The genetic algorithm was employed to Multidisciplinary Design Optimization of transonic internally cooled turbine blades based on the conjugate heat transfer (CHT) method. Firstly, a parametric modeling method was employed to model the internal-cooled blade.Comparison of the SST turbulence model with and withoutγ-θtransition model was conducted, and the influence and reason between turbulent region and heat transfer distribution was analyzed.The result shows that separation appeared after middle region of the suction surface, because of the pressure after shock wave decrease abruptly that reduce adverse pressure gradient resistance capacity of laminar flow, it leads to instability and transition, and then enter a state of turbulence, same to the heat transfer coefficient with the phenomenon of abrupt increase that impact the temperature distribution, consequently SST model with γ-θ transition is better to showcase the change of aerodynamic and heat transfer in the transition region; Then,comparing the cooling effectiveness with different number cooling holes of internal-cooled blade , four cooling channels case was the best choice in consideration of the cooling effectiveness and the manufacturing process and the cost of the blade; In the end, Automatic optimization process was set up ,andseveral optimization frameworks were achieved. With the cooling flow increase in 0.011849 kg/s, average temperature and maximum temperature were reduced by 4.92% and 1.55% respectively in the boundary conditionsoptimization, in addition to optimized the cooling flow and the cooling effectiveness, temperature distribution in the part of contrastive analysis of turbulence model was verifiable, Simultaneously it is important guiding significance for the geometry parameters optimization.
A New Approach to Multidisciplinary Design Optimization of Solid Propulsion System Including Heat Transfer and Ablative Cooling
