Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods

[1]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[2]  Johan A. K. Suykens,et al.  Multiclass least squares support vector machines , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[3]  George D. Magoulas,et al.  Improving the Convergence of the Backpropagation Algorithm Using Learning Rate Adaptation Methods , 1999, Neural Computation.

[4]  Johan A. K. Suykens,et al.  Training multilayer perceptron classifiers based on a modified support vector method , 1999, IEEE Trans. Neural Networks.

[5]  Johan A. K. Suykens,et al.  Least squares support vector machine classifiers: a large scale algorithm , 1999 .

[6]  J. Eastman,et al.  Measuring Thermal Conductivity of Fluids Containing Oxide Nanoparticles , 1999 .

[7]  Johan A. K. Suykens,et al.  An empirical assessment of kernel type performance for least squares support vector machine classifiers , 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

[8]  J. Suykens,et al.  Recurrent least squares support vector machines , 2000 .

[9]  Johan A. K. Suykens,et al.  Weighted least squares support vector machines: robustness and sparse approximation , 2002, Neurocomputing.

[10]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[11]  G. Peterson,et al.  Experimental investigation of temperature and volume fraction variations on the effective thermal conductivity of nanoparticle suspensions (nanofluids) , 2006 .

[12]  Chi-Man Vong,et al.  Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference , 2006, Eng. Appl. Artif. Intell..

[13]  G. Peterson,et al.  The effect of particle size on the effective thermal conductivity of Al2O3-water nanofluids , 2007 .

[14]  Xue-Fei Yang,et al.  Effect of nanoparticles in nanofluid on thermal performance in a miniature thermosyphon , 2007 .

[15]  M. A. El-aziz.  Temperature dependent viscosity and thermal conductivity effects on combined heat and mass transfer in MHD three-dimensional flow over a stretching surface with Ohmic heating , 2007 .

[16]  Li Yu-hua,et al.  Temperature dependence of thermal conductivity of nanofluids , 2008 .

[17]  Yanhui Yuan,et al.  The effect of particle size on the thermal conductivity of alumina nanofluids , 2009 .

[18]  K. Movagharnejad,et al.  A comparative study between LS-SVM method and semi empirical equations for modeling the solubility of different solutes in supercritical carbon dioxide , 2011 .

[19]  Shrikantha S. Rao,et al.  Experimental studies on CHF enhancement in pool boiling with CuO-water nanofluid , 2012 .

[20]  C. Senabre,et al.  Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study , 2012 .

[21]  J. Hao,et al.  Thermal conductivity and rheological properties of graphite/oil nanofluids , 2012 .

[22]  Teuvo Kohonen,et al.  Essentials of the self-organizing map , 2013, Neural Networks.

[23]  Lei Su,et al.  Using BP network for ultrasonic inspection of flip chip solder joints , 2013 .

[24]  Matthias H. Buschmann,et al.  Nanofluids in thermosyphons and heat pipes: Overview of recent experiments and modelling approaches , 2013 .

[25]  T. Ala‐Nissila,et al.  Influence of particle size and shape on turbulent heat transfer characteristics and pressure losses in water-based nanofluids , 2013 .

[26]  Ayush Jain,et al.  Mathematical modelling of thermal conductivity for nanofluid considering interfacial nano-layer , 2013 .

[27]  Shuangfeng Wang,et al.  Silicone based nanofluids containing functionalized graphene nanosheets , 2013 .

[28]  O. Mahian,et al.  Thermal conductivity of Al2O3/water nanofluids , 2014, Journal of Thermal Analysis and Calorimetry.

[29]  Vikas Chaudhary,et al.  A novel Self-Organizing Map (SOM) learning algorithm with nearest and farthest neurons , 2014 .

[30]  Robello Samuel,et al.  Formation and characterization of thermal and electrical properties of CuO and ZnO nanofluids in xanthan gum , 2014 .

[31]  Siamak Kazemzadeh Hannani,et al.  Open-Loop Pulsating Heat Pipes Charged With Magnetic Nanofluids: Powerful Candidates for Future Electronic Coolers , 2014 .

[32]  Junmei Wu,et al.  Numerical simulation on single bubble behavior during Al2O3/H2O nanofluids flow boiling using Moving Particle Simi-implicit method , 2015 .

[33]  A. Minakov,et al.  Measurement of the heat transfer coefficient of a nanofluid based on water and copper oxide particles in a cylindrical channel , 2015, High Temperature.

[34]  Lin Shi,et al.  Effect of temperature on the effective thermal conductivity of n-tetradecane-based nanofluids containing copper nanoparticles , 2015 .

[35]  M. Afrand,et al.  Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of Al2O3–water nanofluids using experimental data , 2015 .

[36]  A. Bahadori,et al.  A LSSVM approach for determining well placement and conning phenomena in horizontal wells , 2015 .

[37]  M. Farbod,et al.  Morphology dependence of thermal and rheological properties of oil-based nanofluids of CuO nanostructures , 2015 .

[38]  A. Bahadori,et al.  A rigorous model to predict the amount of Dissolved Calcium Carbonate Concentration throughout oil field brines: Side effect of pressure and temperature , 2015 .

[39]  Davood Toghraie,et al.  Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO2 using experimental data , 2016 .

[40]  A. Jacobi,et al.  Aluminum surface wettability changes by pool boiling of nanofluids , 2016 .

[41]  Y. Jian,et al.  Streaming potential and heat transfer of nanofluids in parallel plate microchannels , 2016 .

[42]  M. Hatami,et al.  Prediction of hydrodynamic and optical properties of TiO2/water suspension considering particle size distribution , 2016 .

[43]  R. Warkhedkar,et al.  Studies on heat transfer in flow of silver nanofluid through a straight tube with twisted tape inserts , 2016 .

[44]  J. Xu,et al.  Experimental investigation on the correlation between nano-fluid characteristics and thermal properties of Al2O3 nano-particles dispersed in ethylene glycol–water mixture , 2016 .

[45]  Y. Noorollahi,et al.  The effect of employing nanofluid on reducing the bore length of a vertical ground-source heat pump , 2016 .

[46]  M. R. Saad,et al.  Numerical analysis of the forced convective heat transfer on Al2O3–Cu/water hybrid nanofluid , 2017 .

[47]  A. Minea,et al.  A numerical study on ZnO based nanofluids behavior on natural convection , 2017 .

[48]  P. Joy,et al.  Role of base fluid on the thermal conductivity of oleic acid coated magnetite nanofluids , 2017 .

[49]  Suhaib Umer Ilyas,et al.  Stability and thermal analysis of MWCNT-thermal oil-based nanofluids , 2017 .

[50]  M. I. Pryazhnikov,et al.  Thermal conductivity measurements of nanofluids , 2017 .

[51]  Mohamad Azlin Bin Ali,et al.  Convective heat transfer enhancement with graphene nanoplatelet/platinum hybrid nanofluid , 2017 .

[52]  Mohammad Hadi Hajmohammad,et al.  Rheological characteristics of MgO/oil nanolubricants: Experimental study and neural network modeling , 2017 .

[53]  N. Agrawal,et al.  Sensitivity of thermal conductivity for Al2O3 nanofluids , 2017 .

[54]  Hamid Teimouri,et al.  Predicting the effects of magnesium oxide nanoparticles and temperature on the thermal conductivity of water using artificial neural network and experimental data , 2017 .

[55]  S. Wongwises,et al.  Prediction of Thermal Conductivity of Carbon Nanotube-EG Nanofluid Using Experimental Data by ANN , 2017 .

[56]  M. Afrand,et al.  Prediction of rheological behavior of SiO2-MWCNTs/10W40 hybrid nanolubricant by designing neural network , 2018, Journal of Thermal Analysis and Calorimetry.

[57]  K. Vafai,et al.  Analysis of single phase, discrete and mixture models, in predicting nanofluid transport , 2017 .

[58]  Hwai Chyuan Ong,et al.  Synthesis and thermal conductivity characteristic of hybrid nanofluids – A review , 2017 .

[59]  M. Biglari,et al.  An inspection of thermal conductivity of CuO-SWCNTs hybrid nanofluid versus temperature and concentration using experimental data, ANN modeling and new correlation , 2017 .

[60]  Liu Yang,et al.  Recent developments on viscosity and thermal conductivity of nanofluids , 2017 .

[61]  M. H. Esfe,et al.  Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT–Al2O3 nanoparticles by correlation and ANN methods using experimental data , 2017, Journal of Thermal Analysis and Calorimetry.

[62]  M. Tawfik.  Experimental studies of nanofluid thermal conductivity enhancement and applications: A review , 2017 .

[63]  D. Dasgupta,et al.  Thermal circuits based model for predicting the thermal conductivity of nanofluids , 2017 .

[64]  M. Afrand,et al.  Experimental evaluation, new correlation proposing and ANN modeling of thermal properties of EG based hybrid nanofluid containing ZnO-DWCNT nanoparticles for internal combustion engines applications , 2017 .

[65]  Li Haiyan,et al.  Saturated flow boiling heat transfer investigation for nanofluid in minichannel , 2017 .

[66]  S. Saedodin,et al.  Investigation of rheological behavior of MWCNT (COOH-functionalized)/MgO - Engine oil hybrid nanofluids and modelling the results with artificial neural networks , 2017 .

[67]  S. Ghosh,et al.  Review of interfacial layer’s effect on thermal conductivity in nanofluid , 2017 .

[68]  Muhammad Noor Afiq Witri Muhammad Yazid,et al.  A review on the use of carbon nanotubes nanofluid for energy harvesting system , 2017 .

[69]  A. Al-Rashed,et al.  New temperature, interfacial shell dependent dimensionless model for thermal conductivity of nanofluids , 2017 .

[70]  P. V. Walke,et al.  Heat transfer characteristics in nanofluid—A review , 2017 .

[71]  S. Esfandeh,et al.  Experimental evaluation, sensitivity analyzation and ANN modeling of thermal conductivity of ZnO-MWCNT/EG-water hybrid nanofluid for engineering applications , 2017 .

[72]  Jian Yin,et al.  Using scanning acoustic microscopy and LM-BP algorithm for defect inspection of micro solder bumps , 2017, Microelectron. Reliab..

[73]  M. Shafii,et al.  Visualization and comparative investigations of pulsating ferro-fluid heat pipe , 2017 .

[74]  T. Ala‐Nissila,et al.  Influence of particle properties on convective heat transfer of nanofluids , 2018 .

[75]  A. Pattamatta,et al.  Enhancement of natural convection heat transfer in a square cavity using MWCNT/Water nanofluid: an experimental study , 2018 .

[76]  Xun Yu,et al.  Comparison of compressive strength and electrical resistivity of cementitious composites with different nano- and micro-fillers , 2018 .

[77]  M. Ahmadi,et al.  Heat transfer measurment in water based nanofluids , 2018 .

[78]  Hossein Rostamian,et al.  Rheological behavior characteristics of ZrO 2 -MWCNT/10w40 hybrid nano-lubricant affected by temperature, concentration, and shear rate: An experimental study and a neural network simulating , 2017, Physica E: Low-dimensional Systems and Nanostructures.

[79]  M. H. Esfe,et al.  Price-performance evaluation of thermal conductivity enhancement of nanofluids with different particle sizes , 2018 .

[80]  J. Koo,et al.  Further elucidation of nanofluid thermal conductivity measurement using a transient hot-wire method apparatus , 2018 .

[81]  A. Tatar,et al.  A comparison of performance of several artificial intelligence methods for predicting the dynamic viscosity of TiO2/SAE 50 nano-lubricant , 2018 .