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At least 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Parallel Implicit Hydrodynamics for High Explosive Burn Calculations

High explosives in hostile environments will require calculational capabilities that model processes, which evolve on timescales from minutes to nanoseconds. Eventually the HE will begin to move metal. To handle this temporal evolution an implicit hydrodynamics coupled to the chemical release of the HE energy is required. In addition, the use of chemical kinetics to model the transition from, the initially, slow heating of a confined high explosive through to deflagration and on to detonation requires many computational zones to model high explosive engineering systems. This requirement means that a fully parallel implicit hydrodynamics is essential. In this paper we present the calculation of a nonlinear matrix equation for the advanced particle pressure that has been made parallel and implemented in our AMR code, BABBO. This new parallel implicit hydrodynamics has been applied to a cookoff problem, as well as, one and two dimensional shock problems. Results are presented and discussed.

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

A Parametric, Data-Driven, Non-Intrusive Reduced-Order Model Framework for Crystal Plasticity Simulations of Voids

The influence of the internal structure at micrometer length scales on the deformation of polycrystalline materials can be effectively captured using crystal plasticity finite element methods (CPFEM). However, the complexity and nonlinearity of the deformation equations CPFEM solves demand significant computational power and resources to achieve accurate predictions, limiting its broader application. To address this challenge, we have identified a reduced-order representation of the complex data in order to establish a computationally efficient reduced-order models (ROM) and drastically reduce the computational expense of CPFEM. Specifically, in this work, we developed a parametric, data-driven, and non-intrusive ROM framework for CPFEM using proper orthogonal decomposition (POD) and sparse variational Gaussian process (SVGP) regression for single-crystal microstructures under tensile loading conditions. The developed protocol enables one to compress field into a latent/low-dimensional space described by principal component analysis (PCA) via the singular value decomposition (SVD) algorithm. As a result, the high-dimensional data are reduced to a significantly smaller amount of dimensions with POD bases and POD coefficients. Furthermore, we deployed an ensemble of SVGPs—extended from the classical Gaussian process (GP) regression for scalability and handling big data—in a massively parallel manner to train and predict latent POD coefficients using known POD bases from a set of previously obtained simulations results. Lastly, using the predicted POD coefficients, we reconstructed the full-field results and showed reasonable agreement compared with the true values obtained from running CPFEM. The developed framework is validated with a set of CPFEM simulations of a single embedded void in single-crystal aluminum alloy. While the framework is broadly applicable, this work specifically focuses on single-crystal microstructures, a single load case (e.g., tensile), and a specific void geometry (spherical).

Anisotropy↗
DOE OSTI2022

Untangling the threads of cellulose mercerization

Naturally occurring plant cellulose, our most abundant renewable resource, consists of fibers of long polymer chains that are tightly packed in parallel arrays in either of two crystal phases collectively referred to as cellulose I. During mercerization, a process that involves treatment with sodium hydroxide, cellulose goes through a conversion to another crystal form called cellulose II, within which every other chain has remarkably changed direction. We designed a neutron diffraction experiment with deuterium labelling in order to understand how this change of cellulose chain direction is possible. Here we show that during mercerization of bacterial cellulose, chains fold back on themselves in a zigzag pattern to form crystalline anti-parallel domains. This result provides a molecular level understanding of one of the most widely used industrial processes for improving cellulosic materials.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2003

NASA Tech Briefs, August 2003

Topics covered include: Stable, Thermally Conductive Fillers for Bolted Joints; Connecting to Thermocouples with Fewer Lead Wires; Zipper Connectors for Flexible Electronic Circuits; Safety Interlock for Angularly Misdirected Power Tool; Modular, Parallel Pulse-Shaping Filter Architectures; High-Fidelity Piezoelectric Audio Device; Photovoltaic Power Station with Ultracapacitors for Storage; Time Analyzer for Time Synchronization and Monitor of the Deep Space Network; Program for Computing Albedo; Integrated Software for Analyzing Designs of Launch Vehicles; Abstract-Reasoning Software for Coordinating Multiple Agents; Software Searches for Better Spacecraft-Navigation Models; Software for Partly Automated Recognition of Targets; Antistatic Polycarbonate/Copper Oxide Composite; Better VPS Fabrication of Crucibles and Furnace Cartridges; Burn-Resistant, Strong Metal-Matrix Composites; Self-Deployable Spring-Strip Booms; Explosion Welding for Hermetic Containerization; Improved Process for Fabricating Carbon Nanotube Probes; Automated Serial Sectioning for 3D Reconstruction; and Parallel Subconvolution Filtering Architectures.

Source record↗
DOE OSTI2023

Parallelized domain decomposition for multi-dimensional Lagrangian random walk mass-transfer particle tracking schemes

Lagrangian particle tracking schemes allow a wide range of flow and transport processes to be simulated accurately, but a major challenge is numerically implementing the inter-particle interactions in an efficient manner. This article develops a multi-dimensional, parallelized domain decomposition (DDC) strategy for mass-transfer particle tracking (MTPT) methods in which particles exchange mass dynamically. We show that this can be efficiently parallelized by employing large numbers of CPU cores to accelerate run times. In order to validate the approach and our theoretical predictions we focus our efforts on a well-known benchmark problem with pure diffusion, where analytical solutions in any number of dimensions are well established. In this work, we investigate different procedures for “tiling” the domain in two and three dimensions (2-D and 3-D), as this type of formal DDC construction is currently limited to 1-D. An optimal tiling is prescribed based on physical problem parameters and the number of available CPU cores, as each tiling provides distinct results in both accuracy and run time. We further extend the most efficient technique to 3-D for comparison, leading to an analytical discussion of the effect of dimensionality on strategies for implementing DDC schemes. Increasing computational resources (cores) within the DDC method produces a trade-off between inter-node communication and on-node work. For an optimally subdivided diffusion problem, the 2-D parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to around 2700 cores, reducing a 5 h simulation to 8 s, while the 3-D algorithm maintains appreciable speedup up to 1700 cores.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Asynchronous multilevel adaptive methods for solving partial differential equations on multiprocessors - Performance results

The fast adaptive composite grid method (FAC) is an algorithm that uses various levels of uniform grids (global and local) to provide adaptive resolution and fast solution of PDEs. Like all such methods, it offers parallelism by using possibly many disconnected patches per level, but is hindered by the need to handle these levels sequentially. The finest levels must therefore wait for processing to be essentially completed on all the coarser ones. A recently developed asynchronous version of FAC, called AFAC, completely eliminates this bottleneck to parallelism. This paper describes timing results for AFAC, coupled with a simple load balancing scheme, applied to the solution of elliptic PDEs on an Intel iPSC hypercube. These tests include performance of certain processes necessary in adaptive methods, including moving grids and changing refinement. A companion paper reports on numerical and analytical results for estimating convergence factors of AFAC applied to very large scale examples.

Mccormick, S.↗
DOE OSTI2024

TunIO: An AI-powered Framework for Optimizing HPC I/O

I/O operations are a known performance bottleneck of HPC applications. To achieve good performance, users often employ an iterative multistage tuning process to find an optimal I/O stack configuration. However, an I/O stack contains multiple layers, such as high-level I/O libraries, I/O middleware, and parallel file systems, and each layer has many parameters. These parameters and layers are entangled and influenced by each other. The tuning process is time-consuming and complex. In this work, we present TunIO, an AI-powered I/O tuning framework that implements several techniques to balance the tuning cost and performance gain, including tuning the high-impact parameters first. Furthermore, TunIO analyzes the application source code to extract its I/O kernel while retaining all statements necessary to perform I/O. It utilizes a smart selection of high-impact configuration parameters of the given tuning objective. Finally, it uses a novel Reinforcement Learning (RL)-driven early stopping mechanism to balance the cost and performance gain. Experimental results show that TunIO leads to a reduction of up to ≈73% in tuning time while achieving the same performance gain when compared to H5Tuner. It achieves a significant performance gain/cost of 208.4 MBps/min (I/O bandwidth for each minute spent in tuning) over existing approaches under our testing.

Rajesh, Neeraj↗
NASA NTRS2013

Spontaneous Hot Flow Anomalies at Quasi-Parallel Shocks: 2. Hybrid Simulations

Motivated by recent THEMIS observations, this paper uses 2.5-D electromagnetic hybrid simulations to investigate the formation of Spontaneous Hot Flow Anomalies (SHFA) upstream of quasi-parallel bow shocks during steady solar wind conditions and in the absence of discontinuities. The results show the formation of a large number of structures along and upstream of the quasi-parallel bow shock. Their outer edges exhibit density and magnetic field enhancements, while their cores exhibit drops in density, magnetic field, solar wind velocity and enhancements in ion temperature. Using virtual spacecraft in the simulation, we show that the signatures of these structures in the time series data are very similar to those of SHFAs seen in THEMIS data and conclude that they correspond to SHFAs. Examination of the simulation data shows that SHFAs form as the result of foreshock cavitons interacting with the bow shock. Foreshock cavitons in turn form due to the nonlinear evolution of ULF waves generated by the interaction of the solar wind with the backstreaming ions. Because foreshock cavitons are an inherent part of the shock dissipation process, the formation of SHFAs is also an inherent part of the dissipation process leading to a highly non-uniform plasma in the quasi-parallel magnetosheath including large scale density and magnetic field cavities.

Quasi-Parallel↗
NASA NTRS1997

Efficient Parallel Kernel Solvers for Computational Fluid Dynamics Applications

Distributed-memory parallel computers dominate today's parallel computing arena. These machines, such as Intel Paragon, IBM SP2, and Cray Origin2OO, have successfully delivered high performance computing power for solving some of the so-called "grand-challenge" problems. Despite initial success, parallel machines have not been widely accepted in production engineering environments due to the complexity of parallel programming. On a parallel computing system, a task has to be partitioned and distributed appropriately among processors to reduce communication cost and to attain load balance. More importantly, even with careful partitioning and mapping, the performance of an algorithm may still be unsatisfactory, since conventional sequential algorithms may be serial in nature and may not be implemented efficiently on parallel machines. In many cases, new algorithms have to be introduced to increase parallel performance. In order to achieve optimal performance, in addition to partitioning and mapping, a careful performance study should be conducted for a given application to find a good algorithm-machine combination. This process, however, is usually painful and elusive. The goal of this project is to design and develop efficient parallel algorithms for highly accurate Computational Fluid Dynamics (CFD) simulations and other engineering applications. The work plan is 1) developing highly accurate parallel numerical algorithms, 2) conduct preliminary testing to verify the effectiveness and potential of these algorithms, 3) incorporate newly developed algorithms into actual simulation packages. The work plan has well achieved. Two highly accurate, efficient Poisson solvers have been developed and tested based on two different approaches: (1) Adopting a mathematical geometry which has a better capacity to describe the fluid, (2) Using compact scheme to gain high order accuracy in numerical discretization. The previously developed Parallel Diagonal Dominant (PDD) algorithm and Reduced Parallel Diagonal Dominant (RPDD) algorithm have been carefully studied on different parallel platforms for different applications, and a NASA simulation code developed by Man M. Rai and his colleagues has been parallelized and implemented based on data dependency analysis. These achievements are addressed in detail in the paper.

Sun, Xian-He↗
DOE OSTI2025

The impact of non-local parallel electron transport on plasma-impurity reaction rates in tokamak scrape-off layer plasmas

Abstract Plasma-impurity reaction rates are a crucial part of modelling tokamak scrape-off layer (SOL) plasmas. To avoid calculating the full set of rates for the large number of important processes involved, a set of effective rates are typically derived which assume Maxwellian electrons. However, non-local parallel electron transport may result in non-Maxwellian electrons, particularly close to divertor targets. Here, the validity of using Maxwellian-averaged rates in this context is investigated by computing the full set of rate equations for a fixed plasma background from kinetic and fluid SOL simulations. We consider the effect of the electron distribution as well as the impact of the electron transport model on plasma profiles. Results are presented for lithium, beryllium, carbon, nitrogen, neon and argon. It is found that electron distributions with enhanced high-energy tails can result in significant modifications to the ionisation balance and radiative power loss rates from excitation, on the order of 50%–75% for the latter. Fluid electron models with Spitzer-Härm or flux-limited Spitzer-Härm thermal conductivity, combined with Maxwellian electrons for rate calculations, can increase or decrease this error, depending on the impurity species and plasma conditions. Based on these results, we also discuss some approaches to experimentally observing non-local electron transport in SOL plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1998

GSFC Cutting Edge Avionics Technologies for Spacecraft

With the launch of NASA's first fiber optic bus on SAMPEX in 1992, GSFC has ushered in an era of new technology development and insertion into flight programs. Predating such programs the Lewis and Clark missions and the New Millenium Program, GSFC has spearheaded the drive to use cutting edge technologies on spacecraft for three reasons: to enable next generation Space and Earth Science, to shorten spacecraft development schedules, and to reduce the cost of NASA missions. The technologies developed have addressed three focus areas: standard interface components, high performance processing, and high-density packaging techniques enabling lower cost systems. To realize the benefits of standard interface components GSFC has developed and utilized radiation hardened/tolerant devices such as PCI target ASICs, Parallel Fiber Optic Data Bus terminals, MIL-STD-1773 and AS1773 transceivers, and Essential Services Node. High performance processing has been the focus of the Mongoose I and Mongoose V rad-hard 32-bit processor programs as well as the SMEX-Lite Computation Hub. High-density packaging techniques have resulted in 3-D stack DRAM packages and Chip-On-Board processes. Lower cost systems have been demonstrated by judiciously using all of our technology developments to enable "plug and play" scalable architectures. The paper will present a survey of development and insertion experiences for the above technologies, as well as future plans to enable more "better, faster, cheaper" spacecraft. Details of ongoing GSFC programs such as Ultra-Low Power electronics, Rad-Hard FPGAs, PCI master ASICs, and Next Generation Mongoose processors.

Luers, Philip J.↗
DOE OSTI2022

Bayesian model updating with finite element vs surrogate models: Application to a miter gate structural system

Bayesian finite element (FE) model updating using direct model evaluations of large-scale high-fidelity FE models is extremely computationally expensive. Surrogate models can be used as fast emulators of FE models to accelerate the model calibration process. The physics/mechanics-based FE models are still the underpinning behind the surrogate models. Here, this paper evaluates the loss in accuracy and the gain in computational time while performing Bayesian model updating by using surrogate model evaluations compared to using direct FE model evaluations. This evaluation is crucial before entirely relying on surrogate models in model updating for structural health monitoring (SHM) and damage prognosis (DP) purposes. This paper also demonstrates Bayesian updating and surrogate model construction of large-scale high-fidelity FE models of infrastructure systems. In this regard, the miter gate structural system is considered as the testbed structure. Three predominant damage modes (loss of contact between gate and wall, loss of thickness due to corrosion, and loss of tension in the diagonal rods) are considered for model updating purposes. Bayesian model updating is performed using direct FE evaluations by leveraging parallel computing. Two types of surrogates, namely polynomial chaos expansion (PCE) and Gaussian process regression (GPR), are developed for the miter gate. Model updating is performed again using the trained surrogate models, and the updating results are compared with their counterparts obtained using the direct FE evaluation results. The posterior distribution of the FE model parameters obtained using the trained surrogates are sufficiently accurate with respect to the posterior obtained utilizing the direct FE evaluations. In addition, an approximate 4-fold decrease in the computational time was observed when using surrogate model evaluations instead of direct FE evaluations for model updating.

42 ENGINEERING↗
DOE OSTI2025

A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints

Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address this issue, we propose a novel approach: the water balance equivalence (WABE) method, which generates spatially continuous WBET for correcting biases in RSET products. The WABE method computes synthetic WBET by integrating observed WBET and flux tower-derived FLUXCOM ET, which fills the spatial gaps of observed WBET and generates a spatially continuous WBET dataset. Synthetic WBET (2002–2015 annual average) of eight-digit hydrologic unit code (HUC8) basins across the conterminous United States (CONUS), constituting 44 % (887 out of 2035 basins) of CONUS basins, was determined within 2.0 % (RMSE = 12 %) of observed WBET at CONUS and between 1–12 % (RMSE = 3–33 %) across 18 regions in CONUS. With WABE-based bias corrections, the overall annual bias of RSET decreased from 10 % (RMSE = 34 %) to 6 % (RMSE = 26 %) across 37 flux tower sites. The WABE method offers a new approach for RSET accuracy improvement and shows great promise for large area implementations with a potential to yield substantial benefits for building accurate basin water budgets and water management decisions.

Khand, Kul↗
DOE OSTI2020

How eigenmode self-interaction affects zonal flows and convergence of tokamak core turbulence with toroidal system size

Self-interaction is the process by which a microinstability eigenmode that is extended along the direction parallel to the magnetic field interacts non-linearly with itself. This effect is particularly significant in gyrokinetic simulations accounting for kinetic passing electron dynamics and is known to generate stationary $E\times B$ zonal flow shear layers at radial locations near low-order mode rational surfaces (Weikl et al. Phys. Plasmas, vol. 25, 2018, 072305). Here, we find that self-interaction, in fact, plays a very significant role in also generating fluctuating zonal flows, which is critical to regulating turbulent transport throughout the radial extent. Unlike the usual picture of zonal flow drive in which microinstability eigenmodes coherently amplify the flow via modulational instabilities, the self-interaction drive of zonal flows from these eigenmodes are uncorrelated with each other. It is shown that the associated shearing rate of the fluctuating zonal flows therefore reduces as more toroidal modes are resolved in the simulation. In simulations accounting for the full toroidal domain, such an increase in the density of toroidal modes corresponds to an increase in the toroidal system size, leading to a finite system size effect that is distinct from the well-known profile shearing effect.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Global Impacts of Marine Methanethiol Emissions and Chemistry in the Atmosphere

Oceanic emissions of dimethyl sulfide (DMS) have long been known to influence aerosol particle composition, cloud condensation nuclei (CCN) concentration, and Earth’s radiative budget. However, the impact of oceanic emissions of methanethiol (MeSH), a sulfur compound produced by the same oceanic precursor as DMS, has been relatively less explored. The gas-phase oxidation of MeSH has a higher effective yield of SO 2 and a shorter oxidative lifetime compared to DMS, highlighting the relevance of this pathway for the modeled representation of particle formation, growth, and CCN abundance in the marine atmosphere. Here, we use the global chemical transport model GEOS-Chem to explore possible scenarios representative of specific environmental conditions and MeSH emission schemes based on previous experimental studies. We further implement and test previously reported chemical mechanisms for MeSH oxidation, along with additional improvements, highlighting key uncertainties and sensitivities for regional and global sulfur budgets. We place our results in the context of recent modeling updates to DMS chemistry and cloud processing, which further impact SO 2 production in the marine atmosphere in parallel with MeSH oxidation. Within the overall marine sulfur budget, our findings highlight that MeSH plays a significant role in SO 2 production in the marine atmosphere, contributing to regional surface layer concentration increases of up to 40–60%. These results point to the importance of MeSH for efforts aimed at improving the modeled representation of sulfur spatiotemporal patterns relevant to air quality predictions and climate impact assessments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Electronic coupling in square planar La 4 Ni 3 O 8

A study of a dd excitation in La4Ni 3 O 8 (La-438) using x-ray absorption scattering (XAS) and resonant inelastic x-ray scattering (RIXS) at the Ni K-edge is presented here. The incident energy dependence of this dd excitation shows a maximum at the 1s→4p π transition. Its intensity at the main edge is proportional to the amount of incident x-ray polarization parallel to the c-axis. These observations suggest that the RIXS process underlying this excitation includes a strong Ni 3d-Ni 4p Coulomb interaction and excludes the “4p-as-spectator” approximation. The dominant Ni 3d Coulomb interaction is with Ni 4p π with limited or no interaction with the Ni 4p σ . An insulating gap closing is observed as a function of temperature.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Fast neural Poincaré maps for toroidal magnetic fields

Poincaré maps for toroidal magnetic fields are routinely employed to study gross confinement properties in devices built to contain hot plasmas. In most practical applications, evaluating a Poincaré map requires numerical integration of a magnetic field line, a process that can be slow and that cannot be easily accelerated using parallel computations. We propose a novel neural network architecture, the HénonNet, and show that it is capable of accurately learning realistic Poincaré maps from observations of a conventional field-line-following algorithm. After training, such learned Poincaré maps evaluate much faster than the field-line integration method. Moreover, the HénonNet architecture exactly reproduces the primary physics constraint imposed on field-line Poincaré maps: flux preservation. Furthermore, this structure-preserving property is the consequence of each layer in a HénonNet being a symplectic map. We demonstrate empirically that a HénonNet can learn to mock the confinement properties of a large magnetic island by using coiled hyperbolic invariant manifolds to produce a sticky chaotic region at the desired island location. This suggests a novel approach to designing magnetic fields with good confinement properties that may be more flexible than ensuring confinement using KAM tori.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Controlling colloidal crystals via morphing energy landscapes and reinforcement learning

We report a feedback control method to remove grain boundaries and produce circular shaped colloidal crystals using morphing energy landscapes and reinforcement learning–based policies. We demonstrate this approach in optical microscopy and computer simulation experiments for colloidal particles in ac electric fields. First, we discover how tunable energy landscape shapes and orientations enhance grain boundary motion and crystal morphology relaxation. Next, reinforcement learning is used to develop an optimized control policy to actuate morphing energy landscapes to produce defect-free crystals orders of magnitude faster than natural relaxation times. Morphing energy landscapes mechanistically enable rapid crystal repair via anisotropic stresses to control defect and shape relaxation without melting. This method is scalable for up to at least N = 10 3 particles with mean process times scaling as N 0.5 . Further scalability is possible by controlling parallel local energy landscapes (e.g., periodic landscapes) to generate large-scale global defect-free hierarchical structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗