Data-driven time-dependent state estimation for interfacial fluid mechanics in evaporating droplets

Abstract Droplet evaporation plays crucial roles in biodiagnostics, microfabrication, and inkjet printing. Experimentally studying the evolution of a sessile droplet consisting of two or more components needs sophisticated equipment to control the vast parameter space affecting the physical process....

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Autores principales:	Sahar Andalib, Kunihiko Taira, H. Pirouz Kavehpour
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d57b92d6c8a141bea9c30c599fc4638e
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spelling	oai:doaj.org-article:d57b92d6c8a141bea9c30c599fc4638e2021-12-02T16:10:38ZData-driven time-dependent state estimation for interfacial fluid mechanics in evaporating droplets10.1038/s41598-021-92965-82045-2322https://doaj.org/article/d57b92d6c8a141bea9c30c599fc4638e2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92965-8https://doaj.org/toc/2045-2322Abstract Droplet evaporation plays crucial roles in biodiagnostics, microfabrication, and inkjet printing. Experimentally studying the evolution of a sessile droplet consisting of two or more components needs sophisticated equipment to control the vast parameter space affecting the physical process. On the other hand, the non-axisymmetric nature of the problem, attributed to compositional perturbations, introduces challenges to numerical methods. In this work, droplet evaporation problem is studied from a new perspective. We analyze a sessile methanol droplet evolution through data-driven classification and regression techniques. The models are trained using experimental data of methanol droplet evolution under various environmental humidity levels and substrate temperatures. At higher humidity levels, the interfacial tension and subsequently contact angle increase due to higher water uptake into droplet. Therefore, different regimes of evolution are observed due to adsorption–absorption and possible condensation of water which turns the droplet from a single component into a binary system. In this work, machine learning and data-driven techniques are utilized to estimate the regime of droplet evaporation, the time evolution of droplet base diameter and contact angle, and level of surrounding humidity. Droplet regime is estimated by classification algorithms through point-by-point analysis of droplet profile. Decision tree demonstrates a better performance compared to Naïve Bayes (NB) classifier. Additionally, the level of surrounding humidity, as well as the time evolution of droplet base diameter and contact angle, are estimated by regression algorithms. The estimation results show promising performance for four cases of methanol droplet evolution under conditions unseen by the model, demonstrating the model’s capability to capture the complex physics underlying binary droplet evolution.Sahar AndalibKunihiko TairaH. Pirouz KavehpourNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sahar Andalib Kunihiko Taira H. Pirouz Kavehpour Data-driven time-dependent state estimation for interfacial fluid mechanics in evaporating droplets
description	Abstract Droplet evaporation plays crucial roles in biodiagnostics, microfabrication, and inkjet printing. Experimentally studying the evolution of a sessile droplet consisting of two or more components needs sophisticated equipment to control the vast parameter space affecting the physical process. On the other hand, the non-axisymmetric nature of the problem, attributed to compositional perturbations, introduces challenges to numerical methods. In this work, droplet evaporation problem is studied from a new perspective. We analyze a sessile methanol droplet evolution through data-driven classification and regression techniques. The models are trained using experimental data of methanol droplet evolution under various environmental humidity levels and substrate temperatures. At higher humidity levels, the interfacial tension and subsequently contact angle increase due to higher water uptake into droplet. Therefore, different regimes of evolution are observed due to adsorption–absorption and possible condensation of water which turns the droplet from a single component into a binary system. In this work, machine learning and data-driven techniques are utilized to estimate the regime of droplet evaporation, the time evolution of droplet base diameter and contact angle, and level of surrounding humidity. Droplet regime is estimated by classification algorithms through point-by-point analysis of droplet profile. Decision tree demonstrates a better performance compared to Naïve Bayes (NB) classifier. Additionally, the level of surrounding humidity, as well as the time evolution of droplet base diameter and contact angle, are estimated by regression algorithms. The estimation results show promising performance for four cases of methanol droplet evolution under conditions unseen by the model, demonstrating the model’s capability to capture the complex physics underlying binary droplet evolution.
format	article
author	Sahar Andalib Kunihiko Taira H. Pirouz Kavehpour
author_facet	Sahar Andalib Kunihiko Taira H. Pirouz Kavehpour
author_sort	Sahar Andalib
title	Data-driven time-dependent state estimation for interfacial fluid mechanics in evaporating droplets
title_short	Data-driven time-dependent state estimation for interfacial fluid mechanics in evaporating droplets
title_full	Data-driven time-dependent state estimation for interfacial fluid mechanics in evaporating droplets
title_fullStr	Data-driven time-dependent state estimation for interfacial fluid mechanics in evaporating droplets
title_full_unstemmed	Data-driven time-dependent state estimation for interfacial fluid mechanics in evaporating droplets
title_sort	data-driven time-dependent state estimation for interfacial fluid mechanics in evaporating droplets
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d57b92d6c8a141bea9c30c599fc4638e
work_keys_str_mv	AT saharandalib datadriventimedependentstateestimationforinterfacialfluidmechanicsinevaporatingdroplets AT kunihikotaira datadriventimedependentstateestimationforinterfacialfluidmechanicsinevaporatingdroplets AT hpirouzkavehpour datadriventimedependentstateestimationforinterfacialfluidmechanicsinevaporatingdroplets
_version_	1718384439530094592

Data-driven time-dependent state estimation for interfacial fluid mechanics in evaporating droplets

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