  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.
