Abo Bibliothek: Guest
Heat Transfer Research

Erscheint 18 Ausgaben pro Jahr

ISSN Druckformat: 1064-2285

ISSN Online: 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

PREDICTION OF DYNAMIC VISCOSITY OF A NEW NON-NEWTONIAN HYBRID NANOFLUID USING EXPERIMENTAL AND ARTIFICIAL NEURAL NETWORK (ANN) METHODS

Volumen 51, Ausgabe 15, 2020, pp. 1351-1362
DOI: 10.1615/HeatTransRes.2020034645
Get accessGet access

ABSTRAKT

In this paper, an artificial neural network (ANN) has been studied for the viscosity of MWCNTs-ZnO/water-ethylene glycol (80:20 vol.%) non-Newtonian nanofluid. To evaluate the rheological behavior of the nanocoolants, for each solid volume fraction and temperature, all experiments were repeated at different shear rates. After generating the experimental data, an ANN method is applied. The ANN is selected based on the different generating architectures (neuron numbers). The algorithm for choosing the best ANN is presented. Also, using the correlation method, the viscosity of nanofluid is predicted. Finally, ANN and correlation results are compared with the obtained data from the correlation method. It was found that the ANN had a better ability in predicting the viscosity of nanofluid compared with the correlation method because the (MSE) of ANN was 0.0885, and the MSE of the correlation method was 0.9531. However, both approaches are useful, but ANN had a better ability to model the viscosity of nanofluid based on the input values.

REFERENZEN
  1. Aghaei, A., Khorasanizadeh, H., and Sheikhzadeh, G.A., Measurement of the Dynamic Viscosity of Hybrid Engine Oil-CuO-MWCNT Nanofluid, Development of a Practical Viscosity Correlation and Utilizing the Artificial Neural Network, Heat Mass Transf., vol. 54, pp. 151-161, 2018.

  2. Ahmadi, M.H., Gharyehsafa, B.H., 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. FluidMech., vol. 13, pp. 220-228, 2019.

  3. Bahrami, M., Akbari, M., Bagherzadeh, S.A., Karimipour, A., Afrand, M., and Goodarzi, M., Develop 24 Dissimilar ANNs by Suitable Architecture and Training Algorithms via Sensitivity Analysis to Better Statistical Presentation: Measure MSEs between Targets and ANN for Fe-CuO/EG-Water Nanofluid, Physica A, vol. 519, pp. 159-168, 2019.

  4. Dalkilic, A.S., Qebi, A., Celen, A., Yildiz, O., Acikgoz, O., Jumpholkul, C., Bayrak, M., Surana, K., and Wongwises, S., Prediction of Graphite Nanofluids' Dynamic Viscosity by Means of Artificial Neural Networks, Int. Commun. Heat Mass Transf., vol. 73, pp. 33-42, 2016.

  5. Esfahani, M.A. and Toghraie, D., Experimental Investigation for Developing a New Model for the Thermal Conductivity of Silica/Water-Ethylene Glycol (40%-60%) Nanofluid at Different Temperatures and Solid Volume Fractions, J. Mol. Liq., vol. 232, pp. 105-112, 2017.

  6. Esfahani, N.N., Toghraie, D., and Afrand, M., A New Correlation for Predicting the Thermal Conductivity of ZnO-Ag (50%-50%)/Water Hybrid Nanofluid: An Experimental Study, Powder Technol., vol. 323, pp. 367-373, 2018.

  7. Esfe, M.H. and Arani, A.A.A., An Experimental Determination and Accurate Prediction of Dynamic Viscosity of MWCNT (%40)-SiO2 (%60)/5W50 Nano-Lubricant, J. Mol. Liq., vol. 259, pp. 227-237, 2018.

  8. Esfe, M.H., Ahangar, M.R.H., Rejvani, M., Toghraie, D., and Hajmohammad, M.H., Designing an Artificial Neural Network to Predict Dynamic Viscosity of Aqueous Nanofluid of TiO2 Using Experimental Data, Int. Commun. Heat Mass Transf., vol. 75, pp.192-196, 2016a.

  9. Esfe, M.H., Ahangar, M.R.H., Toghraie, D., Hajmohammad, M.H., Rostamian, H., and Tourang, H., Designing Artificial Neural Network on Thermal Conductivity of Al2O3-Water-EG (60-40%) Nanofluid Using Experimental Data, J. Therm. Anal. Calorim., vol. 126, no. 2, pp. 837-843, 2016b.

  10. Esfe, M.H., Hajmohammad, H., Toghraie, D., Rostamian, H., Mahian, O., and Wongwises, S., Multi-Objective Optimization of Nanofluid Flow in Double Tube Heat Exchangers for Applications in Energy Systems, Energy, vol. 137, pp. 160-171, 2017.

  11. Esfe, M.H., Rostamian, H., Esfandeh, S., and Afrand, M., Modeling and Prediction of Rheological Behavior of Al2O3-MWCNT/5W50 Hybrid Nano-Lubricant by Artificial Neural Network Using Experimental Data, Physica A, vol. 510, pp. 625-634, 2018.

  12. Gulum, M., Onay, F.K., and Bilgin, A., Comparison of Viscosity Prediction Capabilities of Regression Models and Artificial Neural Networks, Energy, vol. 161, pp. 361-369, 2018.

  13. Kumar Nayak, S. and Mishra, P.C., Enhanced Heat Transfer from Hot Surface by Nanofluid Based Ultra-Fast Cooling: An Experimental Investigation, J. Enhanced Heat Transf., vol. 26, no. 4, pp. 415-428, 2019.

  14. Kumar, S., Kothiyal, A., Singh Bisht, M., and Kumar, A., Effect of Nanofluid Flow and Protrusion Transverse Ribs on Thermal and Hydrodynamic Performance in Square Channel: An Experimental Investigation, J. Enhanced Heat Transf., vol. 26, no. 1, pp. 75-100, 2019.

  15. Longo, G.A., Ortombina, L., and Zigliotto, M., Application of Artificial Neural Network (ANN) for Modeling H2O/KCOOH (Potassium Formate) Dynamic Viscosity, Int. J. Refrig., vol. 86, pp. 435-440, 2018.

  16. Mohamadian, F., Eftekhar, L., and Bardineh, Y.H., Applying GMDH Artificial Neural Network to Predict Dynamic Viscosity of an Antimicrobial Nanofluid, Nanomed. J., vol. 5, no. 4, pp. 217-222, 2018.

  17. 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. Commun. Heat Mass Transf., vol. 118, pp. 1152-1159, 2018.

  18. Pal, S.K. and Bhattacharyya, S., Enhanced Heat Transfer of Cu-Water Nanofluid in a Channel with Wall Mounted Blunt Ribs, J. Enhanced Heat Transf., vol. 25, no. 1, pp. 61-78, 2018.

  19. Rahman, A.A. and Zhang, X., Prediction of Oscillatory Heat Transfer Coefficient for a Thermoacoustic Heat Exchanger through Artificial Neural Network Technique, Int. J. Heat Mass Transf., vol. 124, pp. 1088-1096, 2018.

  20. Sandhu, H., Gangacharyulu, D., and Singh, M.K., Experimental Investigations on the Cooling Performance of Microchannels Using Alumina Nanofluids with Different Base Fluids, J. Enhanced Heat Transf., vol. 25, no. 3, pp. 283-291, 2018.

  21. Tahani, M., Vakili, M., and Khosrojerdi, S., Experimental Evaluation and ANN Modeling of Thermal Conductivity of Graphene Oxide Nanoplatelets/Deionized Water Nanofluid, Int. Commun. Heat Mass Transf., vol. 76, pp. 358-365, 2016.

  22. Zhao, N. and Li, Z., Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids, Materials (Basel), vol. 10, no. 5, p. 552, 2017. DOI: 10.3390/ma10050552.

  23. Zhao, N., Li, S., Wang, Z., and Cao, Y., Prediction of Viscosity of Nanofluids Using Artificial Neural Networks, ASME 2014 International Mechanical Engineering Congress and Exposition, vol. 8B: Heat Transfer and Thermal Engineering Montreal, Quebec, Canada, pp. 14-20, 2014.

  24. Zhi, L.H., Hu, P., Chen, L.X., and Zhao, G., Viscosity Prediction for Six Pure Refrigerants Using Different Artificial Neural Networks, Int. J. Refrig., vol. 88, pp. 432-440, 2018.

REFERENZIERT VON
  1. Qing Haohua, Hamedi Sajad, Eftekhari S. Ali, Alizadeh S.M., Toghraie Davood, Hekmatifar Maboud, Ahmed Ahmed Najat, Khan Afrasyab, A well-trained feed-forward perceptron Artificial Neural Network (ANN) for prediction the dynamic viscosity of Al2O3–MWCNT (40:60)-Oil SAE50 hybrid nano-lubricant at different volume fraction of nanoparticles, temperatures, and shear rates, International Communications in Heat and Mass Transfer, 128, 2021. Crossref

  2. Soltani Farid, Hajian Mehdi, Toghraie Davood, Gheisari Ali, Sina Nima, Alizadeh As'ad, Applying Artificial Neural Networks (ANNs) for prediction of the thermal characteristics of engine oil –based nanofluids containing tungsten oxide -MWCNTs, Case Studies in Thermal Engineering, 26, 2021. Crossref

  3. Derikvand Mohammad, Solari Mojtaba Shams, Toghraie Davood, Numerical investigation of the effect of a porous block and flow injection using non-Newtonian nanofluid on heat transfer and entropy generation in a microchannel with hydrophobic walls, The European Physical Journal Plus, 136, 8, 2021. Crossref

  4. Hemmat Esfe Mohammad, Kamyab Mohammad Hassan, Alirezaie Ali, Toghraie Davood, Using radial basis function network to model the heat transfer and pressure drop of water based nanofluids containing MgO nanoparticles, Case Studies in Thermal Engineering, 28, 2021. Crossref

  5. Fan Guangli, A.S. El-Shafay, Eftekhari S. Ali, Hekmatifar Maboud, Toghraie Davood, Mohammed Amin Salih, Khan Afrasyab, A well-trained artificial neural network (ANN) using the trainlm algorithm for predicting the rheological behavior of water – Ethylene glycol/WO3 – MWCNTs nanofluid, International Communications in Heat and Mass Transfer, 131, 2022. Crossref

  6. Khalvandi Ali, Saber-Samandari Saeed, Aghdam Mohammad Mohammadi, Application of artificial neural networks to predict Young's moduli of cartilage scaffolds: An in-vitro and micromechanical study, Biomaterials Advances, 136, 2022. Crossref

  7. Esfe Mohammad Hemmat, Khaje khabaz Mohamad, Esmaily Reza, Mahabadi Soheila Tallebi, Toghraie Davood, Rahmanian Alireza, Fazilati Mohammad Ali, Application of artificial intelligence and using optimal ANN to predict the dynamic viscosity of Hybrid nano-lubricant containing Zinc Oxide in Commercial oil, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 647, 2022. Crossref

  8. Fuxi Shi, Sina Nima, Sajadi S. Mohammad, Mahmoud Mustafa Z., Abdelrahman Anas, Aybar Hikmet Ş., Artificial neural network modeling to examine spring turbulators influence on parabolic solar collector effectiveness with hybrid nanofluids, Engineering Analysis with Boundary Elements, 143, 2022. Crossref

  9. Ali Aatif, Ahammad N. Ameer, Tag-Eldin Elsayed, Gamaoun Fehmi, Daradkeh Yousef Ibrahim, Yassen Mansour F., MHD williamson nanofluid flow in the rheology of thermal radiation, joule heating, and chemical reaction using the Levenberg–Marquardt neural network algorithm, Frontiers in Energy Research, 10, 2022. Crossref

  10. Sharifat Farzad, Marchitto Annalisa, Solari Mojtaba Shams, Toghraie Davood, Analysis, prediction, and optimization of heat transfer coefficient and friction factor of water-$${\mathrm{Al}}_{2}{\mathrm{O}}_{3}$$ nanofluid flow in shell-and-tube heat exchanger with helical baffles (using RSM), The European Physical Journal Plus, 137, 8, 2022. Crossref

  11. Hemmat Esfe Mohammad, Hajian Mehdi, Toghraie Davood, Khaje khabaz Mohamad, Rahmanian Alireza, Pirmoradian Mostafa, Rostamian Hossein, Prediction the dynamic viscosity of MWCNT-Al2O3 (30:70)/ Oil 5W50 hybrid nano-lubricant using Principal Component Analysis (PCA) with Artificial Neural Network (ANN), Egyptian Informatics Journal, 23, 3, 2022. Crossref

  12. Raza Ali, Khan Umair, Zaib Aurang, Weera Wajaree, Galal Ahmed M., A comparative study for fractional simulations of Casson nanofluid flow with sinusoidal and slipping boundary conditions via a fractional approach, AIMS Mathematics, 7, 11, 2022. Crossref

Zukünftige Artikel

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
Digitales Portal Digitale Bibliothek eBooks Zeitschriften Referenzen und Berichte Forschungssammlungen Preise und Aborichtlinien Begell House Kontakt Language English 中文 Русский Português German French Spain