Inferring micro-bubble dynamics with physics-informed deep learning
- URL: http://arxiv.org/abs/2105.07179v1
- Date: Sat, 15 May 2021 09:17:56 GMT
- Title: Inferring micro-bubble dynamics with physics-informed deep learning
- Authors: Hanfeng Zhai, Guohui Hu
- Abstract summary: Multiphase flow simulation requires high accuracy due to possible component losses that may be caused by sparse meshing during the computation.
We propose a novel deep learning framework BubbleNet, which entails three main parts: deep neural networks (DNN) with sub nets for predicting different physics fields.
Results indicate our framework can predict the physics fields more accurately, estimating the absolute prediction errors.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Micro-bubbles and bubbly flows are widely observed and applied to medicine,
involves deformation, rupture, and collision of bubbles, phase mixture, etc. We
study bubble dynamics by setting up two numerical simulation cases: bubbly flow
with a single bubble and multiple bubbles, both confined in the microtube, with
parameters corresponding to their medical backgrounds. Both the cases have
their medical background applications. Multiphase flow simulation requires high
computation accuracy due to possible component losses that may be caused by
sparse meshing during the computation. Hence, data-driven methods can be
adopted as a useful tool. Based on physics-informed neural networks (PINNs), we
propose a novel deep learning framework BubbleNet, which entails three main
parts: deep neural networks (DNN) with sub nets for predicting different
physics fields; the physics-informed part, with the fluid continuum condition
encoded within; the time discretized normalizer (TDN), an algorithm to
normalize field data per time step before training. We apply the traditional
DNN and our BubbleNet to train the simulation data and predict the physics
fields of both the two bubbly flow cases. Results indicate our framework can
predict the physics fields more accurately, estimating the prediction absolute
errors. The proposed network can potentially be applied to many other
engineering fields.
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