Global–local collaborative optimization method based on parametric sensitivity analysis and application to optimization of compressor blade

High-dimensional problems are common in designing complex engineering structures, and surrogate models are often used to improve optimization efficiency. However, the higher the dimension is, the lower the accuracy of the surrogate model, resulting in inaccurate optimization results. Therefore, a global‒local collaborative optimization method based on parameter sensitivity analysis is proposed in this work. This method involves decomposing high-dimensional problems into multiple low-dimensional problems. With global‒local collaborative optimization, the modeling cost is effectively reduced, and the modeling efficiency and accuracy are improved. This method is applied to the 3D optimization of a compressor blade and compared with two traditional optimization methods. Because the optimization process fully considers the global and local collaborative effects, the results show that the compressor blade efficiency is improved by 7.2%, and the pressure ratio is improved by 4.03%. In addition, the optimization efficiency is improved by 56.73% while ensuring the accuracy of the surrogate model. Therefore, the proposed method is an effective solution to the problems associated with high-dimensional optimization, and it can be effectively applied to the field of engineering.