Sensitivity analysis and application of machine learning methods to predict the heat transfer performance of CNT/water nanofluid flows through coils

Abstract Nowadays, nanofluids are broadly utilized for various engineering and industrial systems including heat exchangers, power plants, air-conditioning, etc. The helically coiled tube heat exchangers are of the most interesting and efficient kinds of heat exchangers. The current study has focused on proposing model to predict Nusselt number by considering Prandtl number, volumetric concentration, and helical number of helically coiled heat exchanger as input variables. The investigated heat exchanger utilizes water carbon nanofluid. To propose an accurate model, a multilayer perceptron artificial neural network (MLP-ANN), adaptive neuro-fuzzy inference system (ANFIS), and least squares support vector machine (LSSVM) models are used. 72 experimental data are utilized as input data. Results indicate that LSSVM approach has the best performance and the proposed model by this approach has R-squared value equals to 1.

[1]  Alireza Baghban,et al.  ANFIS modeling of rhamnolipid breakthrough curves on activated carbon , 2017 .

[2]  Sarit K. Das,et al.  Model for thermal conductivity of CNT-nanofluids , 2008 .

[3]  Abdolhossein Hemmati-Sarapardeh,et al.  Toward a predictive model for estimating viscosity of ternary mixtures containing ionic liquids , 2014 .

[4]  Mohammad Behshad Shafii,et al.  How to improve the thermal performance of pulsating heat pipes: A review on working fluid , 2018, Renewable and Sustainable Energy Reviews.

[5]  A. Mohammadi,et al.  Rigorous modeling of CO2 equilibrium absorption in ionic liquids , 2017 .

[6]  Mohammad Ghazvini,et al.  Experimental investigation on the convective heat transfer of nanofluid flow inside vertical helically coiled tubes under uniform wall temperature condition , 2012 .

[7]  Mohammad Hossien Ahmadi,et al.  Prediction of power in solar stirling heat engine by using neural network based on hybrid genetic algorithm and particle swarm optimization , 2012, Neural Computing and Applications.

[8]  Hsiang-Wen Tang,et al.  Numerical simulation and optimization of nanofluid in a C-shaped chaotic channel , 2016 .

[9]  Saeed Zeinali Heris,et al.  Comparative study between metal oxide nanopowders on thermal characteristics of nanofluid flow through helical coils , 2013 .

[10]  Saeed Zeinali Heris,et al.  The Study on Application of TiO2/water Nanofluid in Plate Heat Exchanger of Milk Pasteurization Industries , 2016 .

[11]  A. Alimoradi.  Investigation of exergy efficiency in shell and helically coiled tube heat exchangers , 2017 .

[12]  Kobra Zarei,et al.  Predicting the heats of combustion of polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds using bee algorithm and adaptive neuro-fuzzy inference system , 2013 .

[13]  Hamid Niazmand,et al.  Convective Heat Transfer of Nanofluids Flows Through an Isothermally Heated Curved Pipe , 2011 .

[14]  Alireza Baghban,et al.  Phase equilibrium modeling of semi-clathrate hydrates of seven commonly gases in the presence of TBAB ionic liquid promoter based on a low parameter connectionist technique , 2015 .

[15]  Mohammad Ali Ahmadi,et al.  Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods , 2018 .

[16]  Alireza Baghban,et al.  Prediction viscosity of ionic liquids using a hybrid LSSVM and group contribution method , 2017 .

[17]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[18]  Johan A. K. Suykens,et al.  Benchmarking Least Squares Support Vector Machine Classifiers , 2004, Machine Learning.

[19]  Marko S. Jarić,et al.  Research on the shell-side thermal performances of heat exchangers with helical tube coils , 2012 .

[20]  Meysam Maghareh,et al.  Numerical investigation of heat transfer intensification in shell and helically coiled finned tube heat exchangers and design optimization , 2017 .

[21]  Ashkan Alimoradi,et al.  Study of thermal effectiveness and its relation with NTU in shell and helically coiled tube heat exchangers , 2017 .

[22]  Mohammad Hossein Ahmadi,et al.  Experimental investigation of graphene oxide nanofluid on heat transfer enhancement of pulsating heat pipe , 2018 .

[23]  Yulong Ding,et al.  Heat transfer of aqueous suspensions of carbon nanotubes (CNT nanofluids) , 2006 .

[24]  Masoud Nikravesh,et al.  Soft computing and intelligent data analysis in oil exploration , 2003 .

[25]  Alibakhsh Kasaeian,et al.  GMDH algorithm for modeling the outlet temperatures of a solar chimney based on the ambient temperature , 2017 .

[26]  Chaobin Dang,et al.  Experimental investigation of heat transfer of supercritical CO2 cooled in helically coiled tubes based on exergy analysis. , 2018 .

[27]  Kwang Hyung Lee,et al.  First Course on Fuzzy Theory and Applications , 2005, Advances in Soft Computing.

[28]  Xing Zhang,et al.  Heat transfer and pressure drop of nanofluids containing carbon nanotubes in laminar flows , 2013 .

[29]  Farzad Veysi,et al.  Prediction of heat transfer coefficients of shell and coiled tube heat exchangers using numerical method and experimental validation , 2016 .

[30]  M. Moawed,et al.  Experimental study of forced convection from helical coiled tubes with different parameters , 2011 .

[31]  Mohammad Ali Ahmadi,et al.  A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach , 2018, Journal of Thermal Analysis and Calorimetry.

[32]  H. Kurt,et al.  Prediction of thermal conductivity of ethylene glycol-water solutions by using artificial neural networks , 2009 .

[33]  Mohammad Ali Ahmadi,et al.  Prediction of performance of Stirling engine using least squares support machine technique , 2016 .

[34]  Saeed Zeinali Heris,et al.  Multiwalled Carbon Nanotube/Water Nanofluid or Helical Coiling Technique, Which of Them Is More Effective? , 2013 .

[35]  Ravi Kumar,et al.  Condensation of R-134a inside dimpled helically coiled tube-in-shell type heat exchanger , 2018 .

[36]  D. Drew,et al.  Theory of Multicomponent Fluids , 1998 .

[37]  A. Mohammadi,et al.  Prediction of CO2 loading capacities of aqueous solutions ofabsorbents using different computational schemes , 2017 .

[38]  Jules Simo,et al.  A Review on the Application of Nanofluids in Coiled Tube Heat Exchangers , 2018 .

[39]  Michel Feidt,et al.  Connectionist intelligent model estimates output power and torque of stirling engine , 2015 .

[40]  S. Suresh,et al.  Study on performance enhancement factors in turbulent flow of CNT/water nanofluid through a tube fitted with helical screw louvered rod inserts , 2018 .

[41]  Ali Naseri,et al.  Reservoir oil viscosity determination using a rigorous approach , 2014 .

[42]  M. Mohanraj,et al.  Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review , 2012, Renewable and Sustainable Energy Reviews.

[43]  Gary Rosengarten,et al.  Experimental investigation of TiO2/water nanofluid droplet impingement on nanostructured surfaces , 2016 .

[44]  Ali Abbas,et al.  Estimation of air dew point temperature using computational intelligence schemes , 2016 .

[45]  Farzad Veysi,et al.  Optimal and critical values of geometrical parameters of shell and helically coiled tube heat exchangers , 2017 .

[46]  Alireza Bahadori,et al.  Assessing the Dynamic Viscosity of Na–K–Ca–Cl–H2O Aqueous Solutions at High-Pressure and High-Temperature Conditions , 2014 .

[47]  Soteris A. Kalogirou,et al.  Applications of artificial neural-networks for energy systems , 2000 .

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

[49]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[50]  P. Razi,et al.  An experimental investigation on thermo-physical properties and overall performance of MWCNT/heat transfer oil nanofluid flow inside vertical helically coiled tubes , 2012 .