Systematic optimization of the square arc angle aquaculture tank combining CFD methodology and multi-objective genetic algorithm

Abstract

The hydrodynamic performance inside the recirculating aquaculture tank is critical for the welfare of aquaculture. It is undoubtedly a preferable alternative to achieve a conducive flow environment inside the aquaculture tank through optimizing the geometry structure of the aquaculture tank. In this study, combining the computational fluid dynamics (CFD) approach and multi-objective genetic algorithm (MOGA), these two parameters, namely α (R/L) and β (C/L), are assessed and optimized based on the average flow velocity and flow velocity uniformity inside the square arc angle aquaculture tank. Physical experiments are set up to verify the accuracy of the numerical models and predicted results. The results indicate that the variation of both α and β affects the flow velocity and vortex distribution. However, the sensitivity of flow velocity uniformity and average flow velocity to α (74.49% and 91.14%) is much higher than that of β (−24.12% and −23.68%). It demonstrates that increasing α can significantly improve the average flow velocity and flow velocity uniformity inside the aquaculture tank compared to β. In addition, according to MOGA optimization results, taking the maximum flow velocity uniformity and average flow velocity as the optimization goals, the final optimization scheme is α = 0.396 and β = 0.074. Finally, combining CFD and MOGA methods can optimize the aquaculture tank structure accurately and efficiently in a comprehensive and systematic approach with relatively minimal computational cost.