INDUSTRIAL MULTI-FIDELITY SURROGATE-BASED OPTIMIZATION FRAMEWORK FOR COMPRESSOR BLADE DESIGN: BALANCING AEROMECHANICS, CONTACT ROBUSTNESS, AND NI-POD MODELING

Abstract

This paper presents the improvements of a modular, multi-disciplinary, and multi-fidelity Surrogate-Based Optimization (SBO) framework for rotor blade design, aiming at reducing computational cost and accelerating the design process in aeromechanical applications. The concept of “useful accuracy” is central to our approach, allowing the selection of appropriate fidelity levels for each stage of the optimization chain, which combines 3D blade parametrization with aerodynamic and mechanical simulations. Robustness to blade-tip/casing contact interactions is first investigated using two fidelity levels: simplified criterion and nonlinear simulations. The ability of the simplified criterion to guide the optimization process is assessed. Results indicate that, although the locations of maxima differ, the minimization procedure consistently converges toward similar regions of the parameter space. For aerodynamic modeling, a multi-fidelity surrogate strategy combining 2.5D and 3D simulations is adopted, leveraging low-fidelity 2.5D simulations to enhance the quality of the surrogate models associated with the 3D outputs while reducing the need for expensive 3D evaluations. Advanced surrogate modeling based on Non-Intrusive Proper Orthogonal Decomposition (NI-POD) are compared with Tuned Radial Basis Functions (TRBFs) approaches for the prediction of criticity fields. Results show that NI-POD improves prediction accuracy across all derived quantities, despite a higher computational cost. Finally, a multi-fidelity aerodynamic optimization is performed using the simplified contact robustness criterion as constraints. Optimized geometries are compared with initial designs, demonstrating the potential of our framework to efficiently guide aeromechanical design while balancing fidelity, computational cost, and accuracy.