Module 6 : Lecture 1 DIMENSIONAL ANALYSIS (Part – I) Overview
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Turbulent Boundary Layers 7 - 1 David Apsley 7.2.3 Radiation
7. HEAT TRANSFER SPRING 2009 7.1 Definitions 7.2 Modes of heat transfer 7.3 The Prandtl number 7.4 Dimensionless numbers in free and forced convection 7.5 The energy equation 7.6 Laminar boundary layers with isothermal walls 7.7 Temperature profile in a turbulent boundary layer 7.8 Heat-transfer coefficients 7.9 Temperature integral results 7.10 Engineering heat-transfer calculations 7.11 References Examples 7.1 Definitions Heat transfer is energy in transit due to a temperature difference. The heat flux qh is the rate of heat transfer per unit area. 7.2 Modes of Heat Transfer 7.2.1 Conduction Heat transfer due to molecular activity in the absence of a bulk motion. Fourier ’s Law = − ∇ qh kh T (1) kh is the conductivity of heat . –1 –1 –1 –1 At 20 ºC, kh(water) = 0.604 W m K ; kh(air) = 0.0257 W m K . 7.2.2 Advection Heat transfer due to bulk motion of fluid. = − qh c p (T Te )u (2) cp is the specific heat capacity (at constant pressure). Te is a reference (usually the external or free-stream) temperature. –1 –1 –1 –1 At 20 ºC, cp(water) = 4182 J kg K ; cp(air) = 1007 J kg K . Turbulent Boundary Layers 7 - 1 David Apsley 7.2.3 Radiation Heat transfer by emission of electromagnetic radiation. Stefan-Boltzmann Law = 4 qh Ts (3) (= 5.670 ×10 -8 W m–2 K–4) is the Stefan-Boltzmann constant . is the emissivity ( = 1 for a perfect black body) 7.2.4 Free and Forced Convection For fluids, conduction and advection are usually combined as convection , which may be either: forced convection – flow driven by external means; free (or natural ) convection – flow driven by buoyancy. -
Chapter 5 Dimensional Analysis and Similarity
Chapter 5 Dimensional Analysis and Similarity Motivation. In this chapter we discuss the planning, presentation, and interpretation of experimental data. We shall try to convince you that such data are best presented in dimensionless form. Experiments which might result in tables of output, or even mul- tiple volumes of tables, might be reduced to a single set of curves—or even a single curve—when suitably nondimensionalized. The technique for doing this is dimensional analysis. Chapter 3 presented gross control-volume balances of mass, momentum, and en- ergy which led to estimates of global parameters: mass flow, force, torque, total heat transfer. Chapter 4 presented infinitesimal balances which led to the basic partial dif- ferential equations of fluid flow and some particular solutions. These two chapters cov- ered analytical techniques, which are limited to fairly simple geometries and well- defined boundary conditions. Probably one-third of fluid-flow problems can be attacked in this analytical or theoretical manner. The other two-thirds of all fluid problems are too complex, both geometrically and physically, to be solved analytically. They must be tested by experiment. Their behav- ior is reported as experimental data. Such data are much more useful if they are ex- pressed in compact, economic form. Graphs are especially useful, since tabulated data cannot be absorbed, nor can the trends and rates of change be observed, by most en- gineering eyes. These are the motivations for dimensional analysis. The technique is traditional in fluid mechanics and is useful in all engineering and physical sciences, with notable uses also seen in the biological and social sciences. -
Model and Prototype Unit E-1: List of Subjects
ES206 Fluid Mechanics UNIT E: Model and Prototype ROAD MAP . E-1: Dimensional Analysis E-2: Modeling and Similitude ES206 Fluid Mechanics Unit E-1: List of Subjects Dimensional Analysis Buckingham Pi Theorem Common Dimensionless Group Experimental Data Page 1 of 9 Unit E-1 Dimensional Analysis (1) p = f (D, , ,V ) SLIDE 1 UNIT F-1 DimensionalDimensional AnalysisAnalysis (1)(1) ➢ To illustrate a typical fluid mechanics problem in which experimentation is required, consider the steady flow of an incompressible Newtonian fluid through a long, smooth-walled, horizontal, circular pipe: ➢ The pressure drop per unit length ( p ) is a function of: ➢ Pipe diameter (D ) SLIDE 2➢ Fluid density ( ) UNIT F-1 ➢ Fluid viscousity ( ) ➢ Mean velocity ( V ) p = f (D, , ,V ) DimensionalDimensional AnalysisAnalysis (2)(2) ➢ To perform experiments, it would be necessaryTextbook (Munson, Young, to and change Okiishi), page 403 one of the variables while holding others constant ??? How can we combine these data to obtain the desired general functional relationship between pressure drop and variables ? Textbook (Munson, Young, and Okiishi), page 347 Page 2 of 9 Unit E-1 Dimensional Analysis (2) Dp VD = 2 V SLIDE 3 UNIT F-1 DimensionalDimensional AnalysisAnalysis (3)(3) ➢ Fortunately, there is a much simpler approach to this problem that will eliminate the difficulties ➢ It is possible to collect variables and combine into two non-dimensional variables (dimensionless products or groups): Dp VD = 2 V SLIDE 4 UNIT F-1 Pressure Reynolds -
Summary of Dimensionless Numbers of Fluid Mechanics and Heat Transfer 1. Nusselt Number Average Nusselt Number: Nul = Convective
Jingwei Zhu http://jingweizhu.weebly.com/course-note.html Summary of Dimensionless Numbers of Fluid Mechanics and Heat Transfer 1. Nusselt number Average Nusselt number: convective heat transfer ℎ퐿 Nu = = L conductive heat transfer 푘 where L is the characteristic length, k is the thermal conductivity of the fluid, h is the convective heat transfer coefficient of the fluid. Selection of the characteristic length should be in the direction of growth (or thickness) of the boundary layer; some examples of characteristic length are: the outer diameter of a cylinder in (external) cross flow (perpendicular to the cylinder axis), the length of a vertical plate undergoing natural convection, or the diameter of a sphere. For complex shapes, the length may be defined as the volume of the fluid body divided by the surface area. The thermal conductivity of the fluid is typically (but not always) evaluated at the film temperature, which for engineering purposes may be calculated as the mean-average of the bulk fluid temperature T∞ and wall surface temperature Tw. Local Nusselt number: hxx Nu = x k The length x is defined to be the distance from the surface boundary to the local point of interest. 2. Prandtl number The Prandtl number Pr is a dimensionless number, named after the German physicist Ludwig Prandtl, defined as the ratio of momentum diffusivity (kinematic viscosity) to thermal diffusivity. That is, the Prandtl number is given as: viscous diffusion rate ν Cpμ Pr = = = thermal diffusion rate α k where: ν: kinematic viscosity, ν = μ/ρ, (SI units : m²/s) k α: thermal diffusivity, α = , (SI units : m²/s) ρCp μ: dynamic viscosity, (SI units : Pa ∗ s = N ∗ s/m²) W k: thermal conductivity, (SI units : ) m∗K J C : specific heat, (SI units : ) p kg∗K ρ: density, (SI units : kg/m³). -
On Dimensionless Numbers
chemical engineering research and design 8 6 (2008) 835–868 Contents lists available at ScienceDirect Chemical Engineering Research and Design journal homepage: www.elsevier.com/locate/cherd Review On dimensionless numbers M.C. Ruzicka ∗ Department of Multiphase Reactors, Institute of Chemical Process Fundamentals, Czech Academy of Sciences, Rozvojova 135, 16502 Prague, Czech Republic This contribution is dedicated to Kamil Admiral´ Wichterle, a professor of chemical engineering, who admitted to feel a bit lost in the jungle of the dimensionless numbers, in our seminar at “Za Plıhalovic´ ohradou” abstract The goal is to provide a little review on dimensionless numbers, commonly encountered in chemical engineering. Both their sources are considered: dimensional analysis and scaling of governing equations with boundary con- ditions. The numbers produced by scaling of equation are presented for transport of momentum, heat and mass. Momentum transport is considered in both single-phase and multi-phase flows. The numbers obtained are assigned the physical meaning, and their mutual relations are highlighted. Certain drawbacks of building correlations based on dimensionless numbers are pointed out. © 2008 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. Keywords: Dimensionless numbers; Dimensional analysis; Scaling of equations; Scaling of boundary conditions; Single-phase flow; Multi-phase flow; Correlations Contents 1. Introduction ................................................................................................................. -
Dimensional Analysis and Modeling
cen72367_ch07.qxd 10/29/04 2:27 PM Page 269 CHAPTER DIMENSIONAL ANALYSIS 7 AND MODELING n this chapter, we first review the concepts of dimensions and units. We then review the fundamental principle of dimensional homogeneity, and OBJECTIVES Ishow how it is applied to equations in order to nondimensionalize them When you finish reading this chapter, you and to identify dimensionless groups. We discuss the concept of similarity should be able to between a model and a prototype. We also describe a powerful tool for engi- ■ Develop a better understanding neers and scientists called dimensional analysis, in which the combination of dimensions, units, and of dimensional variables, nondimensional variables, and dimensional con- dimensional homogeneity of equations stants into nondimensional parameters reduces the number of necessary ■ Understand the numerous independent parameters in a problem. We present a step-by-step method for benefits of dimensional analysis obtaining these nondimensional parameters, called the method of repeating ■ Know how to use the method of variables, which is based solely on the dimensions of the variables and con- repeating variables to identify stants. Finally, we apply this technique to several practical problems to illus- nondimensional parameters trate both its utility and its limitations. ■ Understand the concept of dynamic similarity and how to apply it to experimental modeling 269 cen72367_ch07.qxd 10/29/04 2:27 PM Page 270 270 FLUID MECHANICS Length 7–1 ■ DIMENSIONS AND UNITS 3.2 cm A dimension is a measure of a physical quantity (without numerical val- ues), while a unit is a way to assign a number to that dimension. -
Chapter 1 Governing Equations of Fluid Flow and Heat Transfer
ME 582 Finite Element Analysis in Thermofluids Dr. Cüneyt Sert Chapter 1 Governing Equations of Fluid Flow and Heat Transfer Following fundamental laws can be used to derive governing differential equations that are solved in a Computational Fluid Dynamics (CFD) study [1] conservation of mass conservation of linear momentum (Newton's second law) conservation of energy (First law of thermodynamics) In this course we’ll consider the motion of single phase fluids, i.e. either liquid or gas, and we'll treat them as continuum. The three primary unknowns that can be obtained by solving these equations are (actually there are five scalar unknowns if we count the three velocity components separately) velocity vector ⃗ pressure temperature But in the governing equations that we solve numerically following four additional variables appear density enthalpy (or internal energy ) viscosity thermal conductivity Pressure and temperature can be treated as two independent thermodynamic variables that define the equilibrium state of the fluid. Four additional variables listed above are determined in terms of pressure and temperature using tables, charts or additional equations. However, for many problems it is possible to consider , and to be constants and to be proportional to with the proportionally constant being the specific heat . Due to different mathematical characters of governing equations for compressible and incompressible flows, CFD codes are usually written for only one of them. It is not common to find a code that can effectively and accurately work in both compressible and incompressible flow regimes. In the following two sections we'll provide differential forms of the governing equations used to study compressible and incompressible flows. -
V. MODELING, SIMILARITY, and DIMENSIONAL ANALYSIS to This
V. MODELING, SIMILARITY, AND DIMENSIONAL ANALYSIS To this point, we have concentrated on analytical methods of solution for fluids problems. However, analytical methods are not always satisfactory due to: (1) limitations due to simplifications required in the analysis, (2) complexity and/or expense of a detailed analysis. The most common alternative is to: Use experimental test & verification procedures. However, without planning and organization, experimental procedures can : (a) be time consuming, (b) lack direction, (c) be expensive. This is particularly true when the test program necessitates testing at one set of conditions, geometry, and fluid with the objective to represent a different but similar set of conditions, geometry, and fluid. Dimensional analysis provides a procedure that will typically reduce both the time and expense of experimental work necessary to experimentally represent a desired set of conditions and geometry. It also provides a means of "normalizing" the final results for a range of test conditions. A normalized (non-dimensional) set of results for one test condition can be used to predict the performance at different but dynamically similar conditions (including even a different fluid). The basic procedure for dimensional analysis can be summarized as follows: 1. Compile a list of relevant variables (dependent & independent) for the problem being considered, 2. Use an appropriate procedure to identify both the number and form of the resulting non-dimensional parameters. V-1 Buckingham Pi Theorem The procedure most commonly used to identify both the number and form of the appropriate non-dimensional parameters is referred to as the Buckingham Pi Theorem. The theorem uses the following definitions: n = the number of independent variables relevant to the problem j’ = the number of independent dimensions found in the n variables j = the reduction possible in the number of variables necessary to be considered simultaneously k = the number of independent Π terms that can be identified to describe the problem, k = n - j Summary of Steps: 1. -
MHD Casson Fluid Flow Over a Stretching Sheet with Entropy Generation Analysis and Hall Influence
entropy Article MHD Casson Fluid Flow over a Stretching Sheet with Entropy Generation Analysis and Hall Influence Mohamed Abd El-Aziz 1 and Ahmed A. Afify 2,* 1 Department of Mathematics, Faculty of Science, King Khalid University, Abha 9004, Saudi Arabia; [email protected] 2 Department of Mathematics, Deanship of Educational Services, Qassim University, P.O. Box 6595, Buraidah 51452, Saudi Arabia * Correspondence: afi[email protected] Received: 3 May 2019; Accepted: 12 June 2019; Published: 14 June 2019 Abstract: The impacts of entropy generation and Hall current on MHD Casson fluid over a stretching surface with velocity slip factor have been numerically analyzed. Numerical work for the governing equations is established by using a shooting method with a fourth-order Runge–Kutta integration scheme. The outcomes show that the entropy generation is enhanced with a magnetic parameter, Reynolds number and group parameter. Further, the reverse behavior is observed with the Hall parameter, Eckert number, Casson parameter and slip factor. Also, it is viewed that Bejan number reduces with a group parameter. Keywords: MHD Casson fluid; slip factor; Hall current; entropy generation; Bejan number 1. Introduction The study of magnetohydrodynamic flows with Hall currents has evinced interest attributable to its numerous applications in industries, such as MHD power generators, Hall current accelerators, Hall current sensors, and planetary fluid dynamics. Sato [1] was the first author who investigated the impact of Hall current on the flow of ionized gas between two parallel plates. The influence of Hall current on the efficiency of an MHD generator was investigated by Sherman and Sutton [2]. -
The Dimnum Package
The dimnum package Miguel R. Clemente [email protected] v1.0.1 from 2021/04/05 Note: Prandtl number is redefined from the amsmath package. 1 Introduction This package simplifies the calling of Dimensionless Numbers in math or text mode. In Table 1 you can find all available Dimensionless Numbers. 2 Usage A Dimensionless number is composed of four items: the command, the symbol, the name, its identifier. You can call a Dimensionless Number in three distinct ways: by its symbol { using the command (i.e. \Ar { Ar). by its name (short version) { appending [s] to the command (i.e. \Bi[s] { Biot). by its name and identifier (long version) { appending [l] to the command (i.e. \Kn[l] { Knudsen number). Symbol, short and long versions, all work in math or text mode without the need of further commands. 1 Besides the comprehensive list of included Dimensionless Numbers, this pack- age also introduces a command to create new Dimensionless Numbers. Creating a Dimensionless Number is achieved by using \newdimnum{\command}{symbol}{name}{identifier} for example, to add the Morton number we write \newdimnum{\Mo}{Mo}{Morton}{number} The identifier can be left empty, such as in the case of Drag Coefficient \newdimnum{Cd}{\ensuremath{C_d}}{Drag Coefficient}{} in this example we also introduce an important command. When the Dimension- less Number symbol is always expressed in math mode { either by definition or the use of subscripts or superscripts { we add \ensuremath{} to encompass the symbol, ensuring a proper representation of the Dimensionless Number. You can add your own Dimensionless Numbers to your projects. -
SASO-ISO-80000-11-2020-E.Pdf
SASO ISO 80000-11:2020 ISO 80000-11:2019 Quantities and units - Part 11: Characteristic numbers ICS 01.060 Saudi Standards, Metrology and Quality Org (SASO) ----------------------------------------------------------------------------------------------------------- this document is a draft saudi standard circulated for comment. it is, therefore subject to change and may not be referred to as a saudi standard until approved by the boardDRAFT of directors. Foreword The Saudi Standards ,Metrology and Quality Organization (SASO)has adopted the International standard No. ISO 80000-11:2019 “Quantities and units — Part 11: Characteristic numbers” issued by (ISO). The text of this international standard has been translated into Arabic so as to be approved as a Saudi standard. DRAFT DRAFT SAUDI STANDADR SASO ISO 80000-11: 2020 Introduction Characteristic numbers are physical quantities of unit one, although commonly and erroneously called “dimensionless” quantities. They are used in the studies of natural and technical processes, and (can) present information about the behaviour of the process, or reveal similarities between different processes. Characteristic numbers often are described as ratios of forces in equilibrium; in some cases, however, they are ratios of energy or work, although noted as forces in the literature; sometimes they are the ratio of characteristic times. Characteristic numbers can be defined by the same equation but carry different names if they are concerned with different kinds of processes. Characteristic numbers can be expressed as products or fractions of other characteristic numbers if these are valid for the same kind of process. So, the clauses in this document are arranged according to some groups of processes. As the amount of characteristic numbers is tremendous, and their use in technology and science is not uniform, only a small amount of them is given in this document, where their inclusion depends on their common use. -
Effect of Fluid Suction on an Oscillatory MHD Channel Flow with Heat Transfer
Available at Applications and http://pvamu.edu/aam Applied Mathematics: Appl. Appl. Math. An International Journal ISSN: 1932-9466 (AAM) Vol. 11, Issue 1 (June 2016), pp. 266 - 284 ________________________________________________________________________________________ Effect of Fluid Suction on an Oscillatory MHD Channel Flow with Heat Transfer N. Ahmed1, A. H. Sheikh2 and D. P. Barua3 Department of Mathematics Gauhati University Guwahati-781014 Assam, India [email protected]; [email protected];[email protected] Received: May 3, 2015; Accepted: March 1, 2016 Abstract Magnetohydrodynamics (MHD) is generally concerned with the study of the magnetic properties (behaviour) of electrically conducting fluids (plasmas, liquid metals etc.) moving in an electromagnetic field. The importance of the concept of MHD in various fields such as astrophysics, bio-medical research, missile technology and geophysics motivates the modelling and investigation of MHD flow and transport problems. The role of fluid suction is paramount in laminar flow control and has wide applications in fields such as aeronautical engineering, automobile engineering and rocket science. This fact inspires the study of the effects of fluid suction in flow and transport models. Time dependent flows are widely encountered in engineering applications such as turbines and in physiological studies such as flow of bio-fluid (blood etc.). In the present paper, an attempt has been made to investigate analytically the problem of a time dependent channel flow with heat transfer, where the channel is bounded by two infinite parallel porous walls. The pressure gradient is assumed to be oscillatory in nature. A magnetic field of uniform strength is assumed to be applied normal to the walls.