A honeycomb model for tortuosity of flow path in the leaf venation network

2014 ◽  
Vol 25 (06) ◽  
pp. 1450015 ◽  
Author(s):  
Han Liu ◽  
Ming-Qing Zou ◽  
Da-Lun Wang ◽  
Shan-Shan Yang ◽  
Ming-Chao Liang

A honeycomb model is designed according to the leaf veins, which is expressed as a function of porosity and tortuosity, and there is no empirical constant in this model. We mainly applied it to the leaf venation network, and the prediction in our model are compared with that from available correlations obtained by matching the numerical results, both of which are consistent with each other. Our model and relations may have important significance and potential applications in leaf venation and porous media. They also have a certain guiding significance to fluid heat transfer and thermal diffusion, as well as biotechnology research, e.g. veins and the neural networks of human.

2021 ◽  
Vol 13 (01) ◽  
pp. 2150001 ◽  
Author(s):  
Shoujing Zheng ◽  
Zishun Liu

We propose a machine learning embedded method of parameters determination in the constitutional models of hydrogels. It is found that the developed logistic regression-like algorithm for hydrogel swelling allows us to determine the fitting parameters based on known swelling ratio and chemical potential. We also put forward the neural networks-like algorithm, which, by its own property, can converge faster as the layer deepens. We then develop neural networks-like algorithm for hydrogel under uniaxial load for experimental application purpose. Finally, we propose several machine learning embedded potential applications for hydrogels, which would provide directions for machine learning-based hydrogel research.


2018 ◽  
Vol 49 (1) ◽  
pp. 77-90
Author(s):  
Eren Ucar ◽  
Moghtada Mobedi ◽  
Azita Ahmadi

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