A Multi-Mode Dynamic Fusion Mach Number Prediction Framework

The precise control of Mach numbers in supersonic and hypersonic compressor wind tunnel systems is a critical challenge in aerodynamic research. Although existing studies have improved prediction accuracy to some extent through machine learning methods, they generally neglect the multi-mode characteristics of complex wind tunnel systems, limiting the generalizability of the models. To address this issue, the present study proposes a multi-mode dynamic fusion Mach number prediction framework that integrates strategies of segmented modeling and cross-modal information fusion. First, single-mode segmented prediction models are constructed on the basis of Multi-output Support Vector Regression (MSVR), with hyperparameters optimized to capture the characteristics of individual modes. Second, the Partial Least Squares (PLS) method is employed to explore the correlations between historical and new modes, dynamically selecting the optimal prediction model and updating the historical mode repository. Experimental results demonstrate that the multi-mode dynamic fusion framework reduces the Root Mean Square Error (RMSE) by 70.57%, 56.4%, and 63.64% compared to Support Vector Regression (SVR), PLS, and Long Short-term Memory (LSTM) networks across six operating conditions. The framework proposed in this paper enhances Mach number prediction accuracy while improving model generalizability.