This article (AETiC V5N1 - Paper #1) has been withdrawn based on the request of the author(s). We apologise for any inconvenience caused.

Abstract

Nuclear reactor core depletion and thermal-hydraulics coupling have long been calculation-intensive tasks challenging both nuclear industry development and academic research projects regarding computing budgets of memory and time. Albeit future evolution in smart computation hardware with artificial intelligence and quantum computing facilities embedded could continuously push the predictive modelling limit, the fundamental reactor physics model will still tip the balance in underpinning the prediction accuracy, as evidenced by a benchmark of two computational models in this work for characterising the depletion of highly self-shielded Gadolinium burnable poison-bearing fuel pins in assessing the British first European Pressurised Reactor’s start-up core performance. Specifically, a sub-group multi-annular-ring method is verified to efficiently represent the self-shielded skin effect, which addresses the deficiencies of classic equivalence models. The subgroup method is subsequently applied into a deterministic neutron transport code and a Monte Carlo stochastic code, respectively, for another benchmark. Resulting discrepancies in power peaking factors for the same assembly are less than 2% for the first fuel cycle, the agreement of which well demonstrates the validity of the proposed subgroup model. At the forefront of efforts to quantitatively understand the burnable poisons’ behaviour precisely for fuel optimisation (e.g., mitigating power peaking), this work could also be advantageously used for training purposes in boosting safety philosophy and public engagement in the roadmap for decarbonisation.