Multi-scale Pix2Pix network for high-fidelity prediction of adiabatic cooling effectiveness in turbine cascade

Film cooling is one of the effective cooling methods to ensure the longevity of high thermal load turbines. Due to multiple corresponding design parameters, it is difficult to seek rapid evolution of overall film cooling performance of new design. Currently, some achievements were obtained in plane cooling by implementing deep learning models which have strong nonlinear mapping capability in high-dimensional datasets. To further expand deep learning and achieve high-fidelity prediction on 3D complex cooling configurations, our research introduces deep learning network into the linear cascade of air-cooling turbines. Furthermore, in this work, the Multi-scale Pixel to Pixel (MSPix2Pix) network is proposed to realize the reconstruction of high-resolution adiabatic cooling effectiveness on turbine cascade among sparse dataset, in which high-resolution non-parametric concept and introduce multiple generators and discriminators are utilized. The average structural similarity (SSIM) reached 0.9892 between the predicted images and the CFD results in the test set. The results of the verification experiment show the applicability of MSPix2Pix network for rapid and accurate evaluation of cooling effectiveness on complex three-dimensional geometry with a certain generalization, which provides a certain support for high fidelity prediction of cooling configuration and aero-thermal coupling design in gas-cooling turbine.