| dc.description.abstract | Abstract. The turbine, as a critical component of gas turbines, has been widely used in marine propulsion. However, performance prediction based on small samples remains challenging. While data-driven methods easily overfit turbine characteristics, traditional loss models are difficult to calibrate due to their high-dimensional parameters. To address these issues, a rapid performance prediction method based on loss model theory and small-sample data-driven adaptive calibration was proposed. Various energy loss models were integrated, and the optimal model was identified through systematic multicriteria evaluation. Key parameters were determined using Self-Organizing Map analysis for intelligent dimensionality reduction. This process identified the most sensitive coefficients, reducing the number of parameters from 29 to 8 and cutting calibration time by 50%. For the single-stage turbine, the maximum prediction error was reduced from 3.84% to 0.83%. High accuracy was maintained in multistage turbines, where the inherent overestimation of losses was effectively corrected, as validated by 3D flow details. The model was further validated via 0D dynamic simulation, maintaining an average relative prediction error of less than 1.0% during a 25% load step-change. This research supports the construction and optimization of digital models for gas turbines. | |
