Uncertainty Quantification in Fusion Power Plant Design

Uncertainty Quantification in Fusion Power Plant Design

The Culham Centre for Fusion Energy (CCFE) is one of Europe’s leading nuclear research centres, closely collaborating with universities and industry to develop a European demonstration fusion power plant (DEMO), for construction in the 2030s. Central to the engineering and physics design of a new fusion device is an integrated operating point which respects the limitations placed on performance by all relevant plant systems and their interactions with one another. Such an operating point can be identified and optimised using a systems code. However, the nature of fusion research and the design of new conceptual facilities is that extrapolation beyond current experimental databases (for the physics basis) and development of new technology must be assumed. The nominal operating point is therefore subject to considerable uncertainty. Current methods of developing and testing fusion plant operating points at CCFE are very far from optimised and there is not a good process for evaluation of the scenarios we produce, and although in-house tools exist to generate performance uncertainties there is limited analysis.

The Institute for Risk and Uncertainty at the University of Liverpool develops and maintains the general purpose software for uncertainty quantification COSSAN-X and OpenCossan (see www.cossan.co.uk). They have a long standing experience in quantifying, mitigating and managing risks and uncertainty in many fields.

This PhD project will combine the experience concerning power plant design based at CCFE with the knowledge about uncertainty quantification from Liverpool. The aim is to develop workflows to assess the uncertainty related to DEMO operating point and use the tools and workflows to develop robust nominal and back-up operating scenarios to increase confidence in the successful creation of such devices. In addition, the student will develop novel and efficient simulation and parallelisation strategies to reduce the computational cost of the stochastic analysis. The student will therefore be able to contribute significantly to the success of the DEMO project by ensuring the consistency and realism of the assumptions underpinning the design.

This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission.

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