TY - GEN
T1 - Material model parameters optimization in liquid mercury target dynamics simulation with machine learning surrogates
AU - Lin, Lianshan
AU - Tran, Hoang
AU - Radaideh, Majdi I.
AU - Gorti, Sarma
AU - Simunovic, Srdjan
AU - Jiang, Hao
AU - Winder, Drew
AU - Cousineau, Sarah
N1 - Publisher Copyright:
© 2023 American Society of Mechanical Engineers (ASME). All rights reserved.
PY - 2023
Y1 - 2023
N2 - A pulsed spallation target is subjected to very short (~0.7?s) but intense loads (23.3 kJ) from repeated proton pulses, which knock away neutrons from the mercury atoms nuclei for a wide range application in physics, engineering, medicine, petroleum exploration, biology, chemistry, etc. The effect of this pulsed loading on the stainless-steel target module which contains the flowing mercury target material is difficult to predict not only due to its short but intense explosive-like physical reaction, but also the nonlinear material behavior of the liquid mercury in the structure. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at high power level stage. However, prediction of the resultant loading on the target is more difficult when helium gas is intentionally injected into the mercury. A 2-phase material model that incorporates the Rayleigh-Plesset (R-P) model is expected to address this complex multi-physics dynamics problem by including the bubble dynamics in the liquid mercury. A parameter sensitivity study was firstly employed to understand their impact on the simulation strains. The investigated parameters included E, n, VFgas, and gas cumulative volume curve control parameters a and b. Verification and validation results from sparse polynomial expansions (SPE) method and directional Gaussian smoothing (DGS) optimization show that the surrogate model had training error of ~7% and validation error of ~15%, indicating that machine learning methods and surrogate models can help optimize the uncertain parameters in the complex 2-phase material model. This approach is expected to fill the knowledge gap between unknown liquid-gas mixture material model and measured vessel strain responses. Keywords: mercury target, machine learning, EOS, twophase material modeling, Rayleigh-Plesset model, spallation neutron source, surrogate modeling, derivative-free optimization, bubble dynamics.
AB - A pulsed spallation target is subjected to very short (~0.7?s) but intense loads (23.3 kJ) from repeated proton pulses, which knock away neutrons from the mercury atoms nuclei for a wide range application in physics, engineering, medicine, petroleum exploration, biology, chemistry, etc. The effect of this pulsed loading on the stainless-steel target module which contains the flowing mercury target material is difficult to predict not only due to its short but intense explosive-like physical reaction, but also the nonlinear material behavior of the liquid mercury in the structure. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at high power level stage. However, prediction of the resultant loading on the target is more difficult when helium gas is intentionally injected into the mercury. A 2-phase material model that incorporates the Rayleigh-Plesset (R-P) model is expected to address this complex multi-physics dynamics problem by including the bubble dynamics in the liquid mercury. A parameter sensitivity study was firstly employed to understand their impact on the simulation strains. The investigated parameters included E, n, VFgas, and gas cumulative volume curve control parameters a and b. Verification and validation results from sparse polynomial expansions (SPE) method and directional Gaussian smoothing (DGS) optimization show that the surrogate model had training error of ~7% and validation error of ~15%, indicating that machine learning methods and surrogate models can help optimize the uncertain parameters in the complex 2-phase material model. This approach is expected to fill the knowledge gap between unknown liquid-gas mixture material model and measured vessel strain responses. Keywords: mercury target, machine learning, EOS, twophase material modeling, Rayleigh-Plesset model, spallation neutron source, surrogate modeling, derivative-free optimization, bubble dynamics.
UR - http://www.scopus.com/inward/record.url?scp=85185540376&partnerID=8YFLogxK
U2 - 10.1115/IMECE2023-113604
DO - 10.1115/IMECE2023-113604
M3 - Conference contribution
AN - SCOPUS:85185540376
T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
BT - Mechanics of Solids, Structures and Fluids
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2023 International Mechanical Engineering Congress and Exposition, IMECE 2023
Y2 - 29 October 2023 through 2 November 2023
ER -