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

由于固有的假设和不确定性，用于湍流模拟的低保真度模型在涉及分离、再循环和再附着的复杂流动中缺乏所需的精度。特别是，螺旋桨和转子尖端周围的湍流旋流对大多数低保真螺旋桨模型提出了重大挑战。另一方面，高保真尺度分辨方法，如大涡模拟（LES）或延迟分离涡模拟（DDES）是太多的查询计算任务，如设计优化和不确定性量化的计算密集型。该项目旨在开发一种用于螺旋桨建模的新型数据驱动的多保真度框架，包括最先进的基于伴随的流同化技术和机器学习（ML）模型，增强了螺旋桨和转子尖端周围流的预测能力。我们建议通过引入修正项来增强低保真度螺旋桨模型。使用逆设计，调整低保真螺旋桨模型的校正项，以减少增强模型和给定的高保真样本之间的差异。在流同化过程中计算的平均流特征和校正项之间的输入-输出对用于训练嵌入在求解器中的卷积神经网络，以增强其对螺旋桨和风扇尖端区域周围复杂流的预测，而无需求助于计算密集型尺度解析模拟。在所提出的工作中，除了使用高保真模拟来研究流动物理和噪声产生机制之外，我们还提出利用高保真数据来迭代地提高基于低保真度数据构建的基于ML的螺旋桨噪声模型的预测精度，通过迁移学习和主动学习，并在这样做的过程中，实现了用于螺旋桨噪声预测和最小化的多保真度数据驱动框架。总之，该项目的目的是使用先进的机器学习技术开发一个用于螺旋桨性能预测的多保真度框架，从而提供行业迫切需要的关键使能技术，以有效地预测和设计高效低噪声螺旋桨和风扇。在EPSRC的景观，该项目与工程的研究主题内的流体动力学和空气动力学的研究领域保持一致。它也与“AI for Science and Government”计划保持一致，这是EPSRC AI和机器人主题领域的关键战略投资之一。

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.