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Derivation of Fluid Flow Equations
TPG4150 Reservoir Recovery Techniques 2017 1 Fluid Flow Equations DERIVATION OF FLUID FLOW EQUATIONS Review of basic steps Generally speaking, flow equations for flow in porous materials are based on a set of mass, momentum and energy conservation equations, and constitutive equations for the fluids and the porous material involved. For simplicity, we will in the following assume isothermal conditions, so that we not have to involve an energy conservation equation. However, in cases of changing reservoir temperature, such as in the case of cold water injection into a warmer reservoir, this may be of importance. Below, equations are initially described for single phase flow in linear, one- dimensional, horizontal systems, but are later on extended to multi-phase flow in two and three dimensions, and to other coordinate systems. Conservation of mass Consider the following one dimensional rod of porous material: Mass conservation may be formulated across a control element of the slab, with one fluid of density ρ is flowing through it at a velocity u: u ρ Δx The mass balance for the control element is then written as: ⎧Mass into the⎫ ⎧Mass out of the ⎫ ⎧ Rate of change of mass⎫ ⎨ ⎬ − ⎨ ⎬ = ⎨ ⎬ , ⎩element at x ⎭ ⎩element at x + Δx⎭ ⎩ inside the element ⎭ or ∂ {uρA} − {uρA} = {φAΔxρ}. x x+ Δx ∂t Dividing by Δx, and taking the limit as Δx approaches zero, we get the conservation of mass, or continuity equation: ∂ ∂ − (Aρu) = (Aφρ). ∂x ∂t For constant cross sectional area, the continuity equation simplifies to: ∂ ∂ − (ρu) = (φρ) . ∂x ∂t Next, we need to replace the velocity term by an equation relating it to pressure gradient and fluid and rock properties, and the density and porosity terms by appropriate pressure dependent functions. -
A Simple Method to Estimate Entropy and Free Energy of Atmospheric Gases from Their Action
Article A Simple Method to Estimate Entropy and Free Energy of Atmospheric Gases from Their Action Ivan Kennedy 1,2,*, Harold Geering 2, Michael Rose 3 and Angus Crossan 2 1 Sydney Institute of Agriculture, University of Sydney, NSW 2006, Australia 2 QuickTest Technologies, PO Box 6285 North Ryde, NSW 2113, Australia; [email protected] (H.G.); [email protected] (A.C.) 3 NSW Department of Primary Industries, Wollongbar NSW 2447, Australia; [email protected] * Correspondence: [email protected]; Tel.: + 61-4-0794-9622 Received: 23 March 2019; Accepted: 26 April 2019; Published: 1 May 2019 Abstract: A convenient practical model for accurately estimating the total entropy (ΣSi) of atmospheric gases based on physical action is proposed. This realistic approach is fully consistent with statistical mechanics, but reinterprets its partition functions as measures of translational, rotational, and vibrational action or quantum states, to estimate the entropy. With all kinds of molecular action expressed as logarithmic functions, the total heat required for warming a chemical system from 0 K (ΣSiT) to a given temperature and pressure can be computed, yielding results identical with published experimental third law values of entropy. All thermodynamic properties of gases including entropy, enthalpy, Gibbs energy, and Helmholtz energy are directly estimated using simple algorithms based on simple molecular and physical properties, without resource to tables of standard values; both free energies are measures of quantum field states and of minimal statistical degeneracy, decreasing with temperature and declining density. We propose that this more realistic approach has heuristic value for thermodynamic computation of atmospheric profiles, based on steady state heat flows equilibrating with gravity. -
LECTURES on MATHEMATICAL STATISTICAL MECHANICS S. Adams
ISSN 0070-7414 Sgr´ıbhinn´ıInstiti´uid Ard-L´einnBhaile´ Atha´ Cliath Sraith. A. Uimh 30 Communications of the Dublin Institute for Advanced Studies Series A (Theoretical Physics), No. 30 LECTURES ON MATHEMATICAL STATISTICAL MECHANICS By S. Adams DUBLIN Institi´uid Ard-L´einnBhaile´ Atha´ Cliath Dublin Institute for Advanced Studies 2006 Contents 1 Introduction 1 2 Ergodic theory 2 2.1 Microscopic dynamics and time averages . .2 2.2 Boltzmann's heuristics and ergodic hypothesis . .8 2.3 Formal Response: Birkhoff and von Neumann ergodic theories9 2.4 Microcanonical measure . 13 3 Entropy 16 3.1 Probabilistic view on Boltzmann's entropy . 16 3.2 Shannon's entropy . 17 4 The Gibbs ensembles 20 4.1 The canonical Gibbs ensemble . 20 4.2 The Gibbs paradox . 26 4.3 The grandcanonical ensemble . 27 4.4 The "orthodicity problem" . 31 5 The Thermodynamic limit 33 5.1 Definition . 33 5.2 Thermodynamic function: Free energy . 37 5.3 Equivalence of ensembles . 42 6 Gibbs measures 44 6.1 Definition . 44 6.2 The one-dimensional Ising model . 47 6.3 Symmetry and symmetry breaking . 51 6.4 The Ising ferromagnet in two dimensions . 52 6.5 Extreme Gibbs measures . 57 6.6 Uniqueness . 58 6.7 Ergodicity . 60 7 A variational characterisation of Gibbs measures 62 8 Large deviations theory 68 8.1 Motivation . 68 8.2 Definition . 70 8.3 Some results for Gibbs measures . 72 i 9 Models 73 9.1 Lattice Gases . 74 9.2 Magnetic Models . 75 9.3 Curie-Weiss model . 77 9.4 Continuous Ising model . -
Entropy and H Theorem the Mathematical Legacy of Ludwig Boltzmann
ENTROPY AND H THEOREM THE MATHEMATICAL LEGACY OF LUDWIG BOLTZMANN Cambridge/Newton Institute, 15 November 2010 C´edric Villani University of Lyon & Institut Henri Poincar´e(Paris) Cutting-edge physics at the end of nineteenth century Long-time behavior of a (dilute) classical gas Take many (say 1020) small hard balls, bouncing against each other, in a box Let the gas evolve according to Newton’s equations Prediction by Maxwell and Boltzmann The distribution function is asymptotically Gaussian v 2 f(t, x, v) a exp | | as t ≃ − 2T → ∞ Based on four major conceptual advances 1865-1875 Major modelling advance: Boltzmann equation • Major mathematical advance: the statistical entropy • Major physical advance: macroscopic irreversibility • Major PDE advance: qualitative functional study • Let us review these advances = journey around centennial scientific problems ⇒ The Boltzmann equation Models rarefied gases (Maxwell 1865, Boltzmann 1872) f(t, x, v) : density of particles in (x, v) space at time t f(t, x, v) dxdv = fraction of mass in dxdv The Boltzmann equation (without boundaries) Unknown = time-dependent distribution f(t, x, v): ∂f 3 ∂f + v = Q(f,f) = ∂t i ∂x Xi=1 i ′ ′ B(v v∗,σ) f(t, x, v )f(t, x, v∗) f(t, x, v)f(t, x, v∗) dv∗ dσ R3 2 − − Z v∗ ZS h i The Boltzmann equation (without boundaries) Unknown = time-dependent distribution f(t, x, v): ∂f 3 ∂f + v = Q(f,f) = ∂t i ∂x Xi=1 i ′ ′ B(v v∗,σ) f(t, x, v )f(t, x, v∗) f(t, x, v)f(t, x, v∗) dv∗ dσ R3 2 − − Z v∗ ZS h i The Boltzmann equation (without boundaries) Unknown = time-dependent distribution -
A Molecular Modeler's Guide to Statistical Mechanics
A Molecular Modeler’s Guide to Statistical Mechanics Course notes for BIOE575 Daniel A. Beard Department of Bioengineering University of Washington Box 3552255 [email protected] (206) 685 9891 April 11, 2001 Contents 1 Basic Principles and the Microcanonical Ensemble 2 1.1 Classical Laws of Motion . 2 1.2 Ensembles and Thermodynamics . 3 1.2.1 An Ensembles of Particles . 3 1.2.2 Microscopic Thermodynamics . 4 1.2.3 Formalism for Classical Systems . 7 1.3 Example Problem: Classical Ideal Gas . 8 1.4 Example Problem: Quantum Ideal Gas . 10 2 Canonical Ensemble and Equipartition 15 2.1 The Canonical Distribution . 15 2.1.1 A Derivation . 15 2.1.2 Another Derivation . 16 2.1.3 One More Derivation . 17 2.2 More Thermodynamics . 19 2.3 Formalism for Classical Systems . 20 2.4 Equipartition . 20 2.5 Example Problem: Harmonic Oscillators and Blackbody Radiation . 21 2.5.1 Classical Oscillator . 22 2.5.2 Quantum Oscillator . 22 2.5.3 Blackbody Radiation . 23 2.6 Example Application: Poisson-Boltzmann Theory . 24 2.7 Brief Introduction to the Grand Canonical Ensemble . 25 3 Brownian Motion, Fokker-Planck Equations, and the Fluctuation-Dissipation Theo- rem 27 3.1 One-Dimensional Langevin Equation and Fluctuation- Dissipation Theorem . 27 3.2 Fokker-Planck Equation . 29 3.3 Brownian Motion of Several Particles . 30 3.4 Fluctuation-Dissipation and Brownian Dynamics . 32 1 Chapter 1 Basic Principles and the Microcanonical Ensemble The first part of this course will consist of an introduction to the basic principles of statistical mechanics (or statistical physics) which is the set of theoretical techniques used to understand microscopic systems and how microscopic behavior is reflected on the macroscopic scale. -
Entropy: from the Boltzmann Equation to the Maxwell Boltzmann Distribution
Entropy: From the Boltzmann equation to the Maxwell Boltzmann distribution A formula to relate entropy to probability Often it is a lot more useful to think about entropy in terms of the probability with which different states are occupied. Lets see if we can describe entropy as a function of the probability distribution between different states. N! WN particles = n1!n2!....nt! stirling (N e)N N N W = = N particles n1 n2 nt n1 n2 nt (n1 e) (n2 e) ....(nt e) n1 n2 ...nt with N pi = ni 1 W = N particles n1 n2 nt p1 p2 ...pt takeln t lnWN particles = "#ni ln pi i=1 divide N particles t lnW1particle = "# pi ln pi i=1 times k t k lnW1particle = "k# pi ln pi = S1particle i=1 and t t S = "N k p ln p = "R p ln p NA A # i # i i i=1 i=1 ! Think about how this equation behaves for a moment. If any one of the states has a probability of occurring equal to 1, then the ln pi of that state is 0 and the probability of all the other states has to be 0 (the sum over all probabilities has to be 1). So the entropy of such a system is 0. This is exactly what we would have expected. In our coin flipping case there was only one way to be all heads and the W of that configuration was 1. Also, and this is not so obvious, the maximal entropy will result if all states are equally populated (if you want to see a mathematical proof of this check out Ken Dill’s book on page 85). -
Boltzmann Equation II: Binary Collisions
Physics 127b: Statistical Mechanics Boltzmann Equation II: Binary Collisions Binary collisions in a classical gas Scattering out Scattering in v'1 v'2 v 1 v 2 R v 2 v 1 v'2 v'1 Center of V' Mass Frame V θ θ b sc sc R V V' Figure 1: Binary collisions in a gas: top—lab frame; bottom—centre of mass frame Binary collisions in a gas are very similar, except that the scattering is off another molecule. An individual scattering process is, of course, simplest to describe in the center of mass frame in terms of the relative E velocity V =Ev1 −Ev2. However the center of mass frame is different for different collisions, so we must keep track of the results in the lab frame, and this makes the calculation rather intricate. I will indicate the main ideas here, and refer you to Reif or Landau and Lifshitz for precise discussions. Lets first set things up in the lab frame. Again we consider the pair of scattering in and scattering out processes that are space-time inverses, and so have identical cross sections. We can write abstractly for the scattering out from velocity vE1 due to collisions with molecules with all velocities vE2, which will clearly be proportional to the number f (vE1) of molecules at vE1 (which we write as f1—sorry, not the same notation as in the previous sections where f1 denoted the deviation of f from the equilibrium distribution!) and the 3 3 number f2d v2 ≡ f(vE2)d v2 in each velocity volume element, and then we must integrate over all possible vE0 vE0 outgoing velocities 1 and 2 ZZZ df (vE ) 1 =− w(vE0 , vE0 ;Ev , vE )f f d3v d3v0 d3v0 . -
Continuity Equation in Pressure Coordinates
Continuity Equation in Pressure Coordinates Here we will derive the continuity equation from the principle that mass is conserved for a parcel following the fluid motion (i.e., there is no flow across the boundaries of the parcel). This implies that δxδyδp δM = ρ δV = ρ δxδyδz = − g is conserved following the fluid motion: 1 d(δM ) = 0 δM dt 1 d()δM = 0 δM dt g d ⎛ δxδyδp ⎞ ⎜ ⎟ = 0 δxδyδp dt ⎝ g ⎠ 1 ⎛ d(δp) d(δy) d(δx)⎞ ⎜δxδy +δxδp +δyδp ⎟ = 0 δxδyδp ⎝ dt dt dt ⎠ 1 ⎛ dp ⎞ 1 ⎛ dy ⎞ 1 ⎛ dx ⎞ δ ⎜ ⎟ + δ ⎜ ⎟ + δ ⎜ ⎟ = 0 δp ⎝ dt ⎠ δy ⎝ dt ⎠ δx ⎝ dt ⎠ Taking the limit as δx, δy, δp → 0, ∂u ∂v ∂ω Continuity equation + + = 0 in pressure ∂x ∂y ∂p coordinates 1 Determining Vertical Velocities • Typical large-scale vertical motions in the atmosphere are of the order of 0. 01-01m/s0.1 m/s. • Such motions are very difficult, if not impossible, to measure directly. Typical observational errors for wind measurements are ~1 m/s. • Quantitative estimates of vertical velocity must be inferred from quantities that can be directly measured with sufficient accuracy. Vertical Velocity in P-Coordinates The equivalent of the vertical velocity in p-coordinates is: dp ∂p r ∂p ω = = +V ⋅∇p + w dt ∂t ∂z Based on a scaling of the three terms on the r.h.s., the last term is at least an order of magnitude larger than the other two. Making the hydrostatic approximation yields ∂p ω ≈ w = −ρgw ∂z Typical large-scale values: for w, 0.01 m/s = 1 cm/s for ω, 0.1 Pa/s = 1 μbar/s 2 The Kinematic Method By integrating the continuity equation in (x,y,p) coordinates, ω can be obtained from the mean divergence in a layer: ⎛ ∂u ∂v ⎞ ∂ω ⎜ + ⎟ + = 0 continuity equation in (x,y,p) coordinates ⎝ ∂x ∂y ⎠ p ∂p p2 p2 ⎛ ∂u ∂v ⎞ ∂ω = − ⎜ + ⎟ ∂p rearrange and integrate over the layer ∫p ∫ ⎜ ⎟ 1 ∂x ∂y p1⎝ ⎠ p ⎛ ∂u ∂v ⎞ ω(p )−ω(p ) = (p − p )⎜ + ⎟ overbar denotes pressure- 2 1 1 2 ⎜ ⎟ weighted vertical average ⎝ ∂x ∂y ⎠ p To determine vertical motion at a pressure level p2, assume that p1 = surface pressure and there is no vertical motion at the surface. -
Chapter 3 Newtonian Fluids
CM4650 Chapter 3 Newtonian Fluid 2/5/2018 Mechanics Chapter 3: Newtonian Fluids CM4650 Polymer Rheology Michigan Tech Navier-Stokes Equation v vv p 2 v g t 1 © Faith A. Morrison, Michigan Tech U. Chapter 3: Newtonian Fluid Mechanics TWO GOALS •Derive governing equations (mass and momentum balances •Solve governing equations for velocity and stress fields QUICK START V W x First, before we get deep into 2 v (x ) H derivation, let’s do a Navier-Stokes 1 2 x1 problem to get you started in the x3 mechanics of this type of problem solving. 2 © Faith A. Morrison, Michigan Tech U. 1 CM4650 Chapter 3 Newtonian Fluid 2/5/2018 Mechanics EXAMPLE: Drag flow between infinite parallel plates •Newtonian •steady state •incompressible fluid •very wide, long V •uniform pressure W x2 v1(x2) H x1 x3 3 EXAMPLE: Poiseuille flow between infinite parallel plates •Newtonian •steady state •Incompressible fluid •infinitely wide, long W x2 2H x1 x3 v (x ) x1=0 1 2 x1=L p=Po p=PL 4 2 CM4650 Chapter 3 Newtonian Fluid 2/5/2018 Mechanics Engineering Quantities of In more complex flows, we can use Interest general expressions that work in all cases. (any flow) volumetric ⋅ flow rate ∬ ⋅ | average 〈 〉 velocity ∬ Using the general formulas will Here, is the outwardly pointing unit normal help prevent errors. of ; it points in the direction “through” 5 © Faith A. Morrison, Michigan Tech U. The stress tensor was Total stress tensor, Π: invented to make the calculation of fluid stress easier. Π ≡ b (any flow, small surface) dS nˆ Force on the S ⋅ Π surface V (using the stress convention of Understanding Rheology) Here, is the outwardly pointing unit normal of ; it points in the direction “through” 6 © Faith A. -
Boltzmann Equation
Boltzmann Equation ● Velocity distribution functions of particles ● Derivation of Boltzmann Equation Ludwig Eduard Boltzmann (February 20, 1844 - September 5, 1906), an Austrian physicist famous for the invention of statistical mechanics. Born in Vienna, Austria-Hungary, he committed suicide in 1906 by hanging himself while on holiday in Duino near Trieste in Italy. Distribution Function (probability density function) Random variable y is distributed with the probability density function f(y) if for any interval [a b] the probability of a<y<b is equal to b P=∫ f ydy a f(y) is always non-negative ∫ f ydy=1 Velocity space Axes u,v,w in velocity space v dv have the same directions as dv axes x,y,z in physical du dw u space. Each molecule can be v represented in velocity space by the point defined by its velocity vector v with components (u,v,w) w Velocity distribution function Consider a sample of gas that is homogeneous in physical space and contains N identical molecules. Velocity distribution function is defined by d N =Nf vd ud v d w (1) where dN is the number of molecules in the sample with velocity components (ui,vi,wi) such that u<ui<u+du, v<vi<v+dv, w<wi<w+dw dv = dudvdw is a volume element in the velocity space. Consequently, dN is the number of molecules in velocity space element dv. Functional statement if often omitted, so f(v) is designated as f Phase space distribution function Macroscopic properties of the flow are functions of position and time, so the distribution function depends on position and time as well as velocity. -
Nonlinear Electrostatics. the Poisson-Boltzmann Equation
Nonlinear Electrostatics. The Poisson-Boltzmann Equation C. G. Gray* and P. J. Stiles# *Department of Physics, University of Guelph, Guelph, ON N1G2W1, Canada ([email protected]) #Department of Molecular Sciences, Macquarie University, NSW 2109, Australia ([email protected]) The description of a conducting medium in thermal equilibrium, such as an electrolyte solution or a plasma, involves nonlinear electrostatics, a subject rarely discussed in the standard electricity and magnetism textbooks. We consider in detail the case of the electrostatic double layer formed by an electrolyte solution near a uniformly charged wall, and we use mean-field or Poisson-Boltzmann (PB) theory to calculate the mean electrostatic potential and the mean ion concentrations, as functions of distance from the wall. PB theory is developed from the Gibbs variational principle for thermal equilibrium of minimizing the system free energy. We clarify the key issue of which free energy (Helmholtz, Gibbs, grand, …) should be used in the Gibbs principle; this turns out to depend not only on the specified conditions in the bulk electrolyte solution (e.g., fixed volume or fixed pressure), but also on the specified surface conditions, such as fixed surface charge or fixed surface potential. Despite its nonlinearity the PB equation for the mean electrostatic potential can be solved analytically for planar or wall geometry, and we present analytic solutions for both a full electrolyte, and for an ionic solution which contains only counterions, i.e. ions of sign opposite to that of the wall charge. This latter case has some novel features. We also use the free energy to discuss the inter-wall forces which arise when the two parallel charged walls are sufficiently close to permit their double layers to overlap. -
CONTINUITY EQUATION Another Principle on Which We Can Derive a New Equation Is the Conservation of Mass
ESCI 342 – Atmospheric Dynamics I Lesson 7 – The Continuity and Additional Equations Suggested Reading: Martin, Chapter 3 THE SYSTEM OF EQUATIONS IS INCOMPLETE The momentum equations in component form comprise a system of three equations with 4 unknown quantities (u, v, p, and ). Du1 p fv (1) Dt x Dv1 p fu (2) Dt y p g (3) z They are not a closed set, because there are four dependent variables (u, v, p, and ), but only three equations. We need to come up with some more equations in order to close the set. DERIVATION OF THE CONTINUITY EQUATION Another principle on which we can derive a new equation is the conservation of mass. The equation derived from this principle is called the mass continuity equation, or simply the continuity equation. Imagine a cube at a fixed point in space. The net change in mass contained within the cube is found by adding up the mass fluxes entering and leaving through each face of the cube.1 The mass flux across a face of the cube normal to the x-axis is given by u. Referring to the picture below, these fluxes will lead to a rate of change in mass within the cube given by m u yz u yz (4) t x xx 1 A flux is a quantity per unit area per unit time. Mass flux is therefore the rate at which mass moves across a unit area, and would have units of kg s1 m2. The mass in the cube can be written in terms of the density as m xyz so that m x y z .