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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

GPU acceleration of hybrid functional calculations in the SPARC electronic structure code

We present a Graphics Processing Unit (GPU)-accelerated version of the real-space SPARC electronic structure code for performing hybrid functional calculations in generalized Kohn–Sham density functional theory. In particular, we develop a batch variant of the recently formulated Kronecker product-based linear solver for the simultaneous solution of multiple linear systems. We then develop a modular, math kernel based implementation for hybrid functionals on NVIDIA architectures, where computationally intensive operations are offloaded to the GPUs, while the remaining workload is handled by the central processing units (CPUs). Considering bulk and slab examples, we demonstrate that GPUs enable up to 8× speedup in node-hours and 80× in core-hours compared to CPU-only execution, reducing the time to solution on V100 GPUs to around 300 s for a metallic system with over 6000 electrons, and significantly reducing the computational resources required for a given wall time.

Kohn-Sham density functional theory↗
DOE OSTI2025

Galerkin formulation of path integrals in lattice field theory

We present a mathematical framework for Galerkin formulations of path integrals in lattice field theory. The framework is based on using the degrees of freedom (DOFs) associated to a Galerkin discretization as the fundamental lattice variables. We formulate standard concepts in lattice field theory, such as the partition function and correlation functions, in terms of the DOFs. For example, using continuous finite element spaces, we show that the two-point spatial correlation function can be defined between any two points on the domain (as opposed to at just lattice sites) and furthermore, this two-point function satisfies a weak propagator (or Green’s function) identity, in analogy to the continuum case, as well as a convergence estimate obtained from the standard finite element techniques. Furthermore, this framework leads naturally to higher-order formulations of lattice field theories by considering higher-order finite element spaces for the Galerkin discretization. We consider analytical and numerical examples of scalar field theory to investigate how increasing the order of piecewise polynomial finite element spaces affect the approximation of lattice observables. Finally, we sketch an outline of this Galerkin framework in the context of gauge field theories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Real-space atomic dynamics in liquid gallium studied by inelastic neutron scattering

Gallium is a prototypical liquid metal and has gained renewed attention because of its unique properties. Characterizing and elucidating its atomic dynamics remains elusive despite numerous studies, primarily because of the challenges in quantifying atomic-scale dynamics in liquids. Recent developments in inelastic neutron scattering enable us to measure the Van Hove correlation function that describes the real-space motion of liquid atoms. Here, in this work, we use this approach to reveal the dynamics in gallium liquids and find the co-existence of two dynamical medium-range orders (MROs), which have a dynamical behavior distinct from that of the short-range order (SRO). We propose that these MROs are driven by global forces in the form of two density waves, as a direct consequence of the underlying competition between ionic core repulsion and valence electron cohesion. We suggest that the density wave approach is not only applicable to other metallic liquids exhibiting similar structural anomalies, but also offers a promising direction for elucidating the dynamics of complex liquids and glasses by linking electronic-state fluctuations to atomic dynamics.

Hua, Chengyun [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2024

Multiscale simulation of spatially correlated microstructure via a latent space representation

When deformation gradients act on the scale of the microstructure of a part due to geometry and loading, spatial correlations and finite-size effects in simulation cells cannot be neglected. We propose a multiscale method that accounts for these effects using a variational autoencoder to encode the structure–property map of the stochastic volume elements making up the statistical description of the part. In this paradigm the autoencoder can be used to directly encode the microstructure or, alternatively, its latent space can be sampled to provide likely realizations. Furthermore, we demonstrate the method on three examples using the common additively manufactured material AlSi10Mg in: (a) a comparison with direct numerical simulation of the part microstructure, (b) a push forward of microstructural uncertainty to performance quantities of interest, and (c) a simulation of functional gradation of a part with stochastic microstructure.

Elastoplasticity↗
DOE OSTI2024

Weak-Form Latent Space Dynamics Identification

This software showcases the enhanced capabilities of the Latent Space Dynamics Identification (LaSDI) algorithm through the application of the weak form, resulting in WLaSDI. WLaSDI first compresses the data, then projects it onto test functions, and subsequently learns the local latent space models. Notably, WLaSDI demonstrates significantly improved robustness to noise. Using weak-form equation learning techniques, WLaSDI achieves local latent space modeling. Compared to the standard sparse identification of nonlinear dynamics (SINDy) used in LaSDI, the variance reduction of the weak form ensures robust and precise latent space recovery, enabling fast, robust, and accurate simulations. We demonstrate the efficacy of WLaSDI against LaSDI using several common benchmark examples, including viscid and inviscid Burgers', radial advection, and heat conduction. For instance, in 1D inviscid Burgers' simulations with up to 100% Gaussian white noise, WLaSDI maintains relative errors consistently below 6%, whereas LaSDI errors can exceed 10,000%. Similarly, in radial advection simulations, WLaSDI keeps relative errors below 16%, compared to potential errors of up to 10,000% with LaSDI. Additionally, WLaSDI achieves significant speedups, such as a 140X speedup in 1D Burgers' simulations compared to the corresponding full order model.

Choi, Youngsoo↗
DOE OSTI2025

Accelerating high-order continuum kinetic plasma simulations using multiple GPUs

Kinetic plasma simulations solve the Vlasov-Poisson or Vlasov-Maxwell equations to evolve scalar-variable distribution functions in position-velocity phase space and vector-variable electromagnetic fields in configuration space. The immense computational cost of evolving high-dimensional variables, and their large number of degrees of freedom, often limits the utility of continuum kinetic simulations and presents a challenge when it comes to accurately simulating real-world physical phenomena. To address this challenge, we present techniques that accelerate and minimize the computational work required for a scalable Vlasov-Poisson solver. We show theoretical hardware compute and communication bounds for solving a fourth-order finite-volume Vlasov-Poisson system. These bounds are then used to inform and evaluate the design of performance portable algorithms for a multiple graphics processing unit (GPU) accelerated version of the Vlasov-Poisson solver VCK-CPU [1]. We demonstrate that the multi-GPU Vlasov solver implementation, VCK-GPU, simultaneously minimizes required inter-process data transfer while also being bounded by the machine network performance limits. This results in an overall strong scaling speedup per timestep of up to 40x in three-dimensional phase space (one position, two velocity coordinates) and 54x in four dimensional phase space (two position, two velocity coordinates) and a 341x increase in simulation throughput of the GPU accelerated code over the existing CPU code. The GPU code is also able to weak scale up to 256 compute nodes and 1024 GPUs. In conclusion, we demonstrate that the improved compute performance enables exploring configurations which were previously computationally infeasible, including resolving fine-scale distribution function filamentation and multi-species dynamics with realistic electron-proton mass ratios.

Continuum kinetics↗
DOE OSTI2026

Methanol mixing controlled combustion process enabled by methanol dehydration to dimethyl ether

This work demonstrates an initial proof-of-concept approach for operating a compression ignition off-road and marine relevant engine using neat methanol. The approach utilizes mixing controlled compression ignition (MCCI) of methanol that is enabled by a homogeneous charge compression ignition (HCCI) pre-burn of premixed dimethyl ether (DME). Although two fuels are used, this work explores and evaluates the opportunity and performance to generate the premixed fuel via. methanol catalytic dehydration over an alumina catalyst at engine relevant temperatures, pressures, and space velocities. Conversion purity and species output results from catalytic dehydration bench flow reactor studies were coupled with single cylinder experiments of the characterized output species for pre-burn HCCI performance. Subsequent initial methanol MCCI performance is also evaluated and compared relative to conventional diesel combustion. The detailed flow reactor results show that the catalytic dehydration conversion efficiency of methanol to DME is a function of system pressure, temperature, and space velocity. The engine results demonstrate that a 100% conversion of methanol to DME is not required for successful pre-burn HCCI, and the water formed during the dehydration process does not need to be removed to achieve the desired HCCI event from this pre-burn mixture. Subsequent methanol MCCI combustion results show that the level of methanol slip in the dehydration process affects the pre-burn HCCI phasing, low temperature heat release process, and magnitude of energy released, all of which can dictate the available window for direct injection of methanol for MCCI combustion.

Jatana, Gurneesh [ORNL] (ORCID:0000000288903225)↗
DOE OSTI2025

Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials

In contrast to their empirical counterparts, machine-learning interatomic potentials (MLIAPs) promise to deliver near-quantum accuracy over broad regions of configuration space. However, due to their generic functional forms and extreme flexibility, they can catastrophically fail to capture the properties of novel, out-of-sample configurations, making the quality of the training set a determining factor, especially when investigating materials under extreme conditions. We propose a novel automated dataset generation method based on the maximization of the information entropy of the feature distribution, aiming at an extremely broad coverage of the configuration space in a way that is agnostic to the properties of specific target materials. The ability of the dataset to capture unique material properties is demonstrated on a range of unary materials, including elements with the FCC (Al), BCC (W), HCP (Be, Re and Os), graphite (C), and trigonal (Sb, Te) ground states. MLIAPs trained to this dataset are shown to be accurate over a range of application-relevant metrics, as well as extremely robust over very broad swaths of configurations space, even without dataset fine-tuning or hyper-parameter optimization, making the approach extremely attractive to rapidly and autonomously develop general-purpose MLIAPs suitable for simulations in extreme conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Computing the Instantaneous Collision Probability between Satellites using Characteristic Function Inversion

The probability that two satellites overlap in space at a specified instant of time is called their instantaneous collision probability. Assuming Gaussian uncertainties and spherical satellites, this probability is the integral of a Gaussian distribution over a sphere. This paper shows how to compute the probability using an established numerical procedure called characteristic function inversion. The collision probability in the short-term encounter scenario is also evaluated with this approach, where the instant at which the probability is computed is the time of closest approach between the objects. Python and R code is provided to evaluate the probability in practice. Overall, the approach has been established for over fifty years, is implemented in existing software, does not rely on analytical approximations, and can be used to evaluate two and three dimensional collision probabilities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Statistical Uncertainty of Inhalation Dose Coefficients: Impact of Particle Deposition in ICRP 66 Human Respiratory Tract Model

Inhaled radioactive materials can pose a long-term health concern, as the material can be incorporated into the body’s metabolic pathways and remain in organs and tissues for extended durations. During the retention period, the radioactive material may localize in a source organ and irradiate adjacent target organs and tissues. Distribution of these materials changes over time, requiring biokinetic modeling to evaluate their movement through various tissues and organs. The evolving distribution depends on multiple inputs characterizing the inhaled material, such as particle size and size distribution, particle density, aspect ratio, specific radionuclide, the chemical form, and solubility. In addition, biological parameters such as breathing rate, breathing type (nasal or nasal/oral), respiratory system morphometry, tidal volume, functional residual capacity, and anatomical dead space all influence material transport. These aerosol properties and physiological characteristics of the respiratory tract jointly define a range of initial conditions that influence the time-dependent distribution of radioactive material. To evaluate both uncertainty in the initial conditions of inhalation exposure and the final output (committed effective dose) from biokinetic models, a Python-based software tool, Radiological Exposure Dose Calculator (REDCAL), was developed to propagate uncertainty within the human respiratory tract model. Focusing on deposition fraction uncertainty, the primary objective was to characterize the initial activity distribution across respiratory regions as a function of anticipated particle sizes and distributions. The impact of the deposition fraction uncertainty was propagated to committed effective dose coefficients for selected radionuclides in a companion publication. For each particle size, a lognormal distribution, characterized by its geometric mean as defined within ICRP Publication 66, serves as the basis for introducing uncertainty into the physical processes governing deposition in various lung regions. Finally, this study addresses the deposition process and examines how uncertainty in deposition mechanisms affects activity distribution in the airways, ultimately presenting the expected range and standard deviation of deposited activity as a function of particle size.

International Commission on Radiological Protectio↗
DOE OSTI2025

Data Reproducibility of Spin-Echo Small-Angle Neutron Scattering Instruments

Spin-echo small-angle neutron scattering (SESANS) is a unique method to measure structures of materials in real space with length scales from ∼ 30 μm to ∼20 µm. As shown in Figure 1, the accessible length scale of SESANS is given by its ability to encode the momentum transfer into the Larmor phase, namely Φ = $\vec{𝛿}$ ⋅ $\vec{𝑄}$, where $\vec{𝑄}$ is the momentum transfer and $\vec{𝛿}$ is the encoding vector of the setup and its projection along Q (δQ) is called spin-echo length (SEL). The spin echo length, which is synonymous with the spatial correlation distance probed, is defined as the following 𝛿𝑄 ∝ 𝜆 2 𝐵𝐿cot𝜃 where 𝜆 is neutron wavelength, B is magnetic field, L is length of the parallelogram magnetic field region, and 𝜃 is the angle between the inclined magnetic field boundary and the beam direction, as shown in Figure 1. The result of the SESANS experiment is a Hankel transformation of the SANS scattering function I(Q), which yields the correlation function of the sample in real space.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Estimating Electron Temperature and Density Using Van Allen Probe Data: Typical Behavior of Energetic Electrons in the Inner Magnetosphere

Abstract The Earth's inner magnetosphere contains multiple electron populations influenced by different factors. The cold electrons of the plasmasphere, warm plasma that contributes to the ring current, and the relativistic plasma of the radiation belts often seem to behave independently. Using omni‐directional flux and energy measurements from the HOPE and Magnetic Electron Ion Spectrometer instruments aboard the Van Allen Probes, we provide a detailed density and temperature description of the inner magnetosphere, offering a comprehensive statistical analysis of the entire Van Allen Probe era. While number density and temperature data at geosynchronous orbit are available, this study focuses on the warm plasma in the inner magnetosphere . Values of density and temperature are extracted by fitting energy and phase space density to obtain the distribution function. The fitted distributions are related to the zeroth and second moments to estimate the number density and temperature. Analysis has indicated that a two Maxwellian fit is sufficient over a wide range of and that there are two independent plasma populations. The more energetic population has a median number density of approximately and a temperature of around 130 keV, with a temperature peak observed between L * = 4 and L * = 4.5. This population is relatively uniform in magnetic local time (MLT). In contrast, the less energetic warm electron population has a median number density of about and a temperature of 7.4 keV. Strong statistical trends in density and temperature across both L * and MLT are presented, along with potential sources driving these variations.

58 GEOSCIENCES↗
DOE OSTI2025

Variations in the charge radii of indium isotopes between 𝑁=52 and 82

Measurements of the 5⁢𝑝 2 𝑃 3/2 → 9⁢𝑠 2 𝑆 1/2 and 5⁢𝑝 2 𝑃 1/2 → 8⁢𝑠 2 𝑆 1/2 transitions in the indium atom, combined with new atomic physics calculations, were used to extract the changes in mean-square nuclear charge radii, 𝛿⁡⟨𝑟 2 ⟩, of the indium (𝑍 = 49) isotopes 101–111 In , 113–123 In , and 125–131 In . With a proton hole in the closed nuclear shell of 𝑍 = 50, indium provides a detailed study of the effect of unpaired nucleons adjacent to the proton-shell closure, allowing investigation into the charge radii for isotopes between the two major neutron-shell closures at 𝑁 = 50 and 𝑁 = 82. A study of the variations in charge radii between neighboring isotopes with neutron number (the ‘odd-even staggering') is presented and provides further insight and challenges for the theoretical description of the size of proton-hole nuclei. Two nuclear theories, density functional theory and the valence-space in-medium similarity renormalization group method, were employed to interpret the data. The new information obtained in this work provides valuable insights into the successes and shortcomings of the theoretical approaches employed.

ab initio calculations↗
DOE OSTI2025

Exploring Micro-Environmental Conditions of Urban Agrivoltaics: Advancing Sustainable Green Spaces and Agriculture in Temperate Cities

Agrivoltaics, the integrated land use combining renewable energy production and agriculture, can potentially address key challenges faced by urban communities, including limited access to vacant land, fresh produce, and electricity. However, urban agrivoltaics has rarely been considered a viable solution, primarily due to a lack of experimental systems demonstrating the functionality of urban food production or green spaces alongside solar energy production. To evaluate the potential of solar photovoltaics to help mitigate heat stress on urban agriculture and green spaces in a temperate climate, we examined the microclimatic changes introduced by an urban agrivoltaics system (UrAV) when combined with vegetable crops and turfgrass, respectively. Accordingly, we installed an experimental setup instrumented with environmental sensors to compare full-sun conditions with those of an agrivoltaics system designed for urban environments. We found minimal differences for both land cover types between the control and agrivoltaics sites in air and soil temperature. However, during periods of intensified summer heat, temperatures beneath the panels cooled due to reductions in wind speed and relative humidity, which impeded the movement of hot, moist air and reduced reference evapotranspiration. Further, soil moisture in UrAV was highly spatially heterogeneous, influenced by the layout of the photovoltaic panels and their support structure, which redistributed rainfall and controlled where solar radiation could penetrate and drive evapotranspiration. Overall, our results suggest that PV-induced environmental changes in temperate climates are compatible with cultivating turfgrass or appropriate crops. These findings can help planners and designers integrate agrivoltaics into community gardens, farms, and green spaces in temperate cities.

14 SOLAR ENERGY↗
DOE OSTI2025

Deciphering the Scattering of Mechanically Driven Polymers Using Deep Learning

Here, we present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compressed into a three-dimensional latent space using a Variational Autoencoder (VAE), and two converter networks establish a bidirectional mapping between the polymer parameters (bending modulus, stretching force, and steady shear) and the scattering functions. The training data are generated using off-lattice Monte Carlo simulations to avoid the orientational bias inherent in lattice models, ensuring robust sampling of polymer conformations. The feasibility of this bidirectional mapping is demonstrated by the organized distribution of polymer parameters in the latent space. By integrating the converter networks with the VAE, we obtain a generator that produces scattering functions from given polymer parameters and an inferrer that directly extracts polymer parameters from scattering data. While the generator can be utilized in a traditional least-squares fitting procedure, the inferrer produces comparable results in a single pass and operates 3 orders of magnitude faster. This approach offers a scalable automated tool for polymer scattering analysis and provides a promising foundation for extending the method to other scattering models, experimental validation, and the study of time-dependent scattering data.

Ding, Lijie [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2024

Measuring Magnets Transfer Functions in the NSRL Transport Line

The beamline at the NASA Space Radiation Laboratory (NSRL) is equipped with a range of magnets, including dipole magnets, dipole corrector magnets, quadrupole magnets, sextupole magnets, and octupole magnets. The magnet transfer function defines the relationship between the power supply currents and the corresponding magnet strengths. This note presents the measurements of the transfer functions for the dipole correctors and quadrupole magnet in the NSRL beamline.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Local Thermal Conductivity Patterning in Rotating Lattice Crystals of Anisotropic Sb 2 S 3

The ability to control material heat transport properties over space and time can drive advanced functionalities in thermal management for electronics and system-on-chip, and enable thermal circuits. Despite the technological relevance, there are limited demonstrations of local thermal property control. Rotating lattice single (RLS) crystals—formed via laser-induced crystallization of an amorphous substrate—offer a novel avenue for local crystal engineering, unlocking opportunities for microscale property patterning. Here, thermal conductivity (𝜅) imaging is applied to RLS crystals of Sb2S3 to resolve microscale 𝜅 variations across patterned regions. Amorphous areas exhibit 𝜅 as low as 0.6 Wm −1 K −1 , while crystalline regions display periodic 𝜅 variations from 0.7 to over 2.5 Wm −1 K −1 . These variations correspond to changes in crystal orientation, revealing marked 𝜅 anisotropy. The crystal out-of-plane direction (c axis)—featuring van der Waals bonds—shows amorphous-like transport, whereas in-plane directions (a, b axes) exhibit 3.5x and 1.7x larger 𝜅, respectively. First-principles calculations, in excellent agreement with experiments, suggest that the in-plane anisotropy originates from expressed Sb lone pairs, which impart a corrugation along the b axis affecting bond stiffness and 𝜅. These findings demonstrate microscale control of thermal properties via laser-processed metastructures, with significant implications for next-generation thermal management.

14 SOLAR ENERGY↗
DOE OSTI2026

A robust framework for frictional fault contact in geological formations using a stabilized augmented Lagrangian approach

Numerical simulations are essential to evaluate the performance and safety of engineered subsurface systems such as geological carbon storage sites, enhanced geothermal fields, and oil and gas reservoirs. A key challenge lies in accurately modeling the frictional contact behavior along fault surfaces. This problem involves inequality constraints that arise from the physics of frictional slip, requiring specialized numerical methods to handle the resulting highly nonlinear and path-dependent behavior. Here, in this work, we address this challenge using an Augmented Lagrangian Method (ALM) implemented via the Uzawa algorithm. The formulation employs mixed finite element spaces, combining low-order piecewise linear displacements within the 3D domain cells with piecewise constant tractions defined on the fault surfaces. Furthermore, to ensure stability and satisfy the inf-sup condition, the discrete displacement space is enriched with face bubble functions on both sides of the contact interfaces. This approach offers several advantages over other stabilization techniques that rely on additional terms, and it integrates naturally in the Uzawa framework.

58 GEOSCIENCES↗