Skip to main content
Log in

Flow visualization and analysis of thermal distribution for the nanofluid by the integration of fuzzy c-means clustering ANFIS structure and CFD methods

  • Regular Paper
  • Published:
Journal of Visualization Aims and scope Submit manuscript

Abstract

A nanofluid containing copper (Cu) nanoparticles was simulated in a rectangular cavity using computational fluid dynamic (CFD). The upper and lower walls of the cavity were adiabatic, while the right and left walls had warm and cold temperatures, respectively. This temperature difference causes a thermal flow from the right wall to the left wall. The elements of the coordination system in different directions, including velocity in the Y direction (V) and fluid temperature, were obtained using CFD. Adaptive network-based fuzzy inference system (ANFIS) was used to train the CFD outputs and provided artificial flow field and temperature distribution along the cavity domain. The CFD outputs were used as input and output data for the ANFIS method. The position of the fluid layer in X and Y computing directions and fluid velocity (Y axis) were used as three inputs, and the fluid temperature was taken as the output in the ANFIS method training process. The data were categorized using fuzzy c-means clustering, and different numbers of clusters were taken as a key parameter in this method. Using the fuzzy inference system, it is possible to predict the nodes in the cavity not generated through CFD simulation so that different coordination of the fluid at these points can be computed. Using ANFIS method, it is possible to reduce the computation time of CFD method so that more nodes are predicted in a shorter period of time, while clustering method can enhance the computing time for each neural cell. The ANFIS method can also visualize the flow in the cavity and display the thermal distribution along with the heat source.

Graphic abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Abonyi J et al (1999) Inverse fuzzy-process-model based direct adaptive control. Math Comput Simul 51(1–2):119–132

    MathSciNet  Google Scholar 

  • Abu-Nada E (2008) Application of nanofluids for heat transfer enhancement of separated flows encountered in a backward facing step. Int J Heat Fluid Flow 29(1):242–249

    Google Scholar 

  • Abu-Nada E (2009) Effects of variable viscosity and thermal conductivity of Al2O3–water nanofluid on heat transfer enhancement in natural convection. Int J Heat Fluid Flow 30(4):679–690

    Google Scholar 

  • Aminossadati S, Kargar A, Ghasemi B (2012) Adaptive network-based fuzzy inference system analysis of mixed convection in a two-sided lid-driven cavity filled with a nanofluid. Int J Therm Sci 52:102–111

    Google Scholar 

  • Asgarpour Khansary M et al (2017a) Correlation of sorption-induced swelling in polymeric films with reference to attenuated total reflectance Fourier-transform infrared spectroscopy data. Eur Polym J 91:429–435

    Google Scholar 

  • Asgarpour Khansary M, Marjani A, Shirazian S (2017b) Prediction of carbon dioxide sorption in polymers for capture and storage feasibility analysis. Chem Eng Res Des 120:254–258

    Google Scholar 

  • Asgarpour Khansary M et al (2018) A priority supposition for estimation of time-dependent changes in thickness and weight of polymeric flat sheet membranes fabricated by the nonsolvent induced phase separation (NIPS) technique. Adv Polym Technol 37(6):1963–1969

    Google Scholar 

  • Avila G, Pacheco-Vega A (2009) Fuzzy-C-means-based classification of thermodynamic-property data: a critical assessment. Numer Heat Transf Part A Appl 56(11):880–896

    Google Scholar 

  • Azwadi CSN et al (2013) Adaptive-network-based fuzzy inference system analysis to predict the temperature and flow fields in a lid-driven cavity. Numer Heat Transf Part A Appl 63(12):906–920

    Google Scholar 

  • Babanezhad M et al (2019) Liquid‐phase chemical reactors: development of 3D hybrid model based on CFD‐adaptive network‐based fuzzy inference system. Can J Chem Eng 97(S1):1676–1684

    Google Scholar 

  • Bararnia H, Soleimani S, Ganji DD (2011) Lattice Boltzmann simulation of natural convection around a horizontal elliptic cylinder inside a square enclosure. Int Commun Heat Mass Transf 38(10):1436–1442

    Google Scholar 

  • Bataineh K, Naji M, Saqer M (2011) A comparison study between various fuzzy clustering algorithms. Jordan J Mech Ind Eng 5(4):335–343

    Google Scholar 

  • Ben-Nakhi A, Mahmoud MA, Mahmoud AM (2008) Inter-model comparison of CFD and neural network analysis of natural convection heat transfer in a partitioned enclosure. Appl Math Model 32(9):1834–1847

    MATH  Google Scholar 

  • Boyacioglu MA, Avci D (2010) An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Syst Appl 37(12):7908–7912

    Google Scholar 

  • Brinkmann HC (1952) The viscosity of concentrated suspensions and solutions. J Chem Phys 20:571

    Google Scholar 

  • Choi SU, Eastman JA (1995) Enhancing thermal conductivity of fluids with nanoparticles. Argonne National Lab, Lemont

    Google Scholar 

  • Dashti A, Harami HR, Rezakazemi M (2018) Accurate prediction of solubility of gases within H2-selective nanocomposite membranes using committee machine intelligent system. Int J Hydrogen Energy 43(13):6614–6624

    Google Scholar 

  • Daungthongsuk W, Wongwises S (2007) A critical review of convective heat transfer of nanofluids. Renew Sustain Energy Rev 11(5):797–817

    Google Scholar 

  • Delavar MA, Farhadi M, Sedighi K (2010) Numerical simulation of direct methanol fuel cells using lattice Boltzmann method. Int J Hydrogen Energy 35(17):9306–9317

    Google Scholar 

  • Ding Y et al (2018) Lattice Boltzmann method for rain-induced overland flow. J Hydrol 562:789–795

    Google Scholar 

  • Fasihi M et al (2012) Computational fluid dynamics simulation of transport phenomena in ceramic membranes for SO2 separation. Math Comput Model 56(11):278–286

    Google Scholar 

  • Fattahi E et al (2012) Lattice Boltzmann simulation of natural convection heat transfer in nanofluids. Int J Therm Sci 52:137–144

    Google Scholar 

  • Ghadiri M, Shirazian S, Ashrafizadeh SN (2012) Mass transfer simulation of gold extraction in membrane extractors. Chem Eng Technol 35(12):2177–2182

    Google Scholar 

  • Ghadiri M, Marjani A, Shirazian S (2013) Mathematical modeling and simulation of CO2 stripping from monoethanolamine solution using nano porous membrane contactors. Int J Greenh Gas Control 13:1–8

    Google Scholar 

  • Ghasemi A et al (2017) Using quantum chemical modeling and calculations for evaluation of cellulose potential for estrogen micropollutants removal from water effluents. Chemosphere 178:411–423

    Google Scholar 

  • Haghshenas Fard M, Esfahany MN, Talaie MR (2010) Numerical study of convective heat transfer of nanofluids in a circular tube two-phase model versus single-phase model. Int Commun Heat Mass Transf 37(1):91–97

    Google Scholar 

  • Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Google Scholar 

  • Jang J-S (1996) Input selection for ANFIS learning. In: Proceedings of the 5th IEEE international conference on fuzzy systems. IEEE

  • Jang J (2017) Frequently asked questions–ANFIS in the fuzzy logic toolbox. http://www.cs.nthu.edu.tw/~jang/anfisfaq.htm

  • Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligence. IEEE Trans Autom Control 42(10):1482–1484

    Google Scholar 

  • Jooshani S et al (2018) Contaminant uptake by polymeric passive samplers: a modeling study with experimental validation. Chem Eng Res Des 129:231–236

    Google Scholar 

  • Kaehler S (2006) Fuzzy logic tutorial, the newsletter of the Seattle Robotic Society (encoder). http://www.seattlerobotics.org/encoder/199803/fuz/flindex.html

  • Keblinski P, Eastman JA, Cahill DG (2005) Nanofluids for thermal transport. Mater Today 8(6):36–44

    Google Scholar 

  • Kefayati GR et al (2011) Lattice Boltzmann simulation of natural convection in tall enclosures using water/SiO2 nanofluid. Int Commun Heat Mass Transfer 38(6):798–805

    Google Scholar 

  • Kefayati GHR et al (2012) Lattice Boltzmann simulation of natural convection in an open enclosure subjugated to water/copper nanofluid. Int J Therm Sci 52:91–101

    Google Scholar 

  • Kennedy E, Condon M, Dowling J (2003) Torque-ripple minimisation in switched reluctance motors using a neuro-fuzzy control strategy. In: Proceedings of the IASTED international conference on modelling and simulation (MS 2003), February 24–26, 2003, Palm Springs, California, USA

  • Khanafer K, Vafai K, Lightstone M (2003) Buoyancy-driven heat transfer enhancement in a two-dimensional enclosure utilizing nanofluids. Int J Heat Mass Transf 46(19):3639–3653

    MATH  Google Scholar 

  • Khansary MA, Marjani A, Shirazian S (2017) On the search of rigorous thermo-kinetic model for wet phase inversion technique. J Membr Sci 538:18–33

    Google Scholar 

  • Khodadadi JM, Hosseinizadeh SF (2007) Nanoparticle-enhanced phase change materials (NEPCM) with great potential for improved thermal energy storage. Int Commun Heat Mass Transf 34(5):534–543

    Google Scholar 

  • Lai F-H, Yang Y-T (2011) Lattice Boltzmann simulation of natural convection heat transfer of Al2O3/water nanofluids in a square enclosure. Int J Therm Sci 50(10):1930–1941

    Google Scholar 

  • Lei Y et al (2007) Fault diagnosis of rotating machinery based on multiple ANFIS combination with GAs. Mech Syst Signal Process 21(5):2280–2294

    Google Scholar 

  • Mahmoodi M (2011) Numerical simulation of free convection of a nanofluid in L-shaped cavities. Int J Therm Sci 50(9):1731–1740

    Google Scholar 

  • Marjani A, Shirazian S (2011) Simulation of heavy metal extraction in membrane contactors using computational fluid dynamics. Desalination 281:422–428

    Google Scholar 

  • Mohammed HA et al (2011) Convective heat transfer and fluid flow study over a step using nanofluids: a review. Renew Sustain Energy Rev 15(6):2921–2939

    Google Scholar 

  • Nabavitabatabayi M, Shirani E, Rahimian MH (2011) Investigation of heat transfer enhancement in an enclosure filled with nanofluids using multiple relaxation time lattice Boltzmann modeling. Int Commun Heat Mass Transf 38(1):128–138

    Google Scholar 

  • Nemati H et al (2010) Lattice Boltzmann simulation of nanofluid in lid-driven cavity. Int Commun Heat Mass Transf 37(10):1528–1534

    Google Scholar 

  • Oztop HF, Abu-Nada E (2008) Numerical study of natural convection in partially heated rectangular enclosures filled with nanofluids. Int J Heat Fluid Flow 29(5):1326–1336

    Google Scholar 

  • Panella M, Gallo AS (2005) An input–output clustering approach to the synthesis of ANFIS networks. IEEE Trans Fuzzy Syst 13(1):69–81

    Google Scholar 

  • Pashaie P et al (2012) Nusselt number estimation along a wavy wall in an inclined lid-driven cavity using adaptive neuro-fuzzy inference system (anfis). Int J Eng Trans A Basics 26(4):383

    Google Scholar 

  • Pourtousi M (2012) Simulation of particle motion in incompressible fluid by lattice Boltzmann MRT Model. Universiti Teknologi Malaysia

  • Pourtousi M et al (2012) Simulation of fluid flow inside a back-ward-facing step by MRT-LBM. Int Proc Comput Sci Inf Technol 33:130–135

    Google Scholar 

  • Pourtousi M et al (2015a) Prediction of multiphase flow pattern inside a 3D bubble column reactor using a combination of CFD and ANFIS. RSC Adv 5(104):85652–85672

    Google Scholar 

  • Pourtousi M et al (2015b) A combination of computational fluid dynamics (CFD) and adaptive neuro-fuzzy system (ANFIS) for prediction of the bubble column hydrodynamics. Powder Technol 274:466–481

    Google Scholar 

  • Rezakazemi M, Shirazian S (2019) Multiscale computational modeling of organic compounds separation using microporous membranes. Desalin Water Treat 142:136–139

    Google Scholar 

  • Safdari A, Dabir H, Kim KC (2018) Cubic-Interpolated Pseudo-particle model to predict thermal behavior of a nanofluid. Comput Fluids 164:102–113

    MathSciNet  MATH  Google Scholar 

  • Shan X, Chen H (1993) Lattice Boltzmann model for simulating flows with multiple phases and components. Phys Rev E 47(3):1815

    Google Scholar 

  • Shan X, Doolen G (1996) Diffusion in a multicomponent lattice Boltzmann equation model. Phys Rev E 54(4):3614

    Google Scholar 

  • Sheldareh A et al (2012) Prediction of particle dynamics in lid-driven cavity flow. Int Rev Model Simul 5(3):1344–1347

    Google Scholar 

  • Shirazian S, Ashrafizadeh SN (2015) LTA and ion-exchanged LTA zeolite membranes for dehydration of natural gas. J Ind Eng Chem 22:132–137

    Google Scholar 

  • Shirazian S, Marjani A, Fadaei F (2011) Supercritical extraction of organic solutes from aqueous solutions by means of membrane contactors: CFD simulation. Desalination 277(1):135–140

    Google Scholar 

  • Shirazian S et al (2012) Hydrodynamics and mass transfer simulation of wastewater treatment in membrane reactors. Desalination 286:290–295

    Google Scholar 

  • Sohrabi MR et al (2011) Mathematical modeling and numerical simulation of CO2 transport through hollow-fiber membranes. Appl Math Model 35(1):174–188

    MATH  Google Scholar 

  • Soltani R et al (2020) Synthesis and characterization of novel N-methylimidazolium-functionalized KCC-1: a highly efficient anion exchanger of hexavalent chromium. Chemosphere 239:124735

    Google Scholar 

  • Soltani R et al (2019a) Novel diamino-functionalized fibrous silica submicro-spheres with a bimodal-micro-mesoporous network: ultrasonic-assisted fabrication, characterization, and their application for superior uptake of Congo red. J Mol Liq 294:111617

    Google Scholar 

  • Soltani R, Marjani A, Shirazian S (2019b) Shell-in-shell monodispersed triamine-functionalized SiO2 hollow microspheres with micro-mesostructured shells for highly efficient removal of heavy metals from aqueous solutions. J Environ Chem Eng 7(1):102832

    Google Scholar 

  • Soltani R, Marjani A, Shirazian S (2019c) Facile one-pot synthesis of thiol-functionalized mesoporous silica submicrospheres for Tl(I) adsorption: isotherm, kinetic and thermodynamic studies. J Hazard Mater 371:146–155

    Google Scholar 

  • Vahala L et al (2000) Thermal lattice Boltzmann simulation for multispecies fluid equilibration. Phys Rev E 62(1):507

    Google Scholar 

  • Varol Y et al (2008) Analysis of adaptive-network-based fuzzy inference system (ANFIS) to estimate buoyancy-induced flow field in partially heated triangular enclosures. Expert Syst Appl 35(4):1989–1997

    Google Scholar 

  • Wang X, Xu X, Choi SUS (1999) Thermal conductivity of nanoparticle-fluid mixture. J Thermophys Heat Transfer 13(4):474–480

    Google Scholar 

  • Wasp FJ (1977) Solid–liquid slurry pipeline transportation. Trans. Tech, Berlin

    Google Scholar 

  • Wolf-Gladrow DA (2000) 5. Lattice Boltzmann models. In: Lattice gas cellular automata and lattice Boltzmann models. Springer, Berlin, pp 159–246

    MATH  Google Scholar 

  • Xuan Y, Yao Z (2004) Lattice Boltzmann model for nanofluids. Heat Mass Transf 41(3):199–205

    Google Scholar 

  • Yang Z et al (2000) Evaluation of the Darcy’s law performance for two-fluid flow hydrodynamics in a particle debris bed using a lattice-Boltzmann model. Heat Mass Transf 36(4):295–304

    Google Scholar 

  • Yilmaz I, Kaynar O (2011) Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl 38(5):5958–5966

    Google Scholar 

  • Zabihi S et al (2019) Development of hybrid ANFIS–CFD model for design and optimization of membrane separation of benzoic acid. J Non-Equilib Thermodyn 44(3):285–293

    MathSciNet  Google Scholar 

  • Zarei F, Marjani A, Hassani Joshaghani A (2019a) Triamino-anchored KCC-1: a novel and promising adsorbent for fast and highly effective aqueous CrVI removal. J Environ Chem Eng 7(5):103309

    Google Scholar 

  • Zarei F, Marjani A, Soltani R (2019b) Novel and green nanocomposite-based adsorbents from functionalised mesoporous KCC-1 and chitosan-oleic acid for adsorption of Pb(II). Eur Polym J 119:400–409

    Google Scholar 

  • Zeinali M et al (2016) Influence of piston and magnetic coils on the field-dependent damping performance of a mixed-mode magnetorheological damper. Smart Mater Struct 25(5):055010

    Google Scholar 

  • Zhou L, Xuan Y, Li Q (2010) Multiscale simulation of flow and heat transfer of nanofluid with lattice Boltzmann method. Int J Multiph Flow 36(5):364–374

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Azam Marjani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, P., Babanezhad, M., Yarmand, H. et al. Flow visualization and analysis of thermal distribution for the nanofluid by the integration of fuzzy c-means clustering ANFIS structure and CFD methods. J Vis 23, 97–110 (2020). https://doi.org/10.1007/s12650-019-00614-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12650-019-00614-0

Keywords

Navigation