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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Proposal for grid computing for nuclear applications

The use of computer clusters for computational sciences including computational physics is vital as it provides computing power to crunch big numbers at a faster rate. In compute intensive applications that requires high resolution such as Monte Carlo simulation, the use of computer clusters in a grid form that supplies computational power to any nodes within the grid that needs computing power, has now become a necessity. In this paper, we described how the clusters running on a specific application could use resources within the grid, to run the applications to speed up the computing process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Verification methods for drift–diffusion reaction models for plasma simulations

Abstract Compared to other computational physics areas such as codes for general computational fluid dynamics, the documentation of verification methods for plasma fluid codes remains under developed. Current analytical solutions for plasma are often highly limited in terms of testing highly coupled physics, due to the harsh assumptions needed to derive even simple plasma equations. This work highlights these limitations, suggesting the method of manufactured solutions (MMSs) as a potential option for future verification efforts. To demonstrate the flexibility of MMS in verifying these highly coupled systems, the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework was utilized. Thanks to the MOOSE framework’s robustness and modularity, as well as to its physics module capabilities and ecosystem applications (i.e. Zapdos and the chemical reaction network) developed for plasma physics modeling and simulation, this report lays the groundwork for a structured method of conducting plasma fluid code verification.

DeChant, Corey (ORCID:0000000293779732)↗
DOE OSTI2013

An analytical particle mover for the charge- and energy-conserving, nonlinearly implicit, electrostatic particle-in-cell algorithm

We propose a 1D analytical particle mover for the recent charge- and energy-conserving electrostatic particle-in-cell (PIC) algorithm in Ref. [G. Chen, L. Chacón, D.C. Barnes, An energy- and charge-conserving, implicit, electrostatic particle-in-cell algorithm, Journal of Computational Physics 230 (2011) 7018–7036]. The approach computes particle orbits exactly for a given piece-wise linear electric field. The resulting PIC algorithm maintains the exact charge and energy conservation properties of the original algorithm, but with improved performance (both in efficiency and robustness against the number of particles and timestep). We demonstrate the advantageous properties of the scheme with a challenging multiscale numerical test case, the ion acoustic wave. Using the analytical mover as a reference, we demonstrate that the choice of error estimator in the Crank–Nicolson mover has significant impact on the overall performance of the implicit PIC algorithm. The generalization of the approach to the multi-dimensional case is outlined, based on a novel and simple charge conserving interpolation scheme.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Limited-angle computed tomography with deep image and physics priors

Computed tomography is a well-established x-ray imaging technique to reconstruct the three-dimensional structure of objects. It has been used extensively in a variety of fields, from diagnostic imaging to materials and biological sciences. One major challenge in some applications, such as in electron or x-ray tomography systems, is that the projections cannot be gathered over all the angles due to the sample holder setup or shape of the sample. This results in an ill-posed problem called the limited angle reconstruction problem. Typical image reconstruction in this setup leads to distortion and artifacts, thereby hindering a quantitative evaluation of the results. To address this challenge, we use a generative model to effectively constrain the solution of a physics-based approach. Our approach is self-training that can iteratively learn the nonlinear mapping from partial projections to the scanned object. Because our approach combines the data likelihood and image prior terms into a single deep network, it is computationally tractable and improves performance through an end-to-end training. We also complement our approach with total-variation regularization to handle high-frequency noise in reconstructions and implement a solver based on alternating direction method of multipliers. We present numerical results for various degrees of missing angle range and noise levels, which demonstrate the effectiveness of the proposed approach.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized both local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. Additionally, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2013

Multi-moment advection scheme in three dimension for Vlasov simulations of magnetized plasma

We present an extension of the multi-moment advection scheme [T. Minoshima, Y. Matsumoto, T. Amano, Multi-moment advection scheme for Vlasov simulations, Journal of Computational Physics 230 (2011) 6800–6823] to the three-dimensional case, for full electromagnetic Vlasov simulations of magnetized plasma. The scheme treats not only point values of a profile but also its zeroth to second order piecewise moments as dependent variables, and advances them on the basis of their governing equations. Similar to the two-dimensional scheme, the three-dimensional scheme can accurately solve the solid body rotation problem of a gaussian profile with little numerical dispersion or diffusion. This is a very important property for Vlasov simulations of magnetized plasma. We apply the scheme to electromagnetic Vlasov simulations. Propagation of linear waves and nonlinear evolution of the electron temperature anisotropy instability are successfully simulated with a good accuracy of the energy conservation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Interpretable machine learning for knowledge generation in heterogeneous catalysis

Most applications of machine learning in heterogeneous catalysis thus far have used black-box models to predict computable physical properties (descriptors), such as adsorption or formation energies, that can be related to catalytic performance (that is, activity or stability). Here, extracting meaningful physical insights from these black-box models has proved challenging, as the internal logic of these black-box models is not readily interpretable due to their high degree of complexity. Interpretable machine learning methods that merge the predictive capacity of black-box models with the physical interpretability of physics-based models offer an alternative to black-box models. In this Perspective, we discuss the various interpretable machine learning methods available to catalysis researchers, highlight the potential of interpretable machine learning to accelerate hypothesis formation and knowledge generation, and outline critical challenges and opportunities for interpretable machine learning in heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

What do we mean by the word “Shock”?

From one vantage point, a shock is a continuous but drastic change in state variables that occurs over very small time and length scales. These scales and associated changes in state variables can be measured experimentally. From another vantage point, a shock is a mathematical singularity consisting of instantaneous changes in state variables. This more mathematical view gives rise to analytical solutions to idealized problems. And from a third vantage point, a shock is a structure in a hydrocode prediction. Its width depends on the simulation’s grid resolution and artificial viscosity. These three vantage points can be in conflict when ideas from the associated fields are combined, and yet combining them is an important goal of an integrated modeling program. This presentation explores an example of how models for real materials in the presence of real shocks react to a hydrocode’s numerical shocks of finite width. The presentation will include an introduction to plasticity for the novice, an historical view of plasticity algorithms, a demonstration of how pursuing the meaning of “shock” has resulted in hydrocode improvements, and will conclude by answering some of the questions that arise from that pursuit. After the technical part of the presentation, a few slides advertising LANL’s Computational Physics Student Summer Workshop will be shown.

36 MATERIALS SCIENCE↗
DOE OSTI2020

HPC4Mfg with Carbon Inc.

Continuous Liquid Interface Production (CLIP) is poised to bring additive manufacturing to multiple American manufacturing sectors owing to its unique combination of rapid print speeds and material options that resemble injection molding thermoplastics. In spite of these benefits, CLIP is still a maturing process. To improve our understanding and control of the process, this project developed a multi-physics computational model that encompasses the coupled chemical-physical processes of photopolymerization and fluid flow to predict part outcomes. A physically predictive model can enable rapid optimization of CLIP and reduce the current cycle time and waste associated with optimization by four-fold. Ultimately, this effort will help open the U.S. $400B plastic manufacturing industry to the key benefits of additive manufacturing, namely mass customization, unlimited design space, and a cost- and energy-effective path to mainstream manufacturing. In the course of the project we produced a model which coupled fluid-flow, species transport, and photochemistry to model the Carbon CLIP process. As the development proceeded, we validated the code against theory and experiments. The resulting code was transferred to Carbon, and we expect it to be a useful part of their modeling capability. Future work includes improvements to the fluid solver’s robustness and performance, along with the addition of additional physics models.

42 ENGINEERING↗
DOE OSTI2021

Development of a Multi-physics Model to Optimize Continuous Liquid Interface Production (CLIP) for Additive Manufacturing (Carbon3D HPC4Mfg Final Report)

Continuous Liquid Interface Production (CLIP) is poised to bring additive manufacturing to multiple American manufacturing sectors owing to its unique combination of rapid print speeds and material options that resemble injection molding thermoplastics. In spite of these benefits, CLIP is still a maturing process. To improve our understanding and control of the process, this project developed a multi-physics computational model that encompasses the coupled chemical-physical processes of photopolymerization and fluid flow to predict part outcomes. A physically predictive model can enable rapid optimization of CLIP and reduce the current cycle time and waste associated with optimization by four-fold. Ultimately, this effort will help open the U.S. $400B plastic manufacturing industry to the key benefits of additive manufacturing, namely mass customization, unlimited design space, and a cost- and energy-effective path to mainstream manufacturing. In the course of the project we produced a model which coupled fluid-flow, species transport, and photochemistry to model the Carbon CLIP process. As the development proceeded, we validated the code against theory and experiments. The resulting code was transferred to Carbon, and we expect it to be a useful part of their modeling capability. Future work includes improvements to the fluid solver’s robustness and performance, along with the addition of additional physics models.

36 MATERIALS SCIENCE↗
NASA NTRS1975

Computers vs. wind tunnels for aerodynamic flow simulations

It is pointed out that in other fields of computational physics, such as ballistics, celestial mechanics, and neutronics, computations have already displaced experiments as the principal means of obtaining dynamic simulations. In the case of aerodynamic investigations, the complexity of the computational work involved in solving the Navier-Stokes equations is the reason that such investigations rely currently mainly on wind-tunnel testing. However, because of inherent limitations of the wind-tunnel approach and economic considerations, it appears that at some time in the future aerodynamic studies will chiefly rely on computational flow data provided by the computer. Taking into account projected development trends, it is estimated that computers with the required capabilities for a solution of the complete viscous, time-dependent Navier-Stokes equations will be available in the mid-1980s.

Chapman, D. R.↗
DOE OSTI2016

Energetic Light Fragment Production Capability in MCNP6

Monte Carlo Methods, Codes, and Applications group within the Computational Physics Division at Los Alamos National Laboratory has led the development of the transport code MCNP6 (Monte Carlo N-Particle transport code, version 6). MCNP6 is a general-purpose, continuous energy, generalized-geometry, time-dependent, Monte-Carlo radiation-transport code designed to track many particle types over broad ranges of energies. It is used around the world in applications ranging from radiation protection and dosimetry, nuclear-reactor design, nuclear criticality safety, detector design and analysis, decontamination and decommissioning, accelerator applications, medical physics, space research, and beyond. At lower energies, the code uses tables of evaluated nuclear data, while for higher energies (> 150 MeV), MCNP6 uses the cascade-exciton model, version 03.03 (CEM03.03), and the Los Alamos quark-gluon string model, version 03.03 (LAQGSM03.03) to model nuclear reactions. Emission of energetic heavy clusters heavier than {sup 4}He from nuclear reactions play a critical role in several applications, including electronics performance in space, human radiation dosages in space or other extreme radiation environments, proton- and hadron-therapy in medical physics, accelerator and shielding applications, and more. None of the available models are able to accurately predict emission of light fragments (LF) from arbitrary reactions. The CEM and LAQGSM event generators in MCNP6 describe quite well the spectra of fragments with sizes up to {sup 4}He across a broad range of target masses and incident energies (up to ∼ 5 GeV for CEM and up to ∼ 1 TeV/A for LAQGSM). However, they do not predict the high-energy tails of LF spectra heavier than {sup 4}He well. Most LF with energies above several tens of MeV are emitted during the precompound stage of a reaction. The 03.03 versions of CEM and LAQGSM do not account for precompound emission of LF larger than {sup 4}He. The goal of this research is to enable MCNP6 to produce high-energy light fragments. These energetic LF may be emitted by our models through three processes: Fermi breakup, preequilibrium, and coalescence. We explore the emission of light fragments through each of these mechanisms and demonstrate an improved agreement with experimental data achieved by extending precompound models to include emission of fragments heavier than {sup 4}He. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Computational studies of physical properties of Nb-Si based alloys

The overall goal is to provide physical properties data supplementing experiments for thermodynamic modeling and other simulations such as phase filed simulation for microstructure and continuum simulations for mechanical properties. These predictive computational modeling and simulations may yield insights that can be used to guide materials design, processing, and manufacture. Ultimately, they may lead to usable Nb-Si based alloy which could play an important role in current plight towards greener energy. The main objectives of the proposed projects are: (1) developing a first principles method based supercell approach for calculating thermodynamic and mechanic properties of ordered crystals and disordered lattices including solid solution; (2) application of the supercell approach to Nb-Si base alloy to compute physical properties data that can be used for thermodynamic modeling and other simulations to guide the optimal design of Nb-Si based alloy.

36 MATERIALS SCIENCE↗
NASA NTRS2000

Air, Ocean and Climate Monitoring Enhancing Undergraduate Training in the Physical, Environmental and Computer Sciences

Faculty in the Department of Physical, Environmental and Computer Sciences strongly believe in the concept that undergraduate research and research-related activities must be integrated into the fabric of our undergraduate Science and Technology curricula. High level skills, such as problem solving, reasoning, collaboration and the ability to engage in research, are learned for advanced study in graduate school or for competing for well paying positions in the scientific community. One goal of our academic programs is to have a pipeline of research activities from high school to four year college, to graduate school, based on the GISS Institute on Climate and Planets model.

Hope, W. W.↗
DOE OSTI2014

A Combined Experimental and Computational Study on Selected Physical Properties of Aminosilicones

A number of physical properties of aminosilicones have been determined experimentally and predicted computationally. It was found that COSMO-RS predicted the densities of the materials under study to within about 4% of the experimentally determined values. Vapor pressure measurements were performed, and all of the aminosilicones of interest were found to be significantly less volatile than the benchmark MEA material. COSMO-RS was reasonably accurate for predicting the vapor pressures for aminosilicones that were thermally stable. The heat capacities of all aminosilicones tested were between 2.0 and 2.3 J/(g.degrees C); again substantially lower than a benchmark 30% aqueous MEA solution. Surface energies for the aminosilicones were found to be 23.3-28.3 dyne/cm and were accurately predicted using the parachor method.

Perry, RJ↗
DOE OSTI2015

Dynamic Fracture Simulations of Explosively Loaded Cylinders

This report documents the modeling results of high explosive experiments investigating dynamic fracture of steel (AerMet® 100 alloy) cylinders. The experiments were conducted at Lawrence Livermore National Laboratory (LLNL) during 2007 to 2008 [10]. A principal objective of this study was to gain an understanding of dynamic material failure through the analysis of hydrodynamic computer code simulations. Two-dimensional and three-dimensional computational cylinder models were analyzed using the ALE3D multi-physics computer code.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗