Minimization of loss in small scale axial air turbine using CFD modeling and evolutionary algorithm optimization

Abstract

Small scale axial air driven turbine (less than 10kW) is the crucial component in distributed power generation cycles and in compressed air energy storage systems driven by renewable energies. Efficient small axial turbine design requires precise loss estimation and geometry optimization of turbine blade profile for maximum performance. Loss predictions are vital for improving turbine efficiency. Published loss prediction correlations were developed based on large scale turbines; therefore, this work aims to develop a new approach for losses prediction in a small scale axial air turbine using computational fluid dynamics (CFD) simulations. For loss minimization, aerodynamics of turbine blade shape was optimized based on fully automated CFD simulation coupled with Multi-objective Genetic Algorithm (MOGA) technique. Compare to other conventional loss models, results showed that the Kacker & Okapuu model predicted the closest values to the CFD simulation results thus it can be used in the preliminary design phase of small axial turbine which can be further optimized through CFD modeling. The combined CFD with MOGA optimization for minimum loss showed that the turbine efficiency can be increased by 12.48% compare to the baseline design.