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.
Micro-bubbles and bubbly flows are widely observed and applied in chemical engineering, 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 microchannel , 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 an 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 semi-physics-informed part, with only the fluid continuum condition and the pressure Poisson equation \mathcal{P 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 coarsened simulation data and predict the physics fields of both the two bubbly flow cases. The BubbleNets are trained for both with and without \mathcal{P Results indicate our framework can predict the physics fields more accurately, estimating the prediction absolute errors . Our deep learning predictions outperform traditional numerical methods computed with similar data density meshing . The proposed network can potentially be applied to many other engineering fields.
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to
in chemical engineering,
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microtube
microchannel
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a
an
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physics-informed
semi-physics-informed
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only
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and the pressure Poisson equation \mathcal{P
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coarsened
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The BubbleNets are trained for both with and without \mathcal{P
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. Our deep learning predictions outperform traditional numerical methods computed with similar data density meshing