Chapter 5 – Single Phase Systems ∑
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Ideal-Gas Mixtures and Real-Gas Mixtures
Thermodynamics: An Engineering Approach, 6th Edition Yunus A. Cengel, Michael A. Boles McGraw-Hill, 2008 Chapter 13 GAS MIXTURES Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. Objectives • Develop rules for determining nonreacting gas mixture properties from knowledge of mixture composition and the properties of the individual components. • Define the quantities used to describe the composition of a mixture, such as mass fraction, mole fraction, and volume fraction. • Apply the rules for determining mixture properties to ideal-gas mixtures and real-gas mixtures. • Predict the P-v-T behavior of gas mixtures based on Dalton’s law of additive pressures and Amagat’s law of additive volumes. • Perform energy and exergy analysis of mixing processes. 2 COMPOSITION OF A GAS MIXTURE: MASS AND MOLE FRACTIONS To determine the properties of a mixture, we need to know the composition of the mixture as well as the properties of the individual components. There are two ways to describe the composition of a mixture: Molar analysis: specifying the number of moles of each component Gravimetric analysis: specifying the mass of each component The mass of a mixture is equal to the sum of the masses of its components. Mass fraction Mole The number of moles of a nonreacting mixture fraction is equal to the sum of the number of moles of its components. 3 Apparent (or average) molar mass The sum of the mass and mole fractions of a mixture is equal to 1. Gas constant The molar mass of a mixture Mass and mole fractions of a mixture are related by The sum of the mole fractions of a mixture is equal to 1. -
An Empirical Model Predicting the Viscosity of Highly Concentrated
Rheol Acta &2001) 40: 434±440 Ó Springer-Verlag 2001 ORIGINAL CONTRIBUTION Burkhardt Dames An empirical model predicting the viscosity Bradley Ron Morrison Norbert Willenbacher of highly concentrated, bimodal dispersions with colloidal interactions Abstract The relationship between the particles. Starting from a limiting Received: 7 June 2000 Accepted: 12 February 2001 particle size distribution and viscos- value of 2 for non-interacting either ity of concentrated dispersions is of colloidal or non-colloidal particles, great industrial importance, since it e generally increases strongly with is the key to get high solids disper- decreasing particle size. For a given sions or suspensions. The problem is particle system it then can be treated here experimentally as well expressed as a function of the num- as theoretically for the special case ber average particle diameter. As a of strongly interacting colloidal consequence, the viscosity of bimo- particles. An empirical model based dal dispersions varies not only with on a generalized Quemada equation the size ratio of large to small ~ / Àe g g 1 À / is used to describe particles, but also depends on the g as a functionmax of volume fraction absolute particle size going through for mono- as well as multimodal a minimum as the size ratio increas- dispersions. The pre-factor g~ ac- es. Furthermore, the well-known counts for the shear rate dependence viscosity minimum for bimodal dis- of g and does not aect the shape of persions with volumetric mixing the g vs / curves. It is shown here ratios of around 30/70 of small to for the ®rst time that colloidal large particles is shown to vanish if interactions do not show up in the colloidal interactions contribute maximum packing parameter and signi®cantly. -
Colloidal Suspensions
Chapter 9 Colloidal suspensions 9.1 Introduction So far we have discussed the motion of one single Brownian particle in a surrounding fluid and eventually in an extaernal potential. There are many practical applications of colloidal suspensions where several interacting Brownian particles are dissolved in a fluid. Colloid science has a long history startying with the observations by Robert Brown in 1828. The colloidal state was identified by Thomas Graham in 1861. In the first decade of last century studies of colloids played a central role in the development of statistical physics. The experiments of Perrin 1910, combined with Einstein's theory of Brownian motion from 1905, not only provided a determination of Avogadro's number but also laid to rest remaining doubts about the molecular composition of matter. An important event in the development of a quantitative description of colloidal systems was the derivation of effective pair potentials of charged colloidal particles. Much subsequent work, largely in the domain of chemistry, dealt with the stability of charged colloids and their aggregation under the influence of van der Waals attractions when the Coulombic repulsion is screened strongly by the addition of electrolyte. Synthetic colloidal spheres were first made in the 1940's. In the last twenty years the availability of several such reasonably well characterised "model" colloidal systems has attracted physicists to the field once more. The study, both theoretical and experimental, of the structure and dynamics of colloidal suspensions is now a vigorous and growing subject which spans chemistry, chemical engineering and physics. A colloidal dispersion is a heterogeneous system in which particles of solid or droplets of liquid are dispersed in a liquid medium. -
Chapter 6 - Engineering Equations of State II
Introductory Chemical Engineering Thermodynamics By J.R. Elliott and C.T. Lira Chapter 6 - Engineering Equations of State II. Generalized Fluid Properties The principle of two-parameter corresponding states 50 50 40 40 30 30 T=705K 20 20 ρ T=286K L 10 10 T=470 T=191K T=329K 0 0 0 0.1 0.2 0.3 0.4 0 0.1 0.2 0.3 0.4 0.5 -10 T=133K -10 ρV Density (g/cc) Density (g/cc) VdW Pressure (bars) in Methane VdW Pressure (bars) in Pentane Critical Definitions: Tc - critical temperature - the temperature above which no liquid can exist. Pc - critical pressure - the pressure above which no vapor can exist. ω - acentric factor - a third parameter which helps to specify the vapor pressure curve which, in turn, affects the rest of the thermodynamic variables. ∂ ∂ 2 P = P = 0 and 0 at Tc and Pc Note: at the critical point, ∂ρ ∂ρ 2 T T Chapter 6 - Engineering Equations of State Slide 1 II. Generalized Fluid Properties The van der Waals (1873) Equation Of State (vdW-EOS) Based on some semi-empirical reasoning about the ways that temperature and density affect the pressure, van der Waals (1873) developed the equation below, which he considered to be fairly crude. We will discuss the reasoning at the end of the chapter, but it is useful to see what the equations are and how we use them before deriving the details. The vdW-EOS is: bρ aρ 1 aρ Z =+1 −= − ()1− bρ RT()1− bρ RT van der Waals’ trick for characterizing the difference between subcritical and supercritical fluids was to recognize that, at the critical point, ∂P ∂ 2 P = 0 and = 0 at T , P ∂ρ ∂ρ 2 c c T T Since there are only two “undetermined parameters” in his EOS (a and b), he has reduced the problem to one of two equations and two unknowns. -
On the Van Der Waals Gas, Contact Geometry and the Toda Chain
entropy Article On the van der Waals Gas, Contact Geometry and the Toda Chain Diego Alarcón, P. Fernández de Córdoba ID , J. M. Isidro * ID and Carlos Orea Instituto Universitario de Matemática Pura y Aplicada, Universidad Politécnica de Valencia, 46022 Valencia, Spain; [email protected] (D.A.); [email protected] (P.F.d.C.); [email protected] (C.O.) * Correspondence: [email protected] Received: 7 June 2018; Accepted: 16 July 2018; Published: 26 July 2018 Abstract: A Toda–chain symmetry is shown to underlie the van der Waals gas and its close cousin, the ideal gas. Links to contact geometry are explored. Keywords: van der Waals gas; contact geometry; Toda chain 1. Introduction The contact geometry of the classical van der Waals gas [1] is described geometrically using a five-dimensional contact manifold M [2] that can be endowed with the local coordinates U (internal energy), S (entropy), V (volume), T (temperature) and p (pressure). This description corresponds to a choice of the fundamental equation, in the energy representation, in which U depends on the two extensive variables S and V. One defines the corresponding momenta T = ¶U/¶S and −p = ¶U/¶V. Then, the standard contact form on M reads [3,4] a = dU + TdS − pdV. (1) One can introduce Poisson brackets on the four-dimensional Poisson manifold P (a submanifold of M) spanned by the coordinates S, V and their conjugate variables T, −p, the nonvanishing brackets being fS, Tg = 1, fV, −pg = 1. (2) Given now an equation of state f (p, T,...) = 0, (3) one can make the replacements T = ¶U/¶S, −p = ¶U/¶V in order to obtain ¶U ¶U f − , ,.. -
Lecture 3. the Basic Properties of the Natural Atmosphere 1. Composition
Lecture 3. The basic properties of the natural atmosphere Objectives: 1. Composition of air. 2. Pressure. 3. Temperature. 4. Density. 5. Concentration. Mole. Mixing ratio. 6. Gas laws. 7. Dry air and moist air. Readings: Turco: p.11-27, 38-43, 366-367, 490-492; Brimblecombe: p. 1-5 1. Composition of air. The word atmosphere derives from the Greek atmo (vapor) and spherios (sphere). The Earth’s atmosphere is a mixture of gases that we call air. Air usually contains a number of small particles (atmospheric aerosols), clouds of condensed water, and ice cloud. NOTE : The atmosphere is a thin veil of gases; if our planet were the size of an apple, its atmosphere would be thick as the apple peel. Some 80% of the mass of the atmosphere is within 10 km of the surface of the Earth, which has a diameter of about 12,742 km. The Earth’s atmosphere as a mixture of gases is characterized by pressure, temperature, and density which vary with altitude (will be discussed in Lecture 4). The atmosphere below about 100 km is called Homosphere. This part of the atmosphere consists of uniform mixtures of gases as illustrated in Table 3.1. 1 Table 3.1. The composition of air. Gases Fraction of air Constant gases Nitrogen, N2 78.08% Oxygen, O2 20.95% Argon, Ar 0.93% Neon, Ne 0.0018% Helium, He 0.0005% Krypton, Kr 0.00011% Xenon, Xe 0.000009% Variable gases Water vapor, H2O 4.0% (maximum, in the tropics) 0.00001% (minimum, at the South Pole) Carbon dioxide, CO2 0.0365% (increasing ~0.4% per year) Methane, CH4 ~0.00018% (increases due to agriculture) Hydrogen, H2 ~0.00006% Nitrous oxide, N2O ~0.00003% Carbon monoxide, CO ~0.000009% Ozone, O3 ~0.000001% - 0.0004% Fluorocarbon 12, CF2Cl2 ~0.00000005% Other gases 1% Oxygen 21% Nitrogen 78% 2 • Some gases in Table 3.1 are called constant gases because the ratio of the number of molecules for each gas and the total number of molecules of air do not change substantially from time to time or place to place. -
Gp-Cpc-01 Units – Composition – Basic Ideas
GP-CPC-01 UNITS – BASIC IDEAS – COMPOSITION 11-06-2020 Prof.G.Prabhakar Chem Engg, SVU GP-CPC-01 UNITS – CONVERSION (1) ➢ A two term system is followed. A base unit is chosen and the number of base units that represent the quantity is added ahead of the base unit. Number Base unit Eg : 2 kg, 4 meters , 60 seconds ➢ Manipulations Possible : • If the nature & base unit are the same, direct addition / subtraction is permitted 2 m + 4 m = 6m ; 5 kg – 2.5 kg = 2.5 kg • If the nature is the same but the base unit is different , say, 1 m + 10 c m both m and the cm are length units but do not represent identical quantity, Equivalence considered 2 options are available. 1 m is equivalent to 100 cm So, 100 cm + 10 cm = 110 cm 0.01 m is equivalent to 1 cm 1 m + 10 (0.01) m = 1. 1 m • If the nature of the quantity is different, addition / subtraction is NOT possible. Factors used to check equivalence are known as Conversion Factors. GP-CPC-01 UNITS – CONVERSION (2) • For multiplication / division, there are no such restrictions. They give rise to a set called derived units Even if there is divergence in the nature, multiplication / division can be carried out. Eg : Velocity ( length divided by time ) Mass flow rate (Mass divided by time) Mass Flux ( Mass divided by area (Length 2) – time). Force (Mass * Acceleration = Mass * Length / time 2) In derived units, each unit is to be individually converted to suit the requirement Density = 500 kg / m3 . -
Solutions Mole Fraction of Component a = Xa Mass Fraction of Component a = Ma Volume Fraction of Component a = Φa Typically We
Solutions Mole fraction of component A = xA Mass Fraction of component A = mA Volume Fraction of component A = fA Typically we make a binary blend, A + B, with mass fraction, m A, and want volume fraction, fA, or mole fraction , xA. fA = (mA/rA)/((mA/rA) + (m B/rB)) xA = (mA/MWA)/((mA/MWA) + (m B/MWB)) 1 Solutions Three ways to get entropy and free energy of mixing A) Isothermal free energy expression, pressure expression B) Isothermal volume expansion approach, volume expression C) From statistical thermodynamics 2 Mix two ideal gasses, A and B p = pA + pB pA is the partial pressure pA = xAp For single component molar G = µ µ 0 is at p0,A = 1 bar At pressure pA for a pure component µ A = µ0,A + RT ln(p/p0,A) = µ0,A + RT ln(p) For a mixture of A and B with a total pressure ptot = p0,A = 1 bar and pA = xA ptot For component A in a binary mixture µ A(xA) = µ0.A + RT xAln (xA ptot/p0,A) = µ0.A + xART ln (xA) Notice that xA must be less than or equal to 1, so ln xA must be negative or 0 So the chemical potential has to drop in the solution for a solution to exist. Ideal gasses only have entropy so entropy drives mixing in this case. This can be written, xA = exp((µ A(xA) - µ 0.A)/RT) Which indicates that xA is the Boltzmann probability of finding A 3 Mix two real gasses, A and B µ A* = µ 0.A if p = 1 4 Ideal Gas Mixing For isothermal DU = CV dT = 0 Q = W = -pdV For ideal gas Q = W = nRTln(V f/Vi) Q = DS/T DS = nRln(V f/Vi) Consider a process of expansion of a gas from VA to V tot The change in entropy is DSA = nARln(V tot/VA) = - nARln(VA/V tot) Consider an isochoric mixing process of ideal gasses A and B. -
Calculation of Steam Volume Fraction in Subcooled Boiling
AE-28G UDC 535.248.2 CD 00 CN UJ < Calculation of Steam Volume Fraction in Subcooled Boiling S. Z. Rouhani AKTIEBOLAGET ATOMENERGI STOCKHOLM, SWEDEN 1967 AE -286 CALCULATION OF STEAM VOLUME FRACTION IN SUBCOOLED BOILING by S. Z . Rouhani SUMMARY An analysis of subcooled boiling is presented. It is assumed that he at is removed by vapor generation, heating of the liquid that replaces the detached bubbles, and to some extent by single phase heat transfer. Two regions of subcooled boiling are considered and a criterion is provided for obtaining the limiting value of subcooling between the two regions. Condensation of vapor in the subcooled liquid is analysed and the relative velocity of vapor with respect to the liquid is neglected in these regions. The theoretical arguments result in some equations for the calculation of steam volume frac tion and true liquid subcooling. Printed and distributed in June 1 967. LIST OF CONTENTS Page Introduction 3 Literature Survey 3 Theory 4 Assumptions and the Derivation of the Basic Equations 4 Remarks on the Liquid Subcooling 6 The integrated Equation of Subcooled Void 7 Estimation of the Rate of Bubble Condensation 9 Effects of Void and Channel Geometry on cp and cp^ 1 0 Effect of Subcooling on cp^ and cp^ 1 0 Effects of Mass Velocity, Heat Flux, and Physical Properties on cpj and cp,, 12 The Limiting Value of Subcooling in the First Region 1 3 Resume of Calculation Procedure 14 Calculations of a in the First Region 14 Calculation of O' in the Second Region 1 4 Steam Quality in Subcooled Boiling 1 5 Comparison with Experimental Data 1 5 Conclusion 15 Nomenclature 18 Greek Letters 20 References 22 Figure Captions 23 - 3 - INTRODUCTION The intensity of subcooled boiling depends on many factors, among which the degree of subcooling, surface heat flux, mass veloc ity, and physical properties of the liquid and its vapor. -
Greek Letters
Seader_44460_fm.qxd 8/30/05 5:15 PM Page xxvi xxvi Nomenclature Xm mole fraction of functional group m in y mole fraction in vapor phase; distance; mass UNIFAC method fraction in extract; mass fraction in overflow x mole fraction in liquid phase; mole fraction in y vector of mole fractions in vapor phase any phase; distance; mass fraction in raffinate; Z compressibility factor = Pv/RT; total mass; mass fraction in underflow; mass fraction of height particles Z froth height on a tray C f = / x normalized mole fraction xi xj 1 ZL length of liquid flow path across a tray j=1 Z¯ lattice coordination number in UNIQUAC and x vector of mole fractions in liquid phase UNIFAC equations xn fraction of crystals of size smaller than L z mole fraction in any phase; overall mole frac- Y mole or mass ratio; mass ratio of soluble mate- tion in combined phases; distance; overall mole rial to solvent in overflow; pressure-drop fraction in feed; dimensionless crystal size; factor for packed columns defined by (6-102); length of liquid flow path across tray concentration of solute in solvent; parameter z vector of mole fractions in overall mixture in (9-34) Greek Letters ␣ thermal diffusivity, k/CP ; relative volatility; ij binary interaction parameter in Wilson equation surface area per adsorbed molecule mV/L; radiation wavelength ␣∗ ideal separation factor for a membrane +, − limiting ionic conductances of cation and ␣ij relative volatility of component i with respect anion, respectively to component j for vapor-liquid equilibria; ij energy of interaction -
Comments on Failures of Van Der Waals' Equation at the Gas–Liquid
Comments on Failures of van der Waals’ Equation at the Gas–Liquid Critical Point, L. V. Woodcock, International Journal of Thermophysics (2018) 39:120 I.H. Umirzakov Institute of Thermophysics, Novosibirsk, Russia [email protected] Abstract These comments are a response to the discussion presented in the above paper concerning the “New comment on Gibbs Density Surface of Fluid Argon: Revised Critical Parameters” by Umirzakov. Here we show that: Woodcock’s results obtained for the dependencies for the isochoric heat capacity, excess Gibbs energy and coexisting difference functional of argon, and coexisting densities of liquid and vapor of the van der Waals fluid and presented in all Figures are incorrect; his Table includes incorrect values of coexisting difference functional; his paper includes many incorrect equations, mathematical and logical errors and physically incorrect assertions concerning the temperature dependences of the isochoric heat capacity and entropy of real fluids; most of the his conclusions are based on the above errors, incorrect data, incorrect comparisons and incorrect dependencies; and most of his conclusions are invalid. We also show that the van der Waals equation of state quantitatively describes the dependencies of saturation pressure on vapor density and temperature near critical point, and the equation of state can describe qualitatively the reduced excess Gibbs energy, rigidity and densities of coexisting liquid and vapor of argon, including the region near critical point. Keywords Coexistence · Critical point · First-order phase transition · Liquid · Phase equilibrium · Vapor 1. Introduction Our comments are a response to a discussion of the article “New comment on Gibbs Density Surface of Fluid Argon: Revised Critical Parameters” by Umirzakov [1] held in the paper [2]. -
Convolutional Neural Networks for Atomistic Systems
Convolutional neural networks for atomistic systems Kevin Ryczko Department of Physics, University of Ottawa Kyle Mills Department of Physics, University of Ontario Institute of Technology Iryna Luchak Department of Electrical & Computer Engineering, University of British Columbia Christa Homenick National Research Council of Canada Isaac Tamblyn Department of Physics, University of Ontario Institute of Technology Department of Physics, University of Ottawa National Research Council of Canada Abstract We introduce a new method, called CNNAS (convolutional neural networks for atom- istic systems), for calculating the total energy of atomic systems which rivals the com- putational cost of empirical potentials while maintaining the accuracy of ab initio calculations. This method uses deep convolutional neural networks (CNNs), where the input to these networks are simple representations of the atomic structure. We use this approach to predict energies obtained using density functional theory (DFT) for 2D hexagonal lattices of various types. Using a dataset consisting of graphene, hexagonal boron nitride (hBN), and graphene-hBN heterostructures, with and with- out defects, we trained a deep CNN that is capable of predicting DFT energies to an extremely high accuracy, with a mean absolute error (MAE) of 0.198 meV / atom (maximum absolute error of 16.1 meV / atom). To explore our new methodology, arXiv:1706.09496v2 [cond-mat.mtrl-sci] 20 Mar 2018 Email addresses: [email protected] (Kevin Ryczko), [email protected] (Isaac Tamblyn) Preprint submitted to Computational Materials Science March 21, 2018 we investigate the ability of a deep neural network (DNN) in predicting a Lennard- Jones energy and separation distance for a dataset of dimer molecules in both two and three dimensions.