Inclined discrete cavities are proposed to improve the operating stability of a centrifugal compressor while minimizing the flow loss, and optimized to simultaneously maximize the adiabatic efficiency and stall margin. Aerodynamic analysis was performed using three-dimensional Reynolds-averaged Navier–Stokes equations. The numerical results obtained for the total pressure ratio and adiabatic efficiency were validated with experimental data for the centrifugal compressor with a smooth casing. The angle of the cavity port, axial distance between cavities, and inclined angle of the cavity were selected as design variables based on sensitivity analysis. The stall margin and adiabatic efficiency at the design point were used as the objective functions for the design. Latin hypercube sampling was used to select 24 design points in the design space. Two neural network models, that is, the radial basis and deep neural networks, were tested with respect to surrogate models and their performances were compared using statistical analysis. A hybrid PSO–GA algorithm was used to identify the optimal solutions for the surrogate models. The deep neural network model has a better overall performance than the radial basis neural network model. The optimization results show that the stall margin increases, with increments of up to 6.41% and 11.32% compared with the reference design and compressor with a smooth casing, respectively.

提出了倾斜的离散腔体，以提高离心压缩机的运行稳定性，同时最大程度地减少流量损失，并进行了优化，以同时最大化绝热效率和失速裕度。使用三维雷诺平均Navier-Stokes方程进行空气动力学分析。用光滑壳体的离心压缩机的实验数据验证了获得的总压比和绝热效率的数值结果。基于灵敏度分析，选择腔孔的角度，腔之间的轴向距离以及腔的倾斜角作为设计变量。设计点的失速裕度和绝热效率被用作设计的目标函数。拉丁超立方体采样用于在设计空间中选择24个设计点。针对代理模型测试了两个神经网络模型，即径向基础模型和深度神经网络，并使用统计分析比较了它们的性能。混合的PSO-GA算法用于确定替代模型的最佳解决方案。深度神经网络模型比径向基神经网络模型具有更好的整体性能。优化结果表明，与参考设计和带有光滑机壳的压缩机相比，失速裕度增加了，分别增加了6.41％和11.32％。深度神经网络模型比径向基神经网络模型具有更好的整体性能。优化结果表明，与参考设计和带有光滑机壳的压缩机相比，失速裕度增加了，分别增加了6.41％和11.32％。深度神经网络模型比径向基神经网络模型具有更好的整体性能。优化结果表明，与参考设计和带有光滑机壳的压缩机相比，失速裕度增加了，分别增加了6.41％和11.32％。