Model calibration of the liquid mercury spallation target using evolutionary neural networks and sparse polynomial expansions

Majdi I. Radaideh; Hoang Tran; Lianshan Lin; Hao Jiang; Drew Winder; Sarma Gorti; Guannan Zhang; Justin Mach; Sarah Cousineau

doi:10.1016/j.nimb.2022.06.001

Model calibration of the liquid mercury spallation target using evolutionary neural networks and sparse polynomial expansions

Majdi I. Radaideh, Hoang Tran, Lianshan Lin, Hao Jiang, Drew Winder, Sarma Gorti, Guannan Zhang, Justin Mach, Sarah Cousineau

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

The mercury constitutive model predicting the strain and stress in the target vessel plays a central role in improving the lifetime prediction and future target designs of the mercury targets at the Spallation Neutron Source. We leverage the experiment strain data collected over multiple years to improve the mercury constitutive model through a combination of large scale simulations of the target behavior and the use of machine learning tools for parameter estimation. We present two interdisciplinary approaches for surrogate-based model calibration of expensive simulations using evolutionary neural networks and sparse polynomial expansions. The newly calibrated simulations achieve 7% average improvement on the prediction accuracy and 8% reduction in mean absolute error compared to previously reported reference parameters, with some individual sensors experiencing up to 30% improvement. The calibrated simulations can aid in fatigue analysis to estimate the mercury target lifetime, which reduces abrupt failure and saves tremendous amount of costs.

Original language	English
Pages (from-to)	41-54
Number of pages	14
Journal	Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms
Volume	525
DOIs	https://doi.org/10.1016/j.nimb.2022.06.001
State	Published - Aug 15 2022
Externally published	Yes

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 under grant DE-SC0009915 (Office of Basic Energy Sciences, Scientific User Facilities program). A portion of this research used resources at the Spallation Neutron Source, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. This research used resources of the Computer and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. The authors have also used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Notice: 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/doe-public-access-plan). 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 under grant DE-SC0009915 (Office of Basic Energy Sciences, Scientific User Facilities program). A portion of this research used resources at the Spallation Neutron Source , a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. This research used resources of the Computer and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725 . The authors have also used resources of the Argonne Leadership Computing Facility , which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357 . Notice: 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/doe-public-access-plan ).

Funders	Funder number
CADES
DOE Public Access Plan
Data Environment for Science
U.S. Department of Energy	DE-AC05-00OR22725, DE-AC02-06CH11357
Office of Science	DE-SC0009915
Basic Energy Sciences
Oak Ridge National Laboratory

Keywords

Inverse problems
Liquid mercury
Model calibration
Optimization
Spallation neutron source
Surrogate modeling

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10.1016/j.nimb.2022.06.001

Cite this

Radaideh, M. I., Tran, H., Lin, L., Jiang, H., Winder, D., Gorti, S., Zhang, G., Mach, J., & Cousineau, S. (2022). Model calibration of the liquid mercury spallation target using evolutionary neural networks and sparse polynomial expansions. Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms, 525, 41-54. https://doi.org/10.1016/j.nimb.2022.06.001

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title = "Model calibration of the liquid mercury spallation target using evolutionary neural networks and sparse polynomial expansions",

abstract = "The mercury constitutive model predicting the strain and stress in the target vessel plays a central role in improving the lifetime prediction and future target designs of the mercury targets at the Spallation Neutron Source. We leverage the experiment strain data collected over multiple years to improve the mercury constitutive model through a combination of large scale simulations of the target behavior and the use of machine learning tools for parameter estimation. We present two interdisciplinary approaches for surrogate-based model calibration of expensive simulations using evolutionary neural networks and sparse polynomial expansions. The newly calibrated simulations achieve 7% average improvement on the prediction accuracy and 8% reduction in mean absolute error compared to previously reported reference parameters, with some individual sensors experiencing up to 30% improvement. The calibrated simulations can aid in fatigue analysis to estimate the mercury target lifetime, which reduces abrupt failure and saves tremendous amount of costs.",

keywords = "Inverse problems, Liquid mercury, Model calibration, Optimization, Spallation neutron source, Surrogate modeling",

author = "Radaideh, {Majdi I.} and Hoang Tran and Lianshan Lin and Hao Jiang and Drew Winder and Sarma Gorti and Guannan Zhang and Justin Mach and Sarah Cousineau",

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Radaideh, MI, Tran, H , Lin, L, Jiang, H, Winder, D , Gorti, S , Zhang, G , Mach, J & Cousineau, S 2022, 'Model calibration of the liquid mercury spallation target using evolutionary neural networks and sparse polynomial expansions', Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms, vol. 525, pp. 41-54. https://doi.org/10.1016/j.nimb.2022.06.001

Model calibration of the liquid mercury spallation target using evolutionary neural networks and sparse polynomial expansions. / Radaideh, Majdi I.; Tran, Hoang ; Lin, Lianshan et al.
In: Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms, Vol. 525, 15.08.2022, p. 41-54.

Research output: Contribution to journal › Article › peer-review

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AU - Tran, Hoang

AU - Lin, Lianshan

AU - Jiang, Hao

AU - Winder, Drew

AU - Gorti, Sarma

AU - Zhang, Guannan

AU - Mach, Justin

AU - Cousineau, Sarah

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AB - The mercury constitutive model predicting the strain and stress in the target vessel plays a central role in improving the lifetime prediction and future target designs of the mercury targets at the Spallation Neutron Source. We leverage the experiment strain data collected over multiple years to improve the mercury constitutive model through a combination of large scale simulations of the target behavior and the use of machine learning tools for parameter estimation. We present two interdisciplinary approaches for surrogate-based model calibration of expensive simulations using evolutionary neural networks and sparse polynomial expansions. The newly calibrated simulations achieve 7% average improvement on the prediction accuracy and 8% reduction in mean absolute error compared to previously reported reference parameters, with some individual sensors experiencing up to 30% improvement. The calibrated simulations can aid in fatigue analysis to estimate the mercury target lifetime, which reduces abrupt failure and saves tremendous amount of costs.

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Model calibration of the liquid mercury spallation target using evolutionary neural networks and sparse polynomial expansions

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