To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

中文翻译：

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使用物理引导的深度学习对气泡流进行计算高效的 CFD 预测

为了实现两相流的高效计算流体动力学 (CFD) 预测，本文通过应用物理引导的数据驱动方法提出了多尺度框架。作为该框架的工具，开发了特征相似性测量 (FSM) 技术，用于使用粗网格 CFD 进行两相流模拟中的误差估计，以实现与具有快速运行特征的细网格模拟相当的精度。通过将物理引导参数和可变梯度定义为物理特征，FSM 能够在粗网格 CFD 模拟中捕获潜在的局部模式。大量低保真数据和相应的高保真数据用于探索与主要模拟错误和现象学标度的影响相关的潜在信息。通过从之前的模拟数据中学习，可以开发和训练使用深度前馈神经网络 (DFNN) 的替代模型来估计粗网格 CFD 的仿真误差。记录的研究支持物理引导的深度学习方法用于粗网格 CFD 模拟的可行性，该方法具有高效工业设计的潜力。