ライブラリ登録: Guest
Heat Transfer Research

年間 18 号発行

ISSN 印刷: 1064-2285

ISSN オンライン: 2162-6561

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.4 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00072 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.43 SJR: 0.318 SNIP: 0.568 CiteScore™:: 3.5 H-Index: 28

Indexed in

ANALYZING ACTIVATION ENERGY AND BINARY CHEMICAL REACTION EFFECTS WITH ARTIFICIAL INTELLIGENCE APPROACH IN AXISYMMETRIC FLOW OF THIRD GRADE NANOFLUID SUBJECT TO SORET AND DUFOUR EFFECTS

巻 54, 発行 3, 2023, pp. 75-94
DOI: 10.1615/HeatTransRes.2022045008
Get accessGet access

要約

The use of nanotechnology has led to the design of many modern and more cost-effective implementation, such as solar power generation, the redevelopment of heat exchangers, and the modernization of the medical and pharmaceutical industries. In this study, the combined effects of activation energy with binary chemical reactant in a steady magnetohydrodynamic mixed convective third-grade nanofluid flow by radially radiative stretching plate has been analyzed with an artificial intelligence approach. Heat transfer analysis was conducted with heat generation, Joule heating, and Soret and Dufour effects. By incorporating appropriate transformations, the initial nonlinear coupled partial differential equations expressing the fluid model were formed as a comparable nonlinear ordinary differential equations system. Three different artificial neural network models were proposed in order to predict the skin friction, Nusselt number, and Sherwood number values of the fluid model by the Shooting Runge-Kutta Fehlberg 4, technique using the data set created by taking various values of the relevant parameters. It is worthy of noting that the average deviation values for each output parameter remained less than 5%. Furthermore it is also observed that mean square error values for skin friction coefficient, local Nusselt number, and local Sherwood number values were attained as 3.63 × 10-3, 4.03 × 10-4, and 8.62 × 10-3, respectively. The obtained results show that artificial neural networks are an engineering tool that can be used with high accuracy to estimate the combined effects of activation energy and binary chemical reaction in a fixed magnetohydrodynamic mixed convective third-grade nanofluid flow with a radial radiative stretched plate.

参考
  1. Abbas, Z., Sheikh, M., and Motsa, S.S., Numerical Solution of Binary Chemical Reaction on Stagnation Point Flow of Casson Fluid over a Stretching/Shrinking Sheet with Thermal Radiation, Energy, vol. 95, pp. 12-20,2016.

  2. Abdul Kareem, F.A., Shariff, A.M., Ullah, S., Garg, S., Dreisbach, F., Keong, L.K., and Mellon, N., Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite, Energy Technol., vol. 5, pp. 1373-1391,2017.

  3. Ahmadi, M.H., Sadeghzadeh, M., Raffiee, A.H., and Chau, K., Applying GMDH Neural Network to Estimate the Thermal Resistance and Thermal Conductivity of Pulsating Heat Pipes, Eng. Appl. Comput. FluidMech., vol. 13, pp. 327-336,2019a.

  4. Ahmadi, M.H., Mohseni-Gharyehsafa, B., Farzaneh-Gord, M., Jilte, R.D., Kumar, R., and Chau, K., Applicability of Connectionist Methods to Predict Dynamic Viscosity of Silver/Water Nanofluid by Using ANN-MLP, MARS and MPR Algorithms, Eng. Appl. Comput. Fluid Mech, vol. 13, pp. 220-228,2019b.

  5. Ahmadloo, E. and Azizi, S., Prediction of Thermal Conductivity of Various Nanofluids Using Artificial Neural Network, Int. Commun. Heat Mass Transf., vol. 74, pp. 69-75,2016.

  6. Ali, A., Abdulrahman, A., Garg, S., Maqsood, K., and Murshid, G., Application of Artificial Neural Networks (ANN) for Vapor-Liquid-Solid Equilibrium Prediction for CH4-CO2 Binary Mixture, Greenhouse Gases, vol. 9, pp. 67-78,2019.

  7. Alireza A., Toghraie, D., Sina, N., and Afrand, M., Developing Dissimilar Artificial Neural Networks (ANNs) to Prediction the Thermal Conductivity of MWCNT-TiO2/Water-Ethylene Glycol Hybrid Nanofluid, Powder Technol, vol. 355, pp. 602-610, 2019.

  8. Barati-Harooni, A. and Najafi-Marghmaleki, A., An Accurate RBF-NN Model for Estimation of Viscosity of Nanofluids, J. Mol. Liq, vol. 224, pp. 580-588,2016.

  9. Bestman, A., Natural Convection Boundary Layer with Suction and Mass Transfer in Porous Medium, Int. J. Eng. Res., vol. 14, pp. 389-396,1990.

  10. Bonakdari, H. and Zaji, A.H., Open Channel Junction Velocity Prediction by Using a Hybrid Self-Neuron Adjustable Artificial Neural Network, Flow Measure. Instrum., vol. 49, pp. 46-51,2016.

  11. Canakci, A., Ozsahin, S., and Varol, T., Modeling the Influence of a Process Control Agent on the Properties of Metal Matrix Composite Powders Using Artificial Neural Networks, Powder Technol, vol. 228, pp. 26-35,2012.

  12. Colak, A.B., Developing Optimal Artificial Neural Network (ANN) to Predict the Specific Heat of Water Based Yttrium Oxide (Y2O3) Nanofluid According to the Experimental Data and Proposing New Correlation, Heat Transf. Res., vol. 51, no. 17, pp. 1565-1586,2020.

  13. Colak, A.B., A Novel Comparative Investigation of the Effect of the Number of Neurons on the Predictive Performance of the Artificial Neural Network: An Experimental Study on the Thermal Conductivity of ZrO2 Nanofluid, Int. J. Energy Res., 2021a. DOI: 10.1002/er.6989.

  14. Colak, A.B., An Experimental Study on the Comparative Analysis of the Effect of the Number of Data on the Error Rates of Artificial Neural Networks, Int. J. Energy Res, vol. 45, no. 1, pp. 478-500,2021b.

  15. Colak, A.B., Experimental Analysis with Specific Heat of Water Based Zirconium Oxide Nanofluid on the Effect of Training Algorithm on Predictive Performance of Artificial Neural Network, Heat Transf. Res., vol. 52, no. 7, pp. 67-93,2021c.

  16. Colak, A.B., Yildiz, O., Bayrak, M., and Tezekici, B.S., Experimental Study for Predicting the Specific Heat of Water Based Cu-Al2O3 Hybrid Nanofluid Using Artificial Neural Network and Proposing New Correlation, Int. J. Energy Res, vol. 44, no. 9, pp. 7198-7215,2020.

  17. Colak, A.B., Guzel, T., Yildiz, O., and Ozer, M., An Experimental Study on Determination of the Shottky Diode Current-Voltage Characteristic Depending on Temperature with Artificial Neural Network, Phys. B, vol. 608, p. 412852,2021.

  18. Daryayehsalameh, B., Ayari, M.A., Tounsi, A., Khandakar, A., and Vaferi, B., Differentiation among Stability Regimes of Alumina-Water Nanofluids Using Smart Classifiers, Adv. Nano Res., vol. 12, no. 5, pp. 489-499,2022.

  19. Esmaeilzadeh, F., Teja, A.S., and Bakhtyari, A., The Thermal Conductivity, Viscosity, and Cloud Points of Bentonite Nanofluids withN-Pentadecane as the Base Fluid, J. Mol. Liq., vol. 300, p. 112307,2020.

  20. Guzel, T. and Colak, A.B., Artificial Intelligence Approach on Predicting Current Values of Polymer Interface Schottky Diode Based on Temperature and Voltage: An Experimental Study, Superlatt. Microstruct., vol. 153, p. 106864,2021.

  21. Hsiao, K.L., To Promote Radiation Electrical MHD Activation Energy Thermal Extrusion Manufacturing System Efficiency by Using Carreau-Nanofluid with Parameters Control Method, Energy, vol. 130, pp. 486-499,2017.

  22. Janardhana, R.G., Ashwini, H., and Mahesh, K., Computational Modeling of Unsteady Third-Grade Fluid Flow over a Vertical Cylinder: A Study of Heat Transfer Visualization, Res. Phys, vol. 8, pp. 671-682,2018.

  23. Kahani, M. and Vatankhah, G., Thermal Performance Prediction of Wickless Heat Pipe with Al2O3/Water Nanofluid Using Artificial Neural Network, Chem. Eng. Commun., vol. 206, pp. 509-523,2019.

  24. Kahani, M., Ghazvini, M., Mohseni-Gharyehsafa, B., Ahmadi, M.H., Pourfarhang, A., Shokrgozar, M., and Heris, S.Z., 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, Int. Commun. Heat Mass Transf, vol. 116,p. 104667,2020.

  25. Loganathan, K., Sagayaraj, A.C., Viloria, A., Varela, N., Lezama, O.B.P., and Ortiz-Ospino, L., Computational Analysis of Third-Grade Liquid Flow with Cross Diffusion Effects: Application to Entropy Modeling, Advances in Swarm Intelligence, Y. Tan, Y. Shi, and M. Tuba, Eds., Cham, Switzerland: Springer, 2020. DOI: 10.1007/978-3-030-53956-6.48.

  26. Loni, R., Asli-Ardeh, E.A., Ghobadian, B., Ahmadi, M.H., and Bellos, E., GMDH Modeling and Experimental Investigation of Thermal Performance Enhancement of Hemispherical Cavity Receiver Using MWCNT/Oil Nanofluid, Sol. Energy, vol. 171, pp. 790-803,2018.

  27. Maddah, H., Ghazvini, M., and Ahmadi, M.H., Predicting the Efficiency of CuO/Water Nanofluid in Heat Pipe Heat Exchanger Using Neural Network, Int. Commun. Heat Mass. Transf., vol. 104, pp. 33-40,2019.

  28. Makinde, O.D., Olanrewaju, P.O., and Charles, W.M., Unsteady Convection with Chemical Reaction and Radiative Heat Transfer past a Flat Porous Plate Moving through a Binary Mixture, Afrika Mat., vol. 22, pp. 65-78,2011.

  29. Manjula Devi, R., Murugesan, P., Venkatesan, M., Keerthika, P., Sudha, K., Kannan, J.C., and Suresh, P., Development of MLP-ANN Model to Predict the Nusselt Number of Plain Swirl Tapes Fixed in a Counter Flow Heat Exchanger, Mater. Today: Proc. vol. 46, no. 17, pp. 8854-8857,2021. DOI: 10.1016/j.matpr.2021.04.433.

  30. Mokashi, I., Afzal, A., Khan, S.A., Abdullah, N.A., Azami, M.H.B., Jilte, R.D., and Samuel, O.D., Nusselt Number Analysis from a Battery Pack Cooled by Different Fluids and Multiple Back-Propagation Modelling Using Feed-Forward Networks, Int. J. Therm. Sci., vol. 161, p. 106738,2021.

  31. Naphon, P., Wiriyasart, S., and Arisariyawong, T., Artificial Neural Network Analysis the Pulsating Nusselt Number and Friction Factor of TiO2/Water Nanofluids in the Spirally Coiled Tube with Magnetic Field, Int. J. Heat Mass Transf., vol. 118, pp. 1152-1159,2018.

  32. Ramezannezhad, M., Development of Simple-to-Use Predictive Models to Determine Thermal Properties of Fe2O3/Water-Ethylene Glycol Nanofluid, Comput., vol. 7, p. 18, 2019.

  33. Rostamian, S.H., Biglari, M., Saedodin, S., and Esfe, M.H., An Inspection of Thermal Conductivity of CuO-SWCNTs Hybrid Nanofluid versus Temperature and Concentration Using Experimental Data, ANN Modeling and New Correlation, J. Mol. Liq., vol. 231, pp. 364-369,2017.

  34. Sadeghzadeh, M., Ahmadi, M.H., Kahani, M., Sakhaeinia, H., Chaji, H., and Chen, L., Smart Modeling by Using Artificial Intelligent Techniques on Thermal Performance of Flatplate Solar Collector Using Nanofluid, Energy Sci. Eng., vol. 7, pp. 1649-1658, 2019b.

  35. Sandeep, N., Effect of Aligned Magnetic Field on Liquid Thin Film Flow of Magnetic Nano Fluids Embedded with Grapheme Nanoparticles, Adv. Powder Technol, vol. 28, pp. 865-875,2017.

  36. Shafiq, A., Khan, I., Rasool, G., Sherif, E.S.M., and Sheikh, A.H., Influence of Single- and Multi-Wall Carbon Nanotubes on Magnetohydrodynamic Stagnation Point Nanofluid Flow over Variable Thicker Surface with Concave and Convex Effects, Math., vol. 8 no. 1, p. 104,2020a.

  37. Shafiq, A., Rasool, G., Khalique, C.M., and Aslam, S., Second Grade Bioconvective Nanofluid Flow with Buoyancy Effect and Chemical Reaction, Symmetry, vol. 12, no. 4, p. 621,2020b.

  38. Shafiq, A., Lone, S.A., Sindhu, T.N., Al-Mdallal, Q.M., and Rasool, G., Statistical Modeling for Bioconvective Tangent Hyperbolic Nanofluid towards Stretching Surface with Zero Mass Flux Condition, Sci. Rep., vol. 11, no. 1, pp. 1-11,2021a.

  39. Shafiq, A., Colak, A.B., Sindhu, T.N., Al-Mdallal, Q.M., and Abdeljawad, T., Estimation of Unsteady Hydromagnetic Williamson Fluid Flow in a Radiative Surface through Numerical and Artificial Neural Network Modeling, Sci. Rep., vol. 11, no. 1, pp. 1-21, 2021b.

  40. Shafiq, A., Colak, A.B., and Sindhu, T.N., Designing Artificial Neural Network of Nanoparticle Diameter and Solid Fluid Interfacial Layer on SWCNTs/EG Nanofluid Flow on Thin Slendering Needles, Int. J. Numer. Methods Fluids, 2021c. DOI: 10.1002/fld.5038.

  41. Shafiq, A., Sindhu, T.N., and Al-Mdallal, Q.M., A Sensitivity Study on Carbon Nanotubes Significance in Darcy-Forchheimer Flow towards a Rotating Disk by Response Surface Methodology, Sci. Rep., vol. 11, no. 1,pp. 1-26,2021d.

  42. Vafaei, M., Afrand, M., Sina, N., Kalbasi, R., Sourani, F., and Teimouri, H., Evaluation of Thermal Conductivity of MgO-MWCNTs/EG Hybrid Nanofluids Based on Experimental Data by Selecting Optimal Artificial Neural Networks, Phys. E, vol. 85, pp. 90-96,2017.

  43. Vaferi, B., Eslamloueyan, R., and Ayatollahi, S., Automatic Recognition of Oil Reservoir Models from Well Testing Data by Using Multi-Layer Perceptron Networks, J. Petrol. Sci. Eng., vol. 77, pp. 254-262,2011.

  44. Vaferi, B., Samimi, F., Pakgohar, E., and Mowla, D., Artificial Neural Network Approach for Prediction of Thermal Behavior of Nanofluids Flowing through Circular Tubes, Powder Technol., vol. 267, pp. 1-10,2014.

  45. Wahiduzzaman, M., Special Non-Newtonian Third-Grade Fluid Flow with Magnetic Field: A Numerical Study, J. Nanofluids, vol. 11, no. 5, pp. 657-663,2022. DOI: 10.1166/jon.2022.1868.

近刊の記事

Effective Efficiency Analysis of Artificially Roughed Solar Air Heater MAN AZAD Energy, Exergy-Emission Performance Investigation of Heat Exchanger with Turbulators Inserts and Ternary Hybrid Nanofluid Ranjeet Rai, Vikash Kumar, Rashmi Rekha Sahoo Temperature correction method of radiation thermometer based on the nonlinear model fitted from spectral emissivity measurements of Ni-based alloy Yanfen Xu, kaihua zhang, Kun Yu, Yufang Liu Analysis of Thermal Performance in a Two-phase Thermosyphon loop based on Flow Visualization and an Image Processing Technique Avinash Jacob Balihar, Arnab Karmakar, Avinash Kumar, Smriti Minj, P L John Sangso Investigation of the Effect of Dead State Temperature on the Performance of Boron Added Fuels and Different Fuels Used in an Internal Combustion Engine. Irfan UÇKAN, Ahmet Yakın, Rasim Behçet PREDICTION OF PARAMETERS OF BOILER SUPERHEATER BASED ON TRANSFER LEARNING METHOD Shuiguang Tong, Qi Yang, Zheming Tong, Haidan Wang, Xin Chen A temperature pre-rectifier with continuous heat storage and release for waste heat recovery from periodic flue gas Hengyu Qu, Binfan Jiang, Xiangjun Liu, Dehong Xia Study on the Influence of Multi-Frequency Noise on the Combustion Characteristics of Pool Fires in Ship Engine Rooms Zhilin Yuan, Liang Wang, Jiasheng Cao, Yunfeng Yan, Jiaqi Dong, Bingxia Liu, Shuaijun Wang Experimental study on two-phase nonlinear oscillation behavior of miniaturized gravitational heat pipe Yu Fawen, Chaoyang Zhang, Tong Li, Yuhang Zhang, Wentao Zheng Flow boiling heat transfer Coefficient used for the Design of the Evaporator of a Refrigeration Machine using CO2 as Working Fluid Nadim KAROUNE, Rabah GOMRI Analyzing The Heat and Flow Characteristics In Spray Cooling By Using An Optimized Rectangular Finned Heat Sink Altug Karabey, Kenan Yakut Thermal management of lithium-ion battery packs by using corrugated channels with nano-enhanced cooling Fatih Selimefendigil, Aykut Can, Hakan Öztop Convective heat transfer inside a rotating helical pipe filled with saturated porous media Krishan Sharma, Deepu P, Subrata Kumar Preparation method and thermal performance of a new ultra-thin flexible flat plate heat pipe Xuancong Zhang, Jinwang Li, Qi Chen Influence of Temperature Gradients and Fluid Vibrations on the Thermocapillary Droplet Behavior in a Rotating Cylinder Yousuf Alhendal The Effect of Corrugation on Heat Transfer and Pressure Drop in a Solar Air Heater: A Numerical Investigation Aneeq Raheem, Waseem Siddique, Shoaib A.Warraich, Khalid Waheed, Inam Ul Haq, Muhammad Tabish Raheem, Muhammad Muneeb Yaseen Investigation of the Effect of Using Different Nanofluids on the Performance of the Organic Rankine Cycle Meltem ARISU, Tayfun MENLİK Entropy generation and heat transfer performance of cylindrical tube heat exchanger with perforated conical rings: a numerical study Anitha Sakthivel, Tiju Thomas Molecular dynamics study of the thermal transport properties in the graphene/C3N multilayer in-plane heterostructures Junjie Zhu, Jifen Wang, Xinyi Liu, Kuan Zhao Flow boiling critical heat flux in a small tube for FC-72 Yuki Otsuki, Makoto Shibahara, Qiusheng Liu, Sutopo Fitri STUDY OF FORCED ACOUSTIC OSCILLATIONS INFLUENCE ON METHANE OXIDATION PROCESS IN OXYGEN-CONTAINING FLOW OF HYDROGEN COMBUSTION PRODUCTS Anastasiya Krikunova, Konstantin Arefyev, Ilya Grishin, Maxim Abramov, Vladislav Ligostaev, Evgeniy Slivinskii, Vitaliy Krivets Examining the Synergistic Use of East-West Reflector and Coal Cinder in Trapezoidal Solar Pond through Energy Analysis VINOTH KUMAR J, AMARKARTHIK ARUNACHALAM
Begell Digital Portal Begellデジタルライブラリー 電子書籍 ジャーナル 参考文献と会報 リサーチ集 価格及び購読のポリシー Begell House 連絡先 Language English 中文 Русский Português German French Spain