This paper investigated the performance of a radial flow turbine by coupling metaheuristic algorithms with Computational Fluid Dynamics. We performed the optimization of the casing and wheel of the turbine simultaneously. The computer codes of four metaheuristic algorithms, namely the Genetic Algorithm, Flower Pollination Algorithm, Grey Wolf Optimizer, and the Grasshopper Optimization Algorithm were developed using MATLAB. The optimization results indicated that Grey Wolf Optimizer is the most powerful algorithm to achieve a higher temperature drop in comparison with other algorithms. Revealed by the study, choosing the best angle at the blade inlet is the most influential factor for efficiency improvement. Besides, casing optimization has a positive effect on the pressure recovery of the turbine by eliminating swirling flow. A comparison of the physics of fluid in the optimized and base wheel showed that the flow is more attached to the optimized blade since the backward-facing step produces a favorable pressure gradient mainly in the recirculating bubble. Employing pulsating flow confirmed that the efficiency of the optimized turbine has a significant increase in comparison to the base turbine by more than 2% in high mass flow rates.
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