State-of-the-art electrochromic thin films devices, fabrication techniques and applications: a review

Yaseen, Muhammad; Khattak, Muhammad Arif Khan; Khan, Abbas; Bibi, Shaista; Bououdina, Mohamed; Usman, Muhammad; Khan, Niaz Ali; Pirzado, Azhar Ali Ayaz; Abumousa, Rasha A.; Humayun, Muhammad

doi:10.1080/20550324.2023.2291619

Cited by 9 publications

(1 citation statement)

References 294 publications

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“…With the rapid development of computer technology, machine learning has demonstrated powerful data analysis capabilities, which have made great contributions to the development of many industries. Droplets of different sizes, physical properties, and concentrations are produced in processes involving droplet generation, such as microfluid [5,6], ink-jet printing [7], and spraying [8]. The study of droplet dynamics will generate a large number of valuable but complex droplet datasets.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Predict Fluid Viscosity Based on Droplet Dynamics Features

Qin,

Wang,

Tang

et al. 2024

Applied Sciences

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In recent years, machine learning has made significant progress in the field of micro-fluids, and viscosity prediction has become one of the hotspots of research. Due to the specificity of the application direction, the input datasets required for machine learning models are diverse, which limits the generalisation ability of the models. This paper starts by analysing the most obvious kinetic feature induced by viscosity during flow—the variation in droplet neck contraction with time (hmin/R∼τ). The kinetic processes of aqueous glycerol solutions of different viscosities when dropped in air were investigated by high-speed camera experiments, and the kinetic characteristics of the contraction of the liquid neck during droplet falling were extracted, using the Ohnesorge number (Oh=μ/(ρRσ)1/2) to represent the change in viscosity. Subsequently, the liquid neck contraction data were used as the original dataset, and three models, namely, random forest, multiple linear regression, and neural network, were used for training. The final results showed superior results for all three models, with the multivariate linear regression model having the best predictive ability with a correlation coefficient R2 of 0.98.

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