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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Coupling multi-fidelity xRAGE with machine learning for graded inner shell design optimization in double shell capsules

Bayesian optimization has shown promise for the design optimization of inertial confinement fusion targets. Specifically, in Vazirani et al. [Phys. Plasmas 28 , 122709 (2021)], optimal designs for double shell capsules with graded inner shells were identified using one-dimensional xRAGE simulation yield calculations. While the machine learning models were able to accurately learn and predict one-dimensional simulation target performance, using simulations with higher fidelity would improve design optimization and better match with the expected experimental performance. However, higher fidelity physics modeling, i.e., two-dimensional xRAGE simulations, requires significantly larger computational time/cost, usually at least an order of magnitude, in comparison with one-dimensional simulations. This study presents a multi-fidelity Bayesian optimization, in which the machine learning model leverages low-fidelity (one-dimensional xRAGE) and high-fidelity (two-dimensional xRAGE) simulations to more accurately predict “pre-shot” target performance with respect to the expected experimental performance. By building a multi-fidelity Bayesian optimization framework coupled with xRAGE, the low-fidelity and high-fidelity simulations are able to inform one another, such that we have: (1) improved physics modeling in comparison with using low-fidelity simulations alone, (2) reduced computational time/cost in comparison with using high-fidelity simulations alone, and (3) more confidence in the expected performance of optimized targets during real-world experiments. In the future, we plan to use this robust multi-fidelity Bayesian optimization methodology to expedite the design of graded inner shells further and eventually full capsules as a part of the current double shell campaign at the National Ignition Facility.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Evaluation of 3D Simulation Capabilities within xRAGE using a Suite of Test Problems

This presentation describes a 3D verification and validation suite for xRAGE. Three verification test problems for hydrodynamics are presented: the Kidder ball problem, the Verney shell problem, and a 5-material compression problem. There is excellent agreement between 2D and 3D XRAGE simulation results for these problems, and between the xRAGE results and the benchmark solutions. In addition, two 3D ICF test problems are presented, based on an OMEGA direct drive capsule experiment, and on a NIF indirect drive capsule experiment. These two ICF test problems are used to evaluate two hydrodynamic methods within xRAGE. In addition to hydrodynamics the simulations also include three temperature plasma physics, thermal conduction and radiation diffusion. When simulating either capsule in 3D the newer unsplit hydrodynamic method in xRAGE produces more vorticity relative to the older default method. For the indirect drive capsule the 3D simulations are in reasonable agreement with the experimental values of ion temperature and neutron production. Unstable 3D vortex rings are present near the DT ice/carbon interface which enhance the confinement of the DT ice.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Assessment of Model Confidence of a Laser Source Model in xRAGE Using Omega Direct-Drive Implosion Experiments

Los Alamos National Laboratory is interested in developing high-energy-density physics validation capabilities for its multiphysics code xRAGE. xRAGE was recently updated with the laser package Mazinisin to improve predictability. We assess the current implementation and coupling of the laser package via validation of laser-driven, direct-drive spherical capsule experiments from the Omega laser facility. Here, the ASME V&V 20-2009 standard is used to determine the model confidence of xRAGE, and considerations for high-energy-density physics are identified. With current modeling capabilities in xRAGE, the model confidence is overwhelmed by significant systematic errors from the experiment or model. Validation evidence suggests cross-beam energy transfer as a dominant source of the systematic error.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

GPU Profiling and Optimizing xRAGE (Final Report)

Our project’s objective is to increase the efficiency of GPU-enabled kernels in xRAGE. To do so, we conduct GPU profiling with NSight Systems on xRAGE tests unsplit_sod_1d and unsplit_sedov_2d to identify bottlenecks and understand the behavior of the GPU during code execution. Next, we analyze these generated GPU profiles to locate the lines of code whose optimization have the most potential for improving runtime. We replicate the structure of the code in smaller test problems that are easier to understand, edit, and run quickly. Within these test problems, we implement two different methods of improving performance: transformation of nested loops into a single MDRangePolicy and hierarchical parallelization using teams of threads. Both methods show speedups in the test code, and after transferring them to xRAGE, they both show up to 30x speedups on various computing platforms. Profiling the edited versions of xRAGE reveals that the GPU successfully executed the bottlenecks with greater efficiency

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Evaluation of Runtime Performance for xRage Equations of State Input Settings and Cropped EOS Tables

Using the xRage hydro code with its intrinsic equations of state (EOS) settings does not produce the most accurate representation of the problem due to xRage's internal alterations to the fundamental EOS data. Utilizing EOSPAC with default settings in the hydro code uses the unaltered EOS SESAME data to produce what is considered the most accurate results. However, this setup for the simulation is very computationally inefficient due to xRage's reliance on pressure and temperature as its look-up variables in combination with EOSPAC's use of a rational function interpolator. This report explores the results and performance of running a simulation with the option, within EOSPAC, to invert the table at setup, using a linear or rational interpolator, and using an EOS table cut down to only the region actually sampled in the problem. Air and Deuterium are used as the test materials for the simulations. While using EOS data directly with EOSPAC, the rational interpolator takes about twice as long as the linear one. Additionally, using a windowed EOS table does not show a large increase in performance. An EOS table that has had more energy grid points interpolated and added in by EOSPAC showed more accurate results, suggesting that a more dense EOS table would be more accurate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Verification study of xRAGE’s multi-ion viscosity model

This report on the adequacy of the plasma viscosity model implemented in LANL’s ASC program code, xRAGE is organized as follows. Section I provides a brief introduction on the kinetic theory which underpins plasma viscosity, and Section II describes how plasma kinetic theory can be used to calculate plasma viscosity coefficients accurate within the hydrodynamic limit. Section III describes the existing model for calculating the plasma viscosity coefficient in xRAGE. Next, Section IV shows tests of the xRAGE viscosity model against established hydrodynamic theory in the case of binary and trinary plasma mixtures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

(U) New xRage Capabilities Enabled by Infrastructure Cleanup

For the past several years, the Eulerian Applications Project at LANL has been working to modernize the xRage code base. Initial efforts, done in FY15-16 and presented in previous work, focused primarily on splitting the code into meaningful packages and levelizing the dependency structure. Ongoing efforts include further refactoring across packages, code cleanup within packages, and writing of unit tests (Fig. 1). When we started the modernization process, one of our major goals was to simplify the code base and enable future code changes, in support of advanced architectures and also of new physics methods. As a result, several recent xRage improvements have been enabled by this infrastructure cleanup. This paper describes some of these improvements, and the infrastructure cleanup tasks that enabled them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

xRAGE: A Brief Overview [Slides]

This report provides information about xRAGE, "a multidimensional, multimaterial, massively parallel, Eulerian AMR, multiphysics code." Challenges and future possibilities for xRAGE are also discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

RANS Simulation of Variable Density Turbulent Round Jets with Coflow using xRAGE Hydrodynamic Code and BHR Turbulence Models

This work for the fiscal year 2023 (FY23) is a continuation of previous efforts to evaluate the BHR turbulence models for their ability to accurately simulate variable density turbulent round jets with coflow. As before, RANS simulations are carried out using the xRAGE hydrodynamic code. The following are some of the previous findings. Israel showed that i) the symmetry boundary conditions for the BHR models in axisymmetric simulations were in error, ii) three grids of different resolutions did not lead to converging solutions, and iii) BHR 3.1 simulation exhibited instabilities and did not reach a steady state. Saenz and Rauenzahn derived and implemented into xRAGE the correct BHR boundary conditions at the symmetry axis. Cline conducted sensitivity studies with various parameters including the BHR models (versions 2, 3.1 and 4), gravity, material pressure, specific heat, initial turbulent kinetic energy and initial turbulent length scale, and found that the largest factor impacting on simulation results was the BHR model version, followed by the initial turbulent length scale. In addition, freeze boundary conditions at the exit and the side wall of the computational domain were used to remove anomalous flow behavior. Cline adjusted the inlet jet velocity, initial turbulent kinetic energy and initial turbulent length scale to obtain the best reasonable match with the experimental data of Charonko and Prestridge. It was found that the BHR 2 and 3.1 models performed in a similar manner, but the BHR 2 model produced much lower levels of density-specific-volume covariance and turbulent kinetic energy. The main focus for the FY23 is to study the effects of computational parameters related to the boundary conditions, mesh, domain size and timestep size. The reasoning behind this is that, unless simulation results can be shown to be reasonably independent from the aforementioned computational parameters, it would be difficult to attribute any discrepancies between simulation and experimental results to turbulence models. This important aspect has largely been overlooked in the previous years, and therefore will be studied comprehensively here. Additionally, effects of varying the initial turbulent length scale will be examined because it was previously identified as a major factor affecting the flow fields.

42 ENGINEERING↗
DOE OSTI2020

3D xRAGE simulation of inertial confinement fusion implosion with imposed mode 2 laser drive asymmetry

Low-mode asymmetries represent an important obstacle to achieving high-gain inertial confinement fusion implosions. As a step in learning how to control such effects, an OMEGA experiment with imposed mode 2 laser drive asymmetries was done to study the expected signatures of this type of asymmetry [M. Gatu Johnson et al., PRE 2018]. In the present work, a 3D xRAGE simulation including the stalk mount has been brought to bear on the data from that experiment. Furthermore, comprehensive comparisons between simulated and measured observables are made. Good agreement between simulated and measured x-ray image-inferred shell trajectories, bang times and neutron emission widths are seen, showing that the hydrodynamics are well captured in the simulation. Asymmetries seen in simulated and measured time-resolved and time-integrated x-ray images and areal densities also compare well, showing impact of both stalk and mode 2. On the other hand, important differences in measured and simulated neutron emission histories, yield, and ion temperature (T ion ) asymmetries are seen, suggesting that the simulation is overestimating shock yield. Overall, the results clearly demonstrate the importance of considering all asymmetry sources when interpreting measured signatures of asymmetry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Verification test of the SURF model in xRage: Part IV cylindrically converging detonation wave

Previous verification studies of the SURF reactive burn model in the xRage code used test problems for underdriven or self-supporting detonation waves. Here we consider overdriven detonation waves. As a test problem we use a cylindrical converging detonation wave. Since there is no exact solution, the numerical solutions at different resolutions are compared for 1-D cylindrical geometry and 2-D Cartesian geometry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

FY2019 ASC Physics Validation report: A validation assessment of the xRAGE laser package in 1d using Michel2013

In this report we use standard V&V methodologies to assess the current implementation of the laser-package in xRAGE. Validation assessment of the laser package is currently limited to single-shell, laser-driven, direct-drive experiments from the Omega laser facility (see Michel et al.). Rigorous evaluation was conducted to quantify model accuracy, confidence, and acceptability.

42 ENGINEERING↗
DOE OSTI2019

Eulerian Applications Project - xRage: High Explosives Package [Slides]

At the most elementary level, the high explosive model in xRage (or many other hydro codes) computes the amount of material that changes from a high explosive reactant to a high explosive product during a time step and accounts for the energy added to the flow by this reaction. The actual chemical reactions that occur during this conversion are not modeled. These processes are very complicated and involve levels of spatial and temporal resolution that are generally not practical for an application code. They would also require a state variable representation that includes all of the chemical species that are produced by the reaction and account for multiple stage chemical reactions. Instead the explosive models we generally use are phenomenological and try to represent the most important physics that drive the explosive wave in a very simplified way. These models generally have a number of tunable parameters that must be calibrated to experiment.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗