A Data-Driven Multi-Fidelity Framework for Enhanced Flow Prediction Around Propeller and Fan Tips

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Due to the inherent assumptions and empiricism low-fidelity models for turbulent flow simulation are well known to lack the desired accuracy in complex flows involving separation, recirculation and reattachment. In particular, turbulent swirling flows around the propeller and rotor tips present significant challenges to most low-fidelity propeller models. On the other hand, high-fidelity scale-resolving methods such as the Large Eddy Simulation (LES) or Delayed Detached Eddy Simulation (DDES) are too computationally intensive for many-query computational tasks such as design optimization and uncertainty quantification. The proposed project aims to develop a novel, data-driven multi-fidelity framework for propeller modelling, consisting of a state-of-the-art adjoint-based flow assimilation technique and a machine learning (ML) model, with enhanced predictive capabilities for flows around propeller and rotor tips. We propose to enhance a low-fidelity propeller model by introducing correction terms. Using inverse design, the correction terms to the low-fidelity propeller model are tuned to reduce the discrepancies between the augmented model and a given high-fidelity sample. The input-output pairs between the mean flow features and correction terms computed in the flow assimilation process is used to train a convolutional neural network that is embedded in the solver to enhance its prediction of complex flows around propeller and fan tip regions without resorting to the computationally intensive scale-resolving simulations. In the proposed work, in addition to using high-fidelity simulations to study the flow physics and noise generation mechanisms, we propose to leverage the high-fidelity data to iteratively enhance the predictive accuracy of the ML-based propeller noise model constructed based on low-fidelity data, via transfer learning and active learning, and in so doing, realize a multi-fidelity data-driven framework for propeller noise prediction and minimization. In summary, the aim of this project is to develop a multi-fidelity framework for propeller performance prediction using advanced machine learning techniques, thus providing a key enabling technology the industry urgently need to efficiently predict and design high-efficiency low-noise propellers and fans. In the EPSRC landscape, this project aligns well with the research area of Fluid Dynamics and Aerodynamics within the research theme of Engineering. It is also well aligned with the 'AI for Science and Government' programme - one of the key strategic investments within the EPSRC Thematic Area of AI and Robotics.