On the Thermal Conductivity Assessment of Oil-Based Hybrid Nanofluids using Extended Kalman Filter integrated with feed-forward neural network

Abstract Regarding their ability to enhance conventional thermal oils' thermophysical properties, oil-based hybrid nanofluids have recently been widely investigated by researchers, especially on lubrication and cooling application in the automotive industry. Thermal conductivity is one of the most crucial thermophysical properties of oil-based hybrid nanofluids, which has been studied in a minimal case of studies on the specific types of them. In this research, for the first time, a comprehensive data-intelligence analysis performed on 400 gathered data points of various types of oil-based hybrid nanofluids using a novel hybrid machine learning approach; the Extended Kalman Filter-Neural network (EKF-ANN). The genetic programming (GP) and response surface methodology (RSM) approaches were examined to appraise the main paradigm. In this research, the best subset regression analysis, as a novel feature selection scheme, was provided for finding the best input parameter among all existing predictive variables (the volume fraction, temperature, thermal conductivity of the base fluid, mean diameter, and bulk density of nanoparticles). The provided models were examined using several statistical metrics, graphical tools and trends, and sensitivity analysis. The results assessment indicated that the EKF-ANN in terms of (R = 0.9738, RMSE = 0.0071 W/m.K, and KGE = 0.9630) validation phase outperformed the RSM (R = 0.9671, RMSE = 0.0079 W/m.K, and KGE = 0.9593) and GP (R = 0.9465, RMSE = 0.010 W/m.K, and KGE = 0.9273), for accurate estimation of the thermal conductivity of oil-based hybrid nanofluids.

[1]  R. Ansari,et al.  An analytical model for elastic modulus calculation of SiC whisker-reinforced hybrid metal matrix nanocomposite containing SiC nanoparticles , 2018, Journal of Alloys and Compounds.

[2]  Iman Ahmadianfar,et al.  Prediction of scour depth at piers with debris accumulation effects using linear genetic programming , 2020, Marine Georesources & Geotechnology.

[3]  S. Aberoumand,et al.  Experimental study on synthesis, stability, thermal conductivity and viscosity of Cu–engine oil nanofluid , 2017 .

[4]  C. Willmott.  Some Comments on the Evaluation of Model Performance , 1982 .

[5]  M. Afrand,et al.  On evaluation of thermophysical properties of transformer oil-based nanofluids: A comprehensive modeling and experimental study , 2020 .

[6]  Robert A. Taylor,et al.  Recent advances in modeling and simulation of nanofluid flows-Part I: Fundamentals and theory , 2019, Physics Reports.

[7]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[8]  I. Alarifi,et al.  Effects of ultrasonication time on stability, dynamic viscosity, and pumping power management of MWCNT-water nanofluid: an experimental study , 2020, Scientific Reports.

[9]  Navid Nasajpour Esfahani,et al.  A new correlation for predicting the thermal conductivity of ZnO–Ag (50%–50%)/water hybrid nanofluid: An experimental study , 2018 .

[10]  M. Farbod,et al.  Improved thermal conductivity of Ag decorated carbon nanotubes water based nanofluids , 2016 .

[11]  Mohammad Hemmat Esfe,et al.  An applicable study on the thermal conductivity of SWCNT-MgO hybrid nanofluid and price-performance analysis for energy management , 2017 .

[12]  K. Chau,et al.  Applicability of connectionist methods to predict dynamic viscosity of silver/water nanofluid by using ANN-MLP, MARS and MPR algorithms , 2019, Engineering Applications of Computational Fluid Mechanics.

[13]  I. Ahmadianfar,et al.  Prediction of nanofluids viscosity using random forest (RF) approach , 2020 .

[14]  A. Addali,et al.  A Review on Nanofluids: Fabrication, Stability, and Thermophysical Properties , 2018, Journal of Nanomaterials.

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

[16]  Evangelos Bellos,et al.  A review of concentrating solar thermal collectors with and without nanofluids , 2018, Journal of Thermal Analysis and Calorimetry.

[17]  Yaonan Wang,et al.  Extended and Unscented Kalman filtering based feedforward neural networks for time series prediction , 2012 .

[18]  Farhad Gharagheizi,et al.  Evaluation of Thermal Conductivity of Gases at Atmospheric Pressure through a Corresponding States Method , 2012 .

[19]  K. Javaherdeh,et al.  Al/ oil nanofluids inside annular tube: an experimental study on convective heat transfer and pressure drop , 2018 .

[20]  Munkhbayar Batmunkh,et al.  Investigation of Al2O3-MWCNTs hybrid dispersion in water and their thermal characterization. , 2012, Journal of nanoscience and nanotechnology.

[21]  Huaqing Xie,et al.  Silicon oil based multiwalled carbon nanotubes nanofluid with optimized thermal conductivity enhancement , 2009 .

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

[23]  Masahito Kobayashi,et al.  Mallows' Cp criterion and unbiasedness of model selection , 1990 .

[24]  M. Afrand,et al.  Effect of sonication characteristics on stability, thermophysical properties, and heat transfer of nanofluids: A comprehensive review. , 2019, Ultrasonics sonochemistry.

[25]  D. Dao,et al.  Advances in electrode and electrolyte improvements in vanadium redox flow batteries with a focus on the nanofluidic electrolyte approach , 2020 .

[26]  S. H. Qing,et al.  Thermal conductivity and electrical properties of hybrid SiO2-graphene naphthenic mineral oil nanofluid as potential transformer oil , 2017 .

[27]  T. Saleh,et al.  An approach to predict the isobaric specific heat capacity of nitrides/ethylene glycol-based nanofluids using support vector regression , 2020, Journal of Energy Storage.

[28]  O. K. Crosser,et al.  Thermal Conductivity of Heterogeneous Two-Component Systems , 1962 .

[29]  I. Ahmadianfar,et al.  Prediction of local scour around circular piles under waves using a novel artificial intelligence approach , 2019, Marine Georesources & Geotechnology.

[30]  Rizwan Ul Haq,et al.  Cu-AlO/Water hybrid nanofluid through a permeable surface in the presence of nonlinear radiation and variable thermal conductivity via LSM , 2018, International Journal of Heat and Mass Transfer.

[31]  I. Ahmadianfar,et al.  A rigorous model for prediction of viscosity of oil-based hybrid nanofluids , 2020 .

[32]  H. Ali,et al.  Thermal conductivity of hybrid nanofluids: A critical review , 2018, International Journal of Heat and Mass Transfer.

[33]  A. Addali,et al.  On the Role of Nanofluids in Thermal-Hydraulic Performance of Heat Exchangers—A Review , 2020, Nanomaterials.

[34]  Guanrong Chen,et al.  Extended Kalman Filter and System Identification , 1991 .

[35]  Prasanta Kumar Das,et al.  Development and characterization of Al2Cu and Ag2Al nanoparticle dispersed water and ethylene glycol based nanofluid , 2007 .

[36]  Amin Shokrollahi,et al.  Prediction of viscosity of water-based Al2O3, TiO2, SiO2, and CuO nanofluids using a reliable approach , 2015 .

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

[38]  A. Sousa,et al.  Nanodiamond-Fe3O4 nanofluids: Preparation and measurement of viscosity, electrical and thermal conductivities , 2016 .

[39]  A. Addali,et al.  Aluminium Nanofluids Stability: A Comparison between the Conventional Two-Step Fabrication Approach and the Controlled Sonication Bath Temperature Method , 2019, Journal of Nanomaterials.

[40]  M. Hemmat Esfe,et al.  Modeling of thermal conductivity of MWCNT-SiO2 (30:70%)/EG hybrid nanofluid, sensitivity analyzing and cost performance for industrial applications , 2018, Journal of Thermal Analysis and Calorimetry.

[41]  I. Ahmadianfar,et al.  On the assessment of specific heat capacity of nanofluids for solar energy applications: Application of Gaussian process regression (GPR) approach , 2020 .

[42]  K. Lindsay,et al.  Marangoni convection in water-alumina nanofluids: Dependence on the nanoparticle size , 2018 .

[43]  K. P. Venkitaraj,et al.  Synthesis of Al2O3–Cu/water hybrid nanofluids using two step method and its thermo physical properties , 2011 .

[44]  Khairul Anwar Mohamad Said,et al.  Overview on the Response Surface Methodology (RSM) in Extraction Processes , 2016 .

[45]  D. Rashtchian,et al.  Investigating the rheological properties of nanofluids of water/hybrid nanostructure of spherical silica/MWCNT , 2014 .

[46]  W. Zhong,et al.  Enhancement of fluid thermal conductivity by the addition of single and hybrid nano-additives , 2007 .

[47]  S. Araghinejad.  Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering , 2013 .

[48]  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 .

[49]  I. Alarifi,et al.  Thermal and Fluid Dynamics Performance of MWCNT-Water Nanofluid Based on Thermophysical Properties: An Experimental and Theoretical Study , 2020, Scientific Reports.

[50]  D. Rashtchian,et al.  Synthesis of spherical silica/multiwall carbon nanotubes hybrid nanostructures and investigation of thermal conductivity of related nanofluids , 2012 .

[51]  A. Nemati,et al.  The effect of functionalisation method on the stability and the thermal conductivity of nanofluid hybrids of carbon nanotubes/gamma alumina , 2013 .

[52]  Huaqing Xie,et al.  Enhancement of thermal conductivity of kerosene-based Fe3O4 nanofluids prepared via phase-transfer method , 2010 .

[53]  I. Ahmadianfar,et al.  Accurate prediction of thermal conductivity of ethylene glycol-based hybrid nanofluids using artificial intelligence techniques , 2020, International Communications in Heat and Mass Transfer.

[54]  Somchai Wongwises,et al.  Recent advances in preparation methods and thermophysical properties of oil-based nanofluids: A state-of-the-art review , 2019, Powder Technology.

[55]  I. Ahmadianfar,et al.  On the specific heat capacity estimation of metal oxide-based nanofluid for energy perspective – A comprehensive assessment of data analysis techniques , 2021 .

[56]  C. T. Nguyen,et al.  New temperature dependent thermal conductivity data for water-based nanofluids , 2009 .

[57]  C. Ha,et al.  Fabrication of Carbon Nanotube/SiO2and Carbon Nanotube/SiO2/Ag Nanoparticles Hybrids by Using Plasma Treatment , 2009, Nanoscale research letters.

[58]  G. Karimi,et al.  Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks , 2015 .

[59]  P. Ndungu,et al.  Physicochemical Properties of Oil-Based Nanofluids Containing Hybrid Structures of Silver Nanoparticles Supported on Silica , 2011 .

[60]  V. Vasu,et al.  Investigation of thermal conductivity and rheological properties of vegetable oil based hybrid nanofluids containing Cu–Zn hybrid nanoparticles , 2017 .

[61]  Chen-Xi Song,et al.  Numerical investigation on pre-heating of coal water slurry in shell-and-tube heat exchangers with fold helical baffles , 2018, International Journal of Heat and Mass Transfer.

[62]  Sunday O. Olatunji,et al.  Modeling thermal conductivity enhancement of metal and metallic oxide nanofluids using support vector regression , 2018 .

[63]  Eric C. Okonkwo,et al.  An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids , 2020, Journal of Thermal Analysis and Calorimetry.

[64]  Rozli Zulkifli,et al.  Modelling and measuring the thermal conductivity of multi-metallic Zn/Cu nanofluid , 2013, Research on Chemical Intermediates.

[65]  I. Ahmadianfar,et al.  A meticulous intelligent approach to predict thermal conductivity ratio of hybrid nanofluids for heat transfer applications , 2020, Journal of Thermal Analysis and Calorimetry.

[66]  M. Firouzi,et al.  Empirical study and model development of thermal conductivity improvement and assessment of cost and sensitivity of EG-water based SWCNT-ZnO (30%:70%) hybrid nanofluid , 2017 .

[67]  A. Shokrgozar,et al.  Cu/Oil nanofluids flow over a semi‐infinite plate accounting an experimental model , 2020, Heat Transfer.

[68]  H. Akaike.  A new look at the statistical model identification , 1974 .

[69]  Zafar Hayat Khan,et al.  Numerical study of unsteady MHD flow of Williamson nanofluid in a permeable channel with heat source/sink and thermal radiation , 2018, The European Physical Journal Plus.

[70]  Greg Welch,et al.  An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.

[71]  L. Asirvatham,et al.  The role of hybrid nanofluids in improving the thermal characteristics of screen mesh cylindrical heat pipes , 2016 .

[72]  O. Bamisile,et al.  A neural network-based predictive model for the thermal conductivity of hybrid nanofluids , 2020 .

[73]  A. Sousa,et al.  Thermal conductivity and viscosity of hybrid nanfluids prepared with magnetic nanodiamond-cobalt oxide (ND-Co3O4) nanocomposite , 2016 .

[74]  M. Afrand,et al.  Heat transfer efficiency of Al2O3-MWCNT/thermal oil hybrid nanofluid as a cooling fluid in thermal and energy management applications: An experimental and theoretical investigation , 2018 .

[75]  Wenhua Yu,et al.  The Role of Interfacial Layers in the Enhanced Thermal Conductivity of Nanofluids: A Renovated Maxwell Model , 2003 .

[76]  M. Afrand,et al.  Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks , 2017 .

[77]  D. Toghraie,et al.  Experimental measurements of thermal conductivity of engine oil-based hybrid and mono nanofluids with tungsten oxide (WO3) and MWCNTs inclusions , 2020 .

[78]  Qing Wang,et al.  Investigation of heat transfer mechanisms among particles in horizontal rotary retorts , 2020 .

[79]  Mohammad Hossein Ahmadi,et al.  A review of thermal conductivity of various nanofluids , 2018, Journal of Molecular Liquids.

[80]  S. Aberoumand,et al.  Tungsten (III) oxide (WO3) – Silver/transformer oil hybrid nanofluid: Preparation, stability, thermal conductivity and dielectric strength , 2016 .

[81]  A. Asadi,et al.  An experimental and theoretical investigation on the effects of adding hybrid nanoparticles on heat transfer efficiency and pumping power of an oil-based nanofluid as a coolant fluid , 2018 .

[82]  A. Alsayegh,et al.  Gas Turbine Intercoolers: Introducing Nanofluids—A Mini-Review , 2020, Processes.

[83]  I. Ahmadianfar,et al.  Nanofluids thermal conductivity prediction applying a novel hybrid data-driven model validated using Monte Carlo-based sensitivity analysis , 2020, Engineering with Computers.

[84]  S. Kannaiyan,et al.  Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks , 2019, Chinese Journal of Chemical Engineering.

[85]  Li Li,et al.  Wave propagation in viscous-fluid-conveying piezoelectric nanotubes considering surface stress effects and Knudsen number based on nonlocal strain gradient theory , 2018, The European Physical Journal Plus.

[86]  V. Vasu,et al.  Thermal conductivity and rheological studies for Cu–Zn hybrid nanofluids with various basefluids , 2016 .

[87]  A. Asadi.  A guideline towards easing the decision-making process in selecting an effective nanofluid as a heat transfer fluid , 2018, Energy Conversion and Management.

[88]  Young I Cho,et al.  HYDRODYNAMIC AND HEAT TRANSFER STUDY OF DISPERSED FLUIDS WITH SUBMICRON METALLIC OXIDE PARTICLES , 1998 .

[89]  B. Raj,et al.  Synthesis, characterization, and thermal property measurement of nano-Al95Zn05 dispersed nanofluid prepared by a two-step process , 2011 .

[90]  Yi Lu Murphey,et al.  Battery state of charge estimation based on a combined model of Extended Kalman Filter and neural networks , 2011, The 2011 International Joint Conference on Neural Networks.

[91]  R. Zou,et al.  The Application of Carbon Materials in Latent Heat Thermal Energy Storage (LHTES) , 2017 .

[92]  M. Biglari,et al.  Experimental investigation of thermal conductivity of CNTs-Al2O3/water: A statistical approach ☆ , 2015 .

[93]  P. Ghosh,et al.  A review on hybrid nanofluids: Recent research, development and applications , 2015 .

[94]  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.

[95]  Léon Personnaz,et al.  A recursive algorithm based on the extended Kalman filter for the training of feedforward neural models , 1998, Neurocomputing.

[96]  L. Que,et al.  Hybrid nanomaterial-based nanofluids for micropower generation , 2014 .

[97]  S. Wongwises,et al.  An experimental and theoretical investigation on heat transfer capability of Mg (OH)2/MWCNT-engine oil hybrid nano-lubricant adopted as a coolant and lubricant fluid , 2018 .

[98]  M. Hemmat Esfe,et al.  ANN modeling, cost performance and sensitivity analyzing of thermal conductivity of DWCNT–SiO2/EG hybrid nanofluid for higher heat transfer , 2018, Journal of Thermal Analysis and Calorimetry.

[99]  Xu Yimin,et al.  Enhancement of solar energy absorption using a plasmonic nanofluid based on TiO2/Ag composite nanoparticles , 2014 .

[100]  S. Ramaprabhu,et al.  Synthesis and nanofluid application of silver nanoparticles decorated graphene , 2011 .

[101]  P.B. Luh,et al.  Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method , 2005, IEEE Transactions on Power Systems.

[102]  Mohd Amiruddin Abd Rahman,et al.  Predicting the specific heat capacity of alumina/ethylene glycol nanofluids using support vector regression model optimized with Bayesian algorithm , 2019, Solar Energy.

[103]  Hoshin Vijai Gupta,et al.  Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling , 2009 .

[104]  Anurag Malik,et al.  Simulation of seepage flow through embankment dam by using a novel extended Kalman filter based neural network paradigm: Case study of Fontaine Gazelles Dam, Algeria , 2021 .

[105]  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.

[106]  S. Ramaprabhu,et al.  Experimental investigation of the thermal transport properties of a carbon nanohybrid dispersed nanofluid. , 2011, Nanoscale.

[107]  A. Mahmood,et al.  Nanoconfined phase change materials for thermal energy applications , 2018 .

[108]  I. Alarifi,et al.  On the heat transfer effectiveness and pumping power assessment of a diamond-water nanofluid based on thermophysical properties: An experimental study , 2020 .

[109]  R. Prasher,et al.  Brownian dynamics simulation to determine the effective thermal conductivity of nanofluids , 2004 .

[110]  A. Sousa,et al.  Experimental investigation of the thermal transport properties of graphene oxide/Co3O4 hybrid nanofluids , 2017 .

[111]  S. Kalaiselvam,et al.  Experimental investigation on convective heat transfer and rheological characteristics of Cu–TiO2 hybrid nanofluids , 2014 .

[112]  Darren George,et al.  SPSS for Windows Step by Step: A Simple Guide and Reference , 1998 .

[113]  Mohd Amiruddin Abd Rahman,et al.  Modeling and prediction of the specific heat capacity of Al2 O3/water nanofluids using hybrid genetic algorithm/support vector regression model , 2019, Nano-Structures & Nano-Objects.

[114]  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 .

[115]  M. L. Plume,et al.  SPSS (Statistical Package for the Social Sciences) , 2002, Encyclopedia of Information Systems.

[116]  X. Q. Yuan,et al.  Thermo-physical property evaluation of diathermic oil based hybrid nanofluids for heat transfer applications , 2017 .

[117]  Nils Lid Hjort,et al.  Model Selection and Model Averaging , 2001 .

[118]  M. Afrand,et al.  Measurement of thermal conductivity of ZnO–TiO2/EG hybrid nanofluid , 2016, Journal of Thermal Analysis and Calorimetry.

[119]  E. Bellos,et al.  Recent advances on nanofluids for low to medium temperature solar collectors: energy, exergy, economic analysis and environmental impact , 2021 .

[120]  Zaqiatud Darojah,et al.  The extended Kalman filter algorithm for improving neural network performance in voice recognition classification , 2016, 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA).

[121]  Wei Wang,et al.  Heat transfer and flow analysis of Casson fluid enclosed in a partially heated trapezoidal cavity , 2019, International Communications in Heat and Mass Transfer.

[122]  A. D'Orazio,et al.  An experimental study on thermal conductivity of F-MWCNTs–Fe3O4/EG hybrid nanofluid: Effects of temperature and concentration , 2016 .

[123]  A. Shahsavar,et al.  Experimental investigation and modeling of thermal conductivity and viscosity for non-Newtonian hybrid nanofluid containing coated CNT/Fe3O4 nanoparticles , 2017 .

[124]  Hajir Karimi,et al.  Modeling viscosity of nanofluids using diffusional neural networks , 2012 .

[125]  James Clerk Maxwell.  A Treatise on Electricity and Magnetism by James Clerk Maxwell , 1937 .