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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Making Uintah Performance Portable for Department of Energy Exascale Testbeds

To help ease ports to forthcoming Department of Energy (DOE) exascale systems, testbeds have been made available to select users. These testbeds are helpful for preparing codes to run on the same hardware and similar software as in their respective exascale systems. This paper describes how the Uintah Computational Framework, an open-source asynchronous many-task (AMT) runtime system, has been modified to be performance portable across the DOE Crusher, DOE Polaris, and DOE Sunspot testbeds in preparation for portable simulations across the exascale DOE Frontier and DOE Aurora systems. The Crusher, Polaris, and Sunspot testbeds feature the AMD MI250X, NVIDIA A100, and Intel PVC GPUs, respectively. This performance portability has been made possible by extending Uintah’s intermediate portability layer [18] to additionally support the Kokkos::HIP, Kokkos::OpenMPTarget, and Kokkos::SYCL back-ends. This paper also describes notable updates to Uintah’s support for Kokkos, which were required to make this extension possible. Results are shown for a challenging radiative heat transfer calculation, central to the University of Utah’s predictive boiler simulations. These results demonstrate single-source portability across AMD-, NVIDIA-, and Intel-based GPUs using various Kokkos back-ends.

Holmen, John↗
DOE OSTI2025

Lessons Learned and Scalability Achieved When Porting Uintah to DOE Exascale Systems

A key challenge faced when preparing codes for Department of Energy (DOE) exascale systems was designing scalable applications for systems featuring hardware and software not yet available at leadership-class scale. With such systems now available, it is important to evaluate scalability of the resulting software solutions on these target systems. One such code designed with the exascale DOE Aurora and DOE Frontier systems in mind is the Uintah Computational Framework, an open-source asynchronous many-task (AMT) runtime system. To prepare for exascale, Uintah adopted a portable MPI+X hybrid parallelism approach using the Kokkos performance portability library (i.e., MPI+Kokkos). This paper complements recent work with additional details and an evaluation of the resulting approach on Aurora and Frontier. Results are shown for a challenging benchmark demonstrating interoperability of 3 portable codes essential to Uintah-related combustion research. These results demonstrate single-source portability across Aurora and Frontier with scaling characteristics shown to 3,072 Aurora nodes and 9,216 Frontier nodes. In addition to showing results run to new scales on new systems, this paper also discusses lessons learned through efforts preparing Uintah for exascale systems.

Holmen, John [ORNL] (ORCID:0000000259342641)↗
DOE OSTI2024

Real-time chiral dynamics at finite temperature from quantum simulation

In this study, we explore the real-time dynamics of the chiral magnetic effect (CME) at a finite temperature in the (1+1)-dimensional QED, the massive Schwinger model. By introducing a chiral chemical potential μ 5 through a quench process, we drive the system out of equilibrium and analyze the induced vector currents and their evolution over time. The Hamiltonian is modified to include the time-dependent chiral chemical potential, thus allowing the investigation of the CME within a quantum computing framework. We employ the quantum imaginary time evolution (QITE) algorithm to study the thermal states, and utilize the Suzuki-Trotter decomposition for the real-time evolution. This study provides insights into the quantum simulation capabilities for modeling the CME and offers a pathway for studying chiral dynamics in low-dimensional quantum field theories.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

From PINNs to PIKANs: recent advances in physics-informed machine learning

Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Lastly, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.

Kolmogorov-Arnold networks↗
DOE OSTI2025

Assessment of critical flaw sizes and crack driving forces during additive manufacturing of metallic materials

Additive manufacturing (AM) of complex engineering components is often plagued by a high susceptibility to cracking, particularly in high-strength metallic materials. While alloy design efforts have made progress in mitigating solidification defects, there remains a need for mechanistic guidelines to predict susceptibility to solid-state cracking. To address this gap, driving forces for the growth of melt pool cracks are calculated across a wide range of alloys using an efficient computational framework. Calculations are coupled with rapid single track laser experiments to elucidate trends in cracking from laser melting. The analyses conducted here highlight the important role of material properties in susceptibility to cracking, notably fracture toughness and elastic modulus. An important finding is that residual stresses that are limited in magnitude to the yield stress of the material are likely insufficient to drive cracking during cooling. Furthermore, the implications of these results are discussed in the context of alloy design for AM and residual stress accumulation during AM.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Harnessing dimethyl ether with ultra-low-grade heat for scaling-resistant brine concentration and fractional crystallization

Solvent-driven separations may enable scalable concentration of hypersaline brines, supporting a circular resource economy from the extraction of lithium and rare earth elements from spent battery and magnet leachates. This work analyses a novel solvent-driven water extraction (SDWE) system employing dimethyl ether (DME) and ultra-low-grade heat for brine concentration and fractional crystallization. SDWE exploits DME’s unique properties: (1) a low dielectric constant that promotes water solubility over charged solutes by a factor of 10 3 , and (2) a high volatility that facilitate efficient DME reconcentration with ultra-low-grade heat. The techno-economic viability of SDWE is assessed with a computational framework that encompasses a liquid–liquid separator and a solvent concentrator. We integrate the extended universal quasichemical model with the virial equation of state to predict the compositions of the complex three-phase DME-water mixture at vapor–liquid and liquid–liquid equilibrium. Subsequently, we optimize the thermodynamic and economic performance of SDWE, by controlling the interstage flash pressure, heat source temperature, and the number of concentrating stages. DME-based SDWE concentrates an input saline feed to 5.5 M and regenerates over 99 % of the DME using ultra-low-grade heat below 50 °C, with a DME/water selectivity ratio of 125. Here our calculations reveal that optimal performance is achieved at interstage flash pressures of 0.4 – 0.5 bar for heat source temperatures between 323–373 K, with improved exergetic efficiencies at lower temperatures. At a heat source temperature of 323 K and an interstage pressure of 0.489 bar, DME-driven SDWE achieves an optimal thermodynamic efficiency of 20.5 % and a projected specific cost of US$ 1.93 m -3 . These specific costs suggest that SDWE is competitive with commercialized thermal distillation technologies, while mitigating the traditional risks associated with scaling in heat and mass exchangers with hypersaline brines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Bridging the gap between NMR measured mean silicate chain length and nano-scale silicate polymorphism of calcium silicate hydrates

Variable stoichiometry and silicate polymorphism in calcium-silicate-hydrates (C-S-H) has impeded the revelation of point defects distribution in the silicate tetrahedral network of C-S-H, which resembles tobermorite crystal structure with some bridging tetrahedra (BT) and paired tetrahedra (PT) vacancies in dreierketten chains. Here we use a computational approach to characterize silicate polymorphism by introducing the vacancy ratio of BT to PT (α) and establishing a three-term empirical mean chain length (MCL) formula for different calcium-to‑silicon ratios (Ca/Si) ranging from 1.2 to 2.3. The formula identifies BT and PT controls at low and high α respectively and allows an inverse mapping of polymorphic range based on NMR experiments. The proposed computational framework quantitatively describes silicate polymorphs and links NMR-measured MCL to C-S-H atomistic configurations at the molecular level.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A molecular dynamics study of domain switching in BiFeO 3 nanofilm under DC electric field

The performance of ferroelectric materials is deeply rooted in the domain switching process triggered by an applied electric field. To understand the effect of controlling factors on the domain switching characteristics, nanoscale studies are essential. In this work, an atomistic-scale computational framework is developed to study the domain switching process of bismuth ferrite (BiFeO 3 ) nanofilms. Through a series of molecular dynamics simulations, the effects of duration and magnitude of an electric field, domain size and nanofilm thickness on the domain switching behavior are investigated. The evolution of domains under different magnitudes and durations of the applied electric field is studied, and the results of unswitched and switched cases are summarized in a switching map. Analysis of various nanofilms reveals that by increasing the film thickness, the switching time is increased. Additionally, the nanofilms with different domain sizes and configurations are examined, and the minimum domain size for complete reversal switching is obtained. Our results are helpful to predict and tune the switching process in ferroelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Screening green solvents for multilayer plastic film recycling processes

Multilayer (ML) plastic films are essential packaging materials that help protect products from diverse external factors; however, only 5% of all ML films are recycled in the United States. Solvent-based technologies are a promising alternative for recycling ML films because they enable recovery of constituent polymer resins. For example, the Solvent Targeted Recovery and Precipitation (STRAPTM) process sequentially dissolves and separates polymer components using a series of targeted solvent washes. A crucial design aspect of this process is the impact of selected solvents on human health and on the environment. Here, this work introduces a computational framework that integrates molecular modeling, process modeling, techno-economic analysis (TEA), and life-cycle analysis (LCA) to quickly screen green solvents for solvent-based ML recycling processes. Initial screening for solvents based on selectivity is performed by estimating temperature-dependent solubilities using molecular-scale models. Subsequent screening uses basic estimates of energy use and octanol-water partition coefficients (logP) as key measures of health, safety, and environmental hazards. Detailed process modeling, TEA, and LCA are used on a reduced set of promising solvents identified in early screening steps to more accurately determine how solvent selection and associated operating conditions impact overall economics and environmental impacts. The framework is used for the identification of green solvents (from a database of 1,000 solvents) that separate an industrial ML film composed of polyethylene (PE), ethylene vinyl alcohol (EVOH), and polyethylene terephthalate (PET). Our analysis shows the effectiveness of the framework and reveals fundamental trade-offs between solvent greenness, solubility, and economics. Our work emphasizes the importance of taking a holistic systems view during solvent design and aims to inform the development of new processes for ML film recycling and the identification of new ML films that are easier to recycle.

economics↗
DOE OSTI2025

A deep learning and finite element approach for exploration of inverse structure–property designs of lightweight hybrid composites

Hybrid composites have important applications, such as high-performance and lightweight materials in aerospace and automotive industries. Hybrid composites utilize the synergy of diverse fillers to achieve desired material properties, but usually have more complicated microstructures. While topology optimization can optimize a particular property, designing hybrid composites for customized mechanical performances, e.g. full-range stress–strain curve, remains challenging. Here, a computational framework that integrated finite element analysis (FEA) and artificial intelligence (AI) methods of Conditional Generative Adversarial Networks (cGAN) deep learning and transfer learning was developed to establish inverse structure–property relationships and design tailor-made hybrid composites. Based on FEA-generated datasets of hybrid fiber-particle–matrix microstructures and their corresponding full-range stress–strain curves, a cGAN architecture was trained to generate tailored microstructures and establish structure–property relationships. Similarity in microstructural features and well-matched stress–strain curves based on the AI-generated composites were achieved. In conclusion, transfer learning was used to expand the pre-trained model for designing different materials systems.

Hybrid composites↗
DOE OSTI2025

Resolving Lonsdaleite's decade-long controversy: Atomistic insights into a metastable diamond polymorph

Lonsdaleite, a theoretically proposed hexagonal diamond polymorph, has remained at the center of a five-decade scientific controversy since its 1967 identification. While some studies claim it exhibits superior hardness through compression-induced structural changes, others contend it is merely a stacking-faulted cubic diamond. Meteoritic samples and synthetic preparations have yielded conflicting evidence, with even advanced characterisation techniques like XRD and TEM failing to provide definitive proof. In this work, we employ first-principles density functional theory (DFT) and molecular dynamics (MD) simulations to generate unambiguous theoretical fingerprints through XRD, Raman, and SAED patterns that distinguish true Lonsdaleite from cubic diamond and its defective variants. Our atomistic approach quantifies the thermodynamic metastability of Lonsdaleite under realistic pressure-temperature conditions, reveals distinct spectral signatures through simulated Raman and resolves the structural ambiguity through generalised stacking fault energy analysis. By establishing clear criteria for definitive identification, this study provides long-awaited clarity to the Lonsdaleite debate while offering a robust computational framework for characterising metastable carbon phases in meteoritic, synthetic and industrial materials.

DFT↗
DOE OSTI2022

Optimal high-throughput virtual screening pipeline for efficient selection of redox-active organic materials

As global interest in renewable energy continues to increase, there has been a pressing need for developing novel energy storage devices based on organic electrode materials that can overcome the shortcomings of the current lithium-ion batteries. One critical challenge for this quest is to find materials whose redox potential (RP) meets specific design targets. In this study, we propose a computational framework for addressing this challenge through the effective design and optimal operation of a high-throughput virtual screening (HTVS) pipeline that enables rapid screening of organic materials that satisfy the desired criteria. Starting from a high-fidelity model for estimating the RP of a given material, we show how a set of surrogate models with different accuracy and complexity may be designed to construct a highly accurate and efficient HTVS pipeline. We demonstrate that the proposed HTVS pipeline construction and operation strategies substantially enhance the overall screening throughput.

36 MATERIALS SCIENCE↗
DOE OSTI2026

scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Bioinformatics↗
DOE OSTI2024

Computational modeling of coupled mechanical damage and electrochemistry in ternary oxide composite electrodes

Performance degradation of ternary layered oxide cathodes largely originates from their loss of structural integrity in cyclic usage. Mechanical damage, such as intergranular fracture of the active particles, is not only a mechanical cleavage process but also interferes with electrochemical kinetics such as infiltration of liquid electrolyte, surface corrosion of the constituent primary particles, and may eventually isolate the primary grains from the electron conducting network. Here, in this work, we develop a computational framework that integrates electrochemistry of a LiNi x Mn y Co 1−x−y O 2 (NMC) composite cathode with mechanical damage of the active particles. To fully examine the intricate chemomechanical behavior of the electrode, we evaluate the effects of the anisotropic material properties, the influence of mechanical potential on Li transport, and the concurrent intergranular fracture and electrolyte penetration along the grain boundaries upon multiple cycles. Electrolyte infiltration benefits capacity retention but aggravates further mechanical damage by corrosion. Structural failure mostly occurs in the first charging due to the anisotropic mechanical strain between the primary grains, while the resulting damage remains stable in the later few cycles. The results are consistent with experimental observations and the integration of electrochemistry and mechanical failure enables a step further understanding of the complex mechanism of battery degradation.

Battery degradation↗
DOE OSTI2025

Seismic response of vertical dry storage casks under three-dimensional earthquake motions

Ensuring the long-term seismic safety of dry storage casks (DSCs) is becoming increasingly critical as these systems evolve from temporary to de facto permanent repositories for spent nuclear fuels. Traditional seismic soil–structure interaction (SSI) assessment methods use one-dimensional deconvolution or simplified boundary conditions to model incident waves. Although computationally appealing, simplifying assumptions may alter the seismic risk by neglecting the full complexity of three-dimensional (3D) wave propagation effects. To address this challenge, this paper introduces a novel high-fidelity computational framework that leverages the Domain Reduction Method (DRM) with perfectly matched layers (PML) to accurately transfer complex, 3D seismic wavefields from regional-scale fault-rupture simulations into local-scale finite element models of DSCs. Using broadband, physics-based ground motions from a generic M w 7.0 strike-slip event, both single-cask and multi-cask configurations were investigated under near- and far-field conditions. Emphasis is placed on capturing complex SSI, spatial variability in the ground motion, and nonlinear phenomena such as cask rocking and sliding. Numerical results demonstrate that near-field conditions, where forward directivity and fling-step effects dominate, lead to significantly higher DSC rocking and sliding. Far-field cases, by contrast, generally exhibit modest responses. Incorporating SSI tends to amplify or alter DSC response spectra and introduce response variability, which underscores the need for site-specific evaluations and robust modeling approaches to ensure the seismic integrity of DSCs in interim spent fuel storage installations.

Das, Tonmoy↗
DOE OSTI2023

Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials

This paper provides a coherent and efficient computational framework for stochastic multiscale analysis of material systems in the presence of parametric uncertainties and modeling errors. Uncertainty in those model parameters that are not deduced as upscaled quantities is attributed to an uncertainty “germ”. While such parameters can appear at any scale, they are predominant at the finest analysis scale. Additional uncertainties stemming from statistical estimation, attributed to lack of data and model error, are associated with each submodel contributing to the multiscale system. Here, a robust and efficient framework based on a generalized extended polynomial chaos expansion (gEPCE) is proposed to simultaneously propagate all these uncertainties in order to provide a probabilistic representation of specific quantities of interest (QoI). We characterize the full probability distribution of the QoI and the uncertainty in the failure probability pertaining to its tails. By combining gEPCE with kernel density estimation (KDE) and directional derivatives, we construct sensitivity measures that connect these statistical metrics of QoI to the various sources of uncertainty to assess their individual and combined impacts. An illustrative problem featuring three-point bending of a composite beam is investigated to demonstrate the presented approach.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Thermomechanical analysis and modeling of a high-temperature light trapping planar cavity solar receiver

The development of durable particle-based high-temperature solar receivers is critical for advancing concentrating solar-thermal (CST) technologies to enable high-efficiency power generation and industrial process heat. Here, this study presents a computational framework to evaluate the thermomechanical performance of a proposed enclosed light-trapping planar cavity receiver designed for particle-based thermal energy systems. The receiver incorporates absorptive cavities and fluidized particle-bed channels to enhance heat capture and reduce thermal losses. Finite element analysis (FEA) is employed to assess stress, strain, and creep-fatigue behavior under concentrated solar flux using realistic thermal boundary conditions derived from coupled system models and experimental assembly parameters. The analysis investigates the influence of particle-to-wall heat transfer coefficients (HTC) ranging from 800 to 1800 W/m 2 .K on the thermomechanical response of six candidate high-temperature alloys: Alloy 740H, Alloy 282, Alloy 617, 316H, Alloy 230, and 800H. Results show that increasing HTC reduces thermal gradients, leading to lower stresses and strains and extended minimum predicted creep life. While all materials satisfy fatigue life requirements under the investigated conditions, significant differences in creep resistance are observed. Alloy 740H consistently exhibits the longest minimum predicted creep life and the most favorable durability margins, followed by Alloy 282, with the remaining materials showing reduced creep resistance under identical loading. The reported creep lives are conservative lower-bound estimates intended for comparative material evaluation. This framework highlights the critical roles of material selection and geometry optimization in improving mechanical durability and reliability of solar-thermal receivers, forming a foundation for future experimental validation and design optimization.

14 SOLAR ENERGY↗