This conference paper was submitted for presentation at the NAFEMS World Congress 2025, held in Salzburg, Austria from May 19–22, 2025.

Abstract

Development of commercially viable fusion power will rely heavily on deployment and targeted development of appropriate simulation methods for use across the design lifecycle and extended use into operations. Tools and methods of an appropriate fidelity are needed to efficiently explore the vast array of potential concepts at an early stage. Performing whole system x-in-the-loop virtual operations are needed for upfront and integrated development of control systems, facility HMI design, operation planning and operator training. This is particularly pertinent for Fusion, where doing so through physical prototype systems is either not possible or has prohibitive costs and timescales. Additionally, successful lifetime monitoring and predictive maintenance of fusion components through diagnostic measurements will be limited due to restricted accessibility and operation in a harsh environment. This provides a use case for a digital twin, which can combine data from the physical instrumentation with simulation to provide enhanced augmented diagnostics in '˜real-time'™. For all such simulations to be valuable in assessing the design or operational risk they need to be performed probabilistically, to quantify the uncertainty in the predictions in a formal reliability analysis. Systems simulation and related novel reduced order modelling techniques provides a realistic approach to achieving these aims. Although constantly advancing computational capability opens the door for larger and more complex simulations, the environmental and financial cost must be considered and does not override the principle of 'œappropriate fidelity' and the development of efficient techniques '“ even if to enable more valuable deployment of computational resource for efficient UQ. This paper presents developments of a novel full-field reduced order modelling technique using an augmented Component Mode Synthesis (CMS) reduction and modal coupling method, describing the reduction process and implementation in Modelica language. The approach enables efficient simulation of coupled fluid-thermo-mechanical models of complex components within a systems environment, capturing aspects of non-linear behaviour. This is demonstrated through application for a coupled fluid-thermal-structural simulation of a Fusion power plant plasma facing component, discussing the advantages and limitations of the approach. Finally, plans for further development of these methods and the application for simulation of Fusion systems and in wider industry are discussed in the context of moving towards realisation of a probabilistic real-time digital twin.