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Prediction of rheology of shear thickening fluids using phenomenological and artificial neural network models

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Abstract

Prediction models for the viscosity curve of a shear thickening fluid (STF) over a wide range of shear rate at different temperatures were developed using phenomenological and artificial neural network (ANN) models. STF containing 65% (w/w) silica nanoparticles was prepared using polyethylene glycol (PEG) as dispersion medium, and tested for rheological behavior at different temperatures. The experimental data set was divided into training data and testing data for the model development and validation, respectively. For both the models, the viscosity of STF was estimated for all the zones with good fit between experimental and predicted viscosity, for both training and testing data sets.

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References

  • Al-Zahrani, S.M., 1997, A generalized rheological model for shear thinning fluids, J. Pet. Sci. Eng. 17, 211–215.

    Article  Google Scholar 

  • Al-Zahrani, S.M. and T.F. Al-Fariss, 1998, A general model for the viscosity of waxy oils, Chem. Eng. Process. 37, 433–437.

    Article  Google Scholar 

  • Barnes, H.A., 1989, Shear-thickening (“Dilatancy”) in suspensions of nonaggregating solid particles dispersed in Newtonian liquids, J. Rheol. 33, 329–366.

    Article  Google Scholar 

  • Bender, J. and N.J. Wagner, 1996, Reversible shear thickening in monodisperse and bidisperse colloidal dispersions, J. Rheol. 40, 899–916.

    Article  Google Scholar 

  • Bender, J.W. and N.J. Wagner, 1995, Optical measurement of the contributions of colloidal forces to the rheology of concentrated suspensions, J. Colloid Interface Sci. 172, 171–184.

    Article  Google Scholar 

  • Bird, R.B., W.E. Steward, and E.W. Lightfoot, 1960, Transport Phenomena, 1st ed., Wiley, New York.

    Google Scholar 

  • Boersma, W.H., J. Laven, and H.N. Stein, 1992, Viscoelastic properties of concentrated shear-thickening dispersions, J. Colloid Interface Sci. 149, 10–22.

    Article  Google Scholar 

  • Brown, E. and H.M. Jaeger, 2011, Through thick and thin, Science 333, 1230–1231.

    Article  Google Scholar 

  • Brown, E. and H.M. Jaeger, 2014, Shear thickening in concentrated suspensions: Phenomenology, mechanisms and relations to jamming, Rep. Prog. Phys. 77, 046602.

    Article  Google Scholar 

  • David, J., P. Filip, and A.A. Kharlamov, 2013, Empirical modelling of nonmonotonous behaviour of shear viscosity, Adv. Mater. Sci. Eng. 2013, 658187.

    Article  Google Scholar 

  • Esfe, M.H., S. Saedodin, N. Sina, M. Afrand, and S. Rostami, 2015, Designing an artificial neural network to predict thermal conductivity and dynamic viscosity of ferromagnetic nanofluid, Int. Commun. Heat Mass Transf. 68, 50–57.

    Article  Google Scholar 

  • Galindo-Rosales, F.J., 2016, Complex fluids in energy dissipating systems, Appl. Sci.-Basel 6, 206.

    Article  Google Scholar 

  • Galindo-Rosales, F.J., F.J. Rubio-Hernández, and A. Sevilla, 2011a, An apparent viscosity function for shear thickening fluids, J. Non-Newton. Fluid Mech. 166, 321–325.

    Article  Google Scholar 

  • Galindo-Rosales, F.J., F.J. Rubio-Hernández, A. Sevilla, and R.H. Ewoldt, 2011b, How Dr. Malcom M. Cross may have tackled the development of “An apparent viscosity function for shear thickening fluids”, J. Non-Newton. Fluid Mech. 166, 1421–1424.

    Article  Google Scholar 

  • Galindo-Rosales, F.J., S. Martínez-Aranda, and L. Campo-Deaño, 2015, CorkSTFµfluidics -A novel concept for the development of eco-friendly light-weight energy absorbing composites, Mater. Des. 82, 326–334.

    Article  Google Scholar 

  • Gürgen, S. and M.C. Kushan, 2017, The stab resistance of fabrics impregnated with shear thickening fluids including various particle size of additives, Compos. Pt. A-Appl. Sci. Manuf. 94, 50–60.

    Article  Google Scholar 

  • Gürgen, S., M.C. Kushan, and W. Li, 2016a, The effect of carbide particle additives on rheology of shear thickening fluids, Korea-Aust. Rheol. J. 28, 121–128.

    Article  Google Scholar 

  • Gürgen, S., W. Li, and M.C. Kushan, 2016b, The rheology of shear thickening fluids with various ceramic particle additives, Mater. Des. 104, 312–319.

    Article  Google Scholar 

  • Hasanzadeh, M. and V. Mottaghitalab, 2014, The role of shearthickening fluids (STFs) in ballistic and stab-resistance improvement of flexible armor, J. Mater. Eng. Perform. 23, 1182–1196.

    Article  Google Scholar 

  • Head, D.A., A. Ajdari, and M.E. Cates, 2001, Jamming, hysteresis, and oscillation in scalar models for shear thickening, Phys. Rev. E 64, 061509.

    Google Scholar 

  • Heidari, E., M.A. Sobati, and S. Movahedirad, 2016, Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN), Chemometrics Intell. Lab. Syst. 155, 73–85.

    Article  Google Scholar 

  • Hoffman, R.L., 1998, Explanations for the cause of shear thickening in concentrated colloidal suspensions, J. Rheol. 42, 111–123.

    Article  Google Scholar 

  • Jiang, B., D.J. Keffer, B.J. Edwards, and J.N. Allred, 2003, Modeling shear thickening in dilute polymer solutions: Temperature, concentration, and molecular weight dependencies, J. Appl. Polym. Sci. 90, 2997–3011.

    Article  Google Scholar 

  • Kang, T.J., K.H. Hong, and M.R. Yoo, 2010, Preparation and properties of fumed silica/kevlar composite fabrics for application of stab resistant material, Fiber. Polym. 11, 719–724.

    Article  Google Scholar 

  • Khandavalli, S., J.A. Lee, M. Pasquali, and J.P. Rothstein, 2015, The effect of shear-thickening on liquid transfer from an idealized gravure cell, J. Non-Newton. Fluid Mech. 221, 55–65.

    Article  Google Scholar 

  • Laun, H.M., R. Bung, S. Hess, W. Loose, O. Hess, K. Hahn, E. Hädicke, R. Hingmann, F. Schmidt, and P. Lindner, 1992, Rheological and small angle neutron scattering investigation of shear-induced particle structures of concentrated polymer dispersions submitted to plane Poiseuille and Couette flow, J. Rheol. 36, 743–787.

    Article  Google Scholar 

  • Lee, Y.S., E.D. Wetzel, and N.J. Wagner, 2003, The ballistic impact characteristics of Kevlar® woven fabrics impregnated with a colloidal shear thickening fluid, J. Mater. Sci. 38, 2825–2833.

    Article  Google Scholar 

  • Lee, Y.S., E.D. Wetzel, R.G. Egres, and N.J. Wagner, 2002, Advanced body armor utilizing shear thickening fluids, 23rd Army Science Conference, Orlando, Florida.

    Google Scholar 

  • Li, H. and J. Zhang, 2003, A generalized model for predicting non-Newtonian viscosity of waxy crudes as a function of temperature and precipitated wax, Fuel 82, 1387–1397.

    Article  Google Scholar 

  • Liu, X.Q., R.Y. Bao, X.J. Wu, W. Yang, B.H. Xie, and M.B. Yang, 2015, Temperature induced gelation transition of a fumed silica/PEG shear thickening fluid, RSC Adv. 5, 18367–18374.

    Article  Google Scholar 

  • Macosko, C.W., 1994, Rheology: Principles, Measurements, and Applications, Wiley-VCH, New York.

    Google Scholar 

  • Majumdar, A., 2011, Soft computing in fibrous materials engineering, Text. Prog. 43, 1–95.

    Article  Google Scholar 

  • Maranzano, B.J. and N.J. Wagner, 2002, Flow-small angle neu-tron scattering measurements of colloidal dispersion microstructure evolution through the shear thickening transition, J. Chem. Phys. 117, 10291–10302.

    Article  Google Scholar 

  • Meyer, J.P., S.A. Adio, M. Sharifpur, and P.N. Nwosu, 2016, The viscosity of nanofluids: A Review of the theoretical, empirical, and numerical models, Heat Transf. Eng. 37, 387–421.

    Google Scholar 

  • Perlácová, T. and V. Pruša, 2015, Tensorial implicit constitutive relations in mechanics of incompressible non-Newtonian fluids, J. Non-Newton. Fluid Mech. 216, 13–21.

    Article  Google Scholar 

  • Raghavan, S.R., J. Hou, G.L. Baker, and S.A. Khan, 2000, Colloidal interactions between particles with tethered nonpolar chains dispersed in polar media: Direct correlation between dynamic rheology and interaction parameters, Langmuir 16, 1066–1077.

    Article  Google Scholar 

  • Raghavan, S.R. and S.A. Khan, 1995, Shear-induced microstructural changes in flocculated suspensions of fumed silica, J. Rheol. 39, 1311–1325.

    Article  Google Scholar 

  • Ramzi, M., M. Kashaninejad, F. Salehi, A.R.S. Mahoonak, and S.M.A. Razavi, 2015, Modeling of rheological behavior of honey using genetic algorithm-artificial neural network and adaptive neuro-fuzzy inference system, Food Biosci. 9, 60–67.

    Article  Google Scholar 

  • Salehi, F. and S.M.A. Razavi, 2012, Dynamic modeling of flux and total hydraulic resistance in nanofiltration treatment of regeneration waste brine using artificial neural networks, Desalin. Water Treat. 41, 95–104.

    Article  Google Scholar 

  • Skelland, A.H.P., 1967, Non-Newtonian Flow and Heat Transfer, Wiley, New York.

    Google Scholar 

  • Srivastava, A., A. Majumdar, and B.S. Butola, 2011, Improving the impact resistance performance of Kevlar fabrics using silica based shear thickening fluid, Mater. Sci. Eng. A-Struct. Mater. Prop. Microstruct. Process. 529, 224–229.

    Article  Google Scholar 

  • Srivastava, A., A. Majumdar, and B.S. Butola, 2012, Improving the impact resistance of textile structures by using shear thickening fluids: A review, Crit. Rev. Solid State Mat. Sci. 37, 115–129.

    Article  Google Scholar 

  • Tian, T., G. Peng, W. Li, J. Ding, and M. Nakano, 2015, Experimental and modelling study of the effect of temperature on shear thickening fluids, Korea-Aust. Rheol. J. 27, 17–24.

    Article  Google Scholar 

  • Zhang, X.Z., W.H. Li, and X.L. Gong, 2008, The rheology of shear thickening fluid (STF) and the dynamic performance of an STF-filled damper, Smart Mater. Struct. 17, 035027.

    Article  Google Scholar 

  • Zhao, N., X. Wen, J. Yang, S. Li, and Z. Wang, 2015, Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks, Powder Technol. 281, 173–183.

    Article  Google Scholar 

  • Zielinska, D., B. Delczyk-Olejniczak, L. Wierzbicki, B. Wilbik-Halgas, M.H. Struszczyk, and M. Leonowicz, 2014, Investigation of the effect of para-aramid fabric impregnation with shear thickening fluid on quasi-static stab resistance, Text. Res. J. 84, 1569–1577.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Textile Technology, Indian Institute of Technology, Delhi, 110016, India

    Sanchi Arora, Animesh Laha, Abhijit Majumdar & Bhupendra Singh Butola

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  1. Sanchi Arora
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  2. Animesh Laha
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  3. Abhijit Majumdar
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  4. Bhupendra Singh Butola
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Correspondence to Sanchi Arora.

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Arora, S., Laha, A., Majumdar, A. et al. Prediction of rheology of shear thickening fluids using phenomenological and artificial neural network models. Korea-Aust. Rheol. J. 29, 185–193 (2017). https://doi.org/10.1007/s13367-017-0019-x

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  • DOI: https://doi.org/10.1007/s13367-017-0019-x

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