Axial Fan Particle Swarm Optimization for a High-Bypass SOFCC-GT Hybrid System

Optimization of axial fan designs for a high-bypass solid oxide fuel cell combustor (SOFCC) gas turbine (GT) hybrid system is examined using particle swarm optimization (PSO) in MATLAB coupled with the turbomachinery design software AxStream. The optimization focused on maximizing the adiabatic total efficiency for both a fan designed for take-off (TO) conditions and another designed for cruise conditions. For the TO fan, an efficiency gain of 5.17% over the baseline was achieved by varying mass flow rate, shaft speed, and blade count. For the cruise fan, an efficiency gain of 1.94% was achieved by varying the shaft speed and the number of rotor and stator blades. Results indicate that lowering shaft speeds generally leads to higher efficiency compared to varying the mass flow rate. Incorporating blade count as a design variable enhances performance by expanding the design space at the cost of requiring a larger population size. Future work will focus on incorporating additional constraints within the optimization framework, adding enhanced control over blade geometry by the addition of blade angles as design variables, and implementing parallel processing to reduce computational time.