parallelization to provide an efficient solution for traveltime computation, tomography, and source location. In conclusion, we validate the efficacy and accuracy of our approach through multiple synthetic data examples.

58 GEOSCIENCES↗
DOE OSTI2026

Energetics of the nucleation and glide of disconnection modes in symmetric tilt grain boundaries

Grain boundaries (GBs) evolve by the nucleation and glide of disconnections, which are dislocations with a step character. In this work, motivated by recent success in predicting GB properties such as the shear coupling factor and mobility from the intrinsic properties of disconnections, we develop a systematic method to calculate the energy barriers for the nucleation and glide of individual disconnection modes under arbitrary driving forces and a quasi-2D setting. This method combines tools from bicrystallography to enumerate disconnection modes and the Nudged elastic band (NEB) method to calculate their energetics, yielding minimum energy paths and atomistic mechanisms for the nucleation and glide of each disconnection mode. We apply the method to accurately predict shear coupling factors of $[001]$ symmetric tilt grain boundaries in Cu. Particular attention is paid to the boundaries where the dislocation-based disconnection nucleation model produces incorrect nucleation barriers. We demonstrate that the method can accurately compute energy barriers and predict shear-coupling factors in the low-temperature regime. For certain disconnection modes in which the assumptions underlying our method do not hold, we report upper bounds on the energy barriers for disconnection nucleation and glide. In addition, the NEB trajectories reveal interesting phenomena such as the dissociation of a higher energy mode into lower energy modes, and in some cases, shear coupling being mediated by partial disconnections, wherein the GB structure temporarily changes to a metastable state before reverting back to its original structure. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2025

Novel Relativistic Electronic Structure Theories for Actinide-Containing Compounds

Actinides of importance to basic energy sciences contain electrons moving at speed comparable to the speed of light. Reliable computational simulation of these electrons and hence actinide chemistry requires accurate description of relativistic effects. The present project advances computational actinide chemistry with development of new methodologies, algorithms, and computer programs in relativistic quantum chemistry, as well as applications to actinide chemistry and spectroscopy. A new “electrons-only” exact two-component approach has been developed to provide efficient treatments of relativistic effects, while maintaining chemical accuracy. New computational algorithms developed here extend the applicability of relativistic electron-correlation methods to larger molecules. The method-development work in this project also features the first implementation of analytic gradient technique for relativistic electron-correlation methods, which provides significantly enhanced ability to compute properties for molecules containing actinides. The applicability and usefulness of these new methods and computer programs have been demonstrated in calculations of actinide-containing molecules to facilitate understanding of actinide chemistry and spectroscopy.

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

A Comprehensive Comparison of Methods for Evaluating Dispatch of Long-Duration Energy Storage in Power Systems Models

Long-duration energy storage (LDES) could play a pivotal role in the transformation of electricity grids with high shares of variable renewable energy (VRE) such as solar and wind. However, the weather-dependent nature of VRE introduces challenges for grid balancing and stability, which LDES - along with short-duration energy storage (SDES) - can help address. However, modeling LDES in production cost models (PCMs) is particularly challenging due to the need for high temporal resolution over extended optimization windows while preserving chronology, which ensures the alignment of energy storage operations with VRE generation over multi-day periods. This report compares traditional dispatch methods with advanced LDES dispatch strategies, such as the extended horizon approach, across different PCM platforms and examines tradeoffs and scalability. The comparison reveals that the traditional 1-day optimization horizon within the PCM leads to inefficient utilization of LDES. In contrast, extending the optimization horizon as much as possible significantly reduces curtailment and improves storage dispatch, especially in renewable-dense systems. There is also promise in using state-of-charge or end volume targets set by an external model, however this requires an additional modeling set and generally increases computational burden. This paper presents a comparison of these various methods in a number of power systems, showing algorithms initially in small test systems and scaling up to large, country-wide simulations. Overall, the research presents the trade-offs of various computational methods and illustrates how LDES may play an essential role in power systems of the future.

14 SOLAR ENERGY↗
DOE OSTI2025

Efficient proximal subproblem solvers for a nonsmooth trust-region method

In [R. J. Baraldi and D. P. Kouri, Mathematical Programming, (2022), pp. 1-40], we introduced an inexact trust-region algorithm for minimizing the sum of a smooth nonconvex and nonsmooth convex function. The principle expense of this method is in computing a trial iterate that satisfies the so-called fraction of Cauchy decrease condition—a bound that ensures the trial iterate produces sufficient decrease of the subproblem model. In this paper, we expound on various proximal trust-region subproblem solvers that generalize traditional trust-region methods for smooth unconstrained and convex-constrained problems. We introduce a simplified spectral proximal gradient solver, a truncated nonlinear conjugate gradient solver, and a dogleg method. Finally, we compare algorithm performance on examples from data science and PDE-constrained optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Exact enforcement of temporal continuity in sequential physics-informed neural networks

The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs). While this method shows promise, the standard version has been shown to struggle in accurately predicting the dynamic behavior of time-dependent problems. To address this challenge, methods have been proposed that decompose the time domain into multiple segments, employing a distinct neural network in each segment and directly incorporating continuity between them in the loss function of the minimization problem. In this work we introduce a method to exactly enforce continuity between successive time segments via a solution ansatz. This hard constrained sequential PINN (HCS-PINN) method is simple to implement and eliminates the need for any loss terms associated with temporal continuity. The method is tested for a number of benchmark problems involving both linear and non-linear PDEs. Examples include various first order time dependent problems in which traditional PINNs struggle, namely advection, Allen–Cahn, and Korteweg–de Vries equations. Furthermore, second and third order time-dependent problems are demonstrated via wave and Jerky dynamics examples, respectively. Notably, the Jerky dynamics problem is chaotic, making the problem especially sensitive to temporal accuracy. Finally, the numerical experiments conducted with the proposed method demonstrated superior convergence and accuracy over both traditional PINNs and the soft-constrained counterparts.

42 ENGINEERING↗
DOE OSTI2025

HPDR: High-Performance Portable Scientific Data Reduction Framework

The rapid growth in scientific data generation is outpacing advancements in computing systems necessary for efficient storage, transfer, and analysis, particularly in the context of exascale computing. With the deployment of first-generation exascale computing systems and next-generation experimental facilities, this gap is widening and necessitates effective data reduction techniques to manage enormous data volumes. Over the past decade, various data reduction methods, including lossless compression, error-controlled lossy compression, and data refactoring, have been developed to accelerate I/O in scientific workflows. Despite significant reductions in data volume, these methods introduce considerable computational overhead, which can become the new bottleneck in data processing. To mitigate this, GPU-accelerated data reduction algorithms have been introduced. However, challenges remain in their integration into exascale workflows, including limited portability across different GPU architectures, substantial memory transfer overhead, and reduced scalability on dense multi-GPU systems. To address these challenges, we propose HPDR, a high-performance and portable data reduction framework. HPDR is designed to enable the execution of state-of-the-art reduction algorithms across diverse processor architectures while reducing memory transfer overhead to 2.3 % of the original, resulting in up to 3.5× faster throughput compared to existing solutions. It also achieves up to 96% of the theoretical speedup in multi-GPU settings. In addition, evaluations on accelerating I/O operations at scale up to 1,024 nodes of the Frontier supercomputer demonstrate that HPDR can achieve up to 103 TB/s reduction throughput, providing up to 4× acceleration in parallel I/O performance compared to existing data reduction routines. This work highlights the potential of HPDR to significantly enhance data reduction efficiency in exascale computing environments.

Chen, Jieyang [University of Oregon]↗
DOE OSTI2026

Machine learning approach for vibronically renormalized electronic band structures

Here, we present a machine learning (ML) method for efficient computation of vibrational thermal expectation values of physical properties from first principles. Our approach is based on the nonperturbative frozen phonon formulation in which stochastic Monte Carlo algorithm is employed to sample configurations of nuclei in a supercell at finite temperatures based on a first-principles phonon model. A deep-learning neural network is trained to accurately predict physical properties associated with sampled phonon configurations, thus bypassing the time-consuming ab initio calculations. To incorporate the point-group symmetry of the electronic system into the ML model, group-theoretical methods are used to develop a symmetry-invariant descriptor for phonon configurations in the supercell. We apply our ML approach to compute the temperature dependent electronic energy gap of silicon based on density functional theory (DFT). We show that, with less than a hundred DFT calculations for training the neural network model, an order of magnitude larger number of sampling can be achieved for the computation of the vibrational thermal expectation values. Our work highlights the promising potential of ML techniques for finite temperature first-principles electronic structure methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Efficient Signal Processing in BOTDA: Utilizing PCA and PCA-Based Neural Networks for Temperature Monitoring

This work presents a comparative analysis of the various signal processing techniques used in the Brillouin gain spectrum (BGS) peak estimation. Traditional fitting methods such as Lorentzian curve fitting (LCF) are slow and less effective in noisy data. PCA-based methods were tested on the experimental data: A Euclidian distance-based approach, and a probabilistic deep neural network (PDNN) based approach, both using 5 principal components to represent a single BGS. Both methods significantly reduce computational time with respect to LCF, whereas PDNN offers uncertainty insights along with the parameter value. Measuring a range of temperatures, analyzing accuracy, and speed, it can be concluded that PCA trained PDNN outperforms other methods, and appears to be helpful in scenario where large datasets are generated.

Brillouin optical time domain analysis↗
DOE OSTI2025

A Domain-Decomposed A-ϕ Formulation Based on Lagrange Multipliers for Low-Frequency Problems

A domain-decomposed A-ϕ formulation based on Lagrange multipliers is proposed to simulate low-frequency elec- tromagnetic problems. This method partitions the computational domain into smaller subdomains, allowing each subdomain to be independently formulated using Lagrange multipliers as Dirichlet boundary conditions, while ensuring continuity of the fields across the interfaces. A mixed finite element method, utilizing both vector and scalar basis functions, is employed to discretize the formulation, resulting in a global system to be solved. The proposed method is validated using TEAM Problem 7 at 50 Hz, demonstrating its effectiveness in handling complex geometries and addressing the low-frequency breakdown issues commonly encountered in traditional finite element methods.

Hossain, Amzad↗
DOE OSTI2024

Assessing and advancing the potential of quantum computing: A NASA case study

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA’s work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware co-design in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing and describe improved methods for simulating quantum systems of various types on high-performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, as well as insights into fundamental quantum physics that can be harnessed for computational purposes.

Rieffel, Eleanor G.↗
DOE OSTI2025

Machine Learning-Accelerated First-Principles Molecular Dynamics Reveals C–C Coupling Mechanisms toward Ethylene on Cu(100)

Here, the Cu(100) termination has been identified as the most effective facet for converting CO and CO 2 into ethylene. To enhance both the activity and selectivity of ethylene production, we perform machine-learning-accelerated, first-principles molecular dynamics simulations at 298 K in an explicit solvent at pH 7 to elucidate the C–C coupling mechanism─the critical reaction step in forming C 2+ products. Among the six potential C–C coupling pathways, the most feasible are CO* dimerization and CO – CHO* and CHO* – CHO* couplings. Using the computational hydrogen electrode method, we demonstrate that all three pathways are equally accessible at −0.6 V vs RHE. At a potential below −1.0 V vs RHE, the thermodynamic barriers for the CO – CHO* and CHO* – CHO* pathways become negligible. Our computational findings explain the experimental observations, particularly the absence of C 2+ products above −0.4 V vs RHE and the peaks in ethylene production near −0.6 and −1.0 V vs RHE. Since CHO* acts as a key intermediate common to both C–C coupling and CH 4 formation, we propose that suppressing CHO* hydrogenation would inhibit CH 4 pathways, thereby maximizing ethylene selectivity.

CO2 reduction↗
DOE OSTI2025

The Transition Density Formalism in the First Compton Computation on $^4$He

The method and results of the first theory description of 4He Compton scattering at nuclear energies is presented, with a focus on figures. It uses the same Compton kernels familiar from proton, deuteron and 3He Compton scattering in Chiral Effective Field Theory with explicit Delta degrees of freedom, applicable between about 50 and 130MeV. The result compares well to data from HIγS, MAXlab and Illinois. The sensitivity of the cross section on the (static) scalar-isoscalar polarisabilities of the nucleon is explored. The project is part of the synergetic international effort of experimentalists and theorists in Compton scattering on one- and few-nucleon systems.

Griesshammer, Harald [The George Washington Univer↗
DOE OSTI2025

Bayesian optimization of entropy-stabilized metal fluoride conversion cathodes and their synthesis

Fluoride-based conversion cathodes are promising for next-generation Li ion batteries because of their high voltage and energy densities. High entropy earth abundant fluoride cathodes are attractive for sustainable battery because of the potentially higher cycling stability. While Ni and Co containing equimolar alloys stabilized by high entropy were considered previously, Ni and Co free non-equimolar metal alloy compositions might offer new opportunities to increase materials stability and average voltage. This opportunity was pursued using Bayesian optimization of average voltage for the computational prediction of (Fe x1 Cu x2 Zn x3 Mn x4 Mg x5 )F 2 cathode chemistries. Theoretical voltage, room temperature free energy, and electronic structure of potentially high voltage compositions were computed using density functional theory calculations. Here, the experimental realization of non-equimolar quinary Co and Ni free fluorides with stable rutile single phase and improved conversion reaction voltage by up to 28 % compared to the equimolar alloy demonstrated the power of Bayesian optimization methods for the computationally guided experimental synthesis of fluoride cathodes. Finally, non-equimolar cathode chemistries were found with cycling stability superior to known multi-element fluorides.

25 ENERGY STORAGE↗
DOE OSTI2025

Dynamic, symmetry-preserving, and hardware-adaptable circuits for quantum computing many-body states and correlators of the Anderson impurity model

We present a hardware-reconfigurable ansatz on N q -qubits for the variational preparation of many-body states of the Anderson impurity model (AIM) with N imp + N bath = N q /2 sites, which conserves total charge and spin z component within each variational search subspace. The many-body ground state of the AIM is determined as the minimum over all minima of O(N$^2_ q$) distinct charge-spin sectors. Hamiltonian expectation values are shown to require ω(N q ) < N meas. $\leqslant$ O(N imp N bath ) symmetry-preserving, parallelizable measurement circuits, each amenable to postselection. To obtain the one-particle impurity Green’s function we show how initial Krylov vectors can be computed via midcircuit measurement and how Lanczos iterations can be computed using the symmetry-preserving ansatz. For a single-impurity Anderson model with a number of bath sites increasing from one to seven, we show using numerical emulation that the ease of variational ground-state preparation is suggestive of linear scaling in circuit depth and subquartic scaling in optimizer complexity. We therefore expect that, combined with time-dependent methods for Green’s function computation, our ansatz provides a useful tool to account for electronic correlations on early fault-tolerant processors. Finally, with a view towards computing real materials properties of interest like magnetic susceptibilities and electron-hole propagators, we provide a straightforward method to compute many-body, time-dependent correlation functions using a combination of time evolution, midcircuit measurement-conditioned operations, and the Hadamard test.

36 MATERIALS SCIENCE↗
DOE OSTI2024

On the prediction of pressure effects for the combination kinetics of two alkyl radicals with the geometric mean rule

Here, alkyl combination reactions, along with their reverse alkane unimolecular decomposition reactions, are widely present in pyrolysis and combustion systems. These reactions occur mainly at temperatures at which their rate coefficients depend on pressure, and only a few studies have explored this dependence for large size alkyls. The accurate calculation of these kinetic data is limited by the size of the alkyl fragments involved, since the theoretical methods used are computationally expensive. Therefore, little reliable data is available for the modeling of real fuels alkane decomposition kinetics. In this paper, we propose an alternative approach that allows an accurate estimation of pressure-dependent kinetic rates for the combination of long-chain alkyl radicals, based on tabulated reaction models. The rates of combination of linear alkyls radicals forming ethane to pentane were calculated using the CASPT2-F12/cc-pVDZ-F12 level within VRC-TST theory combined with master-equation simulations. The computed high-pressure limit rate coefficients showed that n-propyl could be a minimum-size representative structure for larger n-alkyl chain, but this approach fails to capture the pressure dependence of the reaction that remains size-dependent for larger alkyls. The Geometric Mean Rule (GMR), a simple rule that links the self and cross-reactions of three alkyl radicals, is used to address this issue. Our theoretical calculations show for the first time that the GMR applies not only at the high-pressure limit, but also for every pressure explored. The GMR is shown to be accurate, with a maximum 22 % deviation in the falloff region for the estimation of rates for small alkyl combinations. Using this method, we propose a tabulated reaction rate rule for the combination of alkyl radicals up to C5+C5. The use of computed rate coefficients with the GMR approximation opens ways to develop pressure-dependent reaction rate rules for large n-alkyl combinations, based on accurate and computationally expensive theoretical calculations on smaller alkyl fragments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A flexible class of priors for orthonormal matrices with basis function-specific structure

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.

97 MATHEMATICS AND COMPUTING↗