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FLEXO: A Portably Performant Code for Pulsed Power Target Physics

Stagg, Alan K.; Adams, Marissa B.P.; Bond, Stephen D.; Bova, Steven W.; Cearley, Griffin S.; Cochrane, Kyle; Crockatt, Michael M.; Gardiner, Thomas A.; Granzow, Brian N.; Hamlin, Nathaniel D.; Martin, Matthew R.; Shulenburger, Luke N.; Voth, Thomas E.; Weis, Matthew R.; Woolstrum, Jeffrey M.; Yusuf, Nedim A.

FLEXO (Flux-Limited Extended-MHD Ohm's Law) is a production-line multiphysics code developed at Sandia to enable more predictive modeling of target physics on pulsed-power devices. FLEXO uses an extended magnetohydrodynamics (XMHD) model which includes a generalized Ohm's law (GOL), an electron inertia term, and Hall physics. This report describes the code's numerical methods, its computational performance, and test problems of interest.

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Transport coefficients of warm dense matter from Kohn-Sham density functional theory

Physics of Plasmas

Melton, Cody A.; Clay III, Raymond C.; Cochrane, Kyle; Dumi, Amanda; Gardiner, Thomas A.; Lentz, Meghan K.; Townsend, Joshua P.

We present a comprehensive study of transport coefficients including DC electrical conductivity and related optical properties, electrical contribution to the thermal conductivity, and the shear viscosity via ab initio molecular dynamics and density functional theory calculations on the “priority 1” cases from the “Second Charged-Particle Transport Coefficient Workshop” [Stanek et al., Phys. Plasmas (to be published 2024)]. The purpose of this work is to carefully document the entire workflow used to generate our reported transport coefficients, up to and including our definitions of finite size and statistical convergence, extrapolation techniques, and choice of thermodynamic ensembles. In pursuit of accurate optical properties, we also present a novel, simple, and highly accurate algorithm for evaluating the Kramers-Kronig relations. These heuristics are often not discussed in the literature, and it is hoped that this work will facilitate the reproducibility of our data.

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Gaussian Mixture Model Solvers for the Boltzmann Equation

Gardiner, Thomas A.; Robinson, Allen C.

This report documents our experience constructing a numerical method for the collisional Boltzmann equation that is capable of accurately capturing the collisionless through strongly collisional limits. We explore three different functional representations and present a detailed account of a numerical method based on a spatially dependent Gaussian mixture model (GMM). The Kullback-Leibler divergence is used as a closeness measure and various expectation maximization (EM) solution algorithms are implemented to find a compact representation in velocity space for distribution functions that exhibit significant non-Maxwellian character. We discuss issues that appear with this representation over a range of Knudsen numbers for a prototypical test problem and demonstrate that the strongly collisional limit recovers a solution to Euler's equations. Looking forward, this approach is broadly applicable to the non-relativistic and relativistic collisional Vlasov equations.

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Towards Predictive Plasma Science and Engineering through Revolutionary Multi-Scale Algorithms and Models (Final Report)

Laity, George R.; Robinson, Allen C.; Cuneo, Michael E.; Alam, Kathleen M.; Beckwith, Kristian; Bennett, Nichelle L.; Bettencourt, Matthew T.; Bond, Stephen D.; Cochrane, Kyle; Criscenti, Louise; Cyr, Eric C.; Bays, Nathan R.; Drake, Richard R.; Evstatiev, Evstati G.; Fierro, Andrew S.; Gardiner, Thomas A.; Bays, Nathan R.; Goeke, Ronald S.; Hamlin, Nathaniel D.; Hooper, Russell; Koski, Jason P.; Lane, James M.D.; Larson, Steven R.; Leung, Kevin; Mcgregor, Duncan A.O.; Miller, Philip R.; Miller, Sean; Ossareh, Susan J.; Phillips, Edward; Simpson, Sean; Sirajuddin, David; Smith, Thomas M.; Swan, Matthew S.; Thompson, Aidan P.; Tranchida, Julien; Bortz-Johnson, Asa J.; Welch, Dale; Russell, Alex; Watson, Eric; Rose, David; Mcbride, Ryan

This report describes the high-level accomplishments from the Plasma Science and Engineering Grand Challenge LDRD at Sandia National Laboratories. The Laboratory has a need to demonstrate predictive capabilities to model plasma phenomena in order to rapidly accelerate engineering development in several mission areas. The purpose of this Grand Challenge LDRD was to advance the fundamental models, methods, and algorithms along with supporting electrode science foundation to enable a revolutionary shift towards predictive plasma engineering design principles. This project integrated the SNL knowledge base in computer science, plasma physics, materials science, applied mathematics, and relevant application engineering to establish new cross-laboratory collaborations on these topics. As an initial exemplar, this project focused efforts on improving multi-scale modeling capabilities that are utilized to predict the electrical power delivery on large-scale pulsed power accelerators. Specifically, this LDRD was structured into three primary research thrusts that, when integrated, enable complex simulations of these devices: (1) the exploration of multi-scale models describing the desorption of contaminants from pulsed power electrodes, (2) the development of improved algorithms and code technologies to treat the multi-physics phenomena required to predict device performance, and (3) the creation of a rigorous verification and validation infrastructure to evaluate the codes and models across a range of challenge problems. These components were integrated into initial demonstrations of the largest simulations of multi-level vacuum power flow completed to-date, executed on the leading HPC computing machines available in the NNSA complex today. These preliminary studies indicate relevant pulsed power engineering design simulations can now be completed in (of order) several days, a significant improvement over pre-LDRD levels of performance.

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LDRD Report: Topological Design Optimization of Convolutes in Next Generation Pulsed Power Devices

Cyr, Eric C.; Von Winckel, Gregory; Kouri, Drew P.; Gardiner, Thomas A.; Ridzal, Denis; Shadid, John N.; Miller, Sean

This LDRD project was developed around the ambitious goal of applying PDE-constrained opti- mization approaches to design Z-machine components whose performance is governed by elec- tromagnetic and plasma models. This report documents the results of this LDRD project. Our differentiating approach was to use topology optimization methods developed for structural design and extend them for application to electromagnetic systems pertinent to the Z-machine. To achieve this objective a suite of optimization algorithms were implemented in the ROL library part of the Trilinos framework. These methods were applied to standalone demonstration problems and the Drekar multi-physics research application. Out of this exploration a new augmented Lagrangian approach to structural design problems was developed. We demonstrate that this approach has favorable mesh-independent performance. Both the final design and the algorithmic performance were independent of the size of the mesh. In addition, topology optimization formulations for the design of conducting networks were developed and demonstrated. Of note, this formulation was used to develop a design for the inner magnetically insulated transmission line on the Z-machine. The resulting electromagnetic device is compared with theoretically postulated designs.

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Results 1–25 of 32
Results 1–25 of 32
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