A design and PCA-based optimization framework for variable geometry turbine blade profiles

Variable geometry turbines (VGTs) are widely used in variable cycle engines to modulate core airflow. During mass flow regulation, the guide vane rotation induces pronounced changes in outlet flow angle and profile loading, which can degrade regulation accuracy and adaptability across multiple operating conditions. To address this challenge, this work developed an aerodynamic design optimization framework for VGT guide vane and rotor profiles. For guide vane profile design, a corrected deviation angle model was proposed and embedded in a workflow that couples parametric profile generation with an iterative calculation of the guide vane rotation angle. This enabled the guide vane to meet the prescribed outlet flow angle target at both the closed and open settings, and high-speed linear cascade experiments confirmed prediction-measurement agreement within 0.6°. For multi-condition aerodynamic performance improvement, a PCA (Principal Component Analysis) based optimization method was developed using Kriging surrogates and a multi-objective search, where design space was re-parameterized by principal component analysis to support surrogate retraining. Compared with optimization in the original design space, the proposed PCA-based strategy is robust and can achieve a comparable Pareto-front hypervolume using 46 % fewer samples. The extracted principal components further enhance interpretability by revealing how key geometric variations affect profile loading and loss for both guide vane and rotor. Overall, this study offers a new perspective on multi-condition optimization of blade profiles and provides critical support for developing high-performance variable geometry turbines.