ME 305 Part 7 Similitude and Dimensional Analysis.Pdf

Total Page:16

File Type:pdf, Size:1020Kb

ME 305 Part 7 Similitude and Dimensional Analysis.Pdf Dimensional Analysis ME 305 Fluid Mechanics I • Consider that we are interested in determining how the drag force acting on a smooth sphere immersed in a uniform flow depends on other fluid and flow variables. Part 7 • Important varibles of the problem are shown below (How did we decide on these?). 푉 Dimensional Analysis and Similitude 휇 , 퐹퐷 퐷 These presentations are prepared by Dr. Cüneyt Sert Department of Mechanical Engineering • Drag force 퐹퐷 is thought to depend on the following variables. Middle East Technical University 퐹퐷 = 푓(퐷, 푉, 휇, ) Ankara, Turkey [email protected] • In order to find the actual functional relation we need to perform a set of experiments. You can get the most recent version of this document from Dr. Sert’s web site. • Dimensional analysis helps us to design and perform these experiments in a Please ask for permission before using them to teach. You are NOT allowed to modify them. systematic way. 7-1 7-2 Dimensional Analysis (cont’d) Dimensional Analysis (cont’d) • The following set of controlled experiments should be done. • It is possible to simplify the dependency of drag force on other variables by using nondimensional (unitless) parameters. • Fix 퐷, 휇 and . Change 푉 and measure 퐹퐷. 퐹퐷 푉퐷 • Fix 푉, 휇 and . Change 퐷 and measure 퐹 . = 푓 퐷 푉2퐷2 1 휇 • Fix 퐷, 푉 and 휇 . Change and measure 퐹퐷. Note : These are only illustrative Nondimensional drag figures. They do not correspond to Nondimensional • Fix 퐷, 푉 and . Change 휇 and measure 퐹퐷. any actual experimentation. force (Drag coefficient) Reynolds number (푅푒) 퐹퐷 Constant 퐹퐷 Constant 퐹퐷 Constant 퐹퐷 Constant 퐷, 휇, 푉, 휇, 퐷, 푉, 휇 퐷, 푉, 퐹퐷 푉2퐷2 Illustrative figure 푉 퐷 휇 • We need to perform too many experiments. • Also there are major difficulties such as finding fluids with different densities, but 푅푒 same viscosities. Flow over a sphere at 푅푒 = 15000 7-3 7-4 1 Dimensional Analysis (cont’d) Buckingham Pi Theorem • To find this new relation, we only need to change the Reynolds number. • Buckingham Pi theorem can be used to determine the nondimensional groups of variables (Pi groups) for a given set of dimensional variables. • We can do it in any way we want, e.g. the simplest way is to change the speed of air flow in a wind tunnel. • For the flow over a sphere problem studied previously, dimensional parameter set is (퐹 , 퐷, 푉, 휇, ) and this theorem helps us to find two Pi groups as • All 푅푒 = 15000 flows around a sphere will look like the same and they all provide 퐷 the same nondimensional drag force. It does not matter what fluid we use or how 퐹 푉퐷 Π = 퐷 and Π = big the sphere is (be aware of very extreme cases). 1 푉2퐷2 2 휇 • Dimensional analysis is used to formulate a physical phenomenon as a relation • Let’s explain how this works using “the drag force acting on a sphere” problem. between a set of nondimensional (unitless) groups of variables such that the number of these groups is less than the number of dimensional variables. • Step 1 : List all the dimensional variables involved in the problem. • 푛 푛 = 5 • It is important to develop a systematic and meaningful way to perform experiments. is the number of dimensional variables. for our example. • These variables should be independent of each other. For example if the diameter of • Nature of the experiments are simplified and the number of required experiments is a sphere is in the list, frontal area of the sphere can not be included. reduced. • If body forces are important in a problem, gravitational acceleration should be in the list, although it is a constant. 7-5 7-6 Buckingham Pi Theorem (cont’d) Buckingham Pi Theorem (cont’d) • Step 2 : Express each of the variables in tems of basic dimensions, which are • Step 3: Determine the repeating variables that are allowed to appear in more than one Pi group. 퐿 : length , 푇 : time , 푀 : mass • There should be 푟 many repeating variables. • For problems involving heat transfer Θ (temperature) can also be a basic dimension. • If 퐿 is a primary dimension of the problem, we should select one geometric variable • For the example we are studying basic dimensions of variables are as a repeating variable. 푀퐿 퐿 푀 푀 [퐹 ] = , 퐷 = 퐿 , [푉] = , [] = , [휇] = 퐷 푇2 푇 퐿3 퐿푇 • If 푇 is a primary dimension of the problem, we should select one kinematic variable as a repeating variable. • Our example involves 푟 = 3 primary dimensions. For most fluid mechanics problems • If 푀 is a primary dimension of the problem, we should select one dynamic variable 푟 will be 3. as a repeating variable. • Variables having only 퐿 in their dimension are called geometric variables. • Note that this selection is not unique and the resulting Pi groups will depend on our selection. Certain selections are ‘‘better’’ than others. • Variables having only 푇 or both 퐿 and 푇 are called kinematic variables. • For the problem of interest we can select 퐷 , 푉 and as repeating variables. • Variables having 푀 in their dimension are called dynamic variables. • If there is an obvious dependent variable in the problem, do not select it as a • For our example 퐷 is a geometric, 푉 is a kinematic and 퐹 , 휇 and are dynamic 퐷 repeating variable. In our example 퐹 is a dependent variable. We are trying to variables. 퐷 understand how it depends on other variables. 7-7 7-8 2 Buckingham Pi Theorem (cont’d) Buckingham Pi Theorem (cont’d) • Step 4: Determine (푛 − 푟) many Pi groups by combining repeating variables with • Now determine the second Pi group which has 휇 as the nonrepeating variable. nonrepeating variables and using the fact that Pi groups should be nondimensional. 푎 푏 푐 Π2 = 휇 퐷 푉 • For our example we need to find 5 − 3 = 2 Pi groups. Each Pi group will include only 푀 퐿 푀 푐 one of the nonrepating variables. • Π should be unitless : − = [퐿]푎 [ ]푏 2 퐿푇 푇 퐿3 푎 푏 푐 Π1 = 퐹퐷 퐷 푉 We need to determine Π2 should have no 퐿 dimension : 0 = −1 + 푎 + 푏 − 3푐 푎, 푏 and 푐. 푎 = −1 휇 A nonrepeating Π should have no 푇 dimension : 0 = −1 − 푏 푏 = −1 Π = Unknown combination 2 2 퐷푉 parameter 푐 = −1 of repeating parameters Π2 should have no 푀 dimension : 0 = 1 + 푐 푀퐿 퐿 푀 푐 • Π should be unitless : − = [퐿]푎 [ ]푏 • Therefore the relation of nondimensional groups that we are after is 1 푇2 푇 퐿3 퐹 휇 Π = 푓 Π → 퐷 = 푓 Π should have no 퐿 dimension : 0 = 1 + 푎 + 푏 − 3푐 1 1 2 푉2퐷2 1 푉퐷 1 푎 = −2 퐹퐷 Π should have no 푇 dimension : 0 = −2 − 푏 푏 = −2 Π = 푉퐷 1 1 퐷2푉2 • It is better to write the second Pi group as because it is the well known 푐 = −1 휇 Π1 should have no 푀 dimension : 0 = 1 + 푐 Reynolds number. 7-9 7-10 Exercises for Buckingham Pi Theorem Important Nondimensional Numbers of Fluid Mechanics Exercise : Consider the flow of an incompressible fluid through a long, smooth- • Following nondimensional numbers frequently appear as a Pi group. walled horizontal, circular pipe. We are interested in analyzing the pressure drop, 푉퐿 푉퐿 • Reynolds number : 푅푒 = = . Ratio of inertia forces to viscous forces. ∆푝, over a pipe length of 퐿. Other variables of the problem are pipe diameter (퐷), 휇 휈 average velocity (푉) and fluid properties ( and 휇). Determine the Pi groups by a) Δ푝 • Euler number : 퐸푢 = 1 . Ratio of pressure forces to inertia forces. 푉2 selecting as a repeating parameter, b) selecting 휇 as a repeating parameter. 2 푉 • Froude number : 퐹푟 = . Squareroot of the ratio of inertia forces to gravitational Exercise : In a laboratory experiment a tank is drained through an orifice from initial 푔퐿 liquid level ℎ0. The time, 휏, to drain the tank depends on tank diameter, 퐷, orifice forces. diameter, 푑, gravitational acceleration, 푔, liquid properties, and 휇. Determine the 푉 푉 • Mach number : 푀푎 = = . Squareroot of the ratio of inertia forces to Pi groups. 퐸푣/ 푐 compressibility forces. Exercise : The diameter, 푑, of the dots made by an ink jet printer depends on the ink 푉2퐿 휇 퐷 퐿 • Weber number : 푊푒 = . Squareroot of the ratio of inertia forces to surface properties, and , surface tension, , nozzle diameter, , the distance, , of the nozzle from the paper and the ink jet velocity, 푉. Determine the Pi groups. tension forces. 휔퐿 • Strouhal number : 푆푡 = . Used for flows with oscillatory (periodic) behavior. Exercise : The power, 풫, required to drive a propeller is known to depend on the 푉 following variables: freestream speed, 푉, propeller diameter, 퐷, angular speed, 휔, 푝−푝푣 • Cavitation number : 퐶푎 = 1 . Used for possibly cavitating flows. 휇 푐 푉2 fluid properties, and , and the speed of sound . Determine the Pi groups. 2 7-11 7-12 3 Model and Prototype Three Basic Laws of Similitude • In experimental fluid mechanics we sometimes can not work with real sized objects, • A similitude analysis is done to make sure that the results obtained from an known as prototypes. experiment can correctly be transferred to the real flow field. • Instead we use scaled down (or up) versions of them, called models. • Three basic laws of similitude must be satisfied in order to achieve complete • Also sometimes in experiments we use fluids that are different than actual working similarity between prototype and model flow fields. fluids, e.g. we use regular tap water instead of salty sea water to test the 1.
Recommended publications
  • 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.
    [Show full text]
  • RHEOLOGY #2: Anelasicity
    RHEOLOGY #2: Anelas2city (aenuaon and modulus dispersion) of rocks, an organic, and maybe some ice Chris2ne McCarthy Lamont-Doherty Earth Observatory …but first, cheese Team Havar2 Team Gouda Team Jack Stress and Strain Stress σ(MPa)=F(N)/A(m2) 1 kg = 9.8N 1 Pa= N/m2 or kg/(m s2) Strain ε = Δl/l0 = (l0-l)/l0 l0 Cheese results vs. idealized curve. Not that far off! Cheese results σ n ⎛ −E + PV ⎞ ε = A exp A d p ⎝⎜ RT ⎠⎟ n=1 Newtonian! σ Pa viscosity η = ε s-1 Havarti,Jack η=3*107 Pa s Gouda η=2*108 Pa s Muenster η=6*108 Pa s How do we compare with previous studies? Havarti,Jack η=3*107 Pa s Gouda η=2*108 Pa s Muenster η=6*108 Pa s Despite significant error, not far off published results Viscoelas2city: Deformaon at a range of 2me scales Viscoelas2city: Deformaon at a range of 2me scales Viscoelas2city Elas2c behavior is Viscous behavior; strain rate is instantaneous elas2city and propor2onal to stress: instantaneous recovery. σ = ηε Follows Hooke’s Law: σ = E ε Steady-state viscosity Elas1c Modulus k or E ηSS Simplest form of viscoelas2city is the Maxwell model: t 1 J(t) = + ηSS kE SS kE Viscoelas2city How do we measure viscosity and elascity in the lab? Steady-state viscosity Elas1c Modulus k or EU ηSS σ σ η = η = effective [Fujisawa & Takei, 2009] ε ε1 Viscoelas2city: in between the two extremes? Viscoelas2city: in between the two extremes? Icy satellites velocity (at grounding line) tidal signal glaciers velocity (m per day) (m per velocity Vertical position (m) Vertical Day of year 2000 Anelas2c behavior in Earth and Planetary science
    [Show full text]
  • Turbulence, Entropy and Dynamics
    TURBULENCE, ENTROPY AND DYNAMICS Lecture Notes, UPC 2014 Jose M. Redondo Contents 1 Turbulence 1 1.1 Features ................................................ 2 1.2 Examples of turbulence ........................................ 3 1.3 Heat and momentum transfer ..................................... 4 1.4 Kolmogorov’s theory of 1941 ..................................... 4 1.5 See also ................................................ 6 1.6 References and notes ......................................... 6 1.7 Further reading ............................................ 7 1.7.1 General ............................................ 7 1.7.2 Original scientific research papers and classic monographs .................. 7 1.8 External links ............................................. 7 2 Turbulence modeling 8 2.1 Closure problem ............................................ 8 2.2 Eddy viscosity ............................................. 8 2.3 Prandtl’s mixing-length concept .................................... 8 2.4 Smagorinsky model for the sub-grid scale eddy viscosity ....................... 8 2.5 Spalart–Allmaras, k–ε and k–ω models ................................ 9 2.6 Common models ........................................... 9 2.7 References ............................................... 9 2.7.1 Notes ............................................. 9 2.7.2 Other ............................................. 9 3 Reynolds stress equation model 10 3.1 Production term ............................................ 10 3.2 Pressure-strain interactions
    [Show full text]
  • Similitude and Theory of Models - Washington Braga
    EXPERIMENTAL MECHANICS - Similitude And Theory Of Models - Washington Braga SIMILITUDE AND THEORY OF MODELS Washington Braga Mechanical Engineering Department, Pontifical Catholic University, Rio de Janeiro, RJ, Brazil Keywords: similarity, dimensional analysis, similarity variables, scaling laws. Contents 1. Introduction 2. Dimensional Analysis 2.1. Application 2.2 Typical Dimensionless Numbers 3. Models 4. Similarity – a formal definition 4.1 Similarity Variables 5. Scaling Analysis 6. Conclusion Glossary Bibliography Biographical Sketch Summary The concepts of Similitude, Dimensional Analysis and Theory of Models are presented and used in this chapter. They constitute important theoretical tools that allow scientists from many different areas to go further on their studies prior to actual experiments or using small scale models. The applications discussed herein are focused on thermal sciences (Heat Transfer and Fluid Mechanics). Using a formal approach based on Buckingham’s π -theorem, the paper offers an overview of the use of Dimensional Analysis to help plan experiments and consolidate data. Furthermore, it discusses dimensionless numbers and the Theory of Models, and presents a brief introduction to Scaling Laws. UNESCO – EOLSS 1. Introduction Generally speaking, similitude is recognized through some sort of comparison: observing someSAMPLE relationship (called similarity CHAPTERS) among persons (for instance, relatives), things (for instance, large commercial jets and small executive ones) or the physical phenomena we are interested.
    [Show full text]
  • Dynamic Similarity, the Dimensionless Science
    Dynamic similarity, the dimensionless feature science Diogo Bolster, Robert E. Hershberger, and Russell J. Donnelly Dimensional analysis, a framework for drawing physical parallels between systems of disparate scale, affords key insights into natural phenomena too expansive and too energetic to replicate in the lab. Diogo Bolster is an assistant professor of civil engineering and geological sciences at the University of Notre Dame in Notre Dame, Indiana. Robert Hershberger is a research assistant in the department of physics at the University of Oregon in Eugene. Russell Donnelly is a professor of physics at the University of Oregon. Many experiments seem daunting at first glance, in accordance with general relativity, is deflected as it passes owing to the sheer number of physical variables they involve. through the gravitational field of the Sun. Assuming the Sun To design an apparatus that circulates fluid, for instance, one can be treated as a point of mass m and that the ray of light must know how the flow is affected by pressure, by the ap- passes the mass with a distance of closest approach r, dimen- paratus’s dimensions, and by the fluid’s density and viscosity. sional reasoning can help predict the deflection angle θ.1 Complicating matters, those parameters may be temperature Expressed in terms of mass M, length L, and time T, the and pressure dependent. Understanding the role of each variables’ dimensions—denoted with square brackets—are parameter in such a high-dimensional space can be elusive or prohibitively time consuming. Dimensional analysis, a concept historically rooted in Box 1. A brief history of dimensional analysis the field of fluid mechanics, can help to simplify such prob- Going back more than 300 years, discussions of dimensional lems by reducing the number of system parameters.
    [Show full text]
  • Rheology Bulletin 2010, 79(2)
    The News and Information Publication of The Society of Rheology Volume 79 Number 2 July 2010 A Two-fer for Durham University UK: Bingham Medalist Tom McLeish Metzner Awardee Suzanne Fielding Rheology Bulletin Inside: Society Awards to McLeish, Fielding 82nd SOR Meeting, Santa Fe 2010 Joe Starita, Father of Modern Rheometry Weissenberg and Deborah Numbers Executive Committee Table of Contents (2009-2011) President Bingham Medalist for 2010 is 4 Faith A. Morrison Tom McLeish Vice President A. Jeffrey Giacomin Metzner Award to be Presented 7 Secretary in 2010 to Suzanne Fielding Albert Co 82nd Annual Meeting of the 8 Treasurer Montgomery T. Shaw SOR: Santa Fe 2010 Editor Joe Starita, Father of Modern 11 John F. Brady Rheometry Past-President by Chris Macosko Robert K. Prud’homme Members-at-Large Short Courses in Santa Fe: 12 Ole Hassager Colloidal Dispersion Rheology Norman J. Wagner Hiroshi Watanabe and Microrheology Weissenberg and Deborah 14 Numbers - Their Definition On the Cover: and Use by John M. Dealy Photo of the Durham University World Heritage Site of Durham Notable Passings 19 Castle (University College) and Edward B. Bagley Durham Cathedral. Former built Tai-Hun Kwon by William the Conqueror, latter completed in 1130. Society News/Business 20 News, ExCom minutes, Treasurer’s Report Calendar of Events 28 2 Rheology Bulletin, 79(2) July 2010 Standing Committees Membership Committee (2009-2011) Metzner Award Committee Shelley L. Anna, chair Lynn Walker (2008-2010), chair Saad Khan Peter Fischer (2009-2012) Jason Maxey Charles P. Lusignan (2008-2010) Lisa Mondy Gareth McKinley (2009-2012) Chris White Michael J.
    [Show full text]
  • Chapter 8 Dimensional Analysis and Similitude
    Chapter 8 Dimensional Analysis and Similitude Ahmad Sana Department of Civil and Architectural Engineering Sultan Qaboos University Sultanate of Oman Email: [email protected] Webpage: http://ahmadsana.tripod.com Significant learning outcomes Conceptual Knowledge State the Buckingham Π theorem. Identify and explain the significance of the common π-groups. Distinguish between model and prototype. Explain the concepts of dynamic and geometric similitude. Procedural Knowledge Apply the Buckingham Π theorem to determine number of dimensionless variables. Apply the step-by-step procedure to determine the dimensionless π- groups. Apply the exponent method to determine the dimensionless π-groups. Distinguish the significant π-groups for a given a flow problem. Applications (typical) Drag force on a blimp from model testing. Ship model tests to evaluate wave and friction drag. Pressure drop in a prototype nozzle from model measurements. CIVL 4046 Fluid Mechanics 2 8.1 Need for dimensional analysis • Experimental studies in fluid problems • Model and prototype • Example: Flow through inverted nozzle CIVL 4046 Fluid Mechanics 3 Pressure drop through the nozzle can shown as: p p d V d 1 2 f 0 , 1 0 2 V / 2 d1 p p d For higher Reynolds numbers 1 2 f 0 V 2 / 2 d 1 CIVL 4046 Fluid Mechanics 4 8.2 Buckingham pi theorem In 1915 Buckingham showed that the number of independent dimensionless groups of variables (dimensionless parameters) needed to correlate the variables in a given process is equal to n - m, where n is the number of variables involved and m is the number of basic dimensions included in the variables.
    [Show full text]
  • Dimensional Analysis and Similitude Lecture 39: Geomteric and Dynamic Similarities, Examples
    Objectives_template Module 11: Dimensional analysis and similitude Lecture 39: Geomteric and dynamic similarities, examples Dimensional analysis and similitude–continued Similitude: file:///D|/Web%20Course/Dr.%20Nishith%20Verma/local%20server/fluid_mechanics/lecture39/39_1.htm[5/9/2012 3:44:14 PM] Objectives_template Module 11: Dimensional analysis and similitude Lecture 39: Geomteric and dynamic similarities, examples Dimensional analysis and similitude–continued Example 2: pressure–drop in pipe–flow depends on length, inside diameter, velocity, density and viscosity of the fluid. If the roughness-effects are ignored, determine a symbolic expression for the pressure–drop using dimensional analysis. Answer: We will apply Buckingham Pi-theorem Variables: Primary dimensions: No of dimensionless (independent) group: file:///D|/Web%20Course/Dr.%20Nishith%20Verma/local%20server/fluid_mechanics/lecture39/39_2.htm[5/9/2012 3:44:14 PM] Objectives_template Module 11: Dimensional analysis and similitude Lecture 39: Geomteric and dynamic similarities, examples Similitude: To scale–up or down a model to the prototype, two types of similarities are required from the perspective of fluid dynamics: (1) geometrical similarity (2) dynamic similarity 1. Geometric similarity: The model and the prototype must be similar in shape. (Fig. 39a) This is essential because one can use a constant scale factor to relate the dimensions of model and prototype. 2. Dynamic similarity: The flow conditions in two cases are such that all forces (pressure viscous, surface tension, etc) must be parallel and may also be scaled by a constant scaled factor at all corresponding points. Such requirement is restrictive and may be difficult to implement under certain experiential conditions. Dimensional analysis can be used to identify the dimensional groups to achieve dynamic similarity between geometrically similar flows.
    [Show full text]
  • Rheology and Mixing of Suspension and Pastes
    RHEOLOGY AND MIXING OF SUSPENSION AND PASTES Pr Ange NZIHOU, EMAC France USACH, March 2006 PLAN 1- Rheology and Reactors Reactor performance problems caused by rheological behaviors of suspensions et pastes 2- Rheology of complex fluids Definition Classification of mixtures Non-Newtonian behaviors Behavior laws of viscoplastic fluids Thixotropy Viscosity equations Rheological measurements 3-Factors influencing the rheological behavior of fluids 4- Mixing of pastes in agitated vessels Agitator and utilization Geometric parameters Dimensional numbers Dimensionless numbers 1- Rheology and Reactor DESIGN OF REACTOR FOR SCALE UP Flux Production Flux input output Accumulation Mass balance: ⎛⎞Aj ⎛⎞AAj,,in ⎛j out ⎞⎛Aj ⎞ ⎜⎟+=⎜⎟⎜ ⎟+⎜ ⎟ Flux ⎜⎟Flux Accumulation ⎝⎠⎝⎠Production ⎝ ⎠⎝ ⎠ 1 DIMENSIONS OF REACTOR IN VIEW OF SCALE CHANGE PERFORMANCE OF REACTOR: Thermodynamic and kinetic Hydrodynamic of the reaction Operating parameters: Composition Nature of reagents Conversion rate Pressure, temperature RTD Concentrations R Output Flow In Out Residence time Mass and heat transfer Geometric of reactor SIMILARITY PRINCIPLE: Geometric similitude Energetic similitude Kinematic similitude Thermal similitude 2 ENCOUNTERED PROBLEMS WITH REACTOR Existence of dead matter and recirculation: Stagnant fluid R Recirculation Presence of preferred passages R OBJECTIVE: Correct the flows or take it into consideration while designing the reactor 3 Ribbon impellers (agitators) for mixing Complex fluids 4 anchor Helicoidal ribbon Archemedian ribbon impeller 5 6 PLAN
    [Show full text]
  • An Introduction to Dimensional Analysis David Dureisseix
    An introduction to dimensional analysis David Dureisseix To cite this version: David Dureisseix. An introduction to dimensional analysis. Engineering school. Lyon, France. 2016, pp.20. cel-01380149v3 HAL Id: cel-01380149 https://cel.archives-ouvertes.fr/cel-01380149v3 Submitted on 12 Apr 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution - NoDerivatives| 4.0 International License An introduction to dimensional analysis David Dureisseix D´epartement G´enieM´ecanique, INSA de Lyon April 12, 2019 This document is a short (and hopefully concise) introduction to dimensional analysis and is not expected to be printed. Indeed, it relies on URL links (in colored text) to refer to information sources and complementary studies, so it does not provide a large bibliography, nor many pictures. It has been realized with the kind help of Ton Lubrecht and Marie-Pierre Noutary. Photography by KoS, 2008, distributed under a CC BY-SA 3.0 license 1 Contents 1 Goals of dimensional analysis3 2 Physical quantities and
    [Show full text]
  • Dimensional Analysis and Similitude
    Fluid Mechanics Chapter 8 Dimensional Analysis and Similitude Dr. Amer Khalil Ababneh Introduction Because of the complexity of fluid mechanics, the design of many fluid systems relies heavily on experimental results. Tests are typically carried out on a subscale model, and the results are extrapolated to the full-scale system (prototype). The principles underlying the correspondence between the model and the prototype are addressed in this chapter. Dimensional analysis is the process of grouping of variables into significant dimensionless groups, thus reducing problem complexity. Similitude (Similarity) is the process by which geometric and dynamic parameters are selected for the subscale model so that meaningful correspondence can be made to the full size prototype. 8.2 Buckingham Π Theorem In 1915 Buckingham showed that the number of independent dimensionless groups (dimensionless parameters) can be reduced from a set of variables in a given process is n - m, where n is the number of variables involved and m is the number of basic dimensions included in the variables. Buckingham referred to the dimensionless groups as Π, which is the reason the theorem is called the Π theorem. Henceforth dimensionless groups will be referred to as π-groups. If the equation describing a physical system has n dimensional variables and is expressed as then it can be rearranged and expressed in terms of (n - m) π- groups as 1 ( 2 , 3 ,..., nm ) Example If there are five variables (F, V, ρ, μ, and D) to describe the drag on a sphere and three basic dimensions (L, M, and T) are involved. By the Buckingham Π theorem there will be 5 - 3 = 2 π-groups that can be used to correlate experimental results in the form F= f(V, r, m, D) 8.3 Dimensional Analysis Dimensional analysis is the process used to obtain the π-groups.
    [Show full text]
  • 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.
    [Show full text]