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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Strain rate dependent nanostructure of hydrogels with reversible hydrophobic associations during uniaxial extension

An energy dissipation mechanism during deformation is required to impart toughness to hydrogels. Here we describe how in situ small angle X-ray scattering (SAXS) provides insight into possible energy dissipation mechanisms for a tough hydrogel based on an amphiphilic copolymer where nanoscale associations of the hydrophobic moieties act as effective crosslinks. The mechanical properties of the hydrogels are intimately coupled with the nanostructure that provides reversible crosslinks and evolves during deformation. As the extension rate increases, more mechanical energy is dissipated from rearrangements of the crosslinks. The scattering is consistent with hopping of hydrophobes between the nanoscale aggregates as the primary rearrangement mechanism. This rearrangement changes the network conformation that leads to non-affine deformation, where the change in the nanostructure dimension from SAXS is less than 15% of the total macroscopic strain. These nanostructure changes are rate dependent and correlated with the relaxation time of the hydrogel. At low strain rate (0.15% s -1 ), no significant change of the nanostructure was observed, whereas at higher strain rates (1.5% s -1 and 8.4% s -1 ) significant nanostructure anisotropy occurred during extension. These differences are attributed to the ability for the network chains to rearrange on the time scale of the deformation; when the characteristic time for extension is longer than the average segmental relaxation time, no significant change in nanostructure occurs on uniaxial extension. These results illustrate the importance of strain rate in the mechanical characterization and consideration of relaxation time in the design of tough hydrogels with reversible crosslinks.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Variational deep learning of equilibrium transition path ensembles

Here, we present a time-dependent variational method to learn the mechanisms of equilibrium reactive processes and efficiently evaluate their rates within a transition path ensemble. This approach builds off of the variational path sampling methodology by approximating the time-dependent commitment probability within a neural network ansatz. The reaction mechanisms inferred through this approach are elucidated by a novel decomposition of the rate in terms of the components of a stochastic path action conditioned on a transition. This decomposition affords an ability to resolve the typical contribution of each reactive mode and their couplings to the rare event. The associated rate evaluation is variational and systematically improvable through the development of a cumulant expansion. We demonstrate this method in both over- and under-damped stochastic equations of motion, in low-dimensional model systems, and in the isomerization of a solvated alanine dipeptide. In all examples, we find that we can obtain quantitatively accurate estimates of the rates of the reactive events with minimal trajectory statistics and gain unique insights into transitions through the analysis of their commitment probability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗
DOE OSTI2020

Ad Hoc File Systems for High-Performance Computing

Storage backends of parallel compute clusters are still based mostly on magnetic disks, while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory (NVRAM) are deployed within compute nodes. Including these new storage technologies into scientific workflows is unfortunately today a mostly manual task, and most scientists therefore do not take advantage of the faster storage media. One approach to systematically include nodelocal SSDs or NVRAMs into scientific workflows is to deploy ad hoc file systems over a set of compute nodes, which serve as temporary storage systems for single applications or longer-running campaigns. This paper presents results from the Dagstuhl Seminar 17202 “Challenges and Opportunities of User-Level File Systems for HPC” and discusses application scenarios as well as design strategies for ad hoc file systems using node-local storage media. The discussion includes open research questions, such as how to couple ad hoc file systems with the batch scheduling environment and how to schedule stage-in and stage-out processes of data between the storage backend and the ad hoc file systems. Also presented are strategies to build ad hoc file systems by using reusable components for networking and how to improve storage device compatibility. Various interfaces and semantics are presented, for example those used by the three ad hoc file systems BeeOND, GekkoFS, and BurstFS. Their presentation covers a range from file systems running in production to cutting-edge research focusing on reaching the performance limits of the underlying devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Renewable-battery hybrid power plants in congested electricity markets: Implications for plant configuration

Examining coupled renewable-battery power plants (“hybrids”) in congested areas provides insights into a future of increased wind and solar penetration. Our study focuses on two types of congested regions, Variable Renewable Energy (VRE)-rich Areas and Load Centers, and explores likely plant configuration choices for developers and transmission network planners. Here, this paper examines how hybrid value, comprising energy and capacity value, varies by plant configuration and congested region type considering factors such as storage duration, battery degradation, and ability to charge from the grid. We select plant locations from across the seven main U.S. independent system operators (ISOs). Hybrid value for each configuration is computed based on profit-maximizing plant operation given perfect foresight, according to observed wholesale power market real time prices from 2018 to 2021. In VRE-rich Areas, the median increase in energy value from extending storage duration from one to 4h is 29.4% for solar and 26.8% for wind, assuming low battery degradation costs and storage sized to 100% of the plant's nameplate generation capacity. Increasing storage duration beyond 4h does not substantially increase its value from energy markets, even in VRE-rich Areas. We find that solar hybrids reach a 90% capacity credit with 4h of storage, while wind hybrids require 8h of storage, based on the capacity factor of each hybrid during the top 100 net load hours.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Permutation invariant polynomial neural network based diabatic ansatz for the ( E + A ) × ( e + a ) Jahn–Teller and Pseudo-Jahn–Teller systems

In this work, the permutation invariant polynomial neural network (PIP-NN) approach is employed to construct a quasi-diabatic Hamiltonian for system with non-Abelian symmetries. It provides a flexible and compact NN-based diabatic ansatz from the related approach of Williams, Eisfeld, and co-workers. The example of $H^3_+$ is studied, which is an (E + A) × (e + a) Jahn–Teller and Pseudo-Jahn–Teller system. The PIP-NN diabatic ansatz is based on the symmetric polynomial expansion of Viel and Eisfeld, the coefficients of which are expressed with neural network functions that take permutation-invariant polynomials as input. This PIP-NN-based diabatic ansatz not only preserves the correct symmetry but also provides functional flexibility to accurately reproduce ab initio electronic structure data, thus resulting in excellent fits. Further, the adiabatic energies, energy gradients, and derivative couplings are well reproduced. A good description of the local topology of the conical intersection seam is also achieved. Therefore, this diabatic ansatz completes the PIP-NN based representation of DPEM with correct symmetries and will enable us to diabatize even more complicated systems with complex symmetries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

From broadband biphotons to frequency combs via spectral compression with time-varying cavities

Biphoton frequency combs are promising resources for quantum networking due in large part to their compatibility with the telecommunication infrastructure. Here, in this work, we propose a method to periodically compress broadband frequency-entangled photons into biphoton frequency combs by utilizing time-varying linear cavities. Our approach hinges on rapid modulation of the input cavity coupling, yielding high spectral purity in each output comb line similar to that achieved with narrowband filters, but without the associated loss in flux. We examine the dependence of spectral purity and compression on coupling strength, cavity loss, and switching speed, finding realistic regimes supporting purities in excess of 0.999 and peak enhancement factors of 100× and beyond.

Myilswamy, Karthik V. [Purdue Univ., West Lafayett↗
DOE OSTI2023

Model simulations of Plum Island Ecosystems LTER low marsh site using ELM-PFLOTRAN

Model simulations using the E3SM Land Model (ELM) coupled to the PFLOTRAN reactive transport model via the Alquimia interface. The simulations were conducted for a tidal salt marsh at the Plum Island Ecosystems LTER near Rowley, Massachusetts, USA. Model simulations were forced using site-specific tidal cycles and salinity, and the simulations used a biogeochemical reaction network including aerobic decomposition, sulfate reduction, iron reduction, and methanogenesis. Model outputs include simulated carbon stocks, carbon dioxide and methane fluxes, and porewater concentrations of key solutes related to sulfur, iron, and carbon cycling. The model simulations included a saline simulation (with tidal sulfate inputs), a fresh simulation (with low salinity and low sulfate inputs), and a saline simulation with lower vegetation productivity to represent the effect of salinity on vegetation. These simulations were conducted to demonstrate that a new model framework incorporating subsurface redox and biogeochemical interactions into a land surface model could reproduce measured surface greenhouse gas fluxes and biogeochemical dynamics in tidal marsh ecosystems, and to test whether including redox interactions in a land surface model would allow the model to resolve contrasts in biogeochemical cycling and greenhouse gas production between saline and freshwater wetlands.The data package includes gzipped tar archives (which can be expanded using standard tar and gzip utilities) of model outputs from three model configurations: saline subsurface and reduced vegetation productivity related to salinity; saline subsurface with vegetation productivity not reduced; and freshwater. Also included are code for the modified E3SM model, Alquimia interface, and PFLOTRAN reactive transport simulator in gzipped tar format; plain text parameter and configuration files; python code files for visualizing model output and defining model configurations; and model output, tide and salinity forcing, and configuration files in netCDF format. See the README.md file in the data package for a detailed description of all files contained in the package. All files are in netCDF (.nc), gzipped tar archive (.tar.gz or .tgz), or text (all other files).Updated: May 13, 2024. Model output, E3SM code, PFLOTRAN input files, and python codes for visualizing results were updated to reflect changes made for the manuscript revision. The updated archive reflects the code and model output from the final accepted manuscript. Changes included updated reaction parameters reflecting improved parameterization and additional comparisons with field measurements. E3SM code changes included better support for multiple grid cells and improved flow and transport parameterization.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Integrated Cyber/Physical Grid Resiliency Modeling

This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Simulator for Hydrologic Unstructured Domains (SHUD v1.0): numerical modeling of watershed hydrology with the finite volume method

Abstract. Hydrologic modeling is an essential strategy for understanding and predicting natural flows, particularly where observations are lacking in either space or time or where complex terrain leads to a disconnect in the characteristic time and space scales of overland and groundwater flow. However, significant difficulties remain for the development of efficient and extensible modeling systems that operate robustly across complex regions. This paper introduces the Simulator for Hydrologic Unstructured Domains (SHUD), an integrated, multiprocess, multiscale, flexible-time-step model, in which hydrologic processes are fully coupled using the finite volume method. SHUD integrates overland flow, snow accumulation/melt, evapotranspiration, subsurface flow, groundwater flow, and river routing, thus allowing physical processes in general watersheds to be realistically captured. SHUD incorporates one-dimensional unsaturated flow, two-dimensional groundwater flow, and a fully connected river channel network with hillslopes supporting overland flow and baseflow. The paper introduces the design of SHUD, from the conceptual and mathematical description of hydrologic processes in a watershed to the model's computational structures. To demonstrate and validate the model performance, we employ three hydrologic experiments: the V-catchment experiment, Vauclin's experiment, and a model study of the Cache Creek Watershed in northern California. Ongoing applications of the SHUD model include hydrologic analyses of hillslope to regional scales (1 m2 to 106 km2), water resource and stormwater management, and interdisciplinary research for questions in limnology, agriculture, geochemistry, geomorphology, water quality, ecology, climate and land-use change. The strength of SHUD is its flexibility as a scientific and resource evaluation tool where modeling and simulation are required.

58 GEOSCIENCES↗
DOE OSTI2023

CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics

Abstract. Microbial activity and chemical reactions in porous media depend on the local conditions at the pore scale and can involve complex feedback with fluid flow and mass transport. We present a modeling framework that quantitatively accounts for the interactions between the bio(geo)chemical and physical processes and that can integrate genome-scale microbial metabolic information into a dynamically changing, spatially explicit representation of environmental conditions. The model couples a lattice Boltzmann implementation of Navier–Stokes (flow) and advection–diffusion-reaction (mass conservation) equations. Reaction formulations can include both kinetic rate expressions and flux balance analysis, thereby integrating reactive transport modeling and systems biology. We also show that the use of surrogate models such as neural network representations of in silico cell models can speed up computations significantly, facilitating applications to complex environmental systems. Parallelization enables simulations that resolve heterogeneity at multiple scales, and a cellular automaton module provides additional capabilities to simulate biofilm dynamics. The code thus constitutes a platform suitable for a range of environmental, engineering and – potentially – medical applications, in particular ones that involve the simulation of microbial dynamics.

58 GEOSCIENCES↗
DOE OSTI2025

Essential barrier height and a probabilistic approach in characterizing potential landscape

In this work we propose a probabilistic approach to investigate the shape of landscapes of multi-dimensional potential functions. Under a suitable coupling scheme, two copies of the overdamped Langevin dynamics associated with the potential function are coupled, and the coupling times are collected. Assuming a set of intuitive yet technically challenging conditions on the coupling scheme, it is shown that the tail distributions of the coupling times exhibit qualitatively different dependencies on the noise magnitude for single-well versus multi-well potential functions. More specifically, for convex single-well potentials, the negative tail exponent of the coupling time distribution is uniformly bounded away from zero by the convexity parameter and is independent of the noise magnitude. In contrast, for multi-well potentials, the negative tail exponent decreases exponentially as the noise vanishes, with the decay rate governed by the essential barrier height, a quantity introduced in this paper to characterize the non-convex nature of the potential function. Numerical investigations are conducted for a variety of examples, including the Rosenbrock function, interacting particle systems, and loss functions arising in artificial neural networks. These examples not only illustrate the theoretical results in various contexts but also provide crucial numerical validation of the conjectured assumptions, which are essential to the theoretical analysis yet lie beyond the reach of standard technical tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Terrestrial Gamma‐Ray Flash Following Intense Negative Return Stroke Observed With BIMAP‐3D

We report ground-based observations of a terrestrial gamma-ray flash (TGF) associated with a strong negative cloud-to-ground lightning strike (-CG) which occurred near Los Alamos National Laboratory (LANL) in New Mexico, USA. Gamma photons were detected by the Terrestrial High-energy Observations of Radiation (THOR) instrument, developed at the University of California, Santa Cruz, which has been hosted at LANL since 2022. Simultaneous measurements were also made with the 3-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) system, which includes 3D lightning mapping, two fast antennas, and an additional plastic scintillator. Additional field change waveforms were obtained from the Earth Networks Total Lightning Network (ENTLN). The lightning initiated about 1 km above the ground, leading to a powerful -CG with an ENTLN peak current of −237 kA and bipolar field change matching the recently described “energetic compact stroke” (ECS) shape. The TGF was then observed ∼35 µs after the start of the return stroke. We also observe an electric field pulse likely produced by the TGF either in isolation or by coupling to the lightning channel. Based on modeling of the radiated electric field and photon propagation through the atmosphere we infer that the TGF source was on the order of 1⁢0 17 photons. This TGF and associated lightning are extremely similar to some recently reported ECS TGFs in coastal Japan in winter, but we report the first observation of this phenomenon outside of Japan in a different climate, terrain, and season.

58 GEOSCIENCES↗
DOE OSTI2026

Machine-learning force-field models for dynamical simulations of metallic magnets

We review recent advances in machine-learning (ML) force-field methods for Landau–Lifshitz–Gilbert simulations of itinerant electron magnets, focusing on their scalability and transferability. Built on the principle of locality, a deep neural-network model is developed to efficiently and accurately predict electron-mediated forces governing spin dynamics. Symmetry-aware descriptors constructed through a group-theoretical approach ensure rigorous incorporation of both lattice and spin-rotation symmetries. The framework is demonstrated using the prototypical s-d exchange model widely employed in spintronics. ML-enabled large-scale simulations reveal novel nonequilibrium phenomena, including anomalous coarsening of tetrahedral spin order on the triangular lattice and the freezing of phase-separation dynamics in lightly hole-doped, strong-coupling square-lattice systems. These results establish ML force-field frameworks as scalable, accurate, and versatile tools for modeling nonequilibrium spin dynamics in itinerant magnets.

Artificial neural networks↗
DOE OSTI2021

Quantum magnetism on small-world networks

While classical spin systems in random networks have been intensively studied, much less is known about quantum magnets in random graphs. In this work, we investigate interacting quantum spins on small-world networks, building on mean-field theory and extensive quantum Monte Carlo simulations. Starting from one-dimensional (1D) rings, we consider two situations: All-to-all interacting and long-range interactions randomly added. The effective infinite dimension of the lattice leads to a magnetic ordering at finite temperature Tc with mean-field criticality. Nevertheless, in contrast to the classical case, we find two distinct power-law behaviors for Tc versus the average strength of the extra couplings. This is controlled by a competition between a characteristic length scale of the random graph and the thermal correlation length of the underlying 1D system, thus challenging mean-field theories. We also investigate the fate of a gapped 1D spin chain against the small-world effect.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

AMAROK: A Radio Frequency Development Platform for High-Power, Full-Scale Positive Ion Sources for DIII-D Neutral Beam Injectors

Next-generation neutral beam injection (NBI) systems demand RF ion sources capable of efficiently coupling >120 kW at 2–4-MHz frequencies, yet existing designs face voltage standoff and impedance-matching challenges. To address this, the advanced multicoil antenna for RF operations at kilowatts (AMAROK) was developed as a high-power RF inductively coupled plasma (ICP) source delivering up to 200 kW in the 2–4-MHz range via four phase-controlled generators for flexible power sharing. Two antenna designs—a single-strap multiturn (MT) and a multistrap single-turn (ST) design—were evaluated to optimize resonance, impedance matching, and power coupling across plasma loads. Here, a semi-analytical self-resonant frequency (SRF) model, validated experimentally, predicts resonance trends for arbitrary turn counts and tubing diameters, enabling rapid antenna optimization. Strap-to-strap mutual inductance in the ST configuration showed strong spatial dependence, guiding generator operation and total load inductance. These insights informed a custom π -topology matching network, achieving stable impedance matching over a wide range of plasma-driven loads. Collectively, these results position AMAROK as a versatile testbed for advancing high-power RF source technology in fusion NBI applications.

DIII-D↗
DOE OSTI2025

The structure and migration of twin boundaries in tetragonal β -Sn: An application of machine learning based interatomic potentials

Although atomistic simulations have contributed significantly to our understanding of twin boundary structure and migration in metals and alloys with hexagonal close packed (HCP) crystal structures, few direct atomistic studies of twinning have been conducted for other types of low symmetry materials, in large part due to a lack of reliable interatomic potentials. In this work, we examine twin boundary structure and migration in a tetragonal material, β-Sn, comparing high resolution Transmission Electron Microscopy (TEM) images of deformation twins in β-Sn to the results of direct atomistic simulations using multiple interatomic potentials. ML-based potentials developed in this work are found to give results consistent with our experimental data, revealing faceted twin boundary structures formed by the nucleation and motion of twinning disconnections. We use bicrystallographic methods in combination with atomistic simulations to analyze the structure, energy and shear coupled migration of observed twin facets in β-Sn. In analogy to Prismatic-Basal (PB/BP) interfaces in HCP metals, we discover low energy asymmetric Prismatic-A-plane (PA/AP) interfaces important to twin growth in β-Sn. Finally, a Moment Tensor Potential (MTP) and Rapid Artificial Neural Network (RANN) interatomic potential suitable for studying twinning and phase transformations in Sn are made publicly available as part of this work.

36 MATERIALS SCIENCE↗