Laminar-turbulent transition and corner separation play a critical role in the aerodynamics of the compressor and are quite sensitive to the changes of flow conditions and external disturbances. However, a deep understanding of such fine flow phenomena poses a great challenge for turbulent methods and computer resources. In order to clarify the impacts of incoming flow states on the three-dimensional transitional flow in a compressor cascade, we construct a parallel Large-Eddy-Simulation (LES) methodology and apply it to a full-span compressor cascade. Both the turbulent and laminar incoming endwall boundary layers are considered at a free-stream turbulence level of 4%, which is typical in the multistage axial-flow compressor environment. The parallel performance of the MPI (Message Passing Interface) model and hybrid MPI-OpenMP (Open Multi-Processing) model is particularly analyzed at a parallel scale of 10 000 CPU (Central Processing Unit) cores. The parallel performance test shows that the efficiency of the MPI model is evidently higher than that of the hybrid MPI-OpenMP model. The LES results indicate that the incoming laminar endwall boundary layer results in a more remarkable reduction in the blade loading near the endwall and a larger total pressure loss than the turbulent one. The incoming endwall boundary layer state shows a significant impact on the evolution process of the endwall turbulence and a small impact on the corner separation and the suction-surface transition. This study demonstrates the ability of the parallel LES method to capture complex transitional-flow structures in compressor cascades and its potential application to the deeper understandings of compressor aerodynamics.

As the flow state in the volute plays an important role in the energy loss of the squirrel cage fan, the optimization design of the fan volute has drawn great attention. To further improve the overall performance of the fan, the aerodynamic match between the volute and the inlet nozzle as well as the local flow pattern within the fan components should be considered in the optimization. In the present work, a high-dimensional optimization method based on the genetic algorithm, support vector regression surrogate model, Latin hypercube sampling method and the mixed adaptive sampling algorithm is developed. Then, the integrated optimization for inlet nozzle and volute of the fan is carried out, in which both the aerodynamic performance and the impeller-volute interaction are considered. The optimization results show that the total efficiency of the fan increases by 2.24% and the static pressure recover coefficient increases by 2.18%. The reversed flow at the impeller outlet is restrained, and the flow uniformity at the impeller inlet is improved. This work is helpful for the design of squirrel cage fans with high aerodynamic performance.