关键词: 宽速域, γ-Reθt转捩模型, 本征正交分解, 高斯过程回归, 分层–融合, 特征增强

Abstract: During atmospheric re-entry, vehicles experience complex boundary-layer transition phenomena characterized by a wide range of flight speeds and strong compressibility effects. Accurate prediction of transition location is crucial for aerodynamic heating design and performance analysis; however, employing a single global correction coefficient limits model generalization across broad speed regimes. To address this issue, this paper introduces a two-stage feature-enhanced surrogate modeling approach based on Proper Orthogonal Decomposition (POD) combined with Gaussian Process Regression (GPR). By integrating dominant modal coefficients of streamwise velocity with Mach number and flight altitude, the method achieves high-fidelity prediction of the correction coeffi-cient. Moreover, a hierarchical fusion framework based on Mach number is established, significantly enhancing the physical con-sistency and generalization capability of the correction coefficient predictions under hypersonic conditions. Utilizing these high-fidelity correction coefficients within the production term of the intermittency transport equation effectively ensures the physical con-sistency of transition prediction at high Mach numbers. Numerical validation conducted for typical blunt-body re-entry vehicle sce-narios demonstrates that the proposed integrated methodology—comprising high-fidelity surrogate modeling and the corrected tran-sition model—exhibits robust stability and adaptability within complex parameter spaces. The approach accurately captures key tran-sition characteristics, including the transition onset position, and provides reasonable distribution trends of flow variables such as wall friction, highlighting its substantial potential for engineering applications.

Key words: broad Mach number range, γ–Reθt transition model, proper orthogonal decomposition (POD), Gaussian process regression (GPR), hierarchical fusion, feature enhancement