Multidisciplinary Surrogate-Based Optimization of a Compressor Rotor Blade Considering Ice Impact

Abstract

Ice ingestion can lead to a range of detrimental effects, from diminished engine performance to potentially catastrophic failure, especially for the first compressor stages. As the ice is traveling through the engine, whether due to direct hail particle ingestion or due to shedding of ice accretions, the impact with engine components that rotate at high speed can result in severe mechanical damage. The leading edge of the blades in the low-pressure compressor is particularly prone to local damage. Therefore, it is important to consider ice impact simulations as soon as possible in the design process. In this context, the authors propose in this work a multidisciplinary surrogate-based optimization strategy for a compressor rotor that includes ice impact simulations. For this purpose, a dedicated three-dimensional (3D) blade parametrization is first introduced. Then, based on this set of design parameters, the proposed optimization chain is presented, and each chain block is detailed. In particular, (1) aerodynamical simulations are considered at three different flight conditions, (2) maximum static stresses and frequency margins with respect to specific engine orders are evaluated, (3) the blade robustness to blade/casing contact events is assessed using the clearance consumption simplified criteria, and (4) ice impact simulations are carried out using transient response computations with an explicit time integration scheme and the ice fragment modeled with the smooth particle hydrodynamics (SPH) method. A dedicated lower-fidelity model has been developed for the specific purpose of optimization loops to maintain reasonable computations times. The proposed strategy is applied to design a rotor blade of a low-pressure compressor. The aim is to satisfy all mechanical constraints to ensure the blade robustness in operation, without deteriorating its aerodynamic performance. The optimization process is detailed in the paper, and an in-depth analysis of the optimization results is presented.