Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data

Bao, Han; Feng, Jinyong; Dinh, Nam; Zhang, Hongbin

doi:10.1016/j.ijmultiphaseflow.2020.103489

Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data

Journal Article · Fri Oct 09 00:00:00 EDT 2020 · International Journal of Multiphase Flow

DOI:https://doi.org/10.1016/j.ijmultiphaseflow.2020.103489· OSTI ID:1891497

^[1]; Feng, Jinyong ^[2]; Dinh, Nam ^[3]; ^[1]

Idaho National Lab. (INL), Idaho Falls, ID (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
North Carolina State Univ., Raleigh, NC (United States)

Multiphase flow phenomena have been widely observed in the industrial applications while it remains a challenging yet unsolved problems. Three-dimensional computational fluid dynamics (CFD) approaches resolve the flow fields on a finer special and temporal scales which can complement the dedicated experimental study. However, closures have to be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent dispersion and wall lubrication forces, play in important role on the bubble’s distribution and migration in liquid-vapor two-phase flow. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions which usually cannot deliver a universal solution across wide range of flow conditions. In this paper, a data-driven approach, named as Feature Similarity Measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low fidelity data. Experimental data and fine mesh CFD simulations results are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper which reveals that FSM can substantially improve the prediction of coarse mesh CFD model regardless of the choice of interfacial closures and it provides scalability and consistency across discontinuous flow regimes. Furthermore, it demonstrates that data-driven method can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.

View Accepted Manuscript (DOE)

Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1891497

Report Number(s):: INL/JOU--20-57725-Rev000

Journal Information:: International Journal of Multiphase Flow, Journal Name: International Journal of Multiphase Flow Vol. 135; ISSN 0301-9322

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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