Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning. (October 2020) - Bao, Han; Feng, Jinyong; Dinh, Nam; Zhang, Hongbin
Highlights: Physics-guided deep learning is applied to enable computationally efficient computational fluid dynamics (CFD) simulation. Simulation errors of velocities and void fraction are predicted to improve coarse-mesh CFD simulation of two-phase flow. Computational costs of fine-mesh CFD simulation and the physics-guided data-driven approach are compared. Data similarity measurement is introduced to construct the training database of deep neural networks. Abstract: 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. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical "peaks" in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. 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.