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Explosively driven Richtmyer–Meshkov instability jet suppression and enhancement via coupling machine learning and additive manufacturing

Journal Article · · Journal of Applied Physics
DOI:https://doi.org/10.1063/5.0213123· OSTI ID:2407214
 [1];  [1];  [1];  [1];  [1];  [1];  [1];  [2];  [1];  [1];  [1];  [1];  [1]
  1. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
  2. Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Colorado School of Mines, Golden, CO (United States)
+ Show Author Affiliations
The ability to control the behavior of fluid instabilities at material interfaces, such as the shock-driven Richtmyer–Meshkov instability, is a grand technological challenge with a broad number of applications ranging from inertial confinement fusion experiments to explosively driven shaped charges. In this work, we use a linear-geometry shaped charge as a means of studying methods for controlling material jetting that results from the Richtmyer–Meshkov instability. A shaped charge produces a high-velocity jet by focusing the energy from the detonation of high explosives. The interaction of the resulting detonation wave with a hollowed cavity lined with a thin metal layer produces the unstable jetting effect. By modifying the characteristics of the detonation wave prior to striking the lined cavity, the kinetic energy of the jet can be enhanced or reduced. Modifying the geometry of the liner material can also be used to alter jetting properties. We apply optimization methods to investigate several design parameterizations for both enhancing or suppressing the shaped-charge jet. This is accomplished using 2D and 3D hydrodynamic simulations to investigate the design space that we consider. We also apply new additive manufacturing methods for producing the shaped-charge assemblies, which allow for the experimental testing of complicated design geometries obtained through computational optimization. We present a direct comparison of our optimized designs with experimental results carried out at the High Explosives Application Facility at Lawrence Livermore National Laboratory.
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:
2407214
Report Number(s):
LLNL--JRNL-862011; 1094152
Journal Information:
Journal of Applied Physics, Journal Name: Journal of Applied Physics Journal Issue: 3 Vol. 136; ISSN 0021-8979
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (23)

FIG. 1(p. 4)figure FIG. 1
FIG. 2(p. 5)figure FIG. 2
TABLE I(p. 6)table TABLE I
FIG. 3(p. 7)figure FIG. 3
FIG. 4(p. 10)figure FIG. 4
TABLE II(p. 10)table TABLE II
FIG. 5(p. 11)figure FIG. 5
FIG. 6(p. 11)figure FIG. 6
FIG. 7(p. 13)figure FIG. 7
FIG. 8(p. 13)figure FIG. 8
FIG. 9(p. 15)figure FIG. 9
TABLE III(p. 15)table TABLE III
TABLE IV(p. 16)table TABLE IV
FIG. 10(p. 17)figure FIG. 10
FIG. 11(p. 17)figure FIG. 11
FIG. 12(p. 17)figure FIG. 12
FIG. 13(p. 17)figure FIG. 13
FIG. 14(p. 18)figure FIG. 14
FIG. 15(p. 18)figure FIG. 15
FIG. 16(p. 18)figure FIG. 16
FIG. 17(p. 19)figure FIG. 17
FIG. 18(p. 19)figure FIG. 18
FIG. 19(p. 20)figure FIG. 19

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Related Subjects

36 MATERIALS SCIENCE
computational optimization
explosives
fluid dynamics
fluid instabilities
hydrodynamics simulations
machine learning
optimization problems
radiography
shock waves