Validating the design optimisation of ultrasound-based flow meters using computational fluid dynamics and surrogate modelling

Small-diameter ultrasonic flow meters, with an intrusive two-stand configuration, present an interesting industrial internal-flow problem due to their unique geometry and complex interactions with fluid flow. In order to efficiently optimise these systems, their flow physics and operation must be accurately predicted. In this study, Design and Analysis of Computer Experiments (DACE) by computational fluid dynamics is used to predict the turbulent flow and to perform robust design optimisation of the flow meter. The optimisation is performed by surrogate modelling based on Kringing, Latin Hypercube Sampling (LHS), and a Multi-Objective Evolutionary Algorithm (MOEA), where minimisation of pressure drop and maximisation of the flow meter accuracy, are taken as objective functions. The optimisation results are shown and compared numerically and experimentally against a baseline geometry, displaying performance gains and geometrical changes in the 3D space. The applied methodology provides a robust and time-efficient framework to analyse and optimise internal-flow problems with similar features.