Non-Newtonian fluid simulation and reconstruction from monocular videos

Zhang, Yalan; Dong, Zirui; Ban, Xiaojuan; Obaidat, Mohammad S.

doi:10.1016/j.simpat.2022.102688

Simulation Modelling Practice and Theory

2023

DOI: 10.1016/j.simpat.2022.102688

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Non-Newtonian fluid simulation and reconstruction from monocular videos

Yalan Zhang

Zirui Dong²,

Xiaojuan Ban³

et al.

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2023

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Cited by 3 publications

References 19 publications

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Computational Sensing, Understanding, and Reasoning: An Artificial Intelligence Approach to Physics-Informed World Modeling

Moya,

Badías,

González

et al. 2023

Arch Computat Methods Eng

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Computational Sensing, Understanding, and Reasoning: An Artificial Intelligence Approach to Physics-Informed World Modeling

Moya,

Badías,

González

et al. 2023

Arch Computat Methods Eng

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A deep learning framework for solving forward and inverse problems of power-law fluids

Zhai,

Yin,

Pang

2023

Physics of Fluids

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We for the first time leverage deep learning approaches to solve forward and inverse problems of two-dimensional laminar flows for power-law fluids. We propose a deep-learning framework, called Power-Law-Fluid-Net (PL-Net). We develop a surrogate model to solve the forward problems of the power-law fluids, and solve the inverse problems utilizing only a small set of measurement data under the assumption that boundary conditions (BCs) can be partially known. In the design of the methods, we incorporate the hard boundary condition constraints to accelerate the iteration of stochastic gradient descent methods for minimizing loss functions. For the forward problems, by incorporating the constitutive parameters into the input variables of neural networks, the PL-Net serves as a surrogate model for simulating the pressure-driven flows inside pipes having cross sections of varying shapes. We investigate the influences of the BC type, activation function type, and number of collocation points on the accuracy of numerical solutions. For the inverse problems, the PL-Net infers the physical quantities or constitutive parameters from a small number of measurements of flow field variables. The BCs of the inverse problems can even be partially known. We demonstrate the effects of BC type, number of sensors, and noise level on accuracy of inferred quantities. Computational examples indicate the high accuracy of the PL-Net in tackling both the forward and inverse problems of the power-law fluids.

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Non-Newtonian ViRheometry via Similarity Analysis

Hamamichi,

Nagasawa,

Okada

et al. 2023

ACM Trans. Graph.

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We estimate the three Herschel-Bulkley parameters (yield stress σ Y , power-law index n , and consistency parameter η ) for shear-dependent fluid-like materials possibly with large-scale inclusions, for which rheometers may fail to provide a useful measurement. We perform experiments using the unknown material for dam-break (or column collapse) setups and capture video footage. We then use simulations to optimize for the material parameters. For better match up with the simple shear flow encountered in a rheometer, we modify the flow rule for the elasto-viscoplastic Herschel-Bulkley model. Analyzing the loss landscape for optimization, we realize a similarity relation ; material parameters far away within this relation would result in matched simulations, making it hard to distinguish the parameters. We found that by exploiting the setup dependency of the similarity relation, we can improve on the estimation using multiple setups, which we propose by analyzing the Hessian of the similarity relation. We validate the efficacy of our method by comparing the estimations to the measurements from a rheometer (for materials without large-scale inclusions) and show applications to materials with large-scale inclusions, including various salad or pasta sauces, and congee.

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Non-Newtonian fluid simulation and reconstruction from monocular videos

Cited by 3 publications

References 19 publications

Computational Sensing, Understanding, and Reasoning: An Artificial Intelligence Approach to Physics-Informed World Modeling

Computational Sensing, Understanding, and Reasoning: An Artificial Intelligence Approach to Physics-Informed World Modeling

A deep learning framework for solving forward and inverse problems of power-law fluids

Non-Newtonian ViRheometry via Similarity Analysis

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