Richard L. Rowley

Brigham Young University - Provo Main Campus, Provo, UT, United States

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Publications (91)164.4 Total impact

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    ABSTRACT: A first-order temperature-dependent group contribution method was developed to predict Henry’s law constants of hydrocarbons, alcohols, ketones, and formates in which none of the functional groups are attached directly to a benzene ring. Efforts to expand this method to include ester and ether groups were unsuccessful. Second-order groups were developed at a reference condition of 298.15 K and 100 kPa. A second-order temperature-dependent group contribution method was then developed for hydrocarbons, ketones, esters, ethers, and alcohols. These methods were compared to existing literature prediction methods.
    Journal of Chemical & Engineering Data 12/2013; 59(4):1052–1061. · 2.00 Impact Factor
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    ABSTRACT: The effectiveness of chemical process designs and the reliability of mixture models are no better than the accuracy of the pure-chemical thermophysical properties used. A systems-analysis approach can be used in evaluating data from all available sources to triangulate on the best values which can be of higher accuracy than the individual values. This approach includes applied constraints on property values from interproperty relationships, expected smooth trends of properties between related chemicals, and the impact of chemical similarities and differences. These constraints are simultaneously included in the evaluation of the raw experimental data to provide recommended values of constant properties and correlations for temperature-dependent properties. This paper will illustrate how this systems approach has been used to provide recommended property values in the DIPPR 801 evaluated database. In particular, a recent re-evaluation of the properties of the 1,n-alkanediol compounds in the DIPPR database, prompted by the availability of more recently measured critical temperatures and pressures of three members of this family, is used as an illustrative case study of this approach, and some of the newly recommended property values and temperature-dependent correlations for this family are provided.
    Journal of Chemical & Engineering Data 11/2013; 59(4):1031–1037. · 2.00 Impact Factor
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    ABSTRACT: Ternary LLE data have been experimentally measured for several systems consisting of biodiesel compounds. Systems measured include mixtures with the methyl esters of lauric, myristic, palmitic, and oleic acids, each with glycerin and water. Data were collected at atmospheric pressure and 60 °C. These ternary systems have been correlated using the NRTL equation. These data and correlation parameters can be used to improve separations efficiency in transesterified biodiesel fuels.
    Journal of Chemical & Engineering Data 03/2013; 58(4):1001–1004. · 2.00 Impact Factor
  • Sonal Patel, W Vincent Wilding, Richard L Rowley
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    ABSTRACT: Two-phase molecular dynamics simulations employing a Monte Carlo volume sampling method were performed using an ab initio based force field model parameterized to reproduce quantum-mechanical dimer energies for methanol and 1-propanol at temperatures approaching the critical temperature. The intermolecular potential models were used to obtain the binodal vapor-liquid phase dome at temperatures to within about 10 K of the critical temperature. The efficacy of two all-atom, site-site pair potential models, developed solely from the energy landscape obtained from high-level ab initio pair interactions, was tested for the first time. The first model was regressed from the ab initio landscape without point charges using a modified Morse potential to model the complete interactions; the second model included point charges to separate Coulombic and dispersion interactions. Both models produced equivalent phase domes and critical loci. The model results for the critical temperature, density, and pressure, in addition to the sub-critical equilibrium vapor and liquid densities and vapor pressures, are compared to experimental data. The model's critical temperature for methanol is 77 K too high while that for 1-propanol is 80 K too low, but the critical densities are in good agreement. These differences are likely attributable to the lack of multi-body interactions in the true pair potential models used here.
    The Journal of Chemical Physics 12/2011; 135(23):234514. · 3.12 Impact Factor
  • Jiangping Liu, W. Vincent Wilding, Richard L. Rowley
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    ABSTRACT: A local composition model is developed for mixture dielectric constants based on the nonrandom two-liquid (NRTL) model commonly used for correlating activity coefficients in vapor−liquid equilibrium (VLE) data regression. In this model the NRTL local compositions, which represent an effective local molecular structure, are the counterpart of the Kirkwood factor g, which in dielectric constant theory characterizes local molecular orientations and their effect on the static dielectric constant. The resultant model requires values for the pure-component dielectric constant and binary NRTL model parameters available from VLE data compilations or predicted from the universal functional activity coefficient model (UNIFAC). It is predictive in that no mixture dielectric constant data are used and there are no adjustable parameters. Predictions made on 16 binary and six ternary systems at various compositions and temperatures compare favorably with extant correlations that require experimental values to fit an adjustable parameter in the mixing rule and are significantly improved over values predicted by Oster's equation that also has no adjustable parameters.
    Journal of Chemical & Engineering Data. 03/2011; 56(5).
  • J R Rowley, R L Rowley, W V Wilding
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    ABSTRACT: A new method of estimating the lower flammability limit (LFL) of general organic compounds is presented. The LFL is predicted at 298 K for gases and the lower temperature limit for solids and liquids from structural contributions and the ideal gas heat of formation of the fuel. The average absolute deviation from more than 500 experimental data points is 10.7%. In a previous study, the widely used modified Burgess-Wheeler law was shown to underestimate the effect of temperature on the lower flammability limit when determined in a large-diameter vessel. An improved version of the modified Burgess-Wheeler law is presented that represents the temperature dependence of LFL data determined in large-diameter vessels more accurately. When the LFL is estimated at increased temperatures using a combination of this model and the proposed structural-contribution method, an average absolute deviation of 3.3% is returned when compared with 65 data points for 17 organic compounds determined in an ASHRAE-style apparatus.
    Journal of hazardous materials 02/2011; 186(1):551-7. · 4.33 Impact Factor
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    Sonal Patel, W Vincent Wilding, Richard L Rowley
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    ABSTRACT: Molecular dynamics simulations were performed to determine two-phase configurations of model propane molecules below the critical point and in the near-critical, two-phase region. A postprocessor that uses a Monte Carlo method for determination of volumes attributable to each molecule was used to obtain density histograms of the particles from which the bulk coexisting equilibrium vapor and liquid densities were determined. This method of analyzing coexisting densities in a two-phase simulation is straightforward and can be easily implemented for complex, multisite models. Various degrees of internal flexibility in the propane models have little effect on the coexisting densities at temperatures 40 K or more below the critical point, but internal flexibility (angle bending and bond vibrations) does affect the saturated liquid densities in the near-critical region, changing the critical temperature by approximately 20 K. Shorter cutoffs were also found to affect the phase dome and the location of the critical point.
    The Journal of Chemical Physics 01/2011; 134(2):024101. · 3.12 Impact Factor
  • Jason C Thomas, Richard L Rowley
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    ABSTRACT: A transient molecular dynamics (TMD) method for obtaining fluid viscosity is extended to multisite, force-field models of both nonpolar and polar liquids. The method overlays a sinusoidal velocity profile over the peculiar particle velocities and then records the transient decay of the velocity profile. The viscosity is obtained by regression of the solution of the momentum equation with an appropriate constitutive equation and initial and boundary conditions corresponding to those used in the simulation. The transient velocity decays observed appeared to include both relaxation and retardation effects. The Jeffreys viscoelastic model was found to model accurately the transient responses obtained for multisite models for n-butane, isobutane, n-hexane, water, methanol, and 1-hexanol. TMD viscosities obtained for saturated liquids over a wide range of densities agreed well for the polar fluids, both with nonequilibrium molecular dynamics (NEMD) results using the same force-field models and with correlations based on experimental data. Viscosities obtained for the nonpolar fluids agreed well with the experimental and NEMD results at low to moderate densities, but underpredicted experimental values at higher densities where shear-thinning effects and viscous heating may impact the TMD simulations.
    The Journal of Chemical Physics 01/2011; 134(2):024526. · 3.12 Impact Factor
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    ABSTRACT: Henry's law constant is important in engineering and environmental calculations. It can be used to determine the fate and transport of chemicals in air and water, and it can also be used in the design of air-stripping columns. An extensive literary review was conducted to compile a database of experimental Henry's law constants. Reported values of each chemical are compared in addition to the methods of measurement. Methods and family trends are considered in order to designate a recommended value for each compound in the database. Existing predictive methods are compared, and experimental and modeling needs are identified.
    2010 AIChE Annual Meeting; 11/2010
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    ABSTRACT: Pure-compound property data are at present available only for a small fraction of compounds, pertaining to such diverse areas as chemistry and chemical engineering, environmental engineering and environmental impact assessment, hazard and operability analysis. Therefore, methods for reliable prediction of property data are needed. Current methods used to predict physical and thermodynamic properties can be classified into "group contribution" methods (see, for example, Marrero and Gani, 2001), methods based on the "corresponding-states principle", (Teja, Sandler and Patel, 1981, Poling et al., 2001), "asymptotic behavior" correlations (Marano and Holder, 1997) and Quantitative Structure Property Relationships (QSPRs, Dearden et al., 2003). These methods typically provide a model that can predict one property, and separate models have to be derived for every property. Our objective is to develop a system which can be used as a general prediction tool irrespective of the property to be predicted. The recently developed targeted QSPR method (Brauner, et al., 2006, Shacham et al., 2007) is to be employed in such a system. Prediction of a property (the target property) for a particular compound (the target compound) using the TQSPR method is carried out in two stages. The first stage involves the identification of a similarity group (typically of around 20 compounds) structurally related to the target compound. For identification of the similarity group, a large database of molecular descriptors is used. The similarity between potential predictive compounds and the target compound is measured by the partial correlation coefficient between the vector of the molecular descriptors of the target compound and that of a potential predictive compound. The training set is established by selecting the first n (typically 10) compounds with the highest correlation coefficient values, for which target property data are available. In the second stage of the TQSPR method a stepwise regression program (SROV, Shacham and Brauner, 2003) is used to derive a linear regression model (TQSPR) which best represents the target property value in the training set in terms of selected molecular descriptors. One to three descriptors are typically used in the TQSPR. Finally, the TQSPR is used for predicting the target property for the target compound. Kahrs et al., 2008, carried out an evaluation of the TQSPR method using a database which contained 1630 molecular descriptors for 259 hydrocarbons. Only the prediction of the critical temperature (Tc) was tested in that study. The objective of the present study is to carry out a similar evaluation with a much wider variety of chemical compounds and for a large number of pure component constant properties. To this aim, a new database that contains physical property data for 1798 compounds has been established. Included in this data base are numerical values and data uncertainty for 31 properties (critical properties, normal melting and boiling temperatures, heat of formation, flammability limits etc.). All the property data is from the DIPPR database (Rowley et al., 2010). The database contains 3224 molecular descriptors generated by the Dragon, version 5.5. software (DRAGON is copyrighted by TALETE srl, http://www.talete.mi.it) from minimum energy 3-D molecular models. The 3-D molecular structures were optimized for about a 1000 compounds in Gaussian 03 (Frisch, et al., 2004) using B3LYP/6-311+G (3df, 2p), a density functional method with a large basis set. Most of the other compounds were optimized using HF/6-31G*, a Hartree-Fock ab initio method with a medium-sized basis set. To carry out the study, a MATLAB based Graphical User Interface (GUI) has been developed. This GUI provides user access to the database and enables carrying out the various stages of the TQSPR method using the SROV stepwise regression program. The user selects first the target property and the target compound. After that he can select algorithmic parameters by changing the default settings. Parameters that can be changed include, for example, the level of downsizing of the database by removing noisy descriptors and/or descriptors with low information content (e.g., using principal component analysis), similarity measures for selecting the similarity group and training set (e.g., Euclidean distance, correlation coefficient), the cluster algorithm used, and the regression stopping criteria for addition of descriptors to the TQSPR model. The identification of the TQSPR can be carried out automatically or "manually", where the user can override the programs' recommendations with regard to the descriptors selected to the TQSPR model. Preliminary results show that the TQSPR-based property prediction system can predict most properties for a wide variety of compounds (including hydrocarbons, organic compounds containing O, N, S and Cl atoms, etc.) within DIPPR recommended uncertainty level. Detailed results and their discussion will be provided in the extended abstract and in the conference presentation. We believe that the TQSPR-based property prediction system enables optimal utilization of the property related information available in the DIPPR database for prediction of properties on basis of molecular structure. The system can be used for analysis of the consistency of the data available in databases, as well as prediction of unknown properties of existing or not yet synthesized compounds. References 1. Brauner, N; Stateva, R. P.; Cholakov, G. St.; Shacham, M. Structurally Targeted Quantitative Structure-Property Relationship Method for Property Prediction. Ind. Eng. Chem. Res. 2006, 45, 8430-8437. 2. Brauner, N.; Cholakov, G. St.; Kahrs, O.; Stateva, R. P.; Shacham, M. Linear QSPRs for Predicting Pure Compound Properties in Homologous Series. AIChE J. 2008, 54(4), 978-990. 3. Dearden, J. C. Quantitative StructureProperty Relationships for Prediction of Boiling Point, Vapor Pressure, and Melting Point, Environmental Toxicology and Chemistry, 22( 8), 16961709 (2003). 4. Frisch, M.J.; Trucks, G.W.; Schlegel, H.B.; et al. Gaussian03, Revision A.6; Gaussian, Inc., Pittsburgh, PA, 2004. 5. Kahrs, O.; Brauner, N; Cholakov, G. St.; Stateva, R. P.; Marquardt, W.; Shacham, M. Analysis and Refinement of the Targeted QSPR Method. Computers Chem. Engng. 2008, 32 (7) 1397-1410. 6. Marano, J.J.; Holder, G.D. General Equations for Correlating the Thermo-physical Properties of n-Paraffins, n-Olefins and other Homologous Series. 2. Asymptotic Behavior Correlations for PVT Properties. Ind. Eng. Chem. Res. 1997A, 36, 1895. 7. Marrero, J.; Gani, R. Group-contribution based estimation of pure component properties. Fluid Phase Equilibrium. 2001, 183. 8. Poling, B.E., Prausnitz, J. M., O'Connel, J. P., Properties of Gases and Liquids, 5th Ed., McGraw-Hill, New York (2001). 9. Rowley, R.L.; Wilding, W.V.; Oscarson, J.L.; Giles, N.F. DIPPR Data Compilation of Pure Chemical Properties Design Institute for Physical Properties, (http//dippr.byu.edu), Brigham Young University Provo Utah,2010. 10. Shacham, M.; Brauner, N. The SROV Program for Data Analysis and Regression Model Identification. Computers Chem. Engng. 2003, 27, 701. 11. Shacham, M.; Kahrs, O.; St Cholakov, G.; Stateva, R.; Marquardt, W.; Brauner, N. The Role of the Dominant Descriptor in Targeted Quantitative Structure Property Relationships, Chem. Eng. Sci. 2007, 62, (22), 6222-6233. 12. Teja, A.S.;Sandler, S. I.; Patel, N. C., A Generalized Corresponding States Principle Using Two Nonspherical Reference Fluids, Chem. Eng. J. (Laussanne), 1981, 21, 21-28.
    2010 AIChE Annual Meeting; 11/2010
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    ABSTRACT: Published flash point prediction methods are evaluated for accuracy against experimental data from the DIPPR ® 801 database. The most accurate methods require a vapor pressure correlation, which is often not available. Two new methods are presented, one that uses the vapor pressure, and one based on the normal boiling point and enthalpy of vaporization at the normal boiling point. The vapor pressure method shows little improvement over the previous methods unless group contributions are implemented. The boiling point method predicts the flash point within an absolute average deviation of 1.3% when compared with data for more than 1000 compounds. The previous most accurate method that was not based on vapor pressure exhibited an absolute average deviation of 1.84% for the same test set. Copyright © 2010 John Wiley & Sons, Ltd.
    Fire and Materials 11/2010; 35(6):343 - 351. · 1.07 Impact Factor
  • J.R. Rowley, R.L. Rowley, W.V. Wilding
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    ABSTRACT: The flash point is an important indicator of the flammability of liquids and solids. Many methods of estimating the flash point of pure chemicals have been published, but these methods either rely on accurate thermodynamic data or are limited to hydrocarbons. This work presents a method of estimating the flash point of general organic compounds based entirely on structural contributions. The proposed correlation results in an average absolute deviation of 2.84% from experimental values for more than 1,000 organic compounds. © 2010 American Institute of Chemical Engineers Process Saf Prog, 2010
    Process Safety Progress 09/2010; 29(4):353 - 358. · 0.72 Impact Factor
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    ABSTRACT: The flash point is an important indicator of the flammability of a chemical. For safety purposes, many data compilations report the lowest value and not the most likely. This practice, combined with improper documentation and poor data storage methods, has resulted in compilations filled with fire-hazard data that are inconsistent with related properties and between members of homologous chemical series. In this study, the flash points reported in the DIPPR® 801 database and more than 1,400 other literature values were critically reviewed based on measurement method, inter-property relations, and trends in chemical series. New measurements for seven compounds illustrate the differences between experimental flash points and data commonly found in fire-hazard compilations. With a critically reviewed set of experimental data, published predictive methods for the flash point were evaluated for accuracy. KeywordsData evaluation-Database-DIPPR-Flash point-Prediction
    International Journal of Thermophysics 05/2010; 31(4):875-887. · 0.57 Impact Factor
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    ABSTRACT: The lower flammability limits of 18 CxHyOzNw liquids were measured as a function of initial temperature in an ASHRAE 12 L style apparatus. Results indicate that the calculated adiabatic flame temperature is not constant, as previously reported but rather decreases with increasing temperature. Consequently, the modified Burgess−Wheeler law does not accurately predict the effect of temperature on the lower flammability limit. Though few direct comparisons are possible, previously reported data agree well with the values measured in this study.
    Journal of Chemical and Engineering Data - J CHEM ENG DATA. 02/2010;
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    ABSTRACT: Maintaining currency of thermophysical property values in a chemical database with the rapid pace of published new experimental data is particularly difficult for evaluated databases. Evaluated databases that provide recommended values for each of the properties require labor intensive evaluation of not only the newly published property values, but also all related property values stored in the database. One possible solution to this problem is the establishment of a triage system to evaluate the potential influence of new data on recommended property values. Such a priority assignment system for the DIPPR®801 Pure Chemical Database has been developed. Evaluation of the potential impact of new data on the recommended values is done through a correlation for the Influence Factor (IF) that includes weighting factors for the type of property, the experimental methodology, the quality of the data, the quantity of data upon which the current recommendation is based, and the significance of the potential change. Database IFs help prioritize review work on the DIPPR®801 database and thereby contribute to its quality and currency. KeywordsDatabase-DIPPR-Thermophysical properties-Weighting factor
    International Journal of Thermophysics 01/2010; 31(4):860-874. · 0.57 Impact Factor
  • Sonal Patel, W. Vincent Wilding, Richard L. Rowley
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    ABSTRACT: A molecular dynamics (MD) method [Fern et al., J. Phys. Chem. B 2007, 111, 3469.] recently developed for determination of phase equilibrium in a single, two-phase simulation has been shown to be accurate even near the critical point because complete phase separation and no particle insertions are required. In this work, we modify the method for structured molecules by employing a Monte Carlo (MC) method to determine the associated volume for every particle in the simulation cell. Bulk liquid and vapor densities are then determined by comparing the molecular volume distributions of liquid and vapor in the two-phase simulations to those obtained in single-phase simulations. Two-phase molecular dynamic (MD) simulations were performed on n-Propane using a Transferable Potential for Phase Equilibria (TraPPE) model. The coexisting liquid and vapor densities obtained agreed well with literature values from Gibbs Ensemble Monte Carlo (GEMC) simulations.
    2009 AIChE Annual Meeting; 11/2009
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    ABSTRACT: The dielectric constant (ε) or relative static permittivity of a material represents the capacitance of the material relative to a vacuum and is important in many industrial applications. Nevertheless, accurate experimental values are often unavailable, and current prediction methods lack accuracy and are often unreliable. Reported here is the development and testing of a new QSPR (quantitative structure property relation) correlation of ε for organic chemicals. On the basis of the regression analysis and tests of the correlation in prediction mode, the average absolute percent error is expected to be less than 3 % when applied to hydrocarbons and nonpolar compounds and less than 18 % when applied to polar compounds with ε values ranging from 1.0 to 50.0. The correlation requires values for the dipole moment, solubility parameter, van der Waals area, and refractive index. We show also that density functional calculations of the dipole moment using B3LYP/6-311+G(3df,2p) can be used in the ε correlation, when experimental values are unavailable, with little decrease in accuracy of the predicted values.
    Journal of Chemical and Engineering Data - J CHEM ENG DATA. 09/2009; 55(1).
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    ABSTRACT: In superconformal filling of copper-chip interconnects, organic additives are used to fill high-aspect-ratio trenches or vias from the bottom up. In this study we report on the development of intermolecular potentials and use molecular dynamics simulations to provide insight into the molecular function of an organic additive (3-mercaptopropanesulfonic acid or MPSA) important in superconformal electrodeposition. We also investigate how the presence of sodium chloride affects the surface adsorption and surface action of MPSA as well as the charge distribution in the system. We find that NaCl addition decreases the adsorption strength of MPSA at a simulated copper surface and attenuates the copper-ion association with MPSA. The model also was used to simulate induced-charge effects and adsorption on a nonplanar electrode surface.
    The Journal of Chemical Physics 02/2008; 128(4):044717. · 3.12 Impact Factor
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    ABSTRACT: Intelligent design of chemical-process equipment requires accurate thermophysical property values for pure components and mixtures including solutions. Databases used by practicing engineers should include the best numbers available and estimated uncertainties of these numbers. Many important property values have not been measured and must be estimated. Care should be exercised in selecting the estimation method. Property values that are a function of temperature, pressure, and/or composition can be correlated using appropriate equations. Such equations and the number of adjustable parameters in these equations should be selected with care. Examples of determining uncertainties, estimation techniques used, and correlating equations are given.
    Journal of Thermal Analysis and Calorimetry 01/2008; 92(2):465-470. · 1.98 Impact Factor
  • Jason C Thomas, Richard L Rowley
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    ABSTRACT: A transient molecular dynamics (TMD) method has been developed for simulation of fluid viscosity. In this method a sinusoidal velocity profile is instantaneously overlaid onto equilibrated molecular velocities, and the subsequent decay of that velocity profile is observed. The viscosity is obtained by matching in a least-squares sense the analytical solution of the corresponding momentum transport boundary-value problem to the simulated decay of the initial velocity profile. The method was benchmarked by comparing results obtained from the TMD method for a Lennard-Jones fluid with those previously obtained using equilibrium molecular dynamics (EMD) simulations. Two different constitutive models were used in the macroscopic equations to relate the shear rate to the stress. Results using a Newtonian fluid model agree with EMD results at moderate densities but exhibit an increasingly positive error with increasing density at high densities. With the initial velocity profiles used in this study, simulated transient velocities displayed clear viscoelastic behavior at dimensionless densities above 0.7. However, the use of a linear viscoelastic model reproduces the simulated transient velocity behavior well and removes the high-density bias observed in the results obtained under the assumption of Newtonian behavior. The viscosity values obtained using the viscoelastic model are in excellent agreement with the EMD results over virtually the entire fluid domain. For simplicity, the Newtonian fluid model can be used at lower densities and the viscoelastic model at higher densities; the two models give equivalent results at intermediate densities.
    The Journal of Chemical Physics 12/2007; 127(17):174510. · 3.12 Impact Factor

Publication Stats

503 Citations
164.40 Total Impact Points

Institutions

  • 1986–2011
    • Brigham Young University - Provo Main Campus
      • • Department of Chemical Engineering
      • • Department of Chemistry and Biochemistry
      Provo, UT, United States
  • 2004–2006
    • University of Joensuu
      • Department of Chemistry
      Yoensu, Eastern Finland Province, Finland
    • Rice University
      Houston, Texas, United States
  • 1987–2005
    • Brigham Young University - Hawaii
      Kahuku, Hawaii, United States
  • 1989
    • Brigham Young University - Idaho
      • Department of Chemistry
      Provo, Utah, United States