Data-driven physics-informed constitutive metamodeling of complex fluids: A multifidelity neural network (MFNN) framework
Data-driven physics-informed constitutive metamodeling of complex fluids: A multifidelity neural network (MFNN) framework
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DOI：
10.1122/8.0000138
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发表时间：
2021-03-01
影响因子：
3.3
通讯作者：
Jamali, Safa
中科院分区：
文献类型：
--
作者：
Mahmoudabadbozchelou, Mohammadamin;Caggioni, Marco;Jamali, Safa
在这项工作中，我们介绍了一个全面的机器学习算法，即多保真度神经网络（MFNN）架构的数据驱动的本构元建模的复杂流体。这里开发的基于物理学的神经网络通过合成生成基于低保真度模型的数据点，由基础流变本构模型提供信息。这些基于流变学的算法的性能得到了彻底的研究，并与经典的深度神经网络（DNN）进行了比较。MFNNs被发现恢复实验观察到的多组分复杂流体的流变学组成的几个不同的胶体颗粒，蠕虫状胶束，和其他油和芳香族颗粒。此外，数据驱动的模型能够成功地预测该流体的稳态剪切粘度在广泛的应用剪切速率的基础上，其组成成分。建立在所展示的框架，我们提出了一系列的多组分复杂流体的DNN和MFNN的流变预测。我们表明，通过将适当的物理直观的神经网络，MFNN算法捕获的作用，实验温度，盐浓度添加到混合物中，以及老化范围内和外的训练数据参数。这是通过利用大量符合特定流变模型的合成低保真数据实现的。相比之下，人们一致发现纯粹数据驱动的DNN可以预测错误的流变行为。
In this work, we introduce a comprehensive machine-learning algorithm, namely, a multifidelity neural network (MFNN) architecture for data-driven constitutive metamodeling of complex fluids. The physics-based neural networks developed here are informed by the underlying rheological constitutive models through the synthetic generation of low-fidelity model-based data points. The performance of these rheologically informed algorithms is thoroughly investigated and compared against classical deep neural networks (DNNs). The MFNNs are found to recover the experimentally observed rheology of a multicomponent complex fluid consisting of several different colloidal particles, wormlike micelles, and other oil and aromatic particles. Moreover, the data-driven model is capable of successfully predicting the steady state shear viscosity of this fluid under a wide range of applied shear rates based on its constituting components. Building upon the demonstrated framework, we present the rheological predictions of a series of multicomponent complex fluids made by DNN and MFNN. We show that by incorporating the appropriate physical intuition into the neural network, the MFNN algorithms capture the role of experiment temperature, the salt concentration added to the mixture, as well as aging within and outside the range of training data parameters. This is made possible by leveraging an abundance of synthetic low-fidelity data that adhere to specific rheological models. In contrast, a purely data-driven DNN is consistently found to predict erroneous rheological behavior.