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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

DevOps Pragmatic Practices and Potential Perils in Scientific Software Development

The DevOps movement, which aims to accelerate the continuous delivery of high-quality software, has taken a leading role in reshaping the software industry. Likewise, there is growing interest in applying DevOps tools and practices in the domains of computational science and engineering (CSE) to meet the ever-growing demand for scalable simulation and analysis. Translating insights from industry to research computing, however, remains an ongoing challenge; DevOps for science and engineering demands adaptation and innovation in those tools and practices. There is a need to better understand the challenges faced by DevOps practitioners in CSE contexts in bridging this divide. To that end, we conducted a participatory action research study to collect and analyze the experiences of DevOps practitioners at a major US national laboratory through the use of storytelling techniques. We share lessons learned and present opportunities for future investigation into DevOps practice in the CSE domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Computationally Predicted High-Throughput Free-Energy Phase Diagrams for the Discovery of Solid-State Hydrogen Storage Reactions

The design of multinary solid-state material systems that undergo reversible phase changes via changes in temperature and pressure provides a potential means of safely storing hydrogen. However, fully mapping the stabilities of known or newly targeted compounds relative to competing phases at reaction conditions has previously required many stringent experiments or computationally demanding calculations of each compound’s change in Gibbs energy with respect to temperature, G(T). Here, we have extended the approach of constructing chemical potential phase diagrams based on ΔG f (T) to enable the analysis of phase stability at non-zero temperatures. We first performed density functional theory calculations to compute the formation enthalpies of binary, ternary, and quaternary compounds within several compositional spaces of current interest for solid-state hydrogen storage. Temperature effects on solid compound stability were then accounted for using our recently introduced machine learned descriptor for the temperature-dependent contribution G δ (T) to the Gibbs energy G(T). From these Gibbs energies, we evaluated each compound’s stability relative to competing compounds over a wide range of conditions and show using chemical potential and composition phase diagrams that the predicted stable phases and H2 release reactions are consistent with experimental observations. This demonstrates that our approach rapidly computes the thermochemistry of hydrogen release reactions for compounds at sufficiently high accuracy relative to experiment to provide a powerful framework for analyzing hydrogen storage materials. This framework based on G(T) enables the accelerated discovery of active materials for a variety of technologies that rely on solid-state reactions involving these materials.

08 HYDROGEN↗
DOE OSTI2025

Structure and Synthesizability of Iron–Sulfur Metal–Organic Frameworks

Sulfur-based metal–organic frameworks (MOFs) and coordination polymers (CPs) are an emerging class of hybrid materials that have received growing attention due to their magnetic, conductive, and catalytic properties with potential applications in electrocatalysis and energy storage. In this work, we report a high-throughput virtual screening protocol to predict the synthesizability of candidate metal–sulfur MOFs/CPs by computing the thermodynamically stable structures resulting from a particular combination of metal cluster, linker, cation, and synthetic conditions. Free energies are computed by using all-atom classical mechanical thermodynamic integration. Low-free-energy structures are refined using ab initio density functional theory, and pair distribution functions and powder X-ray diffraction patterns are calculated to complement and guide experimental structure determination. We validate the computational approach by retrospective predictions of the stable structure produced by experimental syntheses, and a subsequent screen predicts Fe 4 S 4 -BDT–TPP as a new thermodynamically stable one-dimensional (1D) CP comprising a redox-active Fe 4 S 4 cluster, a 1,4-benzenedithiolate (BDT) linker, and a tetraphenylphosphonium (TPP) countercation. Furthermore, this material is experimentally synthesized, and the 1D chain structure of the crystal is confirmed using microcrystal electron diffraction. The computational screening pipeline is generically transferable to neutral and ionic MOFs/CPs comprising arbitrary metal clusters, linkers, cations, and synthetic conditions, and we make it freely available as an open source tool to guide and accelerate the discovery and engineering of novel porous materials.

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

A review on machine learning-guided design of energy materials

Abstract The development and design of energy materials are essential for improving the efficiency, sustainability, and durability of energy systems to address climate change issues. However, optimizing and developing energy materials can be challenging due to large and complex search spaces. With the advancements in computational power and algorithms over the past decade, machine learning (ML) techniques are being widely applied in various industrial and research areas for different purposes. The energy material community has increasingly leveraged ML to accelerate property predictions and design processes. This article aims to provide a comprehensive review of research in different energy material fields that employ ML techniques. It begins with foundational concepts and a broad overview of ML applications in energy material research, followed by examples of successful ML applications in energy material design. We also discuss the current challenges of ML in energy material design and our perspectives. Our viewpoint is that ML will be an integral component of energy materials research, but data scarcity, lack of tailored ML algorithms, and challenges in experimentally realizing ML-predicted candidates are major barriers that still need to be overcome.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The 4D Camera: An 87 kHz Direct Electron Detector for Scanning/Transmission Electron Microscopy

We describe the development, operation, and application of the 4D Camera—a 576 by 576 pixel active pixel sensor for scanning/transmission electron microscopy which operates at 87,000 Hz. The detector generates data at ~480 Gbit/s which is captured by dedicated receiver computers with a parallelized software infrastructure that has been implemented to process the resulting 10–700 Gigabyte-sized raw datasets. The back illuminated detector provides the ability to detect single electron events at accelerating voltages from 30 to 300 kV. Through electron counting, the resulting sparse data sets are reduced in size by 10--300× compared to the raw data, and open-source sparsity-based processing algorithms offer rapid data analysis. The high frame rate allows for large and complex scanning diffraction experiments to be accomplished with typical scanning transmission electron microscopy scanning parameters.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

2019 Quantum Materials Young Investigators Workshop Report

The fourth edition of the workshop “Quantum Materials Young Investigators” was held June 6-7, 2019 at the Oak Ridge National Laboratory (ORNL). This workshop followed up on previous meetings that took place at ORNL starting in 2016. This important series of annual workshops, organized by Adam Aczel and Stuart Calder, ensures continuous engagement with the early career principal investigators (PIs) of the quantum materials neutron scattering community. Through a series of short scientific talks presented by the external PIs, members of the Neutron Scattering Division (NSD) learn about their scientific interests and needs. Presentations from ORNL staff members also help to inform the external PIs about the current state of ORNL’s quantum materials neutron scattering program. Topics covered include the current instrument suites, sample environment, data reduction, visualization, and analysis, and future neutron scattering and complementary capabilities. Feedback is solicited from the external PIs in all these key areas and communicated to NSD management in the form of a workshop report. This workshop series also helps to foster collaborations between NSD scientists and the research groups of the external PIs and plays an important role in attracting new neutron scattering users.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Advanced Modeling of Plasma-based Particle Accelerators

Plasma-based acceleration (PBA) driven by an intense laser (LWFA) or particle beam (PWFA) can produce ultra-high accelerating fields in excess of a GV/cm. PBA could substantially reduce the size and cost of future linear collider facilities if deployed successfully. PBA enables compact tabletop accelerators that can provide lower energy GeV-class beams in a laboratory setting. PBA enables high quality beam generation suitable for x-ray free electron lasers (XFEL) via controllable methods of self-injection. Challenges in modeling and optimization of multi-stage PBA motivate the need for exascale computing and state-of-the-art PIC codes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Anderson Acceleration for Distributed Training of Deep Learning Models

Anderson acceleration (AA) is an extrapolation technique that has recently gained interest in the deep learning (DL) community to speed-up the sequential training of DL models. However, when performed at large scale, the DL training is exposed to a higher risk of getting trapped into steep local minima of the training loss function, and standard AA does not provide sufficient acceleration to escape from these steep local minima. This results in poor generalizability and makes AA ineffective. To restore AA’s advantage to speed-up the training of DL models on large scale computing platforms, we combine AA with an adaptive moving average procedure that boosts the training to escape from steep local minima. By monitoring the relative standard deviation between consecutive iterations, we also introduce a criterion to automatically assess whether the moving average is needed. We applied the method to the following DL instantiations for image classification: (i) ResNet50 trained on the open-source CIFAR100 dataset and (ii) ResNet50 trained on the open-source ImageNet1k dataset. Numerical results obtained using up to 1,536 NVIDIA V100 GPUs on the OLCF supercomputer Summit showed the stabilizing effect of the moving average on AA for all the problems above.

Lupo Pasini, Massimiliano↗
DOE OSTI2025

Improving Thermal Management Strategies for Data Centers: A Physical Testbed Incorporating Small Modular Reactor and Microreactor Technology

This study aims to accelerate the demonstration of various thermal management systems for data centers using nuclear-generated heat to enhance energy and grid reliability. Utilizing mobile containerized and stationary test beds at INL's High Performance Computing (HPC) facility, this project integrates with various nuclear-related energy systems testing facilities. Key components include immersion cooling apparatus, absorption chillers, and adjustable thermal management simulators. Tasks involve acquiring necessary hardware, sensors, and cooling apparatus, engaging with data center industry stakeholders, and providing a testing platform for algorithms, models, tools, and software. The objective is to expedite the deployment of nuclear-powered data centers, thereby improving energy reliability and affordability.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2021

Inverse Biot–Savart Optimization for Superconducting Accelerator Magnets

Superconducting (SC) magnets for accelerator concepts are often synthesized by numerically optimizing magnetic field waveforms, a process that requires a subsequent solution of a constrained inverse problem to identify suitable SC magnet windings. When the desired field distribution is intuitive, the inverse process is facilitated by seeding preconceived coil distributions into design optimization methods for refinement. With more complex magnetic field distributions, an initial design may be unknown, and topology optimization tools are required to synthesize current distributions without a priori guidance from a subject matter expert. In this work, we develop a constrained inverse Biot-Savart topology optimization methodology that synthesizes optimal distributions of current density in racetrack-like SC coils. The problem structure is exploited through a computationally efficient quadratic programming formulation, and the method is applied to recently published magnetic field waveforms for a recirculating proton phase shifter, a proton therapy gantry, and dipole magnets with sharp field transitions. The method and results herein identify novel winding configurations that can help magnet designers bring accelerator concepts to fruition.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Power Converter Circuit Design Automation using Parallel Monte Carlo Tree Search

The tidal waves of modern electronic/electrical devices have led to increasing demands for ubiquitous application-specific power converters. A conventional manual design procedure of such power converters is computation- and labor-intensive, which involves selecting and connecting component devices, tuning component-wise parameters and control schemes, and iteratively evaluating and optimizing the design. To automate and speed up this design process, we propose an automatic framework that designs custom power converters from design specifications using Monte Carlo Tree Search. Specifically, the framework embraces the upper-confidence-bound-tree (UCT), a variant of Monte Carlo Tree Search, to automate topology space exploration with circuit design specification-encoded reward signals. Moreover, our UCT-based approach can exploit small offline data via the specially designed default policy and can run in parallel to accelerate topology space exploration. Further, it utilizes a hybrid circuit evaluation strategy to substantially reduce design evaluation costs. Empirically, we demonstrated that our framework could generate energy-efficient circuit topologies for various target voltage conversion ratios. Compared to existing automatic topology optimization strategies, the proposed method is much more computationally efficient --- the sequential version can generate topologies with the same quality while being up to 67% faster. Here, the parallelization schemes can further achieve high speedups compared to the sequential version.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Robust Containment Queries over Collections of Trimmed NURBS Surfaces via Generalized Winding Numbers

Here, we propose a containment query that is robust to the watertightness of regions bound by trimmed NURBS surfaces, as this property is difficult to guarantee for in-the-wild CAD models. Containment is determined through the generalized winding number (GWN), a mathematical construction that is indifferent to the arrangement of surfaces in the shape. Applying contemporary techniques for the 3D GWN to trimmed NURBS surfaces requires some form of geometric discretization, introducing computational inefficiency to the algorithm and even risking containment misclassifications near the surface. In contrast, our proposed method leverages properties of the 3D solid angle to solve the relevant surface integral using a boundary formulation with rapidly converging adaptive quadrature. Batches of queries are further accelerated by memoizing (i.e., caching and reusing) quadrature node positions and tangents as they are evaluated. We demonstrate that our GWN method is robust to complex trimming geometry in a CAD model, and is accurate up to arbitrary precision at arbitrary distances from the surface. The derived containment query is therefore robust to model non-watertightness while respecting all curved features of the input shape.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Enhancements to Overset Methods for Achieving Higher Accuracy and Solution Convergence: Preprint

Overset methods, when utilized within a multi-solver framework, provide a very flexible and powerful toolset for analyzing moving body problem. However, many overset framework suffer from several deficiencies such as (1) convergence degradation of the non-linear iterative schemes (2) difficulty in achieving higher-order accuracy and (3) high computational costs over long simulation times. In this work, we explore approaches that mitigate these deficiencies and provide a pathway for improved accuracy and fidelity of overset methods. A high-order, discontinuous Galerkin solver is integrated into an existing overset framework and applied to an unsteady ROBIN fuselage case. For convergence acceleration, an Overset-GMRES linear solver with a timestep controller is applied to a DLR-F6 aircraft steady simulation and an unsteady, notional rotor-fuselage. Finally the same rotor-fuselage case as well as the JVX rotor/wing case is simulated using a reduced-order aerodynamic model.

aerospace↗
DOE OSTI2020

An accelerated approach to inline non-LTE modeling

A goal across many physics and engineering disciplines is to use radiation-hydrodynamics codes to accurately simulate systems that are expected to deviate from local thermodynamic equilibrium (LTE), such as laser-driven hohlraums, stellar coronae, and supernova ejecta. The computational expense of traditional non-LTE (NLTE) opacity calculations can dominate the cost of simulating these systems and render NLTE physics untenable in most cases. In this paper, we present a new parallel implementation for NLTE opacity calculations that employs advanced load balancing, vectorization, and memory management to significantly reduce the computational costs of these calculations. Using the radiation-hydrodynamics code xRAGE , we demonstrate this new implementation on three test problems and present order-of-magnitude speed improvements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

We present a numerical modeling workflow based on machine learning (ML) which reproduces the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Simulations of the shock-driven Kelvin–Helmholtz instability in inclined gas curtains

In this paper, we present simulation results for the two-dimensional, shock-driven Kelvin–Helmholtz instability. Simulations are performed with a Mach 2.0 shock propagating through a finite-thickness curtain of gas inclined at an angle α0=30° with respect to the shock plane. After the passage of the shock, the gas curtain is accelerated along its axis. A perturbation develops due to shock reflection near the lower wall, and a Kelvin–Helmholtz instability forms near the vertical center of the curtain. This is the first known numerical reproduction of these phenomena that have previously been observed in experiments with an inclined cylindrical gas column. The effects of varying Mach number and column width were explored in detail to complement experimental data. Additionally, the dependence of the Kelvin–Helmholtz wavelength on Mach number closely matches the relationship observed in experiments. This supports the notion that the observed instability is effectively two-dimensional and inviscid (like classical Kelvin–Helmholtz). The growth rate of the perturbations in the gas curtain was also found to be similar for different Mach numbers. The perturbation at the curtain foot, previously unreported in experiments, was found to have a similar relationship to Mach number as the Kelvin–Helmholtz instability. Both perturbation wavelengths are found to be proportional to layer width. Simulations were performed with the fast interfaces and transport in the atmosphere, an exascale ready, graphics processing unit-accelerated compressible flow solver developed at the University of New Mexico.

42 ENGINEERING↗
DOE OSTI2021

Sandia / IBM Discussion on Machine Learning for Materials Applications [Slides]

This report includes a compilation of several slide presentations: 1) Interatomic Potentials for Materials Science and Beyond–Advances in Machine Learned Spectral Neighborhood Analysis Potentials (Wood); 2) Agile Materials Science and Advanced Manufacturing through AI/ML (de Oca Zapiain); 3) Machine Learning for DFT Calculations (Rajamanickam); 4) Structure-preserving ML discovery of a quantum-to-continuum codesign stack (Trask); and 5) IBM Overview of Accelerated Discovery Technology (Pitera)

36 MATERIALS SCIENCE↗