Prediction of pool boiling heat transfer coefficient for various nano-refrigerants utilizing artificial neural networks

In the present research, an artificial neural network model was developed to predict the pool boiling heat transfer coefficient (HTC) of refrigerant-based nanofluids based on a large number of experimental data (1342) extracted from the literature. Diverse training algorithms, e.g., Bayesian regulation backpropagation, Levenberg–Marquardt (LM), Resilient backpropagation and scaled conjugate gradient were utilized. Besides, several transfer functions like log-sigmoid (logsig), radial basis (radbas), soft max transfer function (softmax), hard-limit (hardlim), tan-sigmoid (tansig) and triangular basis (tribas) were applied for the hidden layer, and their influences on model correctness were surveyed. The effects of heat flux, saturation pressure, nanoparticle thermal conductivity, base fluid thermal conductivity, nanoparticle concentration (mass%), nanoparticles size and lubricant concentration (mass%) on the pool boiling HTC of refrigerant-based nanofluids were determined over wide ranges of operating conditions. A network possessing one hidden layer with 19 neurons using tansig and purelin as transfer functions in hidden and output layers in a row was introduced as a model having the best performance. In addition, LM was known as a much more efficient train algorithm in comparison with others resulting in extremely precise prediction. The outcomes indicated the present model could accurately estimate the pool boiling HTC of refrigerant-based nanofluids with a correlation coefficient ( R 2 ) of 0.9948 and overall mean square error of 0.01529.

[1]  A. S. Dalkılıç,et al.  Investigation of pool boiling of nanofluids using artificial neural networks and correlation development techniques , 2012 .

[2]  Nandy Putra,et al.  Pool boiling of nano-fluids on horizontal narrow tubes , 2003 .

[3]  Anthony M. Jacobi,et al.  Flow-boiling heat transfer of R-134a-based nanofluids in a horizontal tube , 2010 .

[4]  Guoliang Ding,et al.  Effect of surfactant additives on nucleate pool boiling heat transfer of refrigerant-based nanofluid , 2011 .

[5]  M. Rahimpour,et al.  Estimation of the saturation pressure of pure ionic liquids using MLP artificial neural networks and the revised isofugacity criterion , 2017 .

[6]  Lin Shi,et al.  Application of nanoparticles in domestic refrigerators , 2008 .

[7]  D. Jung,et al.  Enhancement of nucleate boiling heat transfer using carbon nanotubes , 2007 .

[8]  R. Yusof,et al.  Artificial neural network for modeling the size of silver nanoparticles’ prepared in montmorillonite/starch bionanocomposites , 2015 .

[9]  M. Bayareh,et al.  Mixed Convection Heat Transfer of Water-Alumina Nanofluid in an Inclined and Baffled C-Shaped Enclosure , 2018 .

[10]  M. Bayareh,et al.  Investigating the mixed convection heat transfer of a nanofluid in a square chamber with a rotating blade , 2018, Journal of Thermal Analysis and Calorimetry.

[11]  Dominique Richon,et al.  Use of artificial neural networks for calculating derived thermodynamic quantities from volumetric property data , 2003 .

[12]  D. Mowla,et al.  Estimation of viscosities of pure ionic liquids using an artificial neural network based on only structural characteristics , 2017 .

[13]  D. Richon,et al.  Modeling of thermodynamic properties using neural networks: Application to refrigerants , 2002 .

[14]  R. Mamat,et al.  Development of nanorefrigerants for various types of refrigerant based: A comprehensive review on performance , 2016 .

[15]  Yulong Ding,et al.  Experimental investigation into the pool boiling heat transfer of aqueous based γ-alumina nanofluids , 2005 .

[16]  Afshin Tatar,et al.  Utilization of the RBF network to model the nucleate pool boiling heat transfer properties of refrigerant-oil mixtures with nanoparticles , 2017 .

[17]  P. Keshavarz,et al.  Modeling surface tension of pure refrigerants using feed-forward back-propagation neural networks , 2017 .

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

[19]  F. Sabzi,et al.  Prediction of CO2 sorption in poly(ionic liquid)s using ANN-GC and ANFIS-GC models , 2017 .

[20]  Haitao Hu,et al.  Nucleate pool boiling heat transfer characteristics of refrigerant/oil mixture with diamond nanoparticles. , 2010 .

[21]  Seyfolah Saedodin,et al.  Designing an artificial neural network to predict thermal conductivity and dynamic viscosity of ferromagnetic nanofluid , 2015 .

[22]  W. Roetzel,et al.  Pool boiling characteristics of nano-fluids , 2003 .

[23]  M. Kedzierski.  Effect of CuO Nanolubricant on R134a Pool Boiling Heat Transfer with Extensive Measurement and Analysis Details , 2007 .

[24]  Bin Sun,et al.  Experimental study on the heat transfer characteristics of nanorefrigerants in an internal thread copper tube , 2013 .

[25]  Akram Jahanbakhshi,et al.  Magnetic field effects on natural convection flow of a non-Newtonian fluid in an L-shaped enclosure , 2018, Journal of Thermal Analysis and Calorimetry.

[26]  Guoliang Ding,et al.  Influence of carbon nanotubes on nucleate pool boiling heat transfer characteristics of refrigerant–oil mixture , 2010 .

[27]  A. Hezave,et al.  Trainable cascade-forward back-propagation network modeling of spearmint oil extraction in a packed bed using SC-CO2 , 2013 .

[28]  Sarit K. Das,et al.  Effect of surface orientation on pool boiling heat transfer of nanoparticle suspensions , 2008 .

[29]  F. Hormozi,et al.  Prediction of Al2O3–water nanofluids pool boiling heat transfer coefficient at low heat fluxes by using response surface methodology , 2019, Journal of Thermal Analysis and Calorimetry.

[30]  Chunlei Zhang,et al.  Generalized neural network correlation for flow boiling heat transfer of R22 and its alternative refrigerants inside horizontal smooth tubes , 2006 .

[31]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[32]  Soon-Heung Chang,et al.  Boiling heat transfer performance and phenomena of Al2O 3-water nano-fluids from a plain surface in a pool , 2004 .

[33]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[34]  A. Nazari,et al.  Simulation and determination of optimum conditions of pervaporative dehydration of isopropanol proce , 2011 .

[35]  Stephen U. S. Choi.  Enhancing thermal conductivity of fluids with nano-particles , 1995 .

[36]  P. Keshavarz,et al.  Estimation of CO2 mass transfer rate into various types of Nanofluids in hollow Fiber membrane and packed bed column using adaptive neuro-fuzzy inference system , 2018, International Communications in Heat and Mass Transfer.

[37]  M. Bayareh,et al.  Numerical investigation of mixed convection heat transfer of a nanofluid in a circular enclosure with a rotating inner cylinder , 2018, Journal of Thermal Analysis and Calorimetry.

[38]  O. Mahian,et al.  A review of nanorefrigerants: Flow characteristics and applications , 2014 .

[39]  Reza Eslamloueyan,et al.  Using a Multilayer Perceptron Network for Thermal Conductivity Prediction of Aqueous Electrolyte Solutions , 2011 .

[40]  P. Keshavarz,et al.  A new comprehensive model for relative viscosity of various nanofluids using feed-forward back-propagation MLP neural networks , 2018 .

[41]  Haitao Hu,et al.  Effect of nanoparticle size on nucleate pool boiling heat transfer of refrigerant/oil mixture with nanoparticles , 2011 .

[42]  N. Sidik,et al.  Applications of nanorefrigerant and nanolubricants in refrigeration, air-conditioning and heat pump systems: A review , 2015 .

[43]  Seeram Ramakrishna,et al.  Artificial neural network for modeling the elastic modulus of electrospun polycaprolactone/gelatin scaffolds. , 2014, Acta biomaterialia.

[44]  Dongsoo Jung,et al.  Boiling heat transfer enhancement with carbon nanotubes for refrigerants used in building air-conditioning , 2007 .

[45]  W. Rohsenow.  A Method of Correlating Heat-Transfer Data for Surface Boiling of Liquids , 1952, Journal of Fluids Engineering.

[46]  M. Kedzierski.  Effect of Al2O3 nanolubricant on R134a pool boiling heat transfer , 2009 .

[47]  G. David Garson,et al.  Interpreting neural-network connection weights , 1991 .

[48]  Martin T. Hagan,et al.  Neural network design , 1995 .

[49]  Elham Heidari,et al.  Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN) , 2016 .

[50]  S. A. Alavi Fazel,et al.  Pool boiling heat transfer characteristics of graphene-based aqueous nanofluids , 2018, Journal of Thermal Analysis and Calorimetry.

[51]  G. Ding,et al.  Experimental and Model Research on Nanorefrigerant Thermal Conductivity , 2009 .

[52]  A. T. C. Goh,et al.  Back-propagation neural networks for modeling complex systems , 1995, Artif. Intell. Eng..

[53]  Somchai Wongwises,et al.  Nucleate pool boiling heat transfer of TiO2–R141b nanofluids , 2009 .

[54]  S. Saedodin,et al.  An experimental study of the nanofluid pool boiling on the aluminium surface , 2018, Journal of Thermal Analysis and Calorimetry.

[55]  Mehdi Mohammadi,et al.  Multi-objective optimization of a triple shaft gas compressor station using Imperialist Competitive Algorithm , 2016 .

[56]  S. H. Noie,et al.  Study of flow boiling heat transfer characteristics of critical heat flux using carbon nanotubes and water nanofluid , 2017, Journal of Thermal Analysis and Calorimetry.

[57]  Y. Diao,et al.  Experimental investigation of the nucleate pool boiling heat transfer characteristics of δ-Al2O3-R141b nanofluids on a horizontal plate , 2014 .

[58]  M. Nabipour.  Prediction of surface tension of binary refrigerant mixtures using artificial neural networks , 2018 .

[59]  B. Van der Bruggen,et al.  Application of artificial neural networks for estimation of solubility of acid gases (H2S and CO2) in 32 commonly ionic liquid and amine solutions , 2015 .

[60]  Adnan Sözen,et al.  Calculation for the thermodynamic properties of an alternative refrigerant (R508b) using artificial neural network , 2007 .

[61]  R. Webb,et al.  Nucleate pool boiling data for five refrigerants on plain, integral-fin and enhanced tube geometries , 1992 .

[62]  Abdolreza Moghadassi,et al.  A New Approach Based on Artificial Neural Networks for Prediction of High Pressure Vapor-liquid Equilibrium , 2009 .

[63]  Ali Mohebbi,et al.  A neural network for predicting saturated liquid density using genetic algorithm for pure and mixed refrigerants , 2008 .