DeepAI
Log In Sign Up

Deep Learning Interfacial Momentum Closures in Coarse-Mesh CFD Two-Phase Flow Simulation Using Validation Data

05/07/2020
by   Han Bao, et al.
0

Multiphase flow phenomena have been widely observed in the industrial applications, yet it remains a challenging unsolved problem. Three-dimensional computational fluid dynamics (CFD) approaches resolve of the flow fields on finer spatial and temporal scales, which can complement dedicated experimental study. However, closures must 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 an important role in bubble distribution and migration in liquid-vapor two-phase flows. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions that usually cannot deliver a universal solution across a 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. Validation data (including relevant experimental data and validated fine-mesh CFD simulations results) are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper. These reveal that FSM can substantially improve the prediction of the coarse-mesh CFD model, regardless of the choice of interfacial closures, and it provides scalability and consistency across discontinuous flow regimes. It demonstrates that data-driven methods can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.

READ FULL TEXT

page 24

page 26

10/17/2019

Computationally Efficient CFD Prediction of Bubbly Flow using Physics-Guided Deep Learning

To realize efficient computational fluid dynamics (CFD) prediction of tw...
01/06/2020

Using Deep Learning to Explore Local Physical Similarity for Global-scale Bridging in Thermal-hydraulic Simulation

Current system thermal-hydraulic codes have limited credibility in simul...
12/03/2019

Modeling, simulation and validation of supersonic parachute inflation dynamics during Mars landing

A high fidelity multi-physics Eulerian computational framework is presen...
06/08/2020

Multi-fidelity Generative Deep Learning Turbulent Flows

In computational fluid dynamics, there is an inevitable trade off betwee...
04/30/2021

A Gradient-based Deep Neural Network Model for Simulating Multiphase Flow in Porous Media

Simulation of multiphase flow in porous media is crucial for the effecti...
11/25/2017

Spectral Element Methods for Liquid Metal Reactors Applications

Funded by the U.S. Department of Energy, the Nuclear Energy Advanced Mod...
10/26/2021

Continuous data assimilation for two-phase flow: analysis and simulations

We propose, analyze, and test a novel continuous data assimilation two-p...