2021
DOI: 10.3390/nano11123383
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Boiling Heat Transfer Evaluation in Nanoporous Surface Coatings

Abstract: The present study develops a deep learning method for predicting the boiling heat transfer coefficient (HTC) of nanoporous coated surfaces. Nanoporous coated surfaces have been used extensively over the years to improve the performance of the boiling process. Despite the large amount of experimental data on pool boiling of coated nanoporous surfaces, precise mathematical-empirical approaches have not been developed to estimate the HTC. The proposed method is able to cope with the complex nature of the boiling … Show more

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Cited by 18 publications
(4 citation statements)
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“…However, one main bottleneck commonly confronted is related to feature extraction. Selecting the appropriate descriptors by implementing a Pearson parametric correlation map ensuring that informed predictions can be objective and trusted, towards the development of a model with unbiased establishment of parameters relation [ 99 ]. This is often a good strategy to avoid overfitting issues when developing machine learning models and improve the prediction accuracy, while excluding strongly correlated features [ 36 ].…”
Section: Optimization Of Materials Synthesis Using High Throughput Sc...mentioning
confidence: 99%
“…However, one main bottleneck commonly confronted is related to feature extraction. Selecting the appropriate descriptors by implementing a Pearson parametric correlation map ensuring that informed predictions can be objective and trusted, towards the development of a model with unbiased establishment of parameters relation [ 99 ]. This is often a good strategy to avoid overfitting issues when developing machine learning models and improve the prediction accuracy, while excluding strongly correlated features [ 36 ].…”
Section: Optimization Of Materials Synthesis Using High Throughput Sc...mentioning
confidence: 99%
“…Today, the major focus of young researchers relies on ANNs due to their validation and precision; therefore, there is a plethora of research in the literature on the subject, including but not limited to entropy-generated systems [25], porous fins [26], COVID-19 [27], hydromagnetic Williamson fluid flow [28], carbon nanotubes [29], the Emden-Fowler equation [30], second-order singular functional differential models [31], Darcy-Forchheimer models [32], dissipative fluid flow systems [33], mosquito dispersal models [34] and many others [35][36][37][38]. A deep learning technique for estimating the boiling heat transfer coefficient of nanoporous coated surfaces was developed by the authors of [39]. Over the years, nanoporous-coated surfaces have been widely employed to boost boiling efficiency.…”
Section: Introductionmentioning
confidence: 99%
“…One of the paths to enhance heat transfer efficiency is surface engineering with nanostructured coatings. [25][26][27][28][29][30] There have been few works in terms of passive heat management techniques, such as metamaterials, graphene/Ag hybrid film, and SiO 2 particles, to mitigate the adverse thermal effects. [30][31][32][33] These nanostructures and nanostructured films (nanocrystalline, nanoporous, core-shell, nanocomposites, nanowires, nanorods, and QDs) may be artificial designs or nature-inspired designs from plants (epidermal cell of leaves of shade adapted plants), insects (moth-eye and butterfly wings) and other organisms (bacterial chlorosomes).…”
Section: Introductionmentioning
confidence: 99%