机器学习在高压比离心叶轮优化设计中的应用研究

Application Research of Machine Learning in Optimal Design of High Pressure-Ratio Centrifugal Impeller

随着大数据时代的到来和人工智能的发展,气动优化领域进行了很多有益的尝试与探索。传统叶轮机械叶型优化有着耗时长,叶型参数化复杂,计算成本高等缺点。本文采用机器学习中的SVM(支持向量机)方法,基于离心压气机数据库,构建叶型参数与目标函数值之间的非线性模型,取代了繁琐的CFD计算。之后,利用遗传算法进行全局寻优,得出最优的叶型参数组合。与传统优化方法D3D比较,此方法优化时间大幅减少,优化叶型变化趋势与D3D接近,优化结果经过Numeca数值验证,峰值效率比D3D提高了0.2%,表明了机器学习优化具有较高的可靠性。

With the advent of the era of big data and the development of artificial intelligence,many beneficial attempts and explorations have been made in the field of aerodynamic optimization.The traditional optimization method has the disadvantages of time-consuming,complicated parameteri-zation of the blade,and high calculation cost.This paper uses the SVM(support vector machine)method based on the centrifugal compressor database to construct a nonlinear model between the parameters of blade and the efficiency,which replacs the CFD.After that,the genetic algorithm is used for global optimization to obtain the optimal parameters combination.Compared with the traditional optimization method-D3D,the time consumed by this method is greatly reduced,and the change trend of the optimized blade is close to that of D3D.The optimization results have been numerically verified by Numeca,and the peak efficiency is 0.2%higher than D3D,indicating that machine learning optimization has high reliability.