Inverse Design of Compressor/Fan Blade Profiles Based on Conditional Invertible Neural Networks

Abstract Compressors/fans are crucial in modern aircraft engines, providing power, thrust, and combustion efficiency. Blade profile design is a crucial aspect of the overall process, often involving multiple constraints. Current design methods heavily rely on the expertise of designers and typically require many iterations. This article proposes a blade profile inverse design approach based on a conditional invertible neural network (cINN) to address the limitations of the traditional methods. It can enable the direct generation of blade profiles that satisfy specified requirements, including aerodynamic performance and additional constraints that are relevant to the design of a blade. This method encompasses two primary steps: forward process modeling and inverse process modeling. In the forward process, a surrogate model is established to map design parameters to blade profile performance metrics. Within a specified design space, a predetermined number of designs are sampled, and a dataset is produced through performing high-fidelity computational fluid dynamics (CFD) analyses to obtain aerodynamic performance and other interested metrics. Subsequently, the dataset, after data preprocessing, is employed to train a Gaussian process regression (GPR) model to replace costly CFD analyses. In the inverse modeling step, based on the substantial data generated by the trained GPR model, a cINN is trained, mapping design parameters to a latent space under the specified target conditions. This renders the learning of the inverse process feasible. Upon the completion of model training, the design parameters and blade geometry that meet the desired target can be directly derived. The proposed method is demonstrated using a subsonic blade profile inverse design case study.