Component-Topology-Informed and Season-Aware Weighted Multi-Source Transfer Learning for Small-Sample Turbofan Engine Performance Prediction

Aero-engine performance prediction provides an essential basis for condition assessment and health management during ground testing. Conventional component-level models require costly calibration, whereas purely data-driven models are prone to overfitting under data-scarce conditions and generally offer limited physical interpretability. To address these limitations, this study proposes a turbofan engine performance prediction method that integrates a component-topology prior with season-aware multi-source transfer learning. Based on the main gas-path connections and the mechanical coupling between the high- and low-pressure spools, a component-topology-informed fusion prediction model (Engine-BLT) is developed. It characterizes the dynamic coupling behavior of the entire engine through component-specific feature extraction, feature propagation along the gas-flow direction, and cross-component feature fusion. In addition, multiple source domains are constructed according to seasonal information, including ambient temperature, ambient pressure, and relative humidity. A similarity-based weighting strategy is then employed to improve model adaptability under small-sample transfer-learning conditions. Finally, a component-level GasTurb model is employed to provide an independent aerothermodynamic reference under representative steady-state operating conditions. The results show that Engine-BLT achieves R2 values of 0.9650, 0.9991, and 0.9962 for corrected low-pressure-turbine outlet total temperature (T5,corr), corrected net thrust (FN,corr), and corrected high-pressure-compressor outlet total pressure (Pt3,corr), respectively, yielding the best overall prediction accuracy among the evaluated models. The season-aware weighted multi-source transfer learning (SWMT) strategy also provides the best overall performance among the investigated transfer-learning schemes. Under ground idle, maximum continuous thrust, and maximum takeoff thrust conditions, its mean absolute relative errors for Pt3,corr, T5,corr, and FN,corr are 1.17%, 0.99%, and 0.33%, respectively, which are comparable in magnitude to the steady-state aerothermodynamic reference obtained from the GasTurb model. The proposed method improves prediction accuracy under small-sample acceptance-test conditions while maintaining target-domain adaptability and physical interpretability.