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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Efficient Computation of Doppler-Broadened Elastic Scattering Kernel Moments Using Ladder-Operator Formulation

Anefficient routine for computing Legendre moments of the Doppler-broadened elastic scattering kernel, including resonance scattering effects, has been implemented in the ISOXML module of Griffin. Isotropic scattering in the center-of-mass system and the ideal gas model for target motion are assumed. A ladder-operator formulation is introduced to compute all Legendre moments from order 0 to N simultaneously, enabling near-linear scaling of computational cost with respect to the maximum Legendre order. A physics-based strategy for constructing outgoing energy grids has also been developed, in which a tailored base grid is combined with adaptive refinement to maintain accuracy while limiting the number of outgoing energy points. For energies between resonances, a constant cross-section model is employed to further reduce computational cost. In addition, a quantitative criterion is derived to determine isotope-wise cut-off incident energies based on a prescribed up-scattering probability coverage. For 238U, up to incident energies of approximately 75, 230, and 661 eV at 294, 900, and 2500 K (corresponding to a 2% up-scattering probability threshold), computation of P0 kernels requires 1–8 s and computation of P0–P5 kernels requires 0.4–4 min using a single thread, while maintaining 1–3% relative error in up-scattering probability. These results demonstrate that the proposed formulation enables accurate and computationally practical Doppler-broadened kernel generation for online multigroup cross-section production in Griffin.

Doppler-broadening↗
DOE OSTI2023

Molecular weaving of chicken-wire covalent organic frameworks

Molecular weaving is the interlacing of covalently linked threads to make extended structures. Although weaving based on 3D networks has been reported, the 2D forms remain largely unexplored. Reticular chemistry uses mutually embracing tetrahedral metal complexes as crossing points, which, when linked, typically lead to 3D woven structures. Realizing 2D weaving patterns requires crossing points with an overall planar geometry. We show that polynuclear helicates composed of multiple metal-complex units, and therefore multiple turns, are well suited in this regard. By reticulating helicate units, we successfully obtained 2D weaving structures based on the familiar chicken-wire pattern.

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

Anion-derived contact ion pairing as a unifying principle for electrolyte design

Enabling new electrochemical technologies requires systems that can operate under ever-more demanding conditions, and progress in energy storage applications reveals tantalizing opportunities to reimagine electrolyte design for performance at extreme potentials. Here, a common thread among these innovations is the formation of significant populations of contact ion pairs (CIPs) in the electrolyte, regardless of the specific cation chemistry or solvent system. The examples summarized in this review suggest that a set of general electrolyte design rules likely exists, where the purposeful selection of anion chemistry can yield CIP structures with tunable control over reaction thermodynamics, kinetics, and interphase chemistry. Identifying the relevant descriptors for high-performance, anion-derived CIP structures can be achieved utilizing a combined experimental and computational approach, aided by machine learning and artificial intelligence, to more rapidly survey the vast combinatorial space available and to enable a new generation of electrolytes for decarbonized electrochemical processes at scale.

electrochemistry↗
DOE OSTI2021

A compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems

This work aims to advance computational methods for projection-based reduced-order models (ROMs) of linear time-invariant (LTI) dynamical systems. For such systems, current practice relies on ROM formulations expressing the state as a rank-1 tensor (i.e., a vector), leading to computational kernels that are memory bandwidth bound and, therefore, ill-suited for scalable performance on modern architectures. This weakness can be particularly limiting when tackling many-query studies, where one needs to run a large number of simulations. This work introduces a reformulation, called rank-2 Galerkin, of the Galerkin ROM for LTI dynamical systems which converts the nature of the ROM problem from memory bandwidth to compute bound. We present the details of the formulation and its implementation, and demonstrate its utility through numerical experiments using, as a test case, the simulation of elastic seismic shear waves in an axisymmetric domain. We quantify and analyze performance and scaling results for varying numbers of threads and problem sizes. In conclusion, we present an end-to-end demonstration of using the rank-2 Galerkin ROM for a Monte Carlo sampling study. We show that the rank-2 Galerkin ROM is one order of magnitude more efficient than the rank-1 Galerkin ROM (the current practice) and about 970 times more efficient than the full-order model, while maintaining accuracy in both the mean and statistics of the field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Effect of topology changes on the breakup of a periodic liquid jet

Here, the breakup of a periodic jet is examined computationally, using a front-tracking/finite-volume method, where the interface is represented by connected marker points moving with the fluid, while the governing equations are solved on a fixed grid. Tracking the interface allows control of whether topology changes take place or not. The Reynolds and Capillary numbers are kept relatively low ($Re = 150$ and $Ca = 2$) so most of the flow is well resolved. The effect of topology changes is examined by following the jet until it has mostly disintegrated, for different “coalescence criterion,” based on the thickness of thin films and threads. The evolution of both two-dimensional and fully three-dimensional flows is examined. It is found that although there is a significant difference between the evolution when no breakup takes place and when it does, once breakup takes place the evolution is relatively insensitive to exactly how it is triggered for a range of coalescence criterion, and any differences are mostly confined to the smallest scales.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

GPU-accelerated DNS of compressible turbulent flows

Here, this paper explores strategies to transform an existing CPU-based high-performance computational fluid dynamics solver, HyPar, for compressible flow simulations on emerging exascale heterogeneous (CPU+GPU) computing platforms. The scientific motivation for developing a GPU-enhanced version of HyPar is to simulate canonical turbulent flows at the highest resolution possible on such platforms. We show that optimizing memory operations and thread blocks results in 200x speedup of computationally intensive kernels compared with a CPU core. Using multiple GPUs and CUDA-aware MPI communication, we demonstrate both strong and weak scaling of our GPU-based HyPar implementation on the NVIDIA Volta V100 GPUs. We simulate the decay of homogeneous isotropic turbulence in a triply periodic box on grids with up to 1024 3 points (5.3 billion degrees of freedom) and on up to 1,024 GPUs. We compare the wall times for CPU-only and CPU+GPU simulations. The results presented in the paper are obtained on the Summit and Lassen supercomputers at Oak Ridge and Lawrence Livermore National Laboratories, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Dynamic load balancing with enhanced shared-memory parallelism for particle-in-cell codes

Furthering our understanding of many of today’s interesting problems in plasma physics – including plasma based acceleration and magnetic reconnection with pair production due to quantum electrodynamic effects – requires large-scale kinetic simulations using particle-in-cell (PIC) codes. However, these simulations are extremely demanding, requiring that contemporary PIC codes be designed to efficiently use a new fleet of exascale computing architectures. To this end, the key issue of parallel load balance across computational nodes must be addressed. We discuss the implementation of dynamic load balancing by dividing the simulation space into many small, self-contained regions or ‘‘tiles,’’ along with shared-memory (e.g., OpenMP) parallelism both over many tiles and within single tiles. The load balancing algorithm can be used with three different topologies, including two space-filling curves. Here, we tested this implementation in the code Osiris and show low overhead and improved scalability with OpenMP thread number on simulations with both uniform load and severe load imbalance. Compared to other load-balancing techniques, our algorithm gives order-of-magnitude improvement in parallel scalability for simulations with severe load imbalance issues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Implementing a neural network interatomic model with performance portability for emerging exascale architectures

The two main thrusts of computational science are increasingly accurate predictions and faster calculations; to this end, the zeitgeist in molecular dynamics (MD) simulations is pursuing machine learned and data driven interatomic models, e.g. neural network potentials, and novel hardware architectures, e.g. GPUs. Current implementations of neural network potentials are orders of magnitude slower than traditional interatomic models and while looming exascale computing offers the ability to run large, accurate simulations with these models, achieving portable performance for MD with new and varied exascale hardware requires rethinking traditional algorithms, using novel data structures, and library solutions. We re-implement a neural network interatomic model in CabanaMD, an MD proxy application, built on libraries developed for performance portability. Our implementation shows significantly improved thread scaling in this complex kernel as compared to a current LAMMPS implementation, across both strong and weak scaling. Our single-source solution enables simulations up to 20 million atoms on a single CPU node and 4 million atoms with improved performance on a single GPU. Furthermore, we also explore parallelism and data layout choices (using flexible data structures called AoSoAs) and their effect on performance, seeing up to ~50% and ~5% improvements in performance on a GPU by choosing the right level of parallelism and data layout respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

QTRAJ 1.0: A Lindblad equation solver for heavy-quarkonium dynamics

Here we introduce an open-source package called QTraj that solves the Lindblad equation for heavy-quarkonium dynamics using the quantum trajectories algorithm. The package allows users to simulate the suppression of heavy-quarkonium states using externally-supplied input from 3+1D hydrodynamics simulations. The code uses a split-step pseudo-spectral method for updating the wave-function between jumps, which is implemented using the open-source multi-threaded FFTW3 package. This allows one to have manifestly unitary evolution when using real-valued potentials. In this paper, we provide detailed documentation of QTraj 1.0, installation instructions, and present various tests and benchmarks of the code.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Code modernization strategies for short-range non-bonded molecular dynamics simulations

Modern HPC systems are increasingly relying on greater core counts and wider vector registers. Thus, applications need to be adapted to fully utilize these hardware capabilities. One class of applications that can benefit from this increase in parallelism are molecular dynamics simulations. In this paper, we describe our efforts at modernizing the ESPResSo++ simulation package for molecular dynamics by restructuring its particle data layout for efficient memory accesses and applying vectorization techniques to benefit the calculation of short-range non-bonded forces, which results in an overall three times speedup and serves as a baseline for further optimizations. We also implement fine-grained parallelism for multi-core CPUs through HPX, a C++ runtime system which uses lightweight threads and an asynchronous many-task approach to maximize concurrency. Our goal is to evaluate the performance of an HPX-based approach compared to the bulk-synchronous MPI-based implementation. This requires the introduction of an additional layer to the domain decomposition scheme that defines the task granularity. On spatially inhomogeneous systems, which impose a corresponding load-imbalance in traditional MPI-based approaches, we demonstrate that by choosing an optimal task size, the efficient work-stealing mechanisms of HPX can overcome the overhead of communication resulting in an overall 1.4 times speedup compared to the baseline MPI version.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Computing material volume fractions on a superimposed mesh as applied to Monte Carlo particle transport simulations

Here, we present a newly implemented ray tracing algorithm in OpenMC for efficiently computing material volume fractions on superimposed meshes in complex geometries. By firing rays along each coordinate direction through the geometry, the approach accumulates track-length data in each mesh element, thereby determining the fractional composition of each material. Scaling studies on three different models—a random tetrahedra configuration, the Frascati Neutron Generator ITER dose rate benchmark, and a stellarator design—show excellent parallel performance, with nearly linear speedup on modern multi-threaded and distributed-memory systems. An analysis of the residual error relative to high-resolution reference solutions demonstrated that under optimal conditions it decreases as 1/R, where R is the number of rays fired, making it straightforward to achieve user-prescribed accuracy. This new functionality enables practical, mesh-based approaches for detailed nuclear analyses in production Monte Carlo workflows without resorting to expensive, fully conformal or unstructured meshing.

Monte Carlo↗
DOE OSTI2022

Viscoelastic necking dynamics between attractive microgels

Hypothesis: Microgels can deform and interpenetrate and display colloid/polymer duality. The effective interaction of microgels in the collapsed state is governed by the interplay of polymer–solvent interfacial tension and bulk elasticity. A connecting neck is shown to mediate microgel interaction, but its temporal evolution has not been addressed. Here we hypothesize that the necking dynamics of attractive microgels exhibits liquid-like or solid-like behavior over different time and length scales. Experiments: We simulate the merging and pinching of attractive microgels with different crosslinking densities in explicit solvent using dissipative particle dynamics. The temporal coalescence dynamics of microgels is investigated and compared with simple liquid and polymeric droplets. We model the neck growth on long time scales using Maxwell model of polymer relaxation and compare the theoretical prediction with simulation data. The mechanical strength of the neck is characterized systematically via simulated pinch-off of microgels by steered molecular dynamics. Findings: We evidence a crossover in the coalescence dynamics reflecting the viscoelastic signature of microgels. In contrast to the common knowledge that viscoelastic materials respond elastically on short time scales, the early expansion of the microgel neck exhibits a linear behavior, similar to the viscous coalescence of liquid droplets. However, the late regime with arrested dynamics resembles sintering of solid particles. Through an analytical model relating microgel dynamics to neck growth, we show that the long-term behavior is governed by stress relaxation of the polymers in the neck region and predict an exponential decay in the rate of growth, which agrees favorably with the simulation. Different from coalescence, the thread thinning in microgel breakup primarily highlights its polymeric characteristics.

42 ENGINEERING↗
DOE OSTI2019

Electron channeling contrast imaging investigation of stacking fault pyramids in GaP on Si nucleation layers

Thin gallium phosphide layers were deposited on (0 0 1) Silicon surfaces via organometallic vapor phase epitaxy and characterized by electron channeling contrast imaging (ECCI). Stacking fault pyramids at a density of up to 6 x 10 7 cm -2 were identified in the GaP nucleation layer by varying the diffraction conditions. We show that these defects originate at the GaP/Si interface and propagate on all four {1 1 1} planes. We observed that such stacking fault pyramids interact with the gliding of misfit dislocations during lattice-mismatched growth and enhance the threading dislocation density. The initial pulsing of TEGa and TBP during the nucleation of GaP on Silicon has been found to strongly influence the formation of those pyramidal defects. Changing the number of pulse cycles allowed us to lower their density by two orders of magnitude from 6 x 10 7 cm -2 to 4 x 10 5 cm -2 .

36 MATERIALS SCIENCE↗
DOE OSTI2022

Using electron channeling contrast imaging to inform and improve the growth of high-efficiency GaAs solar cells on nanopatterned GaAs substrates

Patterned substrates provide opportunities for reducing the cost of high-efficiency III-V devices by incorporating mechanically weak layers beneficial for substrate reuse (e.g. by spalling). In this work, the functionality of electron channeling contrast imaging (ECCI) as a tool to efficiently understand and mitigate defect formation is exemplified by developing a process in which high-quality III-V material can be grown on nanopatterned GaAs substrates. Reactive ion etching used in the patterning process was found to damage the GaAs substrate surface, leading to the formation of stacking faults in the epitaxial material as observed by ECCI. Etching the patterned substrates in a 1 NH4OH: 1 H2O2: 50 DI H2O solution for 10 s prior to growth removed the substrate surface damage and stacking faults were no longer present. Growth of solar cell device structures initially produced samples with many macroscale flaws creating shunts in the devices, which complicated the assessment of material quality by device measurements. However, ECCI revealed that the epitaxial material surrounding macroscale flaws was free from any crystallographic defects such as stacking faults and threading dislocations. With this knowledge, we focused on refining the patterning process to eliminate the macroscale flaws. Solar cells were then grown on the improved nanopatterned substrates and exhibited device structures with defect densities less than 5 x 105 cm -2 and average conversion efficiency of 24.8%, nearly identical to devices grown on unpatterned epi-ready substrates (25.0%).

14 SOLAR ENERGY↗
DOE OSTI2026

Stacking fault analysis for the early-stages of PVT growth of 4H-SiC crystals

Here, four types of stacking fault formation mechanisms are identified and discussed for early stage PVT growth of 4H-SiC crystals: Type 1: Shockley / double Shockley stacking fault formation inside the facet; Type 2: Stacking fault formation via 2D nucleation; Type 3: Frank + Shockley (S) stacking fault formation due to deflection of threading mixed dislocation (TMD) by macrosteps; Type 4: “Carrot” defect formation related to overgrowth of the terrace formed by separation of 3c/4 and c/4 step risers after deflection of a TMD by vicinal steps. The results help further reduction of defect generation in 4H-SiC substrates.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Fast shared-memory streaming multilevel graph partitioning

In this report we show that a fast parallel graph partitioner can benefit many applications by reducing data transfers. The online methods for partitioning graphs have to be fast and they often rely on simple one-pass streaming algorithms, while the offline methods for partitioning graphs contain more involved algorithms and the most successful methods in this category belong to the multilevel approaches. In this work, we assess the feasibility of using streaming graph partitioning algorithms within the multilevel framework. Our end goal is to come up with a fast parallel offline multilevel partitioner that can produce competitive cutsize quality. We rely on a simple but fast and flexible streaming algorithm throughout the entire multilevel framework. This streaming algorithm serves multiple purposes in the partitioning process: a clustering algorithm in the coarsening, an effective algorithm for the initial partitioning, and a fast refinement algorithm in the uncoarsening. Its simple nature also lends itself easily for parallelization. The experiments on various graphs show that our approach is on the average up to 5.1x faster than the multi-threaded MeTiS, which comes at the expense of only 2x worse cutsize.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High-speed friction stir butt welding of 25.4 mm thick 7175-T79 aluminum alloy

This presents the first experimental demonstration of friction stir butt welding of 25.4 mm thick high-strength, precipitate-strengthened 7XXX aluminum alloy at a welding speed above 500 mm/min and penetration depth greater than 10 mm. Tool pin threads that terminate away from the shoulder region, helped mitigate nearby stress concentration during tool traversing, enabling a higher welding speed while avoiding pin fracture near shoulder. Two kinds of welds—one-sided with full penetration of the plate thickness and double-sided with partial penetration of the plate thickness—were conducted. Significant grain refinement (grain size 1.3 ± 0.8 µm) was evident at the junction of double-sided friction stir welding (FSW) compared to other regions of weld-nugget or base material. Cross-weld tensile testing with digital image correlation revealed high local strains at the junction of double-sided FSW before macro-yield. This strain concentration impeded the typical early necking on advancing/retreating sides of FSW joints, improving the yield strength of as-welded double-sided FSW by 20–24% over slow- to medium-speed one-sided FSW. In conclusion, the joint efficiency of as-welded, double-sided high-speed FSW was about 76% with respect to the base material’s ultimate tensile strength.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Benchmarking the Performance of Neuromorphic and Spiking Neural Network Simulators

Software simulators play a critical role in the development of new algorithms and system architectures in any field of engineering. Neuromorphic computing, which has shown potential in building brain-inspired energy-efficient hardware, suffers a slow-down in the development cycle due to a lack of flexible and easy-to-use simulators of either neuromorphic hardware itself or of spiking neural networks (SNNs), the type of neural network computation executed on most neuromorphic systems. While there are several openly available neuromorphic or SNN simulation packages developed by a variety of research groups, they have mostly targeted computational neuroscience simulations, and only a few have targeted small-scale machine learning tasks with SNNs. Evaluations or comparisons of these simulators have often targeted computational neuroscience-style workloads. In this work, we seek to evaluate the performance of several publicly available SNN simulators with respect to non-computational neuroscience workloads, in terms of speed, flexibility, and scalability. We evaluate the performance of the NEST, Brian2, Brian2GeNN, BindsNET and Nengo packages under a common front-end neuromorphic framework. Our evaluation tasks include a variety of different network architectures and workload types to mimic the computation common in different algorithms, including feed-forward network inference, genetic algorithms, and reservoir computing. We also study the scalability of each of these simulators when running on different computing hardware, from single core CPU workstations to multi-node supercomputers. Our results show that the BindsNET simulator has the best speed and scalability for most of the SNN workloads (sparse, dense, and layered SNN architectures) on a single core CPU. However, when comparing the simulators leveraging the GPU capabilities, Brian2GeNN outperforms the others for these workloads in terms of scalability. NEST performs the best for small sparse networks and is also the most flexible simulator in terms of reconfiguration capability NEST shows a speedup of at least 2x compared to the other packages when running evolutionary algorithms for SNNs. The multi-node and multi-thread capabilities of NEST show at least 2x speedup compared to the rest of the simulators (single core CPU or GPU based simulators) for large and sparse networks. We conclude our work by providing a set of recommendations on the suitability of employing these simulators for different tasks and scales of operations. We also present the characteristics for a future generic ideal SNN simulator for different neuromorphic computing workloads.

97 MATHEMATICS AND COMPUTING↗