Simulating neutronics heating using physics-informed neural networks to resolve the temperature field

Designing a fusion power plant is a complex task, given the intricate nature of its components and the extensive range of design parameters involved. Simulation plays a crucial role in predicting the performance of fusion reactor components through the life cycle, with the coupling of neutronics with thermal analysis being vital for the design and optimization of various systems such as the blanket module and the vacuum vessel wall. Given the extensive design space for these parts and the diverse requirements of fusion components, there is a pressing need for rapid and precise prediction of specific physics fields to check whether the design satisfies the engineering requirements. This paper introduces physics-informed neural networks as surrogate models to resolve the temperature field of the vacuum vessel wall due to neutronics heating. This research demonstrates the capability of physics-informed neural networks for the rapid prediction of physical fields whilst maintaining good accuracy, with a L2 relative error of 0.0093. The output of this research could be used as a template for further developments in surrogate model based simulation-driven engineering in the fusion industry. • The paper describes the use of two physics-informed neural networks to estimate vacuum vessel wall temperature due to neutronics heating. • The work demonstrates that physics-informed neural networks can be used where heating is non-uniform and has high gradients. • The predictions made by the physics-informed neural networks compare well with results obtained using the finite element method.