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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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Anisotropic Particle Acceleration in Alfvénic Turbulence

Alfvénic turbulence is an effective mechanism for particle acceleration in strongly magnetized, relativistic plasma. In this study, we investigate a scenario where turbulent plasma is influenced by a strong guide magnetic field, resulting in highly anisotropic turbulent fluctuations. In such cases, the magnetic moments of particles are conserved, which means that acceleration can only occur along the direction of the magnetic field. Consistent with previous analytic studies, we find through particle-in-cell simulations of magnetically dominated pair plasma that the momenta of accelerated particles are closely aligned with the magnetic field lines. Notably, the alignment angle decreases as particle energy increases, potentially limited only by the inherent curvature and gradients of the turbulent magnetic fluctuations. This finding has significant implications for interpreting the synchrotron radiation emitted by highly accelerated particles.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Acceleration and evolution of a hollow electron beam in wakefields driven by a Laguerre-Gaussian laser pulse

We show that a ring-shaped hollow electron beam can be injected and accelerated by using a Laguerre-Gaussian laser pulse and ionization-induced injection in a laser wakefield accelerator. The acceleration and evolution of such a hollow, relativistic electron beam are investigated through three-dimensional particle-in-cell simulations. We find that both the ring size and the beam thickness oscillate during the acceleration. The beam azimuthal shape is angularly dependent and evolves during the acceleration. The beam ellipticity changes resulting from the electron angular momenta obtained from the drive laser pulse and the focusing forces from the wakefield. The dependence of beam ring radius on the laser-plasma parameters (e.g., laser intensity, focal size, and plasma density) is studied. Such a hollow electron beam may have potential applications for accelerating and collimating positively charged particles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Laboratory measurements of the physics of auroral electron acceleration by Alfvén waves

While the aurora has attracted attention for millennia, important questions remain unanswered. Foremost is how auroral electrons are accelerated before colliding with the ionosphere and producing auroral light. Powerful Alfvén waves are often found traveling Earthward above auroras with sufficient energy to generate auroras, but there has been no direct measurement of the processes by which Alfvén waves transfer their energy to auroral electrons. Here, we show laboratory measurements of the resonant transfer of energy from Alfvén waves to electrons under conditions relevant to the auroral zone. Experiments are performed by launching Alfvén waves and simultaneously recording the electron velocity distribution. Numerical simulations and analytical theory support that the measured energy transfer process produces accelerated electrons capable of reaching auroral energies. The experiments, theory, and simulations demonstrate a clear causal relationship between Alfvén waves and accelerated electrons that directly cause auroras.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

An electronic-structure-centered perspective is presented on permanent-magnet (PM) design, highlighting two key levers, that is, saturation magnetization (M s ), governed by 3d-band filling and exchange physics, and magnetocrystalline anisotropy energy (MAE), arising from spin-orbit coupling (SOC) on anisotropic orbital populations. Reviewing current practices, including DFT-based MAE/J ij extraction, atomistic-spin and micromagnetic modeling, and high-throughput machine learning (ML) pipelines, three bottlenecks limiting predictive discovery is identified that is i) electronic-structure accuracy for small MAE (sensitive to functional choice, Hubbard U, and many-body effects), ii) finite-temperature and kinetic realism (phonon/magnon renormalization, ordering kinetics), and iii) descriptor and multiscale decoupling (lack of SOC-weighted and orbital-resolved fingerprints). Deep dives into the electronic-structure of Nd─Fe─B and Fe─N show how these fingerprints govern magnetic performance, motivating DFT- and quantum-mechanics-based descriptors for discovery. Unbiased, structure-driven exploration, coupled with high-throughput simulations, ML, generative AI, and reasoning models, accelerates candidate identification and propagates insights across scales. Addressing supply-chain risks, on future needs of designing “critical-element-free” magnets with tailored microstructure and high energy products is emphasized. By integrating electronic fingerprints, AI reasoning, and multiscale modeling, a practical roadmap is provided for rare-earth-lean or rare-earth free, high-performance, sustainable PMs.

Singh, Prashant [Ames Laboratory, and Iowa State U↗
DOE OSTI2020

Thermally Activated Adsorption in Metal–Organic Frameworks with a Temperature‐Tunable Diffusion Barrier Layer

Abstract Modification of the external surfaces of metal–organic frameworks offers a new level of control over their adsorption behavior. It was previously shown that capping of MOFs with ethylenediamine (EDA) can effectively retain small gaseous molecules at room temperature. Reported here is a temperature‐induced variation in the capping‐layer gate‐opening mechanism through a combination of in situ infared experiments and ab initio simulations of the capping layer. An atypical acceleration and increase in the loading of weakly adsorbed molecules upon raising the temperature above room temperature is observed. These findings show the discovery of novel temperature‐dependent kinetics that goes beyond standard kinetics and suggest a new avenue for tailoring selective adsorption by thermally tuning the surface barrier.

Tan, Kui↗
DOE OSTI2018

Effects of Aging and Different Mechanical Recycling Processes on the Structure and Properties of Poly(lactic acid)-clay Nanocomposites

The growing use of poly(lactic acid) (PLA) and PLA-based nanocomposites in packaging has raised the interest of studying the mechanical recycling of the wastes and the properties of the recycled materials. The main objective of this work was to study the effect of two different mechanical recycling processes on the structure and properties of a PLA-montmorillonite nanocomposite. The two recycling processes included accelerated thermal and photochemical aging steps to simulate the degradation experienced by post-consumer plastics during their service life. One of them also included a demanding washing process prior to the reprocessing. A decrease in the molecular weight of PLA was observed in the recycled materials, especially in those subjected to the washing step, which explained the small decrease in microhardness and the increased water uptake at long immersion times. Water absorption at short immersion times was similar in virgin and recycled materials and was accurately described using a Fickian model. The recycled materials showed increased thermal, optical and gas barrier properties due to the improved clay dispersion that was observed by XRD and TEM analysis. The results suggest that recycled PLA-clay nanocomposites can be used in demanding applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

High-throughput validation of phase formability and simulation accuracy of Cantor alloys

High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast compositional spaces. Computational approaches, including Density Functional Theory (DFT) and calculation of phase diagrams (CALPHAD), facilitate screening of phase formability as a function of composition and temperature. However, the integration of computational predictions with experimental validation remains challenging in high-throughput studies. In this work, we introduce a quantitative confidence metric to assess the agreement between predictions and experimental observations, providing a quantitative measure of the confidence of machine learning models trained on either DFT or CALPHAD input in accounting for experimental evidence. The experimental dataset was generated via high-throughput in-situ synchrotron X-ray diffraction on compositionally varied FeNiMnCr alloy libraries, heated from room temperature to ~1000 °C. Agreement between the observed and predicted phases was evaluated using either temperature-independent phase classification or a model that incorporates a temperature-dependent probability of phase formation. This integrated approach demonstrates where strong overall agreement between computation and experiment exists, while also identifying key discrepancies, particularly in FCC/BCC predictions at Mn-rich regions to inform future model refinement.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Accelerating multicanonical sampling with irreversibility

Flat-histogram Monte Carlo simulations are well-established, robust methods to perform random walks in a physical observable or parameter space, making them suitable for finding ground states or studying phase transitions in complex systems in statistical physics. However, their efficiency can be limited by the time to attain the desired flat distribution, which is generally unknown prior to the simulations. In particular, they might suffer from slowing down towards the end of a simulation due to the diffusive nature of random walks. In this work we apply irreversibility to the multicanonical Monte Carlo method via the lifting approach to alleviate this behavior. We achieve a 2–4 times speedup in ground-state search for a two-dimensional (2D) Ising model, and up to an order of magnitude of speedup for finding the ground-state energy in an Edwards–Anderson spin glass, compared to traditional multicanonical sampling. In conclusion, the round-trip times between ground states show a narrower distribution and are significantly shorter compared to the reversible counterpart, suggesting that a lower convergence time with a smaller time variance is feasible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

A model and variance reduction method for computing statistical outputs of stochastic elliptic partial differential equations

We present a model and variance reduction method for the fast and reliable computation of statistical outputs of stochastic elliptic partial differential equations. Our method consists of three main ingredients: (1) the hybridizable discontinuous Galerkin (HDG) discretization of elliptic partial differential equations (PDEs), which allows us to obtain high-order accurate solutions of the governing PDE; (2) the reduced basis method for a new HDG discretization of the underlying PDE to enable real-time solution of the parameterized PDE in the presence of stochastic parameters; and (3) a multilevel variance reduction method that exploits the statistical correlation among the different reduced basis approximations and the high-fidelity HDG discretization to accelerate the convergence of the Monte Carlo simulations. The multilevel variance reduction method provides efficient computation of the statistical outputs by shifting most of the computational burden from the high-fidelity HDG approximation to the reduced basis approximations. Furthermore, we develop a posteriori error estimates for our approximations of the statistical outputs. Based on these error estimates, we propose an algorithm for optimally choosing both the dimensions of the reduced basis approximations and the sizes of Monte Carlo samples to achieve a given error tolerance. We provide numerical examples to demonstrate the performance of the proposed method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

A novel peridynamics-based approach to predict pharmaceutical tablet robustness

The pharmaceutical drug product development process can be greatly accelerated through the use of modeling and simulation techniques to predict the manufacturability and performance of a given formulation. The anticipation and possible mitigation of tablet damage due to manufacturing stresses represents a specific area of interest in the pharmaceutical industry for predicting formulation and tableting performance. While the finite element method (FEM) has been extensively used for predicting the mechanical behavior of powder material in the compaction processes, a shortcoming of the approach is the inherent difficulty to predict discontinuities (e.g., damage or cracking) within a tablet as FEM is a continuum-based approach. In this work, we propose a novel method utilizing peridynamics (PD), a numerical method that can capture discontinuities such as tablet fracture, to predict the evolution of damage and breakage in pharmaceutical tablets. The approach links (1) the finite element method – to elucidate the behavior of powders during die compaction – with (2) the peridynamics modeling technique – to model the discontinuous nature of damage and predict tablet breakage during the critical stages of unloading and ejection from the compression die. This short communication presents a proof of concept including a workflow to calibrate the linked FEM-PD simulation models. Further, it demonstrates promising results from a preliminary experimental validation of the approach. Following further development, this approach could be used to guide the optimization of compression processes through targeted changes to formulation material properties, compression process conditions, and/or tooling geometries to deliver improved process efficiency and tablet robustness.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Foundation models for atomistic simulation of chemistry and materials

Conventional computational methods for modeling chemical and materials systems are limited by system size and timescale, forcing a trade-off between quantum-mechanical accuracy and the sampling needed for realistic observables. Large language and vision foundation models — pre-trained on massive datasets using transformer architectures — have revolutionized many fields. It is thus interesting to ask whether a foundation model — subject to suitable data, parameter scaling and training — could enable learned simulations of chemistry and materials. Here, in this study, we review the field of machine-learned interatomic potentials (MLIPs) and posit that scaling up large and diverse chemical and materials datasets and highly expressive architectures using advanced training strategies should result in models that are: more efficient, transferable, robust to out-of-distribution scenarios, and easier to fine-tune to a variety of downstream physical observables than models trained from scratch on small datasets corresponding to specific, targeted atomistic simulation tasks. We provide specific criteria for creating such large-scale MLIP foundation models, coordinated strategies for their development, evaluation and deployment, and highlight potential emergent capabilities that could transform predictive simulations in chemistry and materials science and accelerate discovery across multiple technological domains.

Yuan, Eric C.-Y. [University of California, Berkel↗
DOE OSTI2014

SIMULATION OF ENERGETIC NEUTRAL ATOMS FROM SOLAR ENERGETIC PARTICLES

Energetic neutral atoms (ENAs) provide the only way to observe the acceleration site of coronal-mass-ejection-driven (CME-driven) shock-accelerated solar energetic particles (SEPs). In gradual SEP events, energetic protons can charge exchange with the ambient solar wind or interstellar neutrals to become ENAs. Assuming a CME-driven shock with a constant speed of 1800 km s{sup –1} and compression ratio of 3.5, propagating from 1.5 to 40 R{sub S} , we calculate the accelerated SEPs at 5-5000 keV and the resulting ENAs via various charge-exchange interactions. Taking into account the ENA losses in the interplanetary medium, we obtain the flux-time profiles of these solar ENAs reaching 1 AU. We find that the arriving ENAs at energies above ∼100 keV show a sharply peaked flux-time profile, mainly originating from the shock source below 5 R{sub S} , whereas the ENAs below ∼20 keV have a flat-top time profile, mostly originating from the source beyond 10 R{sub S} . Assuming the accelerated protons are effectively trapped downstream of the shock, we can reproduce the STEREO ENA fluence observations at ∼2-5 MeV/nucleon. We also estimate the flux of ENAs coming from the charge exchange of energetic storm protons, accelerated by the fast CME-driven shock near 1 AU, with interstellar hydrogen and helium. Our results suggest that appropriate instrumentation would be able to detect ENAs from SEPs and to even make ENA images of SEPs at energies above ∼10-20 keV.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Magnetohydrodynamic effects in a shock-accelerated gas cylinder

This work presents two-dimensional (2D) simulations on a cylindrical Richtmyer-Meshkov instability (RMI) in magnetohydrodynamics (MHD). Three studies are presented in an effort to quantify and qualify the evolution of the MHD RMI by varying the magnetic field orientation, strength of the magnetic field, and strength of the shock wave driving the instability. The orientations considered herein are either parallel or perpendicular to the shock wave motion. The second study varies the magnetic fields between 100, 250, and 500 gauss (G), while the third study considers incident shock wave Mach numbers M = 1.2, M = 1.66, and M = 2.2. These parameter ranges were selected to be easily achievable in experiments while the interface perturbation was selected such that its evolution is independent of either the shock wave or magnetic field orientations independently. It was found that the MHD RMI evolution is dependent upon the magnetic field orientation relative to the shock transit direction as well as their individual magnitudes. This is because the mechanism of suppression, attributed to Alfven waves, is a function of the magnetic field strength and the orientation of the magnetic field, while the mechanism of RMI evolution, baroclinic vorticity deposition, is a function of the Mach number. Stronger magnetic fields were found to provide greater mixing suppression and have significant effects on RMI-like interface morphology. Lastly, increasing the shock wave strength generated competing effects between higher RMI vorticity deposition and greater vorticity removal from the interface by faster Alfven waves.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

First-principles effective Hamiltonian for finite-temperature modeling of nonperovskite ferroelectrics

First-principles-based effective Hamiltonian techniques have been widely employed for over three decades to investigate ferroelectricity and related phenomena in perovskite materials. These techniques offer high accuracy, transferability, compatibility with various finite-temperature algorithms, computational efficiency, and ease in incorporating interactions with external fields. They have been adapted to study diverse phenomena, ranging from topological dipole patterns in ferroelectric nanostructures to multicaloric effects. In this work, we develop an effective Hamiltonian for the nonperovskite ferroelectric HfO 2 (hafnia). Applying this methodology to explore the finite-temperature and finite-electric-field properties of ferroelectric hafnia revealed (1) exceptionally large intrinsic coercive fields, an order of magnitude higher than those observed in perovskite ferroelectrics; (2) their atomistic origin; and (3) the existence of a regime where the relationship between the coercive field and the energy barrier for polarization reversal is counterintuitive. Here, these developments could accelerate progress both in methodological advancements for simulating ferroics and in the atomistic understanding of a broad range of ferroelectrics.

Electric polarization↗
DOE OSTI2021

Performance Evaluation of Distributed Energy Resource Management Algorithm in Large Distribution Networks

This paper presents performance evaluation of hierarchical optimization and control for distributed energy resource management system (DERMS) in large distribution networks via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory environment, including the accurate modeling of a full-scale distribution system of 11,000 nodes, the DERMS software controller, and 90 power hardware photovoltaics (PVs) and battery inverters. The applied DERMS algorithm is designed based on a realtime optimal power flow algorithm and implemented with acceleration design that performs fast dispatch of simulated PVs and real physical hardware DER devices every 4 seconds.

DERMS↗
DOE OSTI2025

Asynchronous-many-task systems: Challenges and opportunities - Scaling an AMR astrophysics code on exascale machines using Kokkos and HPX

Dynamic and adaptive mesh refinement is pivotal in high-resolution, multi-physics, multi-model simulations, necessitating precise physics resolution in localized areas across expansive domains. Today’s supercomputers’ extreme heterogeneity presents a significant challenge for dynamically adaptive codes, highlighting the importance of achieving performance portability at scale. Our research focuses on astrophysical simulations, particularly stellar mergers, to elucidate early universe dynamics. Here, we present Octo-Tiger, leveraging Kokkos, HPX, and SIMD for portable performance at scale in complex, massively parallel adaptive multi-physics simulations. Octo-Tiger supports diverse processors, accelerators, and network backends. Experiments demonstrate exceptional scalability across several heterogeneous supercomputers including Perlmutter, Frontier, and Fugaku, encompassing major GPU architectures and x86, ARM, and RISC-V CPUs. Parallel efficiency of 47.59% (110,080 cores and 6880 hybrid A100 GPUs) on a full-system run on Perlmutter (26% HPCG peak performance) and 51.37% (using 32,768 cores and 2048 MI250X) on Frontier are achieved.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Performance Optimization of a Beam Dynamics PIC Code On Hybrid Computer Architectures

The self-consistent multi-particle tracking based on particle-in-cell method (PIC) has been widely used in particle accelerator beam dynamics study. However, the PIC simulation is time-consuming and needs to use modern parallel computers for high resolution applications. In this paper, we implemented and optimized a parallel beam dynamics PIC code on two types of hybrid parallel computer architectures: one is the GPU and GPU cluster, while the other is the "Knight Landing" CPU cluster.

Liu, Zhicong↗
DOE OSTI2020

Two new SciDAC institutes promote mathematical tools and software technology for high-performance computing

Bigger is often said to be better, and the newest extreme-scale computers certainly are bigger, with millions of processing units. Moreover, the breadth of science performed on the U.S. Department of Energy (DOE) computing facilities is expanding, with new technology such as artificial intelligence emerging. These advances are exciting, creating new opportunities for scientific discovery; however, they also raise new questions for scientists who want to exploit these advances for tackling more complex problems. Will my simulation code be able to utilize the accelerators in extreme-scale computing systems? Can I take advantage of the deepening memory hierarchy in heterogeneous processors? Is there a way around bottlenecks caused by the widening ratio of peak floating-point operations per second to I/0 bandwidth? How can I manage my huge amounts of data effectively? Can I analyze data in situ, or must I transfer it to offline storage for later analysis? To address such questions, DOE announced that it is providing $57.5 million over the next five years for two multidisciplinary teams — FASTMath and RAPIDS2 — to develop new tools and techniques to harness supercomputers for scientific discovery. The teams, called SciDAC Institutes, are part of the Scientific Discovery through Advanced Computing program.

97 MATHEMATICS AND COMPUTING↗