Multifidelity Active Learning for Failure Estimation of TRISO Nuclear Fuel

Autor:	Dhulipala, Somayajulu L. N., Chakroborty, Promit, Shields, Michael D., Jiang, Wen, Spencer, Benjamin W., Hales, Jason D.
Rok vydání:	2022
Předmět:	Statistics - Applications Physics - Computational Physics
Druh dokumentu:	Working Paper
Popis:	The Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel proposed to be used for multiple modern nuclear technologies. Therefore, characterizing its safety is vital for the reliable operation of nuclear technologies. However, the TRISO fuel failure probabilities are small and the computational model is time consuming to evaluate them using traditional Monte Carlo-type approaches. In the paper, we present a multifidelity active learning approach to efficiently estimate small failure probabilities given an expensive computational model. Active learning suggests the next best training set for optimal subsequent predictive performance and multifidelity modeling uses cheaper low-fidelity models to approximate the high-fidelity model output. After presenting the multifidelity active learning approach, we apply it to efficiently predict TRISO failure probability and make comparisons to the reference results.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.11115 Zobrazit plný text záznamu View this record from Arxiv