A relay-based probabilistic prediction model for multi-fidelity scenarios in total pressure loss of a compressor cascade with micro-textured surfaces

The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss (TPL) of aero-engine blades. However, due to the considerable scale gap between micro-texture and an actual aero-engine blade, wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation. Furthermore, the fine micro surface structure brings unavoidable manufacturing errors, and the probability prediction contributes to gaining the confidence interval of the results. Therefore, a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency. Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment, the relay-based modeling has been achieved through knowledge transferring, and the confidence interval can be provided by the Gaussian Process Regression (GPR) model. The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma = 0.64, 0.74, 0.84 have been evaluated using the Relay-Based Probabilistic (RBP) model. The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven (SFDD) prediction model, which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.