Performance optimization of a transonic compressor rotor using a primary-auxiliary surrogate model

In axial compressor blade design optimization research, the surrogate model serves as a critical tool for performance prediction, but its construction often incurs high computational costs. To reduce surrogate model construction costs while ensuring prediction accuracy, this study proposes a primary-auxiliary composite neural network surrogate model based on flow field extraction. During model construction, extensive internal flow field data are extracted and employed to support model training, which enables efficient global optimization. To validate the proposed surrogate model, this study selects the Darmstadt transonic compressor (DTC) as the test case, sets the rotor blade sweep as the optimization variable, and targets increases in both the compressor stable operating range and the efficiency at the peak-efficiency point as the optimization objectives. The study employed the primary-auxiliary composite surrogate model with the non-dominated sorting genetic algorithm II (NSGA-II) to explore the Pareto frontier and execute aerodynamic performance optimization design. Results demonstrate that, under few-shot conditions, the prediction accuracy achieved using the primary-auxiliary composite surrogate model is 61.2% higher compared to directly constructed surrogate models. Optimization of the rotor blade sweep led to a reduction in tip leakage flow intensity, a downstream shift, and weakening of boundary layer separation at the lower blade span. While the design point efficiency experienced a minor increase, the stable operating range of the compressor expanded by 17.7%. This study verifies the applicability of the primary-auxiliary composite surrogate model to compressor blade profile optimization and provides a novel means to boost prediction accuracy under low-cost surrogate model constraints.