APPLICATION OF DEEP LEARNING FOR FAN ROTOR BLADE PERFORMANCE PREDICTION IN TURBOMACHINERY

Abstract Optimizing turbomachinery fans is a multidisciplinary challenge, often requiring theoretical assumptions or extensive computations. As new constraints like boundary layer ingestion emerge, developing surrogate models that effectively use existing data becomes essential. Towards this objective, the present study aimed at predicting the performance of the rotor of a turbo- machine fan stage using Deep Learning (DL) techniques. These approaches have been showing increasingly convincing results in recent times, yet usually applied to toy problems or simplified configurations. Thus, this work evaluates the feasibility of applying DL models to optimise the shape of realistic fan rotor blades. To that end, a pipeline is presented to generate and mesh new geometries, run simulations, and finally train deep neural networks to be used as surrogates for performance prediction. In this framework, a u-net type deep neural network was used to predict 2D wake-flow fields of entropy and two 0D metrics, efficiency and pressure ratio, from the geometry of the blade and its operating conditions. To reduce the complexity of the predictive tasks, a transformative approach is used, by opposition to a generative one. The model was compared to POD techniques. Results showed that the neural network was only a slight improvement on an iso-geometry data-set, but widely outperformed the POD model on the multi-geometry data-set. As a conclusion, it provided a good proof of concept to learn flow field views and global performance metrics on realistic, 3D, fan rotor geometries to be later used for optimisation.