Enabling validated exascale nuclear science

Abstract

The field of fusion energy is about to enter the ITER era, for the first time we will have access to a device capable of producing 500 MW of fusion power, with plasmas lasting more than 300 seconds and with core temperatures in excess of 100-200 Million K. Engineering simulation for fusion, sits in an awkward position, a mixture of commercial and licensed tools are used, often with email driven transfer of data. In order to address the engineering simulation challenges of the future, the community must address simulation in a much more tightly coupled ecosystem, with a set of tools that can scale to take advantage of current petascale and upcoming exascale systems to address the design challenges of the ITER era.

On scalable liquid-metal MHD solvers for fusion breeder blanket multiphysics applications

“…A key step in accelerating the development of magnetic confinement fusion (MCF) power plants is developing the capability to study and design tokamak components in silico, reducing the financial costs of prototyping and testing through predictive modelling [1]. Digital replicas and twins of these components, even extending to full plant simulations, pose a significant challenge, requiring carefully validated multiphysics software that can make efficient use of upcoming exascale high performance computing (HPC) systems.…”

Machine learning techniques for sequential learning engineering design optimisation

“…Many of these systems are first-of-a-kind, presenting large regions of unexplored design space across any number of dimensions. Digital models of these systems are often multiphysics and multiscale, requiring high-fidelity simulations running on high performance computing (HPC) resources to model effectively [1,2].…”