Patel, M. V. | Search NASA

DOE OSTI2024

An improved methodology for modeling short pulse buried layer x-ray emission spectra

Radiation-hydrodynamic and spectroscopic modeling are important aspects of high energy density experimental design. In this paper, we improve the performance and capabilities over those obtainable with a previous methodology used for simulating x-ray emission spectra from buried layer targets heated by short pulse lasers. The improvement incorporates post-processing HYDRA radiation-hydrodynamic output with a non-local thermodynamic equilibrium atomic-kinetics radiation transport code, Cretin. Each code uses an independent radiation field which allows decoupling HYDRA's radiation group structure from Cretin's spectral output to improve the speed and flexibility of the design methodology. The execution time decreases from 2–3 days to a few hours while the flexibility of the improved methodology allows for performing sensitivity studies including a comparison of steady-state and time-dependent atomic kinetics and differences in the radiation group structure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Deep learning for NLTE spectral opacities

Computer simulations of high energy density science experiments are computationally challenging, consisting of multiple physics calculations including radiation transport, hydrodynamics, atomic physics, nuclear reactions, laser–plasma interactions, and more. To simulate inertial confinement fusion (ICF) experiments at high fidelity, each of these physics calculations should be as detailed as possible. However, this quickly becomes too computationally expensive even for modern supercomputers, and thus many simplifying assumptions are made to reduce the required computational time. Much of the research has focused on acceleration techniques for the various packages in multiphysics codes. In this work, we explore a novel method for accelerating physics packages via machine learning. The non-local thermodynamic equilibrium (NLTE) package is one of the most expensive calculations in the simulations of indirect drive inertial confinement fusion, taking several tens of percent of the total wall clock time. We explore the use of machine learning to accelerate this package, by essentially replacing the physics calculation with a deep neural network that has been trained to emulate the physics code. Overall, we demonstrate the feasibility of this approach on a simple problem and perform a side-by-side comparison of the physics calculation and the neural network inline in an ICF Hohlraum simulation. We show that the neural network achieves a 10× speed up in NLTE computational time while achieving good agreement with the physics code for several quantities of interest.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗