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 $\mathcal{P}$, from which we conclude that the 'physics-informed' part can serve as inner supervision. 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.
翻译:在化学工程、医学、数据驱动的方法涉及气泡的变形、破裂和碰撞、阶段性混合物等等。我们通过建立两个数字模拟案例来研究气泡动态:一个气泡和多个气泡的泡泡流,两者都限制在微气流中,其参数与医疗背景相对应。这两个案例都有医疗背景应用程序。多相流模拟需要高计算精度,因为计算过程中的偏差摄像值可能会导致部分损失。因此,数据驱动的方法可以被采纳为有用的工具。基于物理知情的神经网络(PINNS),我们提出一个新的深层次学习框架BubbleNet,它包含三个主要部分:深度神经网络(DNNN),其子网为不同物理场的预测;半物理知情部分,只有流动连续性条件和压力 Poisson 方程式 $mathcal{P} P} 内部编码。 时间分解后,可以将数据解析化的解析器(TDN),用于在培训前一步的正常的实地数据的算算。我们用的是传统的内基流流数据网络和软体外的两种方法。我们所训练的模拟的模拟的轨道数据。