Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces
<p dir="ltr">The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate...
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2021
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| _version_ | 1864513505669939200 |
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| author | Uzair Sajjad (19646296) |
| author2 | Imtiyaz Hussain (19646299) Muhammad Sultan (6028859) Sadaf Mehdi (19646302) Chi-Chuan Wang (787384) Kashif Rasool (2542492) Sayed M. Saleh (2181514) Ashraf Y. Elnaggar (11866546) Enas E. Hussein (19646305) |
| author2_role | author author author author author author author author |
| author_facet | Uzair Sajjad (19646296) Imtiyaz Hussain (19646299) Muhammad Sultan (6028859) Sadaf Mehdi (19646302) Chi-Chuan Wang (787384) Kashif Rasool (2542492) Sayed M. Saleh (2181514) Ashraf Y. Elnaggar (11866546) Enas E. Hussein (19646305) |
| author_role | author |
| dc.creator.none.fl_str_mv | Uzair Sajjad (19646296) Imtiyaz Hussain (19646299) Muhammad Sultan (6028859) Sadaf Mehdi (19646302) Chi-Chuan Wang (787384) Kashif Rasool (2542492) Sayed M. Saleh (2181514) Ashraf Y. Elnaggar (11866546) Enas E. Hussein (19646305) |
| dc.date.none.fl_str_mv | 2021-11-16T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/su132212631 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Determining_the_Factors_Affecting_the_Boiling_Heat_Transfer_Coefficient_of_Sintered_Coated_Porous_Surfaces/26984335 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Fluid mechanics and thermal engineering Information and computing sciences Machine learning pool boiling heat transfer coefficient sintered coated porous surfaces deep neural network Bayesian optimization gaussian process gradient boosting regression trees |
| dc.title.none.fl_str_mv | Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influential parameters into the architecture of the model. In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. The optimized model is then employed to perform sensitivity analysis for finding the most influential parameters in the boiling heat transfer assessment of sintered coated porous surfaces on copper substrate subjected to a variety of high- and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface. The model with all the surface morphological features, liquid thermophysical properties, and pool boiling testing parameters demonstrates the highest correlation coefficient, R<sup>2</sup> = 0.985, for HTC prediction. The superheated wall is noted to have the maximum effect on the predictive accuracy of the boiling heat transfer coefficient. For example, if the wall superheat is dropped from the modeling parameters, the lowest prediction of R<sup>2</sup> (0.893) is achieved. The surface morphological features show relatively less influence compared to the liquid thermophysical properties. The proposed methodology is effective in determining the highly influencing surface and liquid parameters for the boiling heat transfer assessment of porous surfaces.</p><h2>Other Information</h2><p dir="ltr">Published in: Sustainability<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/su132212631" target="_blank">https://dx.doi.org/10.3390/su132212631</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_473d0fecb6d171c0c04016d66485de81 |
| identifier_str_mv | 10.3390/su132212631 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26984335 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous SurfacesUzair Sajjad (19646296)Imtiyaz Hussain (19646299)Muhammad Sultan (6028859)Sadaf Mehdi (19646302)Chi-Chuan Wang (787384)Kashif Rasool (2542492)Sayed M. Saleh (2181514)Ashraf Y. Elnaggar (11866546)Enas E. Hussein (19646305)EngineeringFluid mechanics and thermal engineeringInformation and computing sciencesMachine learningpool boiling heat transfer coefficientsintered coated porous surfacesdeep neural networkBayesian optimizationgaussian processgradient boosting regression trees<p dir="ltr">The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influential parameters into the architecture of the model. In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. The optimized model is then employed to perform sensitivity analysis for finding the most influential parameters in the boiling heat transfer assessment of sintered coated porous surfaces on copper substrate subjected to a variety of high- and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface. The model with all the surface morphological features, liquid thermophysical properties, and pool boiling testing parameters demonstrates the highest correlation coefficient, R<sup>2</sup> = 0.985, for HTC prediction. The superheated wall is noted to have the maximum effect on the predictive accuracy of the boiling heat transfer coefficient. For example, if the wall superheat is dropped from the modeling parameters, the lowest prediction of R<sup>2</sup> (0.893) is achieved. The surface morphological features show relatively less influence compared to the liquid thermophysical properties. The proposed methodology is effective in determining the highly influencing surface and liquid parameters for the boiling heat transfer assessment of porous surfaces.</p><h2>Other Information</h2><p dir="ltr">Published in: Sustainability<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/su132212631" target="_blank">https://dx.doi.org/10.3390/su132212631</a></p>2021-11-16T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/su132212631https://figshare.com/articles/journal_contribution/Determining_the_Factors_Affecting_the_Boiling_Heat_Transfer_Coefficient_of_Sintered_Coated_Porous_Surfaces/26984335CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/269843352021-11-16T03:00:00Z |
| spellingShingle | Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces Uzair Sajjad (19646296) Engineering Fluid mechanics and thermal engineering Information and computing sciences Machine learning pool boiling heat transfer coefficient sintered coated porous surfaces deep neural network Bayesian optimization gaussian process gradient boosting regression trees |
| status_str | publishedVersion |
| title | Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces |
| title_full | Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces |
| title_fullStr | Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces |
| title_full_unstemmed | Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces |
| title_short | Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces |
| title_sort | Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces |
| topic | Engineering Fluid mechanics and thermal engineering Information and computing sciences Machine learning pool boiling heat transfer coefficient sintered coated porous surfaces deep neural network Bayesian optimization gaussian process gradient boosting regression trees |