Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Mechanics of Solids, Structures and Fluids |
| Publisher | American Society of Mechanical Engineers (ASME) |
| ISBN (Electronic) | 9780791887684 |
| DOIs | |
| State | Published - 2023 |
| Event | ASME 2023 International Mechanical Engineering Congress and Exposition, IMECE 2023 - New Orleans, United States Duration: Oct 29 2023 → Nov 2 2023 |
Publication series
| Name | ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) |
|---|---|
| Volume | 11 |
Conference
| Conference | ASME 2023 International Mechanical Engineering Congress and Exposition, IMECE 2023 |
|---|---|
| Country/Territory | United States |
| City | New Orleans |
| Period | 10/29/23 → 11/2/23 |
Funding
The authors are grateful for support from the Neutron Sciences Directorate at ORNL in the investigation of this work. This work was supported by the DOE Office of Science (Office of Basic Energy Sciences, Scientific User Facilities program). This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231 using NERSC award BES-ERCAP 0022875. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doepublic-access-plan).