Multi-objective Optimization Capability for Heterogeneous LWR Fuel Assemblies Supercells and Cores

由于燃料组件数学模型中的边界假设，人们怀疑使用物理系统的更大单元模型可以获得增益。然而，由于对这些系统进行建模需要大量的计算负荷，因此对所谓的超级单元或整个核心进行建模通常并不有效，因为“黑盒”优化（优化器反复测试候选解决方案）需要非常长的时间。博士将研究比通常考虑的更大区域建模的方法，并将涵盖这样做所需的新方法。

Due to boundary assumptions in the mathematical modelling of fuel assemblies it is suspected that gains could be achieved with larger unit models of the physical systems. However, due to the large computational load of modelling these systems it has not typically been efficient to model so-called supercells, or indeed entire cores, because 'black box' optimisation (where the optimiser repeatedly tests candidate solutions) take an exceptionally long period of time.The PhD will look at approaches to modelling larger areas than have been typically considered, and will cover the novel methods required to do this.