Скачать с ютуб Axial Fan Optimization for High Bypass SOFC C GT Hybrid System в хорошем качестве

Axial Fan Optimization for High Bypass SOFC C GT Hybrid System

Sam Chumney One of the most challenging aspects for hybrid systems to overcome is matching the range capabilities of conventional aviation power systems due to their lower energy densities requiring increased system mass. This challenge is amplified for the use of ammonia as a fuel for carbonless emissions, such as proposed in the NASA CLEAN ULI, because ammonia has a lower volumetric and gravimetric energy density than other alternative fuels. Thus, the fan is required to provide more power compared to traditional turbofans to match the required thrust. As this ultra-high bypass turbofan (BPR≈30) has most of the thrust coming from the fan, it is increasingly important to maximize fan efficiency to make this system commercially viable. Optimization focused on maximizing the adiabatic total efficiency and the propulsive efficiency at cruise have been carried out using particle swarm optimization (PSO) and genetic algorithm (GA) techniques. For both efficiency calculations, the shaft speed, number of rotor blades, and number of stator blades were allowed to vary. Thrust and power constraints were enforced through the addition of various static penalty factors. PSO displayed better global search ability and less sensitivity to population size but took more generations to converge while being less sensitive to constraint violations. GA was observed to converge more quickly due to loss of population diversity, sacrificing global search ability. However, constraint violations had a larger effect resulting in more design satisfying requirements. In general, designs that met the thrust and power constraints produced from PSO tended to be more efficient than ones produced from GA. This implies that PSO has a better local search ability than GA as well. This webinar will present the design approach, initial findings, and insight into the techniques used leveraging AxSTREAM software platform.