Linear shaped-charge jet optimization using machine learning methods

Sterbentz, Dane M.; Jekel, Charles F.; White, Daniel A.; Rieben, Robert N.; Belof, Jonathan L.

doi:10.1063/5.0156373

Linear shaped-charge jet optimization using machine learning methods

Journal Article · Mon Jul 24 00:00:00 EDT 2023 · Journal of Applied Physics

DOI:https://doi.org/10.1063/5.0156373· OSTI ID:1999463

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Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Linear shaped charges are used to focus energy into rapidly creating a deep linear incision. The general design of a shaped charge involves detonating a confined mass of high explosive (HE) with a metal-lined concave cavity on one side to produce a high velocity jet for the purpose of striking and penetrating a given material target. This jetting effect occurs due to the interaction of the detonation wave with the cavity geometry, which produces an unstable fluid phenomenon known as the Richtmyer–Meshkov instability and results in the rapid growth of a long narrow jet. We apply machine learning and optimization methods to hydrodynamics simulations of linear shaped charges to improve the simulated jet characteristics. The designs that we propose and investigate in this work generally involve modifying the behavior of the detonation waves prior to interaction with the liner material. These designs include the placement of multiple detonators and the use of metal inclusions within the HE. In conclusion, we are able to produce a linear shaped-charge design with a higher penetration depth than the baseline case that we consider and accomplish this using the same amount of or less HE.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1999463

Report Number(s):: LLNL--JRNL-847005; 1070436

Journal Information:: Journal of Applied Physics, Journal Name: Journal of Applied Physics Journal Issue: 4 Vol. 134; ISSN 0021-8979

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

36 MATERIALS SCIENCE
Artificial neural networks
Computational optimization
Equations of fluid dynamics
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Flow instabilities
Fluid jets
Hydrodynamic waves
Hydrodynamics simulations
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Richtmyer–Meshkov instability
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detonation waves
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Linear shaped-charge jet optimization using machine learning methods

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