Shape optimisation of a hydrodynamic separator using expensive and constrained multi-objective computational modelling

Abstract The shape of a hydrodynamic particle separator has been optimised using unsteady computational fluid dynamics, coupled with Lagrangian particle tracking, combined with a parallelised and robust formulation of Bayesian optimisation. The noise present in the models of the separator required the use of the minimum probability of improvement infill criterion for optimisation of the geometry. This allowed direct inclusion of the objective noise via homoscedastic Gaussian process models. The wall clock time for the fluid modelling suggested that the Bayesian sampling should be parallelised. An existing parallelisation strategy was modified for the infill criterion being used and customised to favour exploitation in the decision space in order to deal with the cliff-edge type landscape that the infill criterion created. A new strategy was developed for convergence failures using Voronoi penalisation, while the approach for handling hardware failures was by manual restart. The presence of unsteady flow in the separator was addressed by time-resolving the flow fields in an Eulerian-Lagrangian manner. The optimisation generated an approximate Pareto front of solutions, which included novel and unexpected geometric shapes, demonstrating the value of Bayesian optimisation in producing innovative designs, which resulted in the filing of a patent.