A Smart Model for the Prediction of Heat Transfer Coefficient during Flow Boiling of Nanofluids in Horizontal Tube
The goal of this study is to improve the accuracy and the validity of the prediction of the heat transfer coefficient (HTC) throughout flow boiling of different water-based nanofluids in a horizontal tube by developing an artificial neural network model using Ag/water, Cu/water, CuO/water, Al2O3/wat...
Saved in:
| Published in | Nano hybrids and composites Vol. 36; pp. 89 - 102 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Zurich
Trans Tech Publications Ltd
20.06.2022
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | The goal of this study is to improve the accuracy and the validity of the prediction of the heat transfer coefficient (HTC) throughout flow boiling of different water-based nanofluids in a horizontal tube by developing an artificial neural network model using Ag/water, Cu/water, CuO/water, Al2O3/water, and TiO2/water nanofluids. The multiple layer perceptron (MLP) neural network was designed and trained by 354 experimental data points that were collected from the literature. Thermal conductivity of nanoparticle, mass flux, volumetric concentration, and heat flux were used to serve as input variables of the model. The heat transfer coefficient (HTC) was used as the output variable. Via the method of the trial-and error, MLP with 8 neurons in the hidden layer was attained as the optimal artificial neural network structure. This developed smart model is more accordant with the experimental data than the correlations of the literature. The accuracy of the developed smart model was validated by the value of mean squared error (MSE=0.042) and the value of determination coefficient (R2= 0.9992 ) for all data. |
|---|---|
| AbstractList | The goal of this study is to improve the accuracy and the validity of the prediction of the heat transfer coefficient (HTC) throughout flow boiling of different water-based nanofluids in a horizontal tube by developing an artificial neural network model using Ag/water, Cu/water, CuO/water, Al2O3/water, and TiO2/water nanofluids. The multiple layer perceptron (MLP) neural network was designed and trained by 354 experimental data points that were collected from the literature. Thermal conductivity of nanoparticle, mass flux, volumetric concentration, and heat flux were used to serve as input variables of the model. The heat transfer coefficient (HTC) was used as the output variable. Via the method of the trial-and error, MLP with 8 neurons in the hidden layer was attained as the optimal artificial neural network structure. This developed smart model is more accordant with the experimental data than the correlations of the literature. The accuracy of the developed smart model was validated by the value of mean squared error (MSE=0.042) and the value of determination coefficient (R2= 0.9992 ) for all data. The goal of this study is to improve the accuracy and the validity of the prediction of the heat transfer coefficient (HTC) throughout flow boiling of different water-based nanofluids in a horizontal tube by developing an artificial neural network model using Ag/water, Cu/water, CuO/water, Al 2 O 3 /water, and TiO 2 /water nanofluids. The multiple layer perceptron (MLP) neural network was designed and trained by 354 experimental data points that were collected from the literature. Thermal conductivity of nanoparticle, mass flux, volumetric concentration, and heat flux were used to serve as input variables of the model. The heat transfer coefficient (HTC) was used as the output variable. Via the method of the trial-and error, MLP with 8 neurons in the hidden layer was attained as the optimal artificial neural network structure. This developed smart model is more accordant with the experimental data than the correlations of the literature. The accuracy of the developed smart model was validated by the value of mean squared error (MSE=0.042) and the value of determination coefficient (R 2 = 0.9992 ) for all data. |
| Author | Bouali, Adel Mohammedi, Brahim Hanini, Salah |
| Author_xml | – givenname: Salah surname: Hanini fullname: Hanini, Salah email: hanini.salah@univ-medea.dz organization: University of Medea : Biomaterials and Transport Phenomena Laboratory – givenname: Brahim surname: Mohammedi fullname: Mohammedi, Brahim email: b.mohammedi@crnb.dz organization: Nuclear Research Center of Birine – givenname: Adel surname: Bouali fullname: Bouali, Adel email: bouali.adel@univ-medea.dz organization: University of Medea : Biomaterials and Transport Phenomena Laboratory |
| BookMark | eNplkF1LwzAUhoNMcM6BPyHgjQjVk6S2zeUczgnzA5zXJU2TLaMmNUkR_fV2VPHCq3MOPO974DlGI-usQuiUwGUKtLhqE75RQDYHaEwpzxOWAox-d5bDEZqGsAMASggnGR0jP8Mvb8JH_OBq1WDtPI5bhZ-9qo2MxlnsNF4qEfHaCxu08njulNZGGmUjrjtv7AYvGveBb5xp9kcfeBTW6aYzdcDG4qXz5svZKBq87ip1gg61aIKa_swJel3crufLZPV0dz-frRJJKcQkz0RWgWRZytNc6JqmooJCSAkSIC80ZNk1MK4ynSouRJ4yVmguij5QkVoINkFnQ2_r3XunQix3rvO2f1ky4CRnHAjtqfOBkt6F4JUuW296I58lgXIvtWzLQWqPXgxo3KuISm7_Gv_B388oeg4 |
| CitedBy_id | crossref_primary_10_54691_sjt_v5i6_5154 crossref_primary_10_1016_j_tsep_2022_101563 |
| Cites_doi | 10.2514/1.t4910 10.1016/j.ijheatmasstransfer.2020.119783 10.1016/j.icheatmasstransfer.2020.104667 10.2298/tsci200620238b 10.1016/j.ijheatmasstransfer.2020.120204 10.1016/j.icheatmasstransfer.2016.05.011 10.1134/s002189441806010x 10.1016/j.icheatmasstransfer.2013.03.014 10.15255/KUI.2019.022 10.1007/s10973-019-08746-z 10.1080/01457632.2014.994990 10.1007/s10973-020-09373-9 10.15255/kui.2019.053 10.1016/j.expthermflusci.2013.09.012 10.1016/j.applthermaleng.2017.12.068 10.1016/j.molliq.2014.03.001 10.15255/cabeq.2014.2069 10.1016/j.icheatmasstransfer.2014.04.005 10.1016/j.cherd.2013.08.007 10.2298/tsci130929122r 10.1016/j.applthermaleng.2015.07.016 10.1016/j.geoderma.2018.05.030 10.1016/j.ijheatmasstransfer.2015.05.043 10.1021/acs.jced.0c00168.s001 10.1016/0954-1810(94)00011-s 10.1016/j.ijheatmasstransfer.2019.119211 10.1016/j.catena.2018.10.047 10.1016/j.applthermaleng.2015.12.052 10.1016/j.powtec.2015.10.022 10.1016/j.ijthermalsci.2020.106581 10.1016/j.ijheatmasstransfer.2006.10.024 10.1134/s0021894417010084 10.15255/kui.2019.024 10.1016/j.molliq.2019.111025 10.1016/j.ijheatmasstransfer.2020.119834 10.1016/j.ijmultiphaseflow.2009.02.015 10.1016/j.ijheatmasstransfer.2009.11.026 10.1007/s00231-014-1325-1 10.1016/j.powtec.2019.08.049 10.1016/j.ijheatmasstransfer.2013.03.054 10.1016/j.ijrefrig.2015.04.011 10.1016/j.icheatmasstransfer.2013.04.015 10.1007/s10973-019-08674-y 10.1016/j.ijheatmasstransfer.2012.01.013 10.1016/j.ijrefrig.2013.08.020 10.15255/kui.2019.002 |
| ContentType | Journal Article |
| Copyright | 2022 Trans Tech Publications Ltd Copyright Trans Tech Publications Ltd. 2022 |
| Copyright_xml | – notice: 2022 Trans Tech Publications Ltd – notice: Copyright Trans Tech Publications Ltd. 2022 |
| DBID | AAYXX CITATION 7SR 7U5 8BQ 8FD F28 FR3 JG9 L7M |
| DOI | 10.4028/p-9ge01g |
| DatabaseName | CrossRef Engineered Materials Abstracts Solid State and Superconductivity Abstracts METADEX Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database Materials Research Database Advanced Technologies Database with Aerospace |
| DatabaseTitle | CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Solid State and Superconductivity Abstracts Engineering Research Database Advanced Technologies Database with Aerospace ANTE: Abstracts in New Technology & Engineering METADEX |
| DatabaseTitleList | CrossRef Materials Research Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2297-3400 |
| EndPage | 102 |
| ExternalDocumentID | 10_4028_p_9ge01g |
| GroupedDBID | .4S 8FE 8FG ABJCF ABJNI ACIWK AEDRD ALMA_UNASSIGNED_HOLDINGS BENPR BGLVJ BPHCQ CZ9 D1I DB1 DKFMR EBS KB. KC. PQQKQ PROAC RTP AAYXX CITATION 7SR 7U5 8BQ 8FD F28 FR3 JG9 L7M |
| ID | FETCH-LOGICAL-c220t-76a6b0c364947afd24ab08acc0c0078f0665039e6f4e9aa74338f9a8364b1daa3 |
| ISSN | 2297-3370 2297-3400 |
| IngestDate | Wed Aug 14 04:40:56 EDT 2024 Wed Aug 28 12:36:08 EDT 2024 Fri Sep 06 21:27:46 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Flow Boiling Heat Transfer Coefficient Artificial Neural Networks Nanofluid |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c220t-76a6b0c364947afd24ab08acc0c0078f0665039e6f4e9aa74338f9a8364b1daa3 |
| PQID | 3091739012 |
| PQPubID | 2029172 |
| PageCount | 14 |
| ParticipantIDs | proquest_journals_3091739012 crossref_primary_10_4028_p_9ge01g transtech_journals_10_4028_p_9ge01g |
| PublicationCentury | 2000 |
| PublicationDate | 20220620 |
| PublicationDateYYYYMMDD | 2022-06-20 |
| PublicationDate_xml | – month: 06 year: 2022 text: 20220620 day: 20 |
| PublicationDecade | 2020 |
| PublicationPlace | Zurich |
| PublicationPlace_xml | – name: Zurich |
| PublicationTitle | Nano hybrids and composites |
| PublicationYear | 2022 |
| Publisher | Trans Tech Publications Ltd |
| Publisher_xml | – name: Trans Tech Publications Ltd |
| References | Yang (4588577); 56 Sadripour (4588554) 2019; 59 Kisi (4588595); 174 He (4588565); 50 Shoghl (4588558); 45 Maleki (4588588) 2020; 143 Khooshechin (4588562); 154 Mukherjee (4686509); 159 Almanassra (4686511); 304 Rajabnia (4588578); 20 Setoodeh (4588599); 90 Belmadani (4588590) 2020; 69 Sarafraz (4686513); 287 4588601 Diao (4588569) 2014; 50 Kahani (4588589); 116 Qiu (4588584); 149 Elias (4588561); 44 Park (4588566); 35 Lin (4588576); 98 Diao (4588575); 89 Henderson (4588579); 53 Okawa (4588567); 55 Dadhich (4588581) 2019; 139 Goh (4588602); 9 Sanikhani (4588598); 330 Bahman (4588586); 155 4588591 Tian (4588563); 356 4588593 Duursma (4588570) 2014; 36 4588594 Baqeri (4588574); 55 Das (4588553); 75 4588596 4588597 Vafaei (4588557); 92 Bouali (4588582); 25 Song (4686507); 141 Sarafraz (4588600); 52 Zarei (4588587) 2019; 139 4588583 Ahn (4686512); 62 Baniamerian (4588580); 31 Barki (4588592) 2019; 68 Longo (4588585); 160 Nikkhah (4588572) 2015; 29 Kandasamy (4686508) 2017; 58 Vanaki (4686510); 196 Sun (4588573); 38 |
| References_xml | – volume: 31 start-page: 136 issn: 1533-6808 issue: 1 ident: 4588580 article-title: Analytical Modeling of Boiling Nanofluids publication-title: Journal of Thermophysics and Heat Transfer doi: 10.2514/1.t4910 contributor: fullname: Baniamerian – volume: 154 start-page: 119783 ident: 4588562 article-title: The influence of surfactant and ultrasonic processing on improvement of stability and heat transfer coefficient of CuO nanoparticles in the pool boiling publication-title: International Journal of Heat and Mass Transfer doi: 10.1016/j.ijheatmasstransfer.2020.119783 contributor: fullname: Khooshechin – volume: 116 start-page: 104667 ident: 4588589 article-title: Application of M5 tree regression, MARS, and artificial neural network methods to predict the Nusselt number and output temperature of CuO based nanofluid flows in a car radiator publication-title: International Communications in Heat and Mass Transfer doi: 10.1016/j.icheatmasstransfer.2020.104667 contributor: fullname: Kahani – volume: 25 start-page: 3911 issn: 2334-7163 issue: 5 Part B ident: 4588582 article-title: Using artificial neural network for predicting heat transfer coefficient during flow boiling in an inclined channel publication-title: Thermal Science doi: 10.2298/tsci200620238b contributor: fullname: Bouali – volume: 160 start-page: 120204 ident: 4588585 article-title: Application of an Artificial Neural Network (ANN) for predicting low-GWP refrigerant boiling heat transfer inside Brazed Plate Heat Exchangers (BPHE) publication-title: International Journal of Heat and Mass Transfer doi: 10.1016/j.ijheatmasstransfer.2020.120204 contributor: fullname: Longo – volume: 75 start-page: 341 ident: 4588553 article-title: Synthesis and characterization of TiO2–water nanofluids with different surfactants publication-title: International Communications in Heat and Mass Transfer doi: 10.1016/j.icheatmasstransfer.2016.05.011 contributor: fullname: Das – volume: 59 start-page: 1049 issn: 1573-8620 issue: 6 year: 2019 ident: 4588554 article-title: Investigation of Flow Characteristics And Heat Transfer Enhancement in a Nanofluid Flow in a Corrugated Duct publication-title: Journal of Applied Mechanics and Technical Physics doi: 10.1134/s002189441806010x contributor: fullname: Sadripour – volume: 44 start-page: 93 ident: 4588561 article-title: Effect of nanoparticle shape on the heat transfer and thermodynamic performance of a shell and tube heat exchanger publication-title: International Communications in Heat and Mass Transfer doi: 10.1016/j.icheatmasstransfer.2013.03.014 contributor: fullname: Elias – ident: 4588594 – ident: 4588593 doi: 10.15255/KUI.2019.022 – ident: 4588601 – volume: 139 start-page: 3757 issn: 1588-2926 issue: 6 year: 2019 ident: 4588587 article-title: Prediction of pool boiling heat transfer coefficient for various nano-refrigerants utilizing artificial neural networks publication-title: Journal of Thermal Analysis and Calorimetry doi: 10.1007/s10973-019-08746-z contributor: fullname: Zarei – volume: 36 start-page: 1252 issn: 1521-0537 issue: 14-15 year: 2014 ident: 4588570 article-title: Flow and Heat Transfer of Single-and Two-Phase Boiling of Nanofluids in Microchannels publication-title: Heat Transfer Engineering doi: 10.1080/01457632.2014.994990 contributor: fullname: Duursma – ident: 4588583 – volume: 143 start-page: 4261 issn: 1588-2926 issue: 6 year: 2020 ident: 4588588 article-title: Thermal conductivity modeling of nanofluids with ZnO particles by using approaches based on artificial neural network and MARS publication-title: Journal of Thermal Analysis and Calorimetry doi: 10.1007/s10973-020-09373-9 contributor: fullname: Maleki – volume: 69 start-page: 355 issn: 1334-9090 issue: 7-8 year: 2020 ident: 4588590 article-title: Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis publication-title: Kemija u industriji doi: 10.15255/kui.2019.053 contributor: fullname: Belmadani – volume: 52 start-page: 205 ident: 4588600 article-title: Scale formation and subcooled flow boiling heat transfer of CuO–water nanofluid inside the vertical annulus publication-title: Experimental Thermal and Fluid Science doi: 10.1016/j.expthermflusci.2013.09.012 contributor: fullname: Sarafraz – volume: 141 start-page: 143 ident: 4686507 article-title: Nucleate pool boiling heat transfer of SES36 fluid on nanoporous surfaces obtained by electrophoretic deposition of Al2O3 publication-title: Applied Thermal Engineering doi: 10.1016/j.applthermaleng.2017.12.068 contributor: fullname: Song – volume: 196 start-page: 32 ident: 4686510 article-title: Effect of nanoparticle shapes on the heat transfer enhancement in a wavy channel with different phase shifts publication-title: Journal of Molecular Liquids doi: 10.1016/j.molliq.2014.03.001 contributor: fullname: Vanaki – volume: 29 start-page: 405 issn: 1846-5153 issue: 3 year: 2015 ident: 4588572 article-title: Application of Spherical Copper Oxide (II) Water Nano-fluid as a Potential Coolant in a Boiling Annular Heat Exchanger publication-title: Chemical and Biochemical Engineering Quarterly doi: 10.15255/cabeq.2014.2069 contributor: fullname: Nikkhah – volume: 55 start-page: 71 ident: 4588574 article-title: Experimental investigation of the forced convective boiling heat transfer of R-600a/oil/nanoparticle publication-title: International Communications in Heat and Mass Transfer doi: 10.1016/j.icheatmasstransfer.2014.04.005 contributor: fullname: Baqeri – volume: 92 start-page: 842 issue: 5 ident: 4588557 article-title: Role of nanoparticles on nanofluid boiling phenomenon: Nanoparticle deposition publication-title: Chemical Engineering Research and Design doi: 10.1016/j.cherd.2013.08.007 contributor: fullname: Vafaei – volume: 20 start-page: 99 issn: 2334-7163 issue: 1 ident: 4588578 article-title: Experimental investigation of subcooled flow boiling of water/TiO2 nanofluid in a horizontal tube publication-title: Thermal Science doi: 10.2298/tsci130929122r contributor: fullname: Rajabnia – volume: 90 start-page: 384 ident: 4588599 article-title: Subcooled flow boiling of alumina/water nanofluid in a channel with a hot spot: An experimental study publication-title: Applied Thermal Engineering doi: 10.1016/j.applthermaleng.2015.07.016 contributor: fullname: Setoodeh – volume: 330 start-page: 52 ident: 4588598 article-title: Non-tuned data intelligent model for soil temperature estimation: A new approach publication-title: Geoderma doi: 10.1016/j.geoderma.2018.05.030 contributor: fullname: Sanikhani – volume: 89 start-page: 110 ident: 4588575 article-title: Experimental investigation on the pool boiling characteristics and critical heat flux of Cu-R141b nanorefrigerant under atmospheric pressure publication-title: International Journal of Heat and Mass Transfer doi: 10.1016/j.ijheatmasstransfer.2015.05.043 contributor: fullname: Diao – ident: 4588597 doi: 10.1021/acs.jced.0c00168.s001 – volume: 9 start-page: 143 issue: 3 ident: 4588602 article-title: Back-propagation neural networks for modeling complex systems publication-title: Artificial Intelligence in Engineering doi: 10.1016/0954-1810(94)00011-s contributor: fullname: Goh – volume: 149 start-page: 119211 ident: 4588584 article-title: An artificial neural network model to predict mini/micro-channels saturated flow boiling heat transfer coefficient based on universal consolidated data publication-title: International Journal of Heat and Mass Transfer doi: 10.1016/j.ijheatmasstransfer.2019.119211 contributor: fullname: Qiu – volume: 174 start-page: 11 ident: 4588595 article-title: The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction publication-title: CATENA doi: 10.1016/j.catena.2018.10.047 contributor: fullname: Kisi – volume: 98 start-page: 944 ident: 4588576 article-title: Experimental research on particle aggregation behavior in nanorefrigerant–oil mixture publication-title: Applied Thermal Engineering doi: 10.1016/j.applthermaleng.2015.12.052 contributor: fullname: Lin – volume: 287 start-page: 412 ident: 4686513 article-title: Comparatively experimental study on the boiling thermal performance of metal oxide and multi-walled carbon nanotube nanofluids publication-title: Powder Technology doi: 10.1016/j.powtec.2015.10.022 contributor: fullname: Sarafraz – volume: 159 start-page: 106581 ident: 4686509 article-title: Experimental investigation on thermo-physical properties and subcooled flow boiling performance of Al2O3/water nanofluids in a horizontal tube publication-title: International Journal of Thermal Sciences doi: 10.1016/j.ijthermalsci.2020.106581 contributor: fullname: Mukherjee – ident: 4588596 – volume: 50 start-page: 2272 issue: 11-12 ident: 4588565 article-title: Heat transfer and flow behaviour of aqueous suspensions of TiO2 nanoparticles (nanofluids) flowing upward through a vertical pipe publication-title: International Journal of Heat and Mass Transfer doi: 10.1016/j.ijheatmasstransfer.2006.10.024 contributor: fullname: He – volume: 58 start-page: 71 issn: 1573-8620 issue: 1 year: 2017 ident: 4686508 article-title: Magnetohydrodynamic and heat transfer effects on the stagnation-point flow of an electrically conducting nanofluid past a porous vertical shrinking/stretching sheet in the presence of variable stream conditions publication-title: Journal of Applied Mechanics and Technical Physics doi: 10.1134/s0021894417010084 contributor: fullname: Kandasamy – ident: 4588591 doi: 10.15255/kui.2019.024 – volume: 304 start-page: 111025 ident: 4686511 article-title: An experimental study on stability and thermal conductivity of water/CNTs nanofluids using different surfactants: A comparison study publication-title: Journal of Molecular Liquids doi: 10.1016/j.molliq.2019.111025 contributor: fullname: Almanassra – volume: 155 start-page: 119834 ident: 4588586 article-title: Prediction of the minimum film boiling temperature using artificial neural network publication-title: International Journal of Heat and Mass Transfer doi: 10.1016/j.ijheatmasstransfer.2020.119834 contributor: fullname: Bahman – volume: 35 start-page: 525 issue: 6 ident: 4588566 article-title: Nucleate boiling heat transfer in aqueous solutions with carbon nanotubes up to critical heat fluxes publication-title: International Journal of Multiphase Flow doi: 10.1016/j.ijmultiphaseflow.2009.02.015 contributor: fullname: Park – volume: 53 start-page: 944 issue: 5-6 ident: 4588579 article-title: Flow-boiling heat transfer of R-134a-based nanofluids in a horizontal tube publication-title: International Journal of Heat and Mass Transfer doi: 10.1016/j.ijheatmasstransfer.2009.11.026 contributor: fullname: Henderson – volume: 50 start-page: 1261 issn: 1432-1181 issue: 9 year: 2014 ident: 4588569 article-title: Experimental investigation of the Cu/R141b nanofluids on the evaporation/boiling heat transfer characteristics for surface with capillary micro-channels publication-title: Heat and Mass Transfer doi: 10.1007/s00231-014-1325-1 contributor: fullname: Diao – volume: 356 start-page: 391 ident: 4588563 article-title: Experimental study of the effect of various surfactants on surface sediment and pool boiling heat transfer coefficient of silica/DI water nano-fluid publication-title: Powder Technology doi: 10.1016/j.powtec.2019.08.049 contributor: fullname: Tian – volume: 62 start-page: 718 ident: 4686512 article-title: The boiling phenomenon of alumina nanofluid near critical heat flux publication-title: International Journal of Heat and Mass Transfer doi: 10.1016/j.ijheatmasstransfer.2013.03.054 contributor: fullname: Ahn – volume: 56 start-page: 213 ident: 4588577 article-title: Experimental study on the heat transfer and flow characteristics of nanorefrigerants inside a corrugated tube publication-title: International Journal of Refrigeration doi: 10.1016/j.ijrefrig.2015.04.011 contributor: fullname: Yang – volume: 45 start-page: 122 ident: 4588558 article-title: Experimental investigation on pool boiling heat transfer of ZnO, and CuO water-based nanofluids and effect of surfactant on heat transfer coefficient publication-title: International Communications in Heat and Mass Transfer doi: 10.1016/j.icheatmasstransfer.2013.04.015 contributor: fullname: Shoghl – volume: 139 start-page: 3197 issn: 1588-2926 issue: 5 year: 2019 ident: 4588581 article-title: Flow boiling heat transfer analysis of Al2O3 and TiO2 nanofluids in horizontal tube using artificial neural network (ANN) publication-title: Journal of Thermal Analysis and Calorimetry doi: 10.1007/s10973-019-08674-y contributor: fullname: Dadhich – volume: 55 start-page: 2719 issue: 9-10 ident: 4588567 article-title: Boiling time effect on CHF enhancement in pool boiling of nanofluids publication-title: International Journal of Heat and Mass Transfer doi: 10.1016/j.ijheatmasstransfer.2012.01.013 contributor: fullname: Okawa – volume: 38 start-page: 206 ident: 4588573 article-title: Flow boiling heat transfer characteristics of nano-refrigerants in a horizontal tube publication-title: International Journal of Refrigeration doi: 10.1016/j.ijrefrig.2013.08.020 contributor: fullname: Sun – volume: 68 start-page: 289 issn: 1334-9090 issue: 7-8 year: 2019 ident: 4588592 article-title: Modelling of Adsorption of Methane, Nitrogen, Carbon Dioxide, Their Binary Mixtures, and Their Ternary Mixture on Activated Carbons Using Artificial Neural Network publication-title: Kemija u industriji doi: 10.15255/kui.2019.002 contributor: fullname: Barki |
| SSID | ssj0002119162 |
| Score | 2.2411246 |
| Snippet | The goal of this study is to improve the accuracy and the validity of the prediction of the heat transfer coefficient (HTC) throughout flow boiling of... |
| SourceID | proquest crossref transtech |
| SourceType | Aggregation Database Publisher |
| StartPage | 89 |
| SubjectTerms | Aluminum oxide Artificial neural networks Boiling Data points Heat flux Heat transfer Heat transfer coefficients Nanofluids Neural networks Thermal conductivity Titanium dioxide |
| Title | A Smart Model for the Prediction of Heat Transfer Coefficient during Flow Boiling of Nanofluids in Horizontal Tube |
| URI | https://www.scientific.net/NHC.36.89 https://www.proquest.com/docview/3091739012/abstract/ |
| Volume | 36 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwELdgPAAPiE9RGMgI3qqAEzsffuwQpUICIa2T9hY5ibNGypIqa4W2v353jhN3ZQ-Dl6hy66Ty_XK-O9_vjpBPUug4lyz0wixmngjj0lORSjwegjla-Dzihij881e0OBE_TsNTF8wx7JJN9jm_upVX8j9ShTGQK7Jk_0Gy401hAD6DfOEKEobrnWQ8mx6fw7BpaFaPCYO_Ozx8GSzBBYYAzI5U6g5ef21qRmAGgGUozuv2z_SorWqb_wz6ti3rbdVnyi7arrpqDWVyuc1u5A3hD6erS6R8XQz0OJMD5tISj1rkbBr9U2iXgdiuMF5eVD241Ko6d4qwqUyTqemxqm2o2sYkwJ1lkRcwp7qCQILqEqwf0reMWd3Ld5Vn30vIbsO-IWL_peHB3UXWwtqTZ5r5Z24XG07u9za3MeUQnB2cm67TfuZ98iCIZYj-ejL_PoblTME704Z2_L99yWKc_GV47E0jxnkmDzcoTqy-u2OkLJ-SJ9a7oLMeKs_IPd08J493ak6-IN2MGtBQAxoKoKEAGupAQ9uSImjoABq6Axrag4YiaKgFDU5woKFVQx1oKILmJTmZf1t-XXi28YaXBwHbeHGkoozlPBJSxKosAqEylqg8ZzmalCUe1zEudVQKLZUCI5QnpVQJTMj8Qin-ihw0baNfExr7QkVMikAVSuQ8VEWkAhXmUhdJoRmfkA_DSqbrvr5Kui-qCTkclji1b99FysHQjTFgF0zIx3HZ3ff7N3lzhwe9JY8cmg_Jwabb6ndgc26y9wYl1_BNhVQ |
| link.rule.ids | 315,786,790,27957,27958,33408 |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Smart+Model+for+the+Prediction+of+Heat+Transfer+Coefficient+during+Flow+Boiling+of+Nanofluids+in+Horizontal+Tube&rft.jtitle=Nano+hybrids+and+composites&rft.au=Bouali%2C+Adel&rft.au=Mohammedi%2C+Brahim&rft.au=Hanini%2C+Salah&rft.date=2022-06-20&rft.issn=2297-3400&rft.eissn=2297-3400&rft.volume=36&rft.spage=89&rft.epage=102&rft_id=info:doi/10.4028%2Fp-9ge01g&rft.externalDBID=n%2Fa&rft.externalDocID=10_4028_p_9ge01g |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fwww.scientific.net%2FImage%2FTitleCover%2F6544%3Fwidth%3D600 |