Dimensional Analysis and Similitude
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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. -
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 -
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. -
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. -
Dimensional Analysis and Similitude
DIMENSIONAL ANALYSIS AND SIMILITUDE Many real fluid flow problems can be solved, at best, only approximately by using analytical or numerical methods. Therefore experiments play a crucial role in verifying these approximate solutions. Solutions of real problems usually involve a combination of analysis and experimental work. First, the real physical flow situation is approximated with a mathematical model that is simple enough to yield a solution. Then experimental measurements are made to check the analytical results. Based on the measurements, refinements are made in the mathematical model and analysis. The experimental results are an essential link in this iterative process. The experimental work in the laboratory is both time consuming and expensive. Hence, the goal is to obtain the maximum information from the fewest experiments. The dimensional analysis is an important tool that often helps in achieving this goal. Dimensional analysis is a packaging or compacting technique used to reduce the complexity of experimental programs and at the same time increase the generality of experimental information. Consider the drag force on a stationary smooth sphere immersed in a uniform stream. What experiments must be conducted to determine the drag force on the sphere? V µ D F ρ ME304 61 We would expect the drag force, F, depends on diameter of the sphere, D, the fluid velocity, V, fluid viscosity, µ and the fluid density ρ. That is, Let us imagine a series of experiments to determine the dependence of F on the variablesD,V,ρ andµ.ToobtainacurveofFversusVforfixedvaluesofρ,µand D, we might need tests at 10 values of V. To explore the diameter effect, each test would be repeat for spheres of ten different diameters. -
Applied Biofluid Mechanics.Pdf
Applied Biofluid Mechanics This page intentionally left blank Applied Biofluid Mechanics Lee Waite, Ph.D., P.E. Jerry Fine, Ph.D. New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Manufactured in the United States of America. Except as permitted under the United States Copyright Act of 1976, no part of this pub- lication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher. 0-07-150951-8 The material in this eBook also appears in the print version of this title: 0-07-147217-7. All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trade- mark owner, with no intention of infringement of the trademark. Where such designations appear in this book, they have been printed with initial caps. McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales promotions, or for use in corporate training programs. For more information, please contact George Hoare, Special Sales, at [email protected] or (212) 904-4069. TERMS OF USE This is a copyrighted work and The McGraw-Hill Companies, Inc. (“McGraw-Hill”) and its licensors reserve all rights in and to the work. Use of this work is subject to these terms. -
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. -
FUNDAMENTALS of FLUID MECHANICS Chapter 7 Dimensional Analysis Modeling, and Similitude
FUNDAMENTALS OF FLUID MECHANICS Chapter 7 Dimensional Analysis Modeling, and Similitude 1 MAIN TOPICS Dimens ional A nal ysi s Buckingham Pi Theorem Determination of Pi Terms Comments about Dimensional Analysis Common Dimensionless Groups in Fluid Mechanics Correlation of Experimental Data Modeling and Similitude Typical Model Studies Similitude Based on Governing Differential Equation 2 Dimensional Analysis 1/4 A typical fluid mechanics problem in which experimentation is required consider the steady flow of an incompressible Newtonian fluid through a long, smoothsmooth-- walled, horizontal, circular pipe. An important characteristic of this system, which would be interest to an engineer designing a pipeline, is the pressure drop per unit length that develops along the pipe as a result of friction. 3 Dimensional Analysis 2/4 The first step in the planning of an experiment to study this problem would be to decide the factors, or variables, that will have an effect on the pressure drop.. Pressure droppp per unit leng th p f (D, , ,V ) Pressure drop per unit length depends on FOUR variables: sphere size (D); speed (V); fluid density ( ρ); fluid viscosity (m) 4 Dimensional Analysis 3/4 To perform the experiments in a meaningful and systematic manner, it would be necessary to change the variable, such as the velocity, which holding all other constant, and measure the corresponding pressure drop. Difficulty to determine the functional relationship between the pressure drop and the various facts that influence it. 5 Series of Tests 6 Dimensional Analysis 4/4 Fortunately, there is a much simpler approach to the problem that will eliminate the difficulties described above. -
Proquest Dissertations
INFORMATION TO USERS This manuscript has been reproduced from the microfilm master. UMi films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter tece, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will t>e noted. Also, if unauthorized copyright material had to t)e removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6* x 9” black arxj white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. Bell & Howell Information and Learning 300 North Zeeb Road, Ann Art)or. Ml 48106-1346 USA 800-521-0600 UMI 3-DIMENSIONAL PARTICLE TRACKING VELOCIMETRY MEASUREMENTS OF NEAR-WALL SHEAR IN HUMAN LEFT CORONARY ARTERIES DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Deborah M. Grzybowski, M.S. The Ohio State University 2000 Dissertation Committee: Approved by Professor Morton H. -
Chapter 8 Similitude and Dimensional Analysis
Ch. 8 Similitude and Dimensional Analysis Chapter 8 Similitude and Dimensional Analysis 8.0 Introduction 8.1 Similitude and Physical Models 8.2 Dimensional Analysis 8.3 Normalization of Equations Objectives: - learn how to begin to interpret fluid flows - introduce concept of model study for the analysis of the flow phenomena that could not be solved by analytical (theoretical) methods - introduce laws of similitude which provide a basis for interpretation of model results - study dimensional analysis to derive equations expressing a physical relationship between quantities 8-1 Ch. 8 Similitude and Dimensional Analysis 8.0 Introduction Why we need to model the real system? Most real fluid flows are complex and can be solved only approximately by analytical methods. Real System Set of assumptions Conceptual Model Simplified version of real system Field Study Physical Modeling Mathematical Model (Scaled and simplified [Governing equations version of prototype) + Boundary (initial) conditions] Analytical solution Numerical solution (model) (FDM/FEM/FVM) 8-2 Ch. 8 Similitude and Dimensional Analysis ▪ Three dilemmas in planning a set of physical experiments or numerical experiments 1) Number of possible and relevant variables or physical parameters in real system is huge and so the potential number of experiments is beyond our resources. 2) Many real flow situations are either too large or far too small for convenient experiment at their true size. → When testing the real thing (prototype) is not feasible, a physical model (scaled version of the prototype) can be constructed and the performance of the prototype simulated in the physical model. 3) The solutions produced by numerical simulations must be verified or numerical models calibrated by use of physical models or measurements in the prototype. -
Model-Prototype Similarity
Model-Prototype Similarity Picture Dr Valentin Heller Fluid Mechanics Section, Department of Civil and Environmental Engineering 4th CoastLab Teaching School, Wave and Tidal Energy, Porto, 17-20th January 2012 Content • Introduction • Similarities • Scale effects • Reaching model-prototype similarity • Dealing with scale effects • Conclusions Lecture follows Heller (2011) 2 1 Introduction Why do quantities between model and prototype disagree? Measurement effects: due to non-identical measurement techniques used for data sampling in the model and prototype (intruding versus non- intruding measurement system etc.). Model effects: due to the incorrect reproduction of prototype features such as geometry (2D modelling, reflections from boundaries), flow or wave generation techniques (turbulence intensity level in approach flow, linear wave approximation) or fluid properties (fresh instead of sea water). Scale effects: due to the inability to keep each relevant force ratio constant between the scale model and its real-world prototype. 3 Introduction Example model effects Reflections from beach or from non-absorbing wave maker (without WEC) Regular water waves in a wave tank: frequency 0.625 Hz, steepness ak = 0.05 40 20 0 0 25 50 75 100 Free water surface[mm] Freewater -20 -40 Time [s] Note: Scale effects are due to the scaling. Reflection is not due to the scaling, it is therefore not a scale but a model effect. 4 2 Introduction Example scale effects Miniature universe Real-world prototype Jet trajectory Air concentration 1:l = 1:30 Scale ratio or scale factor l = LP/LM with LP = a characteristic length in the real- world prototype and LM = corresponding length in the model 5 Introduction Why are scale effects relevant? • Scale model test results may be misleading if up-scaled to full scale (e.g.