Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning

• DOI: 10.1016/j.ijmultiphaseflow.2020.103378
• Publication Date: 2020-06-06T06:14:48Z
• Authors: Han Bao; Jinyong Feng; Nam Dinh; Hongbin Zhang
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Fluid Dynamics (physics.flu-dyn); FOS: Physical sciences; Machine Learning (stat.ML); Physics - Fluid Dynamics; 02 engineering and technology; Computational Physics (physics.comp-ph); 01 natural sciences; Machine Learning (cs.LG); Statistics - Machine Learning; Physics - Data Analysis, Statistics and Probability; 0103 physical sciences; 0202 electrical engineering, electronic engineering, information engineering; Physics - Computational Physics; Data Analysis, Statistics and Probability (physics.data-an)

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.<br/>This paper is under review of International Journal of Multi-phase Flow<br/>