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Journal articles on the topic 'Viscosity modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Schmidt, Kurt A. G., Sergio E. Quiñones-Cisneros, John J. Carroll, and Bjørn Kvamme. "Hydrogen Sulfide Viscosity Modeling." Energy & Fuels 22, no. 5 (September 17, 2008): 3424–34. http://dx.doi.org/10.1021/ef700701h.

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2

Balat, M. "Modeling Vegetable Oil Viscosity." Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 30, no. 20 (September 3, 2008): 1856–69. http://dx.doi.org/10.1080/15567030701457392.

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3

Moshe, Amir, David O. Kazmer, Margaret J. Sobkowicz-Kline, Stephen P. Johnston, and Shmuel Kenig. "Transient modeling of viscosity." Polymer Engineering & Science 57, no. 10 (May 31, 2017): 1110–18. http://dx.doi.org/10.1002/pen.24486.

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4

Duchesne, Marc A., Arne M. Bronsch, Robin W. Hughes, and Patrick J. Masset. "Slag viscosity modeling toolbox." Fuel 114 (December 2013): 38–43. http://dx.doi.org/10.1016/j.fuel.2012.03.010.

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5

Bataille, F., R. Rubinstein, and M. Y. Hussaini. "Eddy viscosity and diffusivity modeling." Physics Letters A 346, no. 1-3 (October 2005): 168–73. http://dx.doi.org/10.1016/j.physleta.2005.07.074.

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6

Konstantinov, Ivan, Carlos Villa, Rongjuan Cong, and Thomas Karjala. "Viscosity Modeling of Polymer Solutions." Macromolecular Symposia 377, no. 1 (February 2018): 1600179. http://dx.doi.org/10.1002/masy.201600179.

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7

Zhang, Jieyi, Mehrdad Moosavi, Abbas Ali Rostami, and Francisco M. Vargas. "Viscosity modeling of water + alkanediols mixtures." Journal of Molecular Liquids 249 (January 2018): 326–33. http://dx.doi.org/10.1016/j.molliq.2017.11.005.

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8

Rubinstein, Robert. "Eddy viscosity modeling and turbulence theory." Radiation Effects and Defects in Solids 172, no. 9-10 (October 3, 2017): 718–22. http://dx.doi.org/10.1080/10420150.2017.1398245.

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9

Schmitt, George, John Wiley, and Jeffrey Gotro. "Viscosity modeling during epoxy resin cure." Polymer Engineering and Science 29, no. 5 (March 1989): 329–33. http://dx.doi.org/10.1002/pen.760290511.

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10

King, Jack. "Viscosity in air-gun bubble modeling." GEOPHYSICS 81, no. 1 (January 1, 2016): T1—T9. http://dx.doi.org/10.1190/geo2015-0199.1.

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I have presented finite volume simulations of an air-gun bubble in which the compressible Navier-Stokes equations were solved numerically. These equations included viscosity. My simulation also applied the no-slip condition at the bubble surface. The effects of the viscous terms were small; however, the effect of the no-slip condition was significant, causing a reduction in the bubble rise rate of 18.1% and an increase in the collapse pressure of 17.9%. The no-slip condition caused boundary layers at the bubble surface and changes in the velocity structure throughout the bubble. The no-slip condition allowed the effect of skin-friction drag on the bubble to be captured, along with Kelvin-Helmholtz instabilities at the surface, which caused a change in the shape of the bubble during collapse. The influence of the no-slip condition suggests that it is important and should be included in air-gun bubble models.

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11

Meng, Xianyang, and Jiangtao Wu. "Viscosity modeling of some oxygenated fuels." Fuel 107 (May 2013): 309–14. http://dx.doi.org/10.1016/j.fuel.2012.10.074.

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12

Kalisz, D. "Modeling Physicochemical Properties of Mold Slag." Archives of Metallurgy and Materials 59, no. 1 (March 1, 2014): 149–55. http://dx.doi.org/10.2478/amm-2014-0024.

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Abstract This paper deals with the mathematical modeling of physicochemical properties of mold slag such as: viscosity, surface tension, temperature liquidus, basicity. Computer simulation of slag viscosity was made by the Nakamoto structural model. The effect of addition CaF2 to the mold slag was estimated by using of Urbain model. The results were compared with the results of the experiment. Surface tension for the basic slag composition: CaO - SiO2 - Al2O3 was calculated with using Nakamoto model. The results of calculations indicate that the content of the SiO2 lowers the surface tension, but increase the content of CaO and Al2O3 in the slag increases its value. Calcium fluoride (CaF2) reduces the viscosity of the slag. The increase in temperature reduces the viscosity of the slag, simultaneously increasing the surface tension.

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13

Kumar, Ashutosh, Amr Henni, and Ezeddin Shirif. "Heavy Oil Viscosity Modeling with Friction Theory†." Energy & Fuels 25, no. 2 (February 17, 2011): 493–98. http://dx.doi.org/10.1021/ef101013m.

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14

Brezzi, F., P. Houston, D. Marini, and E. Süli. "Modeling subgrid viscosity for advection–diffusion problems." Computer Methods in Applied Mechanics and Engineering 190, no. 13-14 (December 2000): 1601–10. http://dx.doi.org/10.1016/s0045-7825(00)00179-1.

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15

Ruiz-Llamas, Aimee, and Ricardo Macías-Salinas. "Modeling the Dynamic Viscosity of Ionic Solutions." Industrial & Engineering Chemistry Research 54, no. 28 (July 8, 2015): 7169–79. http://dx.doi.org/10.1021/acs.iecr.5b01664.

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16

Pettersson Reif, B. A., P. A. Durbin, and A. Ooi. "Modeling rotational effects in eddy-viscosity closures." International Journal of Heat and Fluid Flow 20, no. 6 (December 1999): 563–73. http://dx.doi.org/10.1016/s0142-727x(99)00056-9.

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17

Zhang, Guo-Hua, and Kuo-Chih Chou. "Modeling the Viscosity of Alumino-Silicate Melt." steel research international 84, no. 7 (January 22, 2013): 631–37. http://dx.doi.org/10.1002/srin.201200196.

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18

Ismail, Issam, Jeremy Vandenberg, Ahmed Abdala, and Chris Macosko. "Modeling the intrinsic viscosity of polydisperse disks." Journal of Rheology 61, no. 5 (September 2017): 997–1006. http://dx.doi.org/10.1122/1.4996843.

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19

Gąsior, Władysław. "Viscosity modeling of binary alloys: Comparative studies." Calphad 44 (March 2014): 119–28. http://dx.doi.org/10.1016/j.calphad.2013.10.007.

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20

Farid, Saad B. H. "Modeling of Viscosity and Thermal Expansion of Bioactive Glasses." ISRN Ceramics 2012 (December 4, 2012): 1–5. http://dx.doi.org/10.5402/2012/816902.

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The behaviors of viscosity and thermal expansion for different compositions of bioactive glasses have been studied. The effect of phosphorous pentoxide as a second glass former in addition to silica was investigated. Consequently, the nonlinear behaviors of viscosity and thermal expansion with respect to the oxide composition have been modeled. The modeling uses published data on bioactive glass compositions with viscosity and thermal expansion. -regression optimization technique has been utilized for analysis. Linear and nonlinear relations are shown to establish the viscosity and thermal expansion coefficients associated with oxide components of the glasses under study. The modeling allows the calculation of viscosity for a given temperature and, accordingly, the fusion temperature of these glasses along with the coefficient of thermal expansion. The established model relations also suggest first- and second-order phosphorus-alkali and alkaline earth oxides interaction which is reflected on the model coefficient that calculates viscosity and thermal expansion.

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21

Grümer, Benjamin, and Christian Hopmann. "The influence of recycling on the viscosity of polyamide 6 and a general modeling approach." Progress in Rubber, Plastics and Recycling Technology 34, no. 3 (August 2018): 158–67. http://dx.doi.org/10.1177/1477760618798427.

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The multiple processing and use of regrind material of plastics are widely used in the plastics processing industry. The subsequent changes in material characteristics, certainly in the viscosity of the polymer melt, have been analyzed and are well known. However, to quantify the changing viscosity, a general modeling approach is necessary using a low quantity of lab measurements. For the example of a polyamide 6, rheological measurements have been performed and a general modeling approach for the viscosity based on the Carreau model has been developed. The verification of the modeling was successfully proven by comparing additional viscosity measurements with model prediction results.

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22

Manheimer, W., and D. Colombant. "Effects of viscosity in modeling laser fusion implosions." Laser and Particle Beams 25, no. 4 (December 2007): 541–47. http://dx.doi.org/10.1017/s0263034607000663.

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AbstractThis paper examines the necessity of including ion viscosity in modeling laser fusion implosions. Using the Naval Research Laboratory one-half Mega Joule laser fusion target as an example, it is shown that for virtually the entire implosion up to maximum compression, and the entire rebound after the implosion, ion viscosity is unimportant. However for about half a nanosecond before peak implosion, ion viscosity can have a significant, but by no means dominant effect on both the one-dimensional flow and on the Rayleigh-Taylor instability.

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23

Ding, Yongjie, Baoshan Huang, and Xiang Shu. "Modeling Shear Viscosity of Asphalt through Nonequilibrium Molecular Dynamics Simulation." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 28 (August 22, 2018): 235–43. http://dx.doi.org/10.1177/0361198118793316.

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This paper investigates a quantifiable relationship between the chemical composition and the shear viscosity of the asphalt binder through the nonequilibrium molecular dynamics (NEMD) simulation. The fix Deform and fix NVT/SLLOD ensembles were used to perform a shear effect on the model. The relationship between the shear viscosity and the microstructure characteristics of the asphalt model was studied. The results indicated that the asphalt model exhibited shear thinning as the strain rate increases. The effects of energy and chemical structure (molecular weight, aromatic carbon and heteroatom percentage) on shear viscosity were studied from the perspective of microstructure. The results showed that the larger fused aromatic ring core caused a nonlinear relationship between viscosity and strain rate. The viscosity of the asphalt model increased with the molecular weight, the percentage of aromatic carbon and heteroatoms. The molecular weight had a greater effect on viscosity than aromatic carbon and heteroatom percentage. Considering energy variations with strain rate and chemical structure, the NEMD method can be used to predict the behavior of the asphalt molecules under shear strain at a certain level.

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24

He, Yang-Chun, Xue-Jiao Xu, Li-Jun Yang, and Bing Ding. "Viscosity modeling for ionic liquid solutions by Eyring-Wilson equation." Chemical Industry and Chemical Engineering Quarterly 18, no. 3 (2012): 441–47. http://dx.doi.org/10.2298/ciceq110829019h.

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A semi-theoretical model based on the classical Eyring?s mixture viscosity equation and the Wilson activity coefficient equation is presented for correlating the viscosity of ionic liquids with solvent systems. The accuracy of the proposed model was verified by comparing calculated and experimental viscosity values from literatures for 49mixtures with total 1560 data points. The results show that the equation similar to the Wilson activity coefficient equation can be well applied to describe the non-ideal term in the Eyring?s mixture viscosity equation. The model has a relatively simple mathematical form and can be easily incorporated into process simulation software.

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25

Chen, Genmeng. "Comparison of 2-D numerical viscoelastic waveform modeling with ultrasonic physical modeling." GEOPHYSICS 61, no. 3 (May 1996): 862–71. http://dx.doi.org/10.1190/1.1444011.

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The objective of the study is to test the validity of theoretical models of wave attenuation by comparing their predictions of attenuation against physical model results. The study is confined to a 2-D geometry, and the viscoelastic materials used in physical modeling are those commonly used in the experiment. The physical modeling data of homogeneous media are compared with the numerical results in the frequency domain. The time‐domain comparisons between numerical modeling and physical modeling are also shown by three examples. The theoretical viscoelastic models used in the numerical study are the Kelvin‐Voigt model, the standard linear solid model, and the standard linear solid model with a continuous spectrum of relaxation time. On the comparison of a single model, all the models simulate the physical model fairly well, but the standard linear solid model gives the best result among them. The Kelvin‐Voigt model is easy to use as a quick first‐order simulation of the viscoelastic materials because it has fewer viscosity parameters than the other two models. The disadvantage of the Kelvin‐Voigt model is that it predicts too much attenuation of the high‐frequency components. It is also shown that neglecting the viscosity of some materials like polyvinylcloride plastic (PVC), which has high viscosity, will produce incorrect results in synthetic seismograms.

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26

Jiang, Nan, Songul Kaya, and William Layton. "Analysis of Model Variance for Ensemble Based Turbulence Modeling." Computational Methods in Applied Mathematics 15, no. 2 (April 1, 2015): 173–88. http://dx.doi.org/10.1515/cmam-2014-0029.

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AbstractThis report develops an ensemble or statistical eddy viscosity model. The model is parameterized by an ensemble of solutions of an ensemble-Leray regularization. The combined approach of ensemble time stepping and ensemble eddy viscosity modeling allows direct parametrization of the turbulent viscosity coefficient. We prove unconditional stability and that the model's solution approaches statistical equilibrium as t → ∞; the model's variance converges to zero as t → ∞. The ensemble method is used to interrogate a rotating flow, testing its predictability by computing effective averaged Lyapunov exponents.

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27

Pal, Rajinder. "Modeling the Viscosity of Concentrated Nanoemulsions and Nanosuspensions." Fluids 1, no. 2 (April 12, 2016): 11. http://dx.doi.org/10.3390/fluids1020011.

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28

Bardool, Roghayeh, Ali Bakhtyari, Feridun Esmaeilzadeh, and Xiaopo Wang. "Nanofluid viscosity modeling based on the friction theory." Journal of Molecular Liquids 286 (July 2019): 110923. http://dx.doi.org/10.1016/j.molliq.2019.110923.

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29

Quiñones-Cisneros, Sergio E., Claus K. Zéberg-Mikkelsen, and Erling H. Stenby. "The friction theory (f-theory) for viscosity modeling." Fluid Phase Equilibria 169, no. 2 (March 2000): 249–76. http://dx.doi.org/10.1016/s0378-3812(00)00310-1.

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30

Wei, En-Bo, Yan-Ju Ji, and Jun Zhang. "Modeling effective viscosity reduction behaviour of solid suspensions." Chinese Physics B 21, no. 12 (December 2012): 126601. http://dx.doi.org/10.1088/1674-1056/21/12/126601.

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31

Quiñones-Cisneros, Sergio E., and Ulrich K. Deiters. "Generalization of the Friction Theory for Viscosity Modeling." Journal of Physical Chemistry B 110, no. 25 (June 2006): 12820–34. http://dx.doi.org/10.1021/jp0618577.

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32

Yousefi, Fakhri, Hajir Karimi, and Mohammad Mehdi Papari. "Modeling viscosity of nanofluids using diffusional neural networks." Journal of Molecular Liquids 175 (November 2012): 85–90. http://dx.doi.org/10.1016/j.molliq.2012.08.015.

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33

Hieber, C. A., and H. H. Chiang. "Shear-rate-dependence modeling of polymer melt viscosity." Polymer Engineering and Science 32, no. 14 (July 1992): 931–38. http://dx.doi.org/10.1002/pen.760321404.

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34

Al-Moameri, Harith, Luay Jaf, and Galen J. Suppes. "Viscosity-dependent frequency factor for modeling polymerization kinetics." RSC Advances 7, no. 43 (2017): 26583–92. http://dx.doi.org/10.1039/c7ra01242j.

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The simulation of polymer-forming reactions can be a powerful tool to reduce the time and cost of developing new polymer formulations; formulations that can be potentially both more sustainable and less costly.

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35

Rivlin,, Z., J. Baram,, and H. G. Jiang,. "Modeling the Viscosity Temperature Dependence of Liquid Alloys." High Temperature Materials and Processes 15, no. 3 (July 1996): 153–58. http://dx.doi.org/10.1515/htmp.1996.15.3.153.

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36

Fu, Zhong, and Galen J. Suppes. "Group contribution modeling of viscosity during urethane reaction." Journal of Polymer Engineering 35, no. 1 (January 1, 2015): 11–20. http://dx.doi.org/10.1515/polyeng-2014-0006.

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Abstract Based on experimental viscosity data collected from single material and binary system mixtures, a group contribution method was introduced to estimate viscosities of a range of polyol oligomers and urethane polymers at temperatures from 25 to 150°C. Use of mixture rules then extends the estimation method to multi-component reacting systems. Mixture viscosity data were used to determine the Gibbs free energy (G) in the Grundberg-Nissan equation which can be used to estimate mixture viscosities with correction for some non-idealities. The resulting model is able to accurately predict mixture viscosities based on binary interaction. The goal of this work is to estimate the viscosities of urethane-forming reactions; accurate viscosity information is critical as an intermediate step to predict how successful a foam formulation would be and to ultimately estimate the final physical properties of the foam.

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37

Thodesen, Carl, Feipeng Xiao, and Serji N. Amirkhanian. "Modeling viscosity behavior of crumb rubber modified binders." Construction and Building Materials 23, no. 9 (September 2009): 3053–62. http://dx.doi.org/10.1016/j.conbuildmat.2009.04.005.

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38

Song, Fu, Guo Yang, Qian Weiqi, and Wang Chen. "Recent progress in nonlinear eddy-viscosity turbulence modeling." Acta Mechanica Sinica 19, no. 5 (October 2003): 409–19. http://dx.doi.org/10.1007/bf02484575.

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39

Wei, Minghai, Li Sun, Peipei Qi, Chunguang Chang, and Chunyang Zhu. "Continuous phenomenological modeling for the viscosity of shear thickening fluids." Nanomaterials and Nanotechnology 8 (January 1, 2018): 184798041878655. http://dx.doi.org/10.1177/1847980418786551.

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In general, shear thickening fluids show a marked increase in viscosity beyond a critical shear rate, which can be attributed to the hydrodynamic clustering effects, where in any external energy acting on a shear thickening fluid is dissipated quickly. However, there is a lack of theoretical modeling to predict the viscosity curve of shear thickening fluids, which changes continuously with the increasing shear rate. In this article, a phenomenological continuous viscosity modeling for a class of shear thickening fluids is proposed. The modeling predicts shear thickening and thinning behaviors that are naturally exhibited by shear thickening fluids for high and high enough values of the shear rate. The result shows that the phenomenological modeling provides a very good fit for several independent experimental data sets. Therefore, the proposed modeling can be used in numerical simulations and theoretical analysis across different engineering fields.

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40

PAPARI, MOHAMMAD MEHDI, JALIL MOGHADASI, SOUDABEH NIKMANESH, ELHAM HOSSEINI, and ALI BOUSHEHRI. "MODELING THERMOPHYSICAL PROPERTIES OF NOBLE GAS INVOLVED MIXTURES." International Journal of Computational Methods 08, no. 01 (March 2011): 19–39. http://dx.doi.org/10.1142/s0219876211002393.

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The present work involves in determining isotropic and effective pair potential energy of binary gas mixtures of Kr–Xe , Kr–C2H6 , Xe–C2H6 , Kr–C3H8 , and Xe–C3H8 from thermophysical properties consisting of viscosity and second virial coefficients through inversion method. Typically, the calculated intermolecular potential energy of Kr–Xe system has compared with HFD model potential reported in literature. A desirable harmony between our model potential and HFD model has been obtained. In order to assess the potential energies obtained, transport properties including viscosity, diffusion, thermal diffusion factor, and thermal conductivity of aforementioned mixtures were predicted using the calculated models potential. The deviation percentage of the calculated viscosity and thermal conductivity of above-mentioned mixtures from the literature values are, respectively, within ±2%, ±3%.

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41

Bohiniková, Alžbeta, Iveta Jančigová, and Ivan Cimrák. "Modeling Red Blood Cell Viscosity Contrast Using Inner Soft Particle Suspension." Micromachines 12, no. 8 (August 18, 2021): 974. http://dx.doi.org/10.3390/mi12080974.

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The inner viscosity of a biological red blood cell is about five times larger than the viscosity of the blood plasma. In this work, we use dissipative particles to enable the proper viscosity contrast in a mesh-based red blood cell model. Each soft particle represents a coarse-grained virtual cluster of hemoglobin proteins contained in the cytosol of the red blood cell. The particle interactions are governed by conservative and dissipative forces. The conservative forces have purely repulsive character, whereas the dissipative forces depend on the relative velocity between the particles. We design two computational experiments that mimic the classical viscometers. With these experiments we study the effects of particle suspension parameters on the inner cell viscosity and provide parameter sets that result in the correct viscosity contrast. The results are validated with both static and dynamic biological experiment, showing an improvement in the accuracy of the original model without major increase in computational complexity.

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42

Ghasemi, M., and C. H. H. Whitson. "Modeling Steam-Assisted Gravity Drainage With a Black-Oil Proxy." SPE Reservoir Evaluation & Engineering 16, no. 02 (May 6, 2013): 155–71. http://dx.doi.org/10.2118/147072-pa.

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Summary This paper describes an alternative approach to model steam-assisted gravity drainage (SAGD) with an isothermal black-oil (BO) reservoir simulator. The oil-viscosity reduction caused by heating in the actual SAGD process is emulated by a tuned saturated pseudo-oil viscosity relation in which solution gas/oil ratio (Rs) is used as a “proxy for temperature.” In the BO formulation, fully saturated oil viscosity (μo*) at reservoir pressure equals μo that would be attained at steam-chamber temperature (T*) in the actual SAGD process; initial oil viscosity (μoi) with initial Rs = 0 represents initial oil viscosity at reservoir temperature; and BO gas properties represent steam at T*. After careful analysis of the SAGD process, one finds that oil flows only along a narrow zone along the outer edge of the steam chamber—the “edge oil-flow zone.” The temperature gradient within this narrow zone is perpendicular to the oil-flow direction and is practically impossible to model with any precision because of the large temperature variation and dynamic steam-chamber shape over time. The BO-model solubility gradient also varies, analogous to temperature in a thermal model, from zero to fully saturated (Rs*) with an associated drop in oil viscosity from μoi to μo*. SAGD design requires many hundreds of runs to find operational conditions that maximize economic value (e.g., injector and producer location, rates, pattern spacing, and steam-chamber temperature T*). The proposed BO proxy model runs several times faster than a thermal model while maintaining similar performance behavior. The proxy-model saturated pseudo-oil viscosity μo(p) relation used is found by history matching a full-physics thermal-model performance prediction of oil rate, bottomhole flowing pressure, and cumulative oil for a 2D homogeneous model. We have found a single-constant μo(p) equation that yields a good match to thermal SAGD performance. The tuned pseudo-oil viscosity relation honors the measured initial reservoir and fully heated (at T*) oil viscosities. Its dependence on Rs is not physical, but reflects the use of Rs as a transform variable for temperature, capturing the strong spatial variation of temperature and oil viscosity within the localized steam/oil boundary region in which oil has been mobilized. The pseudo-oil viscosity relation, defined by a single empirical best-fit constant n—for a given T* and a set of thermal properties—appears to be applicable for a wide range of reservoir heterogeneity, injection and production rates, and well placement. Consequently, it should be possible to use the BO proxy model for SAGD optimization of T*, control rates, and injector/producer vertical-depth difference. We also see the potential of using the BO proxy model for solvent-based SAGD, with the pseudo-oil viscosity model depending on both T* and solvent; thermal compositional modeling is yet even slower and less suitable for optimization.

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43

Kim, Wan-Yi, Arthur Pelton, Christopher Bale, Eve Bélisle, and Sergei Decterov. "Modeling the viscosity of silicate melts containing manganese oxide." Journal of Mining and Metallurgy, Section B: Metallurgy 49, no. 3 (2013): 323–37. http://dx.doi.org/10.2298/jmmb120918039k.

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Our recently developed model for the viscosity of silicate melts is applied to describe and predict the viscosities of oxide melts containing manganese oxide. The model requires three pairs of adjustable parameters that describe the viscosities in three systems: pure MnO, MnO-SiO2 and MnO-Al2O3-SiO2. The viscosity of other ternary and multicomponent silicate melts containing MnO is then predicted by the model without any additional adjustable model parameters. Experimental viscosity data are reviewed for melts formed by MnO with SiO2, Al2O3, CaO, MgO, PbO, Na2O and K2O. The deviation of the available experimental data from the viscosities predicted by the model is shown to be within experimental error limits.

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44

Huang, Ao, Yanzhu Huo, Juan Yang, Huazhi Gu, and Guangqiang Li. "Computational Modeling and Prediction on Viscosity of Slags by Big Data Mining." Minerals 10, no. 3 (March 12, 2020): 257. http://dx.doi.org/10.3390/min10030257.

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The viscosity of slag is a key factor affecting metallurgical efficiency and recycling, such as metal-slag reaction and separation, as well as slag wool processing. In order to comprehensively clarify the variation of the slag viscosity, various data mining methods have been employed to predict the viscosity of the slag. In this study, a more advanced dual-stage predictive modeling approach is proposed in order to accurately analyze and predict the viscosity of slag. Compared with the traditional single data mining approach, the proposed method performs better with a higher recall rate and low misclassification rate. The simulation results show that temperature, SiO2, Al2O3, P2O5, and CaO have greater influences on the slag’s viscosity. The critical temperature for onset of the important influence of slag composition is 980 °C. Furthermore, it is found that SiO2 and P2O5 have positive correlations with slag’s viscosity, while temperature, Al2O3, and CaO have negative correlations. A two-equation model of six-degree polynomial combined with Arrhenius formula is also established for the purpose of providing theoretical guidance for industrial application and reutilization of slag.

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45

Kudish, Ilya I., and Ruben G. Airapetyan. "Modeling of Line Contacts With Degrading Lubricant." Journal of Tribology 125, no. 3 (June 19, 2003): 513–22. http://dx.doi.org/10.1115/1.1538193.

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A plane isothermal elastohydrodynamic problem for a lubricated line contact is studied. The lubricant represented by a base stock with some polymer additive undergoes stress-induced degradation due to scission of polymer additive molecules. The polymer molecules have linear structure. The degradation process of a polymer additive dissolved in a lubricant while the lubricant passes through the contact is described by a kinetic equation. The kinetic equation is solved along the lubricant flow streamlines. The solution of the kinetic equation predicts the density of the probabilistic distribution of the polymer molecular weight versus polymer molecule chain length. The changes in the distribution of polymer molecules affect local lubricant properties. In particular, the lubricant viscosity changes as polymer molecules undergo scission. These irreversible changes in the lubricant viscosity alter virtually all parameters of the lubricated contact such as film thickness, frictional stresses and pressure. As a result of the polymer additive degradation the lubricant experiences a significant viscosity loss. The viscosity loss (up to 60 percent), in turn, leads to a noticeable reduction in the lubrication film thickness (up to 12 percent) and frictional stresses applied to contact surfaces in comparison with the case of a nondegrading lubricant. Moreover, the pressure distribution in degrading lubricants exhibits extremely sharp spikes of about 2.15 to 2.82 (depending on the slide-to-roll ratio) times greater than the maximum Hertzian pressure. That may lead to noticeable variations in fatigue life of the contact surfaces.

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Bahri, A., I. V. Zhukov, and V. G. Kharazov. "MODELING OF FRACTIONATING COLUMN OF THE VISCOSITY BREAKING PROCESS." Oil and Gas Studies, no. 5 (November 1, 2016): 113–18. http://dx.doi.org/10.31660/0445-0108-2016-5-113-118.

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In the paper the issues of modeling of fractioning column run for obtaining a static model using the recursive MNK are considered. It is emphasized that the main difficulties in modeling were caused by the lack of information and uncertainty of a number of parameters due to underestimation of technological parameters such as ,the raw material composition, wear and corrosion of the column construction elements, etc.

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Salih, Doaa Mohammed, Sameera M. Hamdalla, and Mohammed H. Al-Kabi. "Modeling of Oil Viscosity for Southern Iraqi Reservoirs using Neural Network Method." Journal of Petroleum Research and Studies 10, no. 1 (March 1, 2020): 1–17. http://dx.doi.org/10.52716/jprs.v10i1.514.

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The calculation of the oil density is more complex due to a wide range of pressuresand temperatures, which are always determined by specific conditions, pressure andtemperature. Therefore, the calculations that depend on oil components are moreaccurate and easier in finding such kind of requirements. The analyses of twenty liveoil samples are utilized. The three parameters Peng Robinson equation of state istuned to get match between measured and calculated oil viscosity. The Lohrenz-Bray-Clark (LBC) viscosity calculation technique is adopted to calculate the viscosity of oilfrom the given composition, pressure and temperature for 20 samples. The tunedequation of state is used to generate oil viscosity values for a range of temperature andpressure extends from the reservoir to surface conditions.The generated viscosity data is utilized in the neural network tool (NN) to get fittingmodel correlates the viscosity of oil with composition, pressure and temperature. Theresulted error and the correlation coefficient of the model constructed are close to 0and 1 respectively. The NN model is also tested with data that are not used in set upthe model. The results proved the validity of the model. Moreover, the model’soutcomes demonstrate its superiority to selected empirical correlations.

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Gounley, John, and Yan Peng. "Computational Modeling of Membrane Viscosity of Red Blood Cells." Communications in Computational Physics 17, no. 4 (April 2015): 1073–87. http://dx.doi.org/10.4208/cicp.2014.m355.

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AbstractDespite its demonstrated importance in the deformation and dynamics of red blood cells, membrane viscosity has not received the same attention in computational models as elasticity and bending stiffness. Recent experiments on red blood cells indicated a power law response due to membrane viscosity. This is potentially much different from the solid viscoelastic models, such as Kelvin-Voigt and standard linear solid (SLS), currently used in computation to describe this aspect of the membrane. Within the context of a framework based on lattice Boltzmann and immersed boundary methods, we introduce SLS and power law models for membrane viscosity. We compare how the Kelvin-Voigt (as approximated by SLS) and power law models alter the deformation and dynamics of a spherical capsule in shear flows.

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Villaret, C., and A. G. Davies. "Modeling Sediment-Turbulent Flow Interactions." Applied Mechanics Reviews 48, no. 9 (September 1, 1995): 601–9. http://dx.doi.org/10.1115/1.3023148.

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Models of widely differing complexity have been used in recent years to quantify sediment transport processes for engineering applications. This paper presents a review of these model types, from simple eddy viscosity models involving the “passive scalar hypothesis” for sediment predication, to complex two-phase flow models. The specific points addressed in this review include, for the suspension layer, the bottom boundary conditions, the relationship between the turbulent eddy viscosity and particle diffusivity, the damping of turbulence by vertical gradients in suspended sediment concentration, and hindered settling. For the high-concentration near-bed layer, the modeling of particle interactions is discussed mainly with reference to two-phase flow models. The paper concludes with a comparison between the predictions of both a classical, one-equation, turbulence k-model and a two-phase flow model, with “starved bed” experimental data sets obtained in steady, open-channel flow.

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Kovaleva, L. A., V. N. Kireev, and A. А. Musin. "Numerical modeling of thermal impact on high-viscosity hydrocarbon systems." Proceedings of the Mavlyutov Institute of Mechanics 5 (2007): 221–26. http://dx.doi.org/10.21662/uim2007.1.026.

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The numerical modeling of the heating of a high-viscosity hydrocarbon liquid, whose viscosity and thermal conductivity depend on the temperature, is carried out in this research. A system of equations for free convection is solved in the linear Boussinesq approximation. The dynamics of changes in the temperature field and convective structures in a liquid is studied.

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