The invention discloses a
turbomachinery aerodynamic performance-blade
load optimization method based on
machine learning, and the method comprises the steps: determining a
turbomachinery working fluid, carrying out the parameterization of a
turbomachinery, obtaining an optimization process input variable and an optimization target, and determining the empirical
design space of the input variable; performing
Bayesian optimization sampling on the turbomachinery in the empirical
design space of the input variable according to the optimization target, selecting a
working fluid in the optimization sampling process, calculating to obtain an optimization target value, and storing all
Bayesian optimization sampling data; constructing a Unet-CNN neural network, and carrying out network training; performing random sampling on optimization process input variables in an empirical
design space, constructing a geometric model to perform unsteady CFD calculation, performing post-
processing to obtain a high-performance
test set and a low-performance
test set of the Unet-CNN neural network, and testing the Unet-CNN neural network; and enabling the Unet-CNN neural network passing the test to be used for turbomachinery optimization, and obtaining the optimal turbomachinery structure. According to the method, the cost and time consumption for constructing the proxy model can be greatly reduced.