Validating the design optimisation of ultrasonic flow meters using computational fluid dynamics and surrogate modelling

TY - JOUR

T1 - Validating the design optimisation of ultrasonic flow meters using computational fluid dynamics and surrogate modelling

AU - Rincón, Mario Javier

AU - Reclari, Martino

AU - I. A. Yang, Xiang

AU - Abkar, Mahdi

PY - 2023/4

Y1 - 2023/4

N2 - Domestic ultrasonic flow meters with an intrusive two-stand configuration present a complex flow behaviour due to their unique geometry, which offers an interesting case to evaluate optimisation methods in wall-bounded turbulent flows. In this study, the design and analysis of computer models by computational fluid dynamics is used to predict the turbulent flow and to perform robust design optimisation of the flow meter. The optimisation is accomplished by surrogate modelling based on Kriging, Latin hypercube sampling, and Bayesian strategies to ensure a high-quality and space-filled response surface. A novel function to quantify flow meter measurement uncertainty is defined and evaluated together with pressure drop in order to define the multi-objective optimisation problem. The optimisation Pareto front is shown and compared numerically and experimentally against pressure drop and laser Doppler velocimetry experiments, displaying performance gains and geometrical changes in the 3D space. From the various improved designs sampled experimentally, a 4.9% measurement uncertainty reduction and a 37.4% pressure drop reduction have been shown compared to the analysed baseline case. The applied methodology provides a robust and efficient framework to evaluate design changes, improving ultrasonic flow meters and internal-flow problems with similar features.

AB - Domestic ultrasonic flow meters with an intrusive two-stand configuration present a complex flow behaviour due to their unique geometry, which offers an interesting case to evaluate optimisation methods in wall-bounded turbulent flows. In this study, the design and analysis of computer models by computational fluid dynamics is used to predict the turbulent flow and to perform robust design optimisation of the flow meter. The optimisation is accomplished by surrogate modelling based on Kriging, Latin hypercube sampling, and Bayesian strategies to ensure a high-quality and space-filled response surface. A novel function to quantify flow meter measurement uncertainty is defined and evaluated together with pressure drop in order to define the multi-objective optimisation problem. The optimisation Pareto front is shown and compared numerically and experimentally against pressure drop and laser Doppler velocimetry experiments, displaying performance gains and geometrical changes in the 3D space. From the various improved designs sampled experimentally, a 4.9% measurement uncertainty reduction and a 37.4% pressure drop reduction have been shown compared to the analysed baseline case. The applied methodology provides a robust and efficient framework to evaluate design changes, improving ultrasonic flow meters and internal-flow problems with similar features.

KW - Computational fluid dynamics

KW - Design optimisation

KW - Experimental validation

KW - Kriging

KW - Surrogate modelling

KW - Ultrasonic flow meter

U2 - 10.1016/j.ijheatfluidflow.2023.109112

DO - 10.1016/j.ijheatfluidflow.2023.109112

M3 - Journal article

SN - 0142-727X

VL - 100

JO - International Journal of Heat and Fluid Flow

JF - International Journal of Heat and Fluid Flow

M1 - 109112

ER -