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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Quantifying the accuracy of the tumor motion and area as a function of acceleration factor for the simulation of the dynamic keyhole magnetic resonance imaging method

Purpose: The dynamic keyhole is a new MR image reconstruction method for thoracic and abdominal MR imaging. To date, this method has not been investigated with cancer patient magnetic resonance imaging (MRI) data. The goal of this study was to assess the dynamic keyhole method for the task of lung tumor localization using cine-MR images reconstructed in the presence of respiratory motion. Methods: The dynamic keyhole method utilizes a previously acquired a library of peripheral k-space datasets at similar displacement and phase (where phase is simply used to determine whether the breathing is inhale to exhale or exhale to inhale) respiratory bins in conjunction with central k-space datasets (keyhole) acquired. External respiratory signals drive the process of sorting, matching, and combining the two k-space streams for each respiratory bin, thereby achieving faster image acquisition without substantial motion artifacts. This study was the first that investigates the impact of k-space undersampling on lung tumor motion and area assessment across clinically available techniques (zero-filling and conventional keyhole). In this study, the dynamic keyhole, conventional keyhole and zero-filling methods were compared to full k-space dataset acquisition by quantifying (1) the keyhole size required for central k-space datasets for constant image quality across sixty four cine-MRI datasets from nine lung cancer patients, (2) the intensity difference between the original and reconstructed images in a constant keyhole size, and (3) the accuracy of tumor motion and area directly measured by tumor autocontouring. Results: For constant image quality, the dynamic keyhole method, conventional keyhole, and zero-filling methods required 22%, 34%, and 49% of the keyhole size (P < 0.0001), respectively, compared to the full k-space image acquisition method. Compared to the conventional keyhole and zero-filling reconstructed images with the keyhole size utilized in the dynamic keyhole method, an average intensity difference of the dynamic keyhole reconstructed images (P < 0.0001) was minimal, and resulted in the accuracy of tumor motion within 99.6% (P < 0.0001) and the accuracy of tumor area within 98.0% (P < 0.0001) for lung tumor monitoring applications. Conclusions: This study demonstrates that the dynamic keyhole method is a promising technique for clinical applications such as image-guided radiation therapy requiring the MR monitoring of thoracic tumors. Based on the results from this study, the dynamic keyhole method could increase the imaging frequency by up to a factor of five compared with full k-space methods for real-time lung tumor MRI.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Accelerating dissipative particle dynamics simulations for soft matter systems

Dissipative particle dynamics (DPD) is a coarse-grained particle-based simulation method that offers microscopic-scale insights into soft matter systems. We present an efficient implementation of a DPD model for graphical processing units (GPUs). As implemented in the LAMMPS molecular dynamics package, it can run effectively on current-generation supercomputers which often have hybrid nodes containing multi-core CPUs and (one or more) GPUs. Using efficient communication of information between the CPUs and GPUs, DPD interactions can be computed on the GPU while other portions of a full simulation model (boundary conditions, constraints, bonded interactions, diagnostic calculations, etc.) can be performed on the CPU. Our GPU-enhanced runs show a speedup of up to 9.5× versus many-core CPU simulations, and can run scalably across thousands of compute nodes. We briefly discuss how the new GPU implementation was validated against the CPU version for thermodynamics, diffusion, and hydrodynamic behavior. We also highlight large-scale models which the faster DPD implementation has enabled, for studies of monolayer self-assembly and thin-film instabilities.

36 MATERIALS SCIENCE↗
DOE OSTI2018

WIPP PA Sensitivity Studies in Support of CRA-2014 Completeness Determination and Compliance Decision - 18209

The Waste Isolation Pilot Plant (WIPP) has been developed by the U.S. Department of Energy (DOE) for the geologic (deep underground) disposal of transuranic (TRU) waste. Containment of TRU waste at the WIPP is regulated by the U.S. Environmental Protection Agency (EPA). The DOE demonstrates compliance with the containment requirements by means of performance assessment (PA) calculations. WIPP PA calculations estimate the probability and consequence of potential radionuclide releases from the repository to the accessible environment for a regulatory period of 10,000 years after facility closure. Since TRU waste was first received at the WIPP in 1999, compliance recertification applications (CRAs) have been required by the EPA on five-year cycle. The latest CRA (CRA-2014) was submitted by the DOE to the EPA in 2014. As part of the application, a formal set of PA calculations (CRA-2014 PA) was performed and results were documented in detail. The CRA-2014 PA incorporated several parameter updates based on new data, as well as the implementation of a different model for panel closures, barriers planned for the opening of each waste panel. The results presented as part of the CRA-2014 showed via the required complementary cumulative distribution function (CCDF) release curves that the estimated releases were within compliance limits. As part of their process for reviewing the completeness of the CRA-2014 [1], the EPA formally requested that the DOE perform sensitivity studies based on EPA-requested changes to model parameters. The DOE performed and documented the results of three sensitivity studies described below: CRA14-SEN2 [2], CRA14-SEN3 [3], and CRA14-SEN4 [4]. In each case, the new analysis results were compared to CRA- 2014 PA (CRA14) results to determine the impact of the EPA-requested changes. CRA14-SEN2: the application of EPA-requested modified parameters in the operations and experimental (non-waste) areas of the repository to simulate an accelerated (instantaneous) creep closure, the inclusion of capillary pressure effects on relative permeability, and an increase in initial/residual brine saturation and residual gas saturation. The modifications to the repository model resulted in increased pressures and decreased brine saturations in waste areas and increased pressures and brine saturations in the operations and experimental areas. However, the overall effects on total high-probability mean releases from the repository were entirely insignificant. It was concluded that the modeling assumptions associated with the operations and experimental areas of the repository have an insignificant effect on the prediction of total releases from the repository and/or adequacy of the current (CRA14) model to demonstrate compliance with the regulatory limits. CRA14-SEN3: the application of EPA-requested modified parameters in the panel closure areas and operations and experimental (non-waste) areas of the repository to simulate an accelerated (instantaneous) creep closure, the inclusion of capillary pressure effects on relative permeability, and an increase in initial/residual brine saturation and residual gas saturation. The modifications to the repository model predominantly result in increased pressures and decreased brine saturations in waste areas, increased pressures and brine saturations in the operations and experimental areas, and decreased brine and gas flows across all panel closures. The pressure increases in repository waste regions yielded slightly decreased brine saturations (on average) in those areas. However, the overall effects on total high-probability mean releases from the repository are entirely insignificant. It was concluded that the modeling assumptions made for this sensitivity study which were associated with the panel closures and north end areas of the repository have only a small effect on the prediction of total releases from the repository and/or adequacy of the current (CRA14) model to demonstrate compliance with the regulatory limits. CRA14-SEN4: the application of several changes to the current WIPP PA model (CRA14), including: parameter changes, use of an updated code, and a revised computational grid for the BRAGFLO code. The modifications to the repository model resulted in increased releases in all primary release mechanisms. The impacts of each EPA-requested change to CRA14 were analyzed with respect to each release mechanism in the CRA14-SEN4 study. Overall, total high-probability predicted mean releases from the repository were increased by about 15%. Total low-probability predicted mean releases were increased by about 107%. It was concluded that the EPA-requested changes to the CRA14 result in increases to the predicted total releases from the repository. However, releases calculated in the CRA14-SEN4 analysis remained below regulatory limits, demonstrating continued compliance of the WIPP. In January 2017, the EPA determined that CRA-2014 was complete and then began a technical evaluation of CRA-2014 to determine whether the WIPP continued to comply with the radiation protection standards for disposal. During their evaluation, the EPA utilized the sensitivity study results to inform their ultimate determination in July 2017 that the WIPP continues to comply with regulatory standards. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

A plasma–vacuum interface tracking algorithm for magnetohydrodynamic simulations of coaxial plasma accelerators

The resistive Magneto-hydrodynamic (MHD) model describes the behavior of a strongly ionized plasma in the presence of external electric and magnetic fields. For problems involving the plasma with an open boundary to very low pressure/vacuum regions, the continuum assumption is no longer valid in the entire domain of interest. For example, this is seen in a plasma accelerator where a dense plasma expands into a vacuum background. A common practice to deal with this issue is to assign small values of density and pressure to the vacuum regions and proceed to solve the continuum based MHD equations throughout the domain. We show that this approach fails to produce solutions consistent with the physics of the problem and can give rise to unacceptable artifacts such as spurious shocks. Here, we develop a plasma–vacuum interface tracking approach to mitigate this problem. The plasma–vacuum interface is tracked in a physically consistent manner and the MHD equations are solved only in the regions that contain the plasma. The interface tracking is achieved using a face-flux formulation derived from the theoretical solution to a 1D free expansion problem. Coupled with a threshold based approach, the interface tracking is implemented for both explicit and implicit time stepping frameworks on generalized unstructured grids. Simulations of magnetized thermal plasma jets expanding into a vacuum background indicate plume profiles devoid of the unphysical shock obtained using the small background density approach. In the context of resistive MHD simulations, the interface tracking approach overcomes the numerical stiffness induced by specifying a large background resistivity in the vacuum regions, resulting in significant wall-clock time gains.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Accelerated Snowmelt Protocol to Simulate Climate Change Induced Impacts on Snowpack Dependent Ecosystems

Field studies that simulate the effects of climate change are important for a predictive understanding of ecosystem responses to a changing environment. Among many concerns, regional warming can result in advanced timing of spring snowmelt in snowpack dependent ecosystems, which could lead to longer snow-free periods and drier summer soils. Past studies investigating these impacts of climate change have manipulated snowmelt with a variety of techniques that include manual snowpack alteration with a shovel, infrared radiation, black sand and fabric covers. Within these studies however, sufficient documentation of methods is limited, which can make experimental reproduction difficult. Here, we outline a detailed plot-scale protocol that utilizes a permeable black geotextile fabric deployed on top of an isothermal spring snowpack to induce advanced snowmelt. The method offers a reliable and cost-effective approach to induce snowmelt by passively increasing solar radiation absorption at the snow surface. In addition, control configurations with no snowpack manipulation are paired adjacent to the induced snowmelt plot for experimental comparison. Past and ongoing deployments in Colorado subalpine ecosystems indicate that this approach can accelerate snowmelt by 14-23 days, effectively mimicking snowmelt timing at lower elevations. This protocol can be applied to a variety of studies to understand the hydrological, ecological, and geochemical impacts of regional warming in snowpack dependent ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation

In this work we address the problem of accelerating complex power-grid simulation through machine learning ( ML). Specifically, we develop a framework, Smart-PGSim,which generates multitask-learning (MTL) neural network (NN)models to predict the initial values of variables critical to the problem convergence. MTL models allow information sharing when predicting multiple dependent variables while including customized layers to predict individual variables. We show that,to achieve the required accuracy, it is paramount to embed domain-specific constraints derived from the specific power-grid components in the MTL model. Smart-PGSim then employs the predicted initial values as a high-quality initial condition for the power-grid numerical solver (warm start), resulting in both higher performance compared to state-of-the-art solutions while maintaining the required accuracy. Smart-PGSim brings 2.60×speedup on average (up to 3.28×) computed over 10,000 problems, without losing solution optimality.

machine learning, neural networks↗
DOE OSTI2017

Influence of Alloying Elements and Effect of Stress on Anisotropic Hydrogen Diffusion in Zr-Based Alloys Predicted by Accelerated Kinetic Monte Carlo Simulations

The formation of hydrides degrades the toughness of Zr-based claddings, and thus is one of the primary concerns regarding cladding integrity during fuel operation and used fuel storage. However, the formation mechanism of hydrides, particularly the nucleation mechanism, is yet to be fully understood. To explore the nucleation process, atomic scale studies including density functional theory, molecular dynamics and kinetic Monte Carlo have been used to investigate H diffusion, segregation, and the possible nucleation paths of hydrides. Here, density functional theory calculations are used to parameterize a kinetic Monte Carlo model, which can accurately predict the effective H diffusivity in various Zr-based claddings including Zircaloy, Zirlo and Zr-2.5Nb. Assisted by density functional theory calculations, a recently developed charge-optimized-many-body potential is further improved and used to investigate the possible nucleation paths of hydrides. It’s shown that the formation of face-centered hydrides starts with coherent zeta-phase, shearing of which in the basal plane changes the hcp stacking into fcc and leads to the formation of gamma-hydride nuclei. The shearing involves a negligible barrier and negligible strain accumulation in the matrix by adopting three equivalent shear partials. The results obtained at the atomic scale are consistent with previous theory and experiments, and improves the current understanding on how and how fast hydride forms in Zr based cladding.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

GPU-accelerated kinetic Hall thruster simulations in WarpX

Abstract Two-dimensional (axial-azimuthal) simulations of a Hall thruster are performed using the open-source particle-in-cell code WarpX. The simulation conditions are chosen to match those of the axial-azimuthal benchmark first reported by Charoy et al. in 2019. A range of numerical and solver parameters is investigated in order to find those which yield the best performance. It is found that WarpX completes the benchmark case in 3.8 days on an Nvidia V100 GPU, and in as low as 1.5 days on a more recent Nvidia H100 GPU. Of the numerical parameters investigated, it is determined that the field-solve tolerance and particle resampling thresholds have the largest effect on the simulation wall time and that particle resampling may artificially widen electron velocity distribution functions, leading to unphysical heating. A semi-implicit scheme for the electrostatic field solve is tested and is found to produce results consistent to within 10% of the benchmark in less than twelve hours. The scaling properties of the electrostatic solver to multiple GPUs are also assessed on a uniform plasma test problem. The results of this work are discussed in the context of advancements in GPU hardware and the suitability of kinetic Hall thruster simulations for engineering applications.

Marks, Thomas A.↗
DOE OSTI2014

Accelerating Monte Carlo molecular simulations by reweighting and reconstructing Markov chains: Extrapolation of canonical ensemble averages and second derivatives to different temperature and density conditions

Accurate determination of thermodynamic properties of petroleum reservoir fluids is of great interest to many applications, especially in petroleum engineering and chemical engineering. Molecular simulation has many appealing features, especially its requirement of fewer tuned parameters but yet better predicting capability; however it is well known that molecular simulation is very CPU expensive, as compared to equation of state approaches. We have recently introduced an efficient thermodynamically consistent technique to regenerate rapidly Monte Carlo Markov Chains (MCMCs) at different thermodynamic conditions from the existing data points that have been pre-computed with expensive classical simulation. This technique can speed up the simulation more than a million times, making the regenerated molecular simulation almost as fast as equation of state approaches. In this paper, this technique is first briefly reviewed and then numerically investigated in its capability of predicting ensemble averages of primary quantities at different neighboring thermodynamic conditions to the original simulated MCMCs. Moreover, this extrapolation technique is extended to predict second derivative properties (e.g. heat capacity and fluid compressibility). The method works by reweighting and reconstructing generated MCMCs in canonical ensemble for Lennard-Jones particles. In this paper, system's potential energy, pressure, isochoric heat capacity and isothermal compressibility along isochors, isotherms and paths of changing temperature and density from the original simulated points were extrapolated. Finally, an optimized set of Lennard-Jones parameters (ε, σ) for single site models were proposed for methane, nitrogen and carbon monoxide.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Accelerating and Parallelizing Lagrangian Simulations of Mixing-Limited Reactive Transport

Recent advances in random walk particle tracking have enabled direct simulation of mixing and reactions by allowing the particles to interact with each other using a multipoint mass transfer scheme. The mass transfer scheme allows separation of mixing and spreading processes, among other advantages, but it is computationally expensive because its speed depends on the number of interacting particle pairs. This note explores methods for relieving the computational bottleneck caused by the mass transfer step, and we use these algorithms to develop a new parallel, interacting particle model. The new model is a combination of a sparse search algorithm and a novel domain decomposition scheme, both of which offer significant speedup relative to the reference case—even when they are executed serially. We combine the strengths of these methods to create a parallel particle scheme that is highly accurate and efficient with run times that scale as 1/P for a fixed number of particles, where P is the number of computational cores (equivalently, subdomains, in this work) being used. Here, the new parallel model is a significant advance because it enables efficient simulation of large particle ensembles that are needed for environmental simulations and also because it can naturally pair with parallel geochemical solvers to create a practical Lagrangian tool for simulating mixing and reactions in complex chemical systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The FastEddy® Resident-GPU Accelerated Large-Eddy Simulation Framework: Moist Dynamics Extension, Validation and Sensitivities of Modeling Non-Precipitating Shallow Cumulus Clouds

Herein we describe the moist dynamics formulation implemented within the graphics processing unit-resident large-eddy simulation FastEddy® model, which includes a simple saturation adjustment scheme for condensation and evaporation processes. Two LES model intercomparison exercises for non-precipitating shallow cumulus clouds are simulated in order to validate this model extension, including a static forcing and a time-dependent forcing case. Overall, we find our dynamical, thermodynamical and microphysical quantities, along with turbulence variability and fluxes, to be commensurate with the corresponding model intercomparison results. In addition, sensitivities to specific model settings are investigated. Among these settings, it is shown that boundary layer and cloud layer structure and characteristics are sensitive to use of higher-order advection schemes impacting the vertical distribution of cloud content and associated turbulence statistics. Increasing the timescale of the saturation scheme leads to enhanced liquid water presence and decreases vertical velocity variance within the cloud deck. In some cases, these sensitivities agree with the model-to-model variability reported in the intercomparison exercises, highlighting the important role of specific model implementation choices in the context of shallow cumulus convection simulations. These analyses and findings also provide the basis for future extensions and applications of FastEddy® for modeling moist convection and precipitation scenarios.

54 ENVIRONMENTAL SCIENCES↗