An Iterative Neural Operator to Predict the Thermo-Fluid Information in Internal Cooling Channels

Abstract

The design of internal cooling channels played an important role in turbine cooling. Distributions of thermo-fluid information, including surface distributions, cross section distributions, and projected distributions are common forms of data for internal cooling research. For over half a century since 2D thermo-fluid data were obtainable, there were very few universal tools to regress images. This study proposed a reduced ordered model to regress thermo-fluid image data by integrating the physics nature of thermo-fluid problems with neural networks. This effort started from a general partial differential equation and utilized a series of derivations to convert the equations into recurrent convolutional neural networks. The tested data included the temperature distribution on the cooled solid surface, the projected heat flux image on the fluid-solid interfaces, and the pressure distribution in the middle cross section. Results indicated an excellent regressing accuracy of the presented model for the three types of data, which was elevated as compared with a widely used conditional generative adversarial networks (cGAN) deep learning model. Most importantly, the proposed model only consumed 1/290 trainable parameters as compared to cGAN model. The key features that led to the success of the proposed reduced ordered model included: the matching between the differential nature of a convention-diffusion phenomenon and the convolution calculation process, and the compliance of the time evolution nature of thermo-fluid images with the recurrent structure of the model.