Predicting Thermal Stress and Failure Risk in Monoblock Divertors Using 2D Finite Difference Modelling and Gradient Boosting Regression for Fusion Energy Applications

TL;DR

This paper combines 2D finite difference modeling with gradient boosting regression to predict thermal stress and failure risks in monoblock divertors, aiding in the design and maintenance of fusion reactor components.

Contribution

It introduces a novel integrated approach using finite difference simulations and machine learning to assess failure risk in divertor materials under fusion conditions.

Findings

Identified high-stress regions at material interfaces.

Developed a predictive model for failure risk assessment.

Provided insights into thermomechanical behavior of divertors.

Abstract

This study presents a combined approach using a 2D finite difference method and Gradient Boosting Regressor (GBR) to analyze thermal stress and identify potential failure points in monoblock divertors made of tungsten, copper, and CuCrZr alloy. The model simulates temperature and heat flux distributions under typical fusion reactor conditions, highlighting regions of high thermal gradients and stress accumulation. These stress concentrations, particularly at the interfaces between materials, are key areas for potential failure, such as thermal fatigue and microcracking. Using the GBR model, a predictive maintenance framework is developed to assess failure risk based on thermal stress data, allowing for early intervention. This approach provides insights into the thermomechanical behavior of divertors, contributing to the design and maintenance of more resilient fusion reactor components.