Critical reassessment of viscosities of 11 common gases

Maitland, Geoffrey C.; Smith, E. B.

doi:10.1021/je60053a015

Cited by 160 publications

(53 citation statements)

References 39 publications

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“…11 gives excellent agreement with the Argon viscosity values recommended by Maitland and Smith [14], Kestin et al [10] and Younglove and Hanley [17]. …”

Section: Dsmc Models and Viscosity Formulaesupporting

confidence: 75%

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Viscosity of argon at temperatures >2000 K from measured shock thickness

Macrossan

Lilley

2003

Physics of Fluids

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Mott-Smith's approximate theory of plane 1D shock structure (Phys. Rev., 82, 885-92, 1951; Phys. Rev., 5, 1325-36, 1962 suggests, for any intermolecular potential, the average number of collisions undergone by a molecule as it cross the shock quickly approaches a limit as the Mach number increases. We check this with DSMC calculations and show that it can be used to estimate the gas viscosity at high temperatures from measurements of shock thickness. We consider a monatomic gas (γ = 5/3) for five different collision models and hence five different viscosity laws µ = µ (T ). The collision models are: the variable hard sphere, σ ∝ 1/g 2υ , with three values of υ; the generalized hard sphere; and the Maitland-Smith potential. For shock Mach numbers M 1 4.48, all these collision models predict a shock thickness ∆ = 11.0λ s , where λ s is a suitably defined 'shock length scale', with a scatter ≈ 2.5% (2 standard deviations). This shock length depends on the upstream flow speed, downstream density and a collision cross-section derived from the viscosity of the gas at a temperature T g , characteristic of the collisions at relative speed g = u 1 − u 2 between upstream and downstream molecules. Using ∆ = 11λ s and the experimental measurements of shock thickness in argon given by Alsmeyer (J. Fluid Mech. 74, 498-513, 1976), we estimate the viscosity of argon at high values of T g . These estimated values agree with the viscosity of argon recommended by the CRC Handbook of Chemistry and Physics (2001) at T ≈ 1, 500 K. For T 2, 000 K, for which there appears to be no reliable direct measurements of viscosity, our estimated values lie be- Taking the error in the experimental measurements of ∆ as the scatter in the results of Alsmeyer (± 2%), we estimate the uncertainty in the viscosity deduced from the shock thickness measurements as less than ± 5%. To this accuracy, our results agree with the power law predictions and disagree with the CRC Handbook values, for T 3,000 K.

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“…11 gives excellent agreement with the Argon viscosity values recommended by Maitland and Smith [14], Kestin et al [10] and Younglove and Hanley [17]. …”

Section: Dsmc Models and Viscosity Formulaesupporting

confidence: 75%

“…DSMC results of shock thickness in a monatomic gas were given by Erwin et al [13], using the deflection angle in each collision calculated from the Maitland-Smith [14] potential…”

Section: Dsmc Models and Viscosity Formulaementioning

confidence: 99%

Viscosity of argon at temperatures >2000 K from measured shock thickness

Macrossan

Lilley

2003

Physics of Fluids

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“…(3), (5), and k = 15kµ/4m. Figure 3 shows the theoretical variation of viscosity with temperature for the two models along with the experimental fit of Maitland and Smith [14]. The LJ model is observed to be in much better agreement with the experimental fit overall, with a maximum error less than 4%.…”

Section: Viscosity and Thermal Conductivity Using Original Parametersmentioning

confidence: 75%

“…[5] It may be written as a linear function of viscosity, k = 15kµ/4m, and therefore the thermal conductivity calculations are expected to follow similar trends as for viscosity. Figure 1 compares the viscosity variation of argon obtained from experiments [11,12,13], predictions using the LJ potential [9], the most commonly used VSS model for argon [5] and a curve fit obtained by Maitland and Smith [14] using data from a number of experiments for argon. A similar comparison is illustrated in figure 1 for molecular nitrogen using the Maitland-Smith fit.…”

Section: Lennard-jones Interaction Modelmentioning

confidence: 99%

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Effect of molecular models on viscosity and thermal conductivity calculations

Weaver

Alexeenko

2014

AIP Conference Proceedings

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Abstract. The effect of molecular models on viscosity and thermal conductivity calculations is investigated. The Direct Simulation Monte Carlo (DSMC) method for rarefied gas flows is used to simulate Couette and Fourier flows as a means of obtaining the transport coefficients. Experimental measurements for argon (Ar) provide a baseline for comparison over a wide temperature range of 100-1,500 K. The variable hard sphere (VHS), variable soft sphere (VSS), and Lennard-Jones (L-J) molecular models have been implemented into a parallel version of Bird's one-dimensional DSMC code, DSMC1, and the model parameters have been recalibrated to the current experimental data set. While the VHS and VSS models only consider the short-range, repulsive forces, the L-J model also includes constributions from the long-range, dispersion forces. Theoretical results for viscosity and thermal conductivity indicate the L-J model is more accurate than the VSS model; with maximum errors of 1.4% and 3.0% in the range 300-1,500 K for L-J and VSS models, respectively. The range of validity of the VSS model is extended to 1,650 K through appropriate choices for the model parameters.

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Simulation of Chemical Vapor Deposition Processes

Kleijn¹

2002

Characterization of Materials

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Chemical vapor deposition (CVD) processes constitute an important technology for the manufacturing of thin solid films. Applications include semiconducting, conducting, and insulating films in the integrated circuit industry, superconducting thin films, antireflection and spectrally selective coatings on optical components, and anticorrosion and antiwear layers on mechanical tools and equipment. Compared to other deposition techniques, such as sputtering, sublimation, and evaporation, CVD is very versatile and offers good control of film structure and composition, excellent uniformity, and sufficiently high growth rates. Perhaps the most important advantage of CVD over other deposition techniques is its capability for conformal deposition—i.e., the capability of depositing films of uniform thickness on highly irregularly shaped surfaces. In CVD processes a thin film is deposited from the gas phase through chemical reactions at the surface. Reactive gases are introduced into the controlled environment of a reactor chamber in which the substrates on which deposition takes place are positioned. Depending on the process conditions, reactions may take place in the gas phase, leading to the creation of gaseous intermediates. The energy required to drive the chemical reactions can be supplied thermally by heating the gas in the reactor (thermal CVD), but also by supplying photons in the form of, e.g., laser light to the gas (photo CVD), or through the application of an electrical discharge (plasma CVD or plasma enhanced CVD). The reactive gas species fed into the reactor and the reactive intermediates created in the gas phase diffuse toward and adsorb onto the solid surface. Here, solid‐catalyzed reactions lead to the growth of the desired film. The application of new materials, the desire to coat and reinforce new ceramic and fibrous materials, and the tremendous increase in complexity and performance of semiconductor and similar products employing thin films are leading to the need to develop new CVD processes and to ever increasing demands on the performance of processes and equipment. This performance is determined by the interacting influences of hydrodynamics and chemical kinetics in the reactor chamber, which in turn are determined by process conditions and reactor geometry. It is generally felt that the development of novel reactors and processes can be greatly improved if simulation is used to support the design and optimization phase. In the last decades, various sophisticated mathematical models have been developed, which relate process characteristics to operating conditions and reactor geometry. When based on fundamental laws of chemistry and physics, rather than empirical relations, such models can provide a scientific basis for design, development, and optimization of CVD equipment and processes. This may lead to a reduction of time and money spent on the development of prototypes and to better reactors and processes. Besides, mathematical CVD models may also provide fundamental insights into the underlying physicochemical processes and may be of help in the interpretation of experimental data.

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Critical reassessment of viscosities of 11 common gases

Cited by 160 publications

References 39 publications

Viscosity of argon at temperatures >2000 K from measured shock thickness

Viscosity of argon at temperatures >2000 K from measured shock thickness

Effect of molecular models on viscosity and thermal conductivity calculations

Simulation of Chemical Vapor Deposition Processes

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Critical reassessment of viscosities of 11 common gases

Cited by 160 publications

References 39 publications

Viscosity of argon at temperatures &gt;2000 K from measured shock thickness

Viscosity of argon at temperatures &gt;2000 K from measured shock thickness

Effect of molecular models on viscosity and thermal conductivity calculations

Simulation of Chemical Vapor Deposition Processes

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Viscosity of argon at temperatures >2000 K from measured shock thickness

Viscosity of argon at temperatures >2000 K from measured shock thickness