Partitioned Topology Optimization of Aero-Engine Rear Cooling Plate Based on Multi-Feature K-Means Algorithm

As a core load-bearing component, the aero-engine rear cooling plate requires its design to simultaneously meet strength requirements and lightweight indicators. The topology optimization method considering stress constraints is the core technical path to achieve this goal, but it suffers from insufficient control precision in key areas, easily leading to material redundancy. To address this issue, a partitioned topology optimization method based on the multi-feature K-means algorithm is proposed. First, by integrating multi-dimensional features including element stress, physical density, and spatial position, an innovative multi-feature K-means algorithm is employed to realize dynamic adaptive partitioning during the optimization process. Secondly, combined with the p-norm method for partitioned stress aggregation, a precise prediction and control method for partitioned stress is adopted to refine stress constraints. Thirdly, a topology optimization model of the rear cooling plate with multi-feature partitioned stress constraints is constructed, and the adjoint method is used to solve the stress sensitivities under centrifugal loads. Finally, the effectiveness of the proposed method is verified using the rear cooling plate model. The rear cooling plate is discretized with 0.5 mm 2D axisymmetric finite elements, the filter radius is 4 mm, and the Method of Moving Asymptotes (MMA) is employed for the solution. The mass fraction of the finally optimized rear cooling plate structure is 0.157, which is 13.7% lower than that obtained by the global stress constraint method and 11.3% lower than that obtained by the topology optimization method considering both the geometric partitioned stress constraints and global stress constraints. The proposed method provides a new approach for the lightweight design of the aero-engine rear cooling plate.