Chapter 3 Equations for Turbulent Flows
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CFD-Driven Symbolic Identification of Algebraic Reynolds-Stress Models
CFD-driven Symbolic Identification of Algebraic Reynolds-Stress Models Isma¨ılBEN HASSAN SA¨IDIa,∗, Martin SCHMELZERb, Paola CINNELLAc, Francesco GRASSOd aInstitut A´erotechnique, CNAM, 15 Rue Marat, 78210 Saint-Cyr-l'Ecole,´ France bFaculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 2, Delft, The Netherlands cInstitut Jean Le Rond D'Alembert, Sorbonne Universit´e,4 Place Jussieu, 75005 Paris, France dLaboratoire DynFluid, CNAM, 151 Boulevard de l'Hopital, 75013 Paris, France Abstract A CFD-driven deterministic symbolic identification algorithm for learning ex- plicit algebraic Reynolds-stress models (EARSM) from high-fidelity data is de- veloped building on the CFD-free SpaRTA algorithm of [1]. Corrections for the Reynolds stress tensor and the production of transported turbulent quantities of a baseline linear eddy viscosity model (LEVM) are expressed as functions of tensor polynomials selected from a library of candidate functions. The CFD- driven training consists in solving a blackbox optimization problem in which the fitness of candidate EARSM models is evaluated by running RANS simula- tions. A preliminary sensitivity analysis is used to identify the most influential terms and to reduce the dimensionality of the search space and the Constrained Optimization using Response Surface (CORS) algorithm, which approximates the black-box cost function using a response surface constructed from a limited number of CFD solves, is used to find the optimal model parameters within a realizable search space. The resulting turbulence models are numerically robust and ensure conservation of mean kinetic energy. Furthermore, the CFD-driven procedure enables training of models against any target quantity of interest com- putable as an output of the CFD model. -
Aerodynamics Material - Taylor & Francis
CopyrightAerodynamics material - Taylor & Francis ______________________________________________________________________ 257 Aerodynamics Symbol List Symbol Definition Units a speed of sound ⁄ a speed of sound at sea level ⁄ A area aspect ratio ‐‐‐‐‐‐‐‐ b wing span c chord length c Copyrightmean aerodynamic material chord- Taylor & Francis specific heat at constant pressure of air · root chord tip chord specific heat at constant volume of air · / quarter chord total drag coefficient ‐‐‐‐‐‐‐‐ , induced drag coefficient ‐‐‐‐‐‐‐‐ , parasite drag coefficient ‐‐‐‐‐‐‐‐ , wave drag coefficient ‐‐‐‐‐‐‐‐ local skin friction coefficient ‐‐‐‐‐‐‐‐ lift coefficient ‐‐‐‐‐‐‐‐ , compressible lift coefficient ‐‐‐‐‐‐‐‐ compressible moment ‐‐‐‐‐‐‐‐ , coefficient , pitching moment coefficient ‐‐‐‐‐‐‐‐ , rolling moment coefficient ‐‐‐‐‐‐‐‐ , yawing moment coefficient ‐‐‐‐‐‐‐‐ ______________________________________________________________________ 258 Aerodynamics Aerodynamics Symbol List (cont.) Symbol Definition Units pressure coefficient ‐‐‐‐‐‐‐‐ compressible pressure ‐‐‐‐‐‐‐‐ , coefficient , critical pressure coefficient ‐‐‐‐‐‐‐‐ , supersonic pressure coefficient ‐‐‐‐‐‐‐‐ D total drag induced drag Copyright material - Taylor & Francis parasite drag e span efficiency factor ‐‐‐‐‐‐‐‐ L lift pitching moment · rolling moment · yawing moment · M mach number ‐‐‐‐‐‐‐‐ critical mach number ‐‐‐‐‐‐‐‐ free stream mach number ‐‐‐‐‐‐‐‐ P static pressure ⁄ total pressure ⁄ free stream pressure ⁄ q dynamic pressure ⁄ R -
Turbulence Momentum Transport and Prediction of the Reynolds Stress in Canonical Flows
Turbulence Momentum Transport and Prediction of the Reynolds Stress in Canonical Flows T.-W. Lee Mechanical and Aerospace Engineering, Arizona State University, Tempe, AZ, 85287 Abstract- We present a unique method for solving for the Reynolds stress in turbulent canonical flows, based on the momentum balance for a control volume moving at the local mean velocity. A differential transform converts this momentum balance to a solvable form. Comparisons with experimental and computational data in simple geometries show quite good agreements. An alternate picture for the turbulence momentum transport is offered, as verified with data, where the turbulence momentum is transported by the mean velocity while being dissipated by viscosity. The net momentum transport is the Reynolds stress. This turbulence momentum balance is verified using DNS and experimental data. T.-W. Lee Mechanical and Aerospace Engineering, SEMTE Arizona State University Tempe, AZ 85287-6106 Email: [email protected] 0 Nomenclature C1 = constants Re = Reynolds number based on friction velocity U = mean velocity in the x direction Ue = free-stream velocity V = mean velocity in the y direction u’ = fluctuation velocity in the x direction urms’ = root-mean square of u’ u’v’ = Reynolds stress = boundary layer thickness m = modified kinematic viscosity Introduction Finding the Reynolds stress has profound implications in fluid physics. Many practical flows are turbulent, and require some method of analysis or computations so that the flow process can be understood, predicted and controlled. This necessity led to several generations of turbulence models including a genre that models the Reynolds stress components themselves, the Reynolds stress models [1, 2]. -
Potential Flow Theory
2.016 Hydrodynamics Reading #4 2.016 Hydrodynamics Prof. A.H. Techet Potential Flow Theory “When a flow is both frictionless and irrotational, pleasant things happen.” –F.M. White, Fluid Mechanics 4th ed. We can treat external flows around bodies as invicid (i.e. frictionless) and irrotational (i.e. the fluid particles are not rotating). This is because the viscous effects are limited to a thin layer next to the body called the boundary layer. In graduate classes like 2.25, you’ll learn how to solve for the invicid flow and then correct this within the boundary layer by considering viscosity. For now, let’s just learn how to solve for the invicid flow. We can define a potential function,!(x, z,t) , as a continuous function that satisfies the basic laws of fluid mechanics: conservation of mass and momentum, assuming incompressible, inviscid and irrotational flow. There is a vector identity (prove it for yourself!) that states for any scalar, ", " # "$ = 0 By definition, for irrotational flow, r ! " #V = 0 Therefore ! r V = "# ! where ! = !(x, y, z,t) is the velocity potential function. Such that the components of velocity in Cartesian coordinates, as functions of space and time, are ! "! "! "! u = , v = and w = (4.1) dx dy dz version 1.0 updated 9/22/2005 -1- ©2005 A. Techet 2.016 Hydrodynamics Reading #4 Laplace Equation The velocity must still satisfy the conservation of mass equation. We can substitute in the relationship between potential and velocity and arrive at the Laplace Equation, which we will revisit in our discussion on linear waves. -
Introduction to Compressible Computational Fluid Dynamics James S
Introduction to Compressible Computational Fluid Dynamics James S. Sochacki Department of Mathematics James Madison University [email protected] Abstract This document is intended as an introduction to computational fluid dynamics at the upper undergraduate level. It is assumed that the student has had courses through three dimensional calculus and some computer programming experience with numer- ical algorithms. A course in differential equations is recommended. This document is intended to be used by undergraduate instructors and students to gain an under- standing of computational fluid dynamics. The document can be used in a classroom or research environment at the undergraduate level. The idea of this work is to have the students use the modules to discover properties of the equations and then relate this to the physics of fluid dynamics. Many issues, such as rarefactions and shocks are left out of the discussion because the intent is to have the students discover these concepts and then study them with the instructor. The document is used in part of the undergraduate MATH 365 - Computation Fluid Dynamics course at James Madi- son University (JMU) and is part of the joint NSF Grant between JMU and North Carolina Central University (NCCU): A Collaborative Computational Sciences Pro- gram. This document introduces the full three-dimensional Navier Stokes equations. As- sumptions to these equations are made to derive equations that are accessible to un- dergraduates with the above prerequisites. These equations are approximated using finite difference methods. The development of the equations and finite difference methods are contained in this document. Software modules and their corresponding documentation in Fortran 90, Maple and Matlab can be downloaded from the web- site: http://www.math.jmu.edu/~jim/compressible.html. -
INCOMPRESSIBLE FLOW AERODYNAMICS 3 0 0 3 Course Category: Programme Core A
COURSE CODE COURSE TITLE L T P C 1151AE107 INCOMPRESSIBLE FLOW AERODYNAMICS 3 0 0 3 Course Category: Programme core a. Preamble : The primary objective of this course is to teach students how to determine aerodynamic lift and drag over an airfoil and wing at incompressible flow regime by analytical methods. b. Prerequisite Courses: Fluid Mechanics c. Related Courses: Airplane Performance Compressible flow Aerodynamics Aero elasticity Flapping wing dynamics Industrial aerodynamics Transonic Aerodynamics d. Course Educational Objectives: To introduce the concepts of mass, momentum and energy conservation relating to aerodynamics. To make the student understand the concept of vorticity, irrotationality, theory of air foils and wing sections. To introduce the basics of viscous flow. e. Course Outcomes: Upon the successful completion of the course, students will be able to: Knowledge Level CO Course Outcomes (Based on revised Nos. Bloom’s Taxonomy) Apply the physical principles to formulate the governing CO1 K3 aerodynamics equations Find the solution for two dimensional incompressible inviscid CO2 K3 flows Apply conformal transformation to find the solution for flow over CO3 airfoils and also find the solutions using classical thin airfoil K3 theory Apply Prandtl’s lifting-line theory to find the aerodynamic CO4 K3 characteristics of finite wing Find the solution for incompressible flow over a flat plate using CO5 K3 viscous flow concepts f. Correlation of COs with POs: COs PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 CO1 H L H L M H H CO2 H L H L M H H CO3 H L H L M H H CO4 H L H L M H H CO5 H L H L M H H H- High; M-Medium; L-Low g. -
Shallow-Water Equations and Related Topics
CHAPTER 1 Shallow-Water Equations and Related Topics Didier Bresch UMR 5127 CNRS, LAMA, Universite´ de Savoie, 73376 Le Bourget-du-Lac, France Contents 1. Preface .................................................... 3 2. Introduction ................................................. 4 3. A friction shallow-water system ...................................... 5 3.1. Conservation of potential vorticity .................................. 5 3.2. The inviscid shallow-water equations ................................. 7 3.3. LERAY solutions ........................................... 11 3.3.1. A new mathematical entropy: The BD entropy ........................ 12 3.3.2. Weak solutions with drag terms ................................ 16 3.3.3. Forgetting drag terms – Stability ............................... 19 3.3.4. Bounded domains ....................................... 21 3.4. Strong solutions ............................................ 23 3.5. Other viscous terms in the literature ................................. 23 3.6. Low Froude number limits ...................................... 25 3.6.1. The quasi-geostrophic model ................................. 25 3.6.2. The lake equations ...................................... 34 3.7. An interesting open problem: Open sea boundary conditions .................... 41 3.8. Multi-level and multi-layers models ................................. 43 3.9. Friction shallow-water equations derivation ............................. 44 3.9.1. Formal derivation ....................................... 44 3.10. -
Lattice Boltzmann Modeling for Shallow Water Equations Using High
Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 2010 Lattice Boltzmann modeling for shallow water equations using high performance computing Kevin Tubbs Louisiana State University and Agricultural and Mechanical College, [email protected] Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Part of the Engineering Science and Materials Commons Recommended Citation Tubbs, Kevin, "Lattice Boltzmann modeling for shallow water equations using high performance computing" (2010). LSU Doctoral Dissertations. 34. https://digitalcommons.lsu.edu/gradschool_dissertations/34 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. LATTICE BOLTZMANN MODELING FOR SHALLOW WATER EQUATIONS USING HIGH PERFORMANCE COMPUTING A Dissertation Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy in The Interdepartmental Program in Engineering Science by Kevin Tubbs B.S. Physics , Southern University, 2001 M.S. Physics, Louisiana State University, 2004 May, 2010 To my family ii ACKNOWLEDGMENTS I want to acknowledge the love and support of my family and friends which was instrumental in completing my degree. I would like to especially thank my parents, John and Veronica Tubbs and my siblings Kanika Tubbs and Keosha Tubbs. I dedicate this dissertation in loving memory of my brother Kendrick Tubbs and my grandmother Gertrude Nicholas. I would also like to thank Dr. -
1 David Apsley 3. APPROXIMATIONS and SIMPLIFIED EQUATIONS
3. APPROXIMATIONS AND SIMPLIFIED EQUATIONS SPRING 2021 3.1 Steady-state vs time-dependent flow 3.2 Two-dimensional vs three-dimensional flow 3.3 Incompressible vs compressible flow 3.4 Inviscid vs viscous flow 3.5 Hydrostatic vs non-hydrostatic flow 3.6 Boussinesq approximation for density 3.7 Depth-averaged (shallow-water) equations 3.8 Reynolds-averaged equations (turbulent flow) Examples Fluid dynamics is governed by equations for mass, momentum and energy. The momentum equation for a viscous fluid is called the Navier-Stokes equation; it is based upon: • continuum mechanics; • the momentum principle; • shear stress proportional to velocity gradient. A fluid for which the last is true is called a Newtonian fluid. This is the case for most fluids in engineering. However, there are important non-Newtonian fluids; e.g. mud, cement, blood, paint, polymer solutions. CFD is very useful for these, as their governing equations are usually impossible to solve analytically. The full equations are time-dependent, 3-dimensional, viscous, compressible, non-linear and highly coupled. However, in most cases it is possible to simplify analysis by adopting a reduced equation set. Some common approximations are listed below. Reduction of dimension: • steady-state; • two-dimensional. Neglect of some fluid property: • incompressible; • inviscid. Simplified forces: • hydrostatic; • Boussinesq approximation for density. Approximations based upon averaging: • depth-averaging (shallow-water equations); • Reynolds-averaging (turbulent flows). The consequences of these approximations are examined in the following sections. CFD 3 – 1 David Apsley 3.1 Steady-State vs Time-Dependent Flow Many flows are naturally time-dependent. Examples include waves, tides, turbines, reciprocating pumps and internal combustion engines. -
A Mixing-Length Formulation for the Turbulent Prandtl Number in Wall-Bounded flows with Bed Roughness and Elevated Scalar Sources ͒ John P
PHYSICS OF FLUIDS 18, 095102 ͑2006͒ A mixing-length formulation for the turbulent Prandtl number in wall-bounded flows with bed roughness and elevated scalar sources ͒ John P. Crimaldia Department of Civil, Environmental, and Architectural Engineering, University of Colorado, Boulder, Colorado 80309-0428 Jeffrey R. Koseff and Stephen G. Monismith Department of Civil and Environmental Engineering, Stanford University, Stanford, California 94305-4020 ͑Received 7 November 2005; accepted 20 June 2006; published online 5 September 2006͒ Turbulent Prandtl number distributions are measured in a laboratory boundary layer flow with bed roughness, active blowing and sucking, and scalar injection near the bed. The distributions are significantly larger than unity, even at large distances from the wall, in apparent conflict with the Reynolds analogy. An analytical model is developed for the turbulent Prandtl number, formulated as the ratio of momentum and scalar mixing length distributions. The model is successful at predicting the measured turbulent Prandtl number behavior. Large deviations from unity are shown in this case to be consistent with measurable differences in the origins of the momentum and scalar mixing length distributions. Furthermore, these deviations are shown to be consistent with the Reynolds analogy when the definition of the turbulent Prandtl number is modified to include the effect of separate mixing length origin locations. The results indicate that the turbulent Prandtl number for flows over complex boundaries can be modeled based on simple knowledge of the geometric and kinematic nature of the momentum and scalar boundary conditions. © 2006 American Institute of Physics. ͓DOI: 10.1063/1.2227005͔ I. INTRODUCTION For flows over rough surfaces, the nature of the roughness 9 has been shown to only slightly alter Prt. -
7. Turbulence Turbulent Jet
7. Turbulence Turbulent Jet Instantaneous Mean Turbulent Boundary Layer y U(y) Instantaneous Mean What is Turbulence? • A “random”, 3-d, time-dependent eddying motion with many scales, superposed on an often drastically simpler mean flow • A solution of the Navier-Stokes equations • The natural state at high Reynolds numbers • An efficient transporter and mixer • A major source of energy loss • A significant influence on drag: ‒ increases frictional drag in non-separated flow ‒ delays, or sometimes prevents, boundary-layer separation on curved surfaces, so reducing pressure drag. ● “The last great unsolved problem of classical physics” Momentum Transfer in Laminar and Turbulent Flow • Laminar: ‒ smooth ‒ no mixing of fluid ‒ momentum transfer by viscous forces • Turbulent: ‒ chaotic ‒ mixing of fluid ‒ momentum transfer mainly by the net effect of intermingling ρ푈퐿 푈퐿 Regime determined by Reynolds number: Re = = μ ν “Low” Re laminar; “high” Re turbulent “High” or “low” depends on the flow and the choice of 푈 and 퐿 Reynolds’ Decomposition v u + mean fluctuation 푢 = 푢ത + 푢′ 푣 = 푣ҧ + 푣′ 푝 = 푝ҧ + 푝′ Alternative Notations • Overbar for mean, prime for fluctuation: 푢 + 푢′ −ρ푢′푣′ • Upper case for mean, lower case for fluctuation: 푈 + 푢 −ρ푢푣 Reynolds Averaging of a Product 푢 = 푢 + 푢′ 푢′ = 0 cf statistics: σ 푢2 2 2 ′2 σ2 = 푢′2 = − 푢ത2 푢 = 푢 + 푢 Variance 푁 σ 푢푣 푢푣 = 푢 푣 + 푢′푣′ ρ ≡ 푢′푣′ = − 푢ത푣ҧ Covariance 푁 푢푣 = (푢 + 푢′)(푣 + 푣′) = 푢 푣 + 푢푣′ + 푢′푣 + 푢′푣′ 푢푣 = 푢 푣 + 푢푣ഥ′ + 푢ഥ′푣 + 푢′푣′ 푢푣 = 푢 푣 + 푢′푣′ Effect of Turbulence on the Mean Flow (i) Mass vA Mass flux: ρ푣퐴 Average mass flux: ρ푣퐴 The mean velocity satisfies the same continuity equation as the instantaneous velocity Effect of Turbulence on the Mean Flow (ii) Momentum vA (푥-)momentum flux: (ρ푣퐴)푢 = ρ(푢푣)퐴 u Average momentum flux : (ρ푣퐴)푢 = ρ(푢 푣 + 푢′푣′)퐴 extra term Net rate of transport of momentum per unit area ρ푢′푣′ from LOWER to UPPER .. -
Intrinsic Plasma Rotation and Reynolds Stress at the Plasma
Measurements of Reynolds Stress and its Contribution to the Momentum Balance in the HSX Stellarator by Robert S. Wilcox A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Electrical Engineering) at the University of Wisconsin–Madison 2014 Date of final oral examination: 12/2/2014 The dissertation is approved by the following members of the Final Oral Committee: David T. Anderson, Professor, Electrical Engineering Amy E. Wendt, Professor, Electrical Engineering Chris C. Hegna, Professor, Engineering Physics Paul W. Terry, Professor, Physics Joseph N. Talmadge, Associate Scientist, Electrical Engineering c Copyright by Robert S. Wilcox 2014 All Rights Reserved i ABSTRACT In a magnetic configuration that has been sufficiently optimized for quasi-symmetry, the neoclas- sical transport and viscosity can be small enough that other terms can compete in the momentum balance to determine the plasma rotation and radial electric field. The Reynolds stress generated by plasma turbulence is identified as the most likely candidate for non-neoclassical flow drive in the HSX stellarator. Using multi-tipped Langmuir probes in the edge of HSX in the quasi-helically symmetric (QHS) configuration, the radial electric field and parallel flows are found to deviate from the values calculated by the neoclassical transport code PENTA using the ambipolarity con- straint in the absence of externally injected momentum. The local Reynolds stress in the parallel and perpendicular directions on a surface is also measured using the fluctuating components of floating potential and ion saturation current measurements. Although plasma turbulence enters the momentum balance as the flux surface averaged radial gradient of the Reynolds stress, the locally measured quantity implies a significant contribution to the momentum balance.