What Is Fluid Mixing? Dimensionless Groups Reynolds Number Froude
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Laws of Similarity in Fluid Mechanics 21
Laws of similarity in fluid mechanics B. Weigand1 & V. Simon2 1Institut für Thermodynamik der Luft- und Raumfahrt (ITLR), Universität Stuttgart, Germany. 2Isringhausen GmbH & Co KG, Lemgo, Germany. Abstract All processes, in nature as well as in technical systems, can be described by fundamental equations—the conservation equations. These equations can be derived using conservation princi- ples and have to be solved for the situation under consideration. This can be done without explicitly investigating the dimensions of the quantities involved. However, an important consideration in all equations used in fluid mechanics and thermodynamics is dimensional homogeneity. One can use the idea of dimensional consistency in order to group variables together into dimensionless parameters which are less numerous than the original variables. This method is known as dimen- sional analysis. This paper starts with a discussion on dimensions and about the pi theorem of Buckingham. This theorem relates the number of quantities with dimensions to the number of dimensionless groups needed to describe a situation. After establishing this basic relationship between quantities with dimensions and dimensionless groups, the conservation equations for processes in fluid mechanics (Cauchy and Navier–Stokes equations, continuity equation, energy equation) are explained. By non-dimensionalizing these equations, certain dimensionless groups appear (e.g. Reynolds number, Froude number, Grashof number, Weber number, Prandtl number). The physical significance and importance of these groups are explained and the simplifications of the underlying equations for large or small dimensionless parameters are described. Finally, some examples for selected processes in nature and engineering are given to illustrate the method. 1 Introduction If we compare a small leaf with a large one, or a child with its parents, we have the feeling that a ‘similarity’ of some sort exists. -
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. -
CALCULUS for the UTTERLY CONFUSED Has Proven to Be a Wonderful Review Enabling Me to Move Forward in Application of Calculus and Advanced Topics in Mathematics
TLFeBOOK WHAT READERS ARE SAYIN6 "I wish I had had this book when I needed it most, which was during my pre-med classes. It could have also been a great tool for me in a few medical school courses." Or. Kellie Aosley8 Recent Hedical school &a&ate "CALCULUS FOR THE UTTERLY CONFUSED has proven to be a wonderful review enabling me to move forward in application of calculus and advanced topics in mathematics. I found it easy to use and great as a reference for those darker aspects of calculus. I' Aaron Ladeville, Ekyiheeriky Student 'I1 am so thankful for CALCULUS FOR THE UTTERLY CONFUSED! I started out Clueless but ended with an All' Erika Dickstein8 0usihess school Student "As a non-traditional student one thing I have learned is the importance of material supplementary to texts. Especially in calculus it helps to have a second source, especially one as lucid and fun to read as CALCULUS FOR THE UTTERtY CONFUSED. Anyone, whether you are a math weenie or not, will get something out of this book. With this book, your chances of survival in the calculus jungle are greatly increased.'I Brad &3~ker,Physics Student Other books in the Utterly Conhrsed Series include: Financial Planning for the Utterly Confrcsed, Fifth Edition Job Hunting for the Utterly Confrcred Physics for the Utterly Confrred CALCULUS FOR THE UTTERLY CONFUSED Robert M. Oman Daniel M. Oman McGraw -Hill New York San Francisco Washington, D.C. Auckland Bogoth Caracas Lisbon London Madrid Mexico City Milan Montreal New Delhi San Juan Singapore Sydney Tokyo Toronto Library of Congress Cataloging-in-Publication Data Oman, Robert M. -
Impeller Power Draw Across the Full Reynolds Number
IMPELLER POWER DRAW ACROSS THE FULL REYNOLDS NUMBER SPECTRUM Thesis Submitted to The School of Engineering of the UNIVERSITY OF DAYTON In Partial Fulfillment of the Requirements for The Degree of Master of Science in Chemical Engineering By Zheng Ma Dayton, OH August, 2014 IMPELLER POWER DRAW ACROSS THE FULL REYNOLDS NUMBER SPECTRUM Name: Ma, Zheng APPROVED BY: ______________________ ________________________ Kevin J. Myers, D.Sc., P.E. Eric E. Janz, P.E. Advisory Committee Chairman Research Advisor Research Advisor & Professor Chemineer Research & Chemical & Materials Engineering Development Manager NOV-Process & Flow Technologies ________________________ Robert J. Wilkens, Ph.D., P.E. Committee Member & Professor Chemical & Materials Engineering _______________________ ___________________________ John G. Weber, Ph.D. Eddy M. Rojas, Ph.D., M.A., P.E. Associate Dean Dean, School of Engineering School of Engineering ii ABSTRACT IMPELLER POWER DRAW ACROSS THE FULL REYNOLDS NUMBER SPECTRUM Name: Ma, Zheng University of Dayton Research Advisors: Dr. Kevin J. Myers Eric E. Janz The objective of this work is to gain information that could be used to design full scale mixing systems, and also could develop a design guide that can provide a reliable prediction of the power draw of different types of impellers. To achieve this goal, the power number behavior,including three operation regimes, the limits of the operation regimes, and the effect of baffling on power number,was compared across the full Reynolds number spectrum for Newtonian fluids in a laboratory-scale agitator. Six industrially significant impellers were tested, including three radial flow impellers: D-6, CD-6, and S-4, and also three axial flow impellers: P-4, SC-3, and HE-3. -
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- I 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 I 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 I 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. I 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 I 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. -
Birds and Frogs Equation
Notices of the American Mathematical Society ISSN 0002-9920 ABCD springer.com New and Noteworthy from Springer Quadratic Diophantine Multiscale Principles of Equations Finite Harmonic of the American Mathematical Society T. Andreescu, University of Texas at Element Analysis February 2009 Volume 56, Number 2 Dallas, Richardson, TX, USA; D. Andrica, Methods A. Deitmar, University Cluj-Napoca, Romania Theory and University of This text treats the classical theory of Applications Tübingen, quadratic diophantine equations and Germany; guides readers through the last two Y. Efendiev, Texas S. Echterhoff, decades of computational techniques A & M University, University of and progress in the area. The presenta- College Station, Texas, USA; T. Y. Hou, Münster, Germany California Institute of Technology, tion features two basic methods to This gently-paced book includes a full Pasadena, CA, USA investigate and motivate the study of proof of Pontryagin Duality and the quadratic diophantine equations: the This text on the main concepts and Plancherel Theorem. The authors theories of continued fractions and recent advances in multiscale finite emphasize Banach algebras as the quadratic fields. It also discusses Pell’s element methods is written for a broad cleanest way to get many fundamental Birds and Frogs equation. audience. Each chapter contains a results in harmonic analysis. simple introduction, a description of page 212 2009. Approx. 250 p. 20 illus. (Springer proposed methods, and numerical 2009. Approx. 345 p. (Universitext) Monographs in Mathematics) Softcover examples of those methods. Softcover ISBN 978-0-387-35156-8 ISBN 978-0-387-85468-7 $49.95 approx. $59.95 2009. X, 234 p. (Surveys and Tutorials in The Strong Free Will the Applied Mathematical Sciences) Solving Softcover Theorem Introduction to Siegel the Pell Modular Forms and ISBN: 978-0-387-09495-3 $44.95 Equation page 226 Dirichlet Series Intro- M. -
Series Representation of Power Function
Series Representation of Power Function Kolosov Petro May 7, 2017 Abstract This paper presents the way to make expansion for the next form function: y = n x ; 8(x; n) 2 N to the numerical series. The most widely used methods to solve this problem are Newton's Binomial Theorem and Fundamental Theorem of Calculus (that is, derivative and integral are inverse operators). The paper provides the other kind of solution, based on induction from particular to general case, except above described theorems. Keywords. power, power function, monomial, polynomial, power series, third power, series, finite difference, divided difference, high order finite difference, derivative, bi- nomial coefficient, binomial theorem, Newton's binomial theorem, binomial expansion, n-th difference of n-th power, number theory, cubic number, cube, Euler number, ex- ponential function, Pascal triangle, Pascal's triangle, mathematics, number theory 2010 Math. Classification Subject. 40C15, 32A05 e-mail: kolosov [email protected] ORCID: http://orcid.org/0000-0002-6544-8880 Social media links Twitter - Kolosov Petro Youtube - Kolosov Petro Mendeley - Petro Kolosov Academia.edu - Petro Kolosov LinkedIn - Kolosov Petro Google Plus - Kolosov Petro Facebook - Kolosov Petro Vimeo.com - Kolosov Petro VK.com - Math Facts Publications on other resources HAL.fr articles - Most recent updated ArXiV.org articles Archive.org articles 1 ViXrA.org articles Figshare.com articles Datahub.io articles 1 Introduction Let basically describe Newton's Binomial Theorem and Fundamental Theorem of Cal- culus and some their properties. In elementary algebra, the binomial theorem (or binomial expansion) describes the algebraic expansion of powers of a binomial. The theorem describes expanding of the power of (x + y)n into a sum involving terms of the form axbyc where the exponents b and c are nonnegative integers with b + c = n, and the coefficient a of each term is a specific positive integer depending on n and b. -
Simulation-Based Design of Bioreactors Using Computational Multiphysics
Simulation-based Design of Bioreactors Using Computational Multiphysics by Kimia Entezari A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Master of Applied Science in Chemical Engineering Waterloo, Ontario, Canada, 2021 c Kimia Entezari 2021 Author's Declaration I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Abstract The Covid-19 pandemic highlighted the importance of quickly scaling up the production of vaccines and other pharmaceutical products. These products are typically made within bioreactors: vessels that carry out bioreactions involving microorganisms or biochemical substances derived from microorganisms. The design, construction, and evaluation of bioreactors for large-scale production, however, is costly and time-consuming. Many builds are often needed to resolve issues such as poor mixing and inhomogeneous nutrient transfer. Nevertheless, computational methods can be used to identify and resolve these limitations early-on in the design process. This is why understanding the flow characteristics inside a bioreactor through computational fluid dynamics (CFD) can save time, money, and lives. Bioreactors contain three phases: 1) a continuous liquid medium which is the host for cells to feed and grow, 2) a dispersed solid phase which is the microorganism particles inside the tank, and 3) a dispersed gas phase which includes the air or oxygen bubbles for microorganisms aspiration. Due to the complexity of solving a three-phase flow problem, most bioreactor multi-phase simulations in the literature neglect the dispersed microor- ganism phase and its effects entirely{thus assuming two phases only. -
Understand the Real World of Mixing
Back to Basics Understand the Real World of Mixing Thomas Post Post Mixing Optimization and Most chemical engineering curricula do not Solutions adequately address mixing as it is commonly practiced in the chemical process industries. This article attempts to fi ll in some of the gaps by explaining fl ow patterns, mixing techniques, and the turbulent, transitional and laminar mixing regimes. or most engineers, college memories about mix- This is the extent of most engineers’ collegiate-level ing are limited to ideal reactors. Ideal reactors are preparation for real-world mixing applications. Since almost Fextreme cases, and are represented by the perfect- everything manufactured must be mixed, most new gradu- mixing model, the steady-state perfect-mixing model, or the ates are ill-prepared to optimize mixing processes. This steady-state plug-fl ow model (Figure 1). These models help article attempts to bridge the gap between the theory of the determine kinetics and reaction rates. ideal and the realities of actual practice. For an ideal batch reactor, the perfect-mixing model assumes that the composition is uniform throughout the Limitations of the perfect-mixing reactor at any instant, and gives the reaction rate as: and plug-fl ow models Classical chemical engineering focuses on commodity (–rA)V = NA0 × (dXA/dt) (1) chemicals produced in large quantities in continuous opera- tions, such as in the petrochemical, polymer, mining, fertil- The steady-state perfect-mixing model for a continuous process is similar, with the composition uniform throughout the reactor at any instant in time. However, it incorporates Feed the inlet molar fl owrate, FA0: Uniformly Mixed (–rA)V = FA0 × XA (2) The steady-state plug-fl ow model of a continuous process assumes that the composition is the same within a differential volume of the reactor in the direction of fl ow, typically within a pipe: Product (–r )V = F × dX (3) Uniformly A A0 A Mixed These equations can be integrated to determine the time required to achieve a conversion of XA. -
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 Autumn 2013
TOPIC T3: DIMENSIONAL ANALYSIS AUTUMN 2013 Objectives (1) Be able to determine the dimensions of physical quantities in terms of fundamental dimensions. (2) Understand the Principle of Dimensional Homogeneity and its use in checking equations and reducing physical problems. (3) Be able to carry out a formal dimensional analysis using Buckingham’s Pi Theorem. (4) Understand the requirements of physical modelling and its limitations. 1. What is dimensional analysis? 2. Dimensions 2.1 Dimensions and units 2.2 Primary dimensions 2.3 Dimensions of derived quantities 2.4 Working out dimensions 2.5 Alternative choices for primary dimensions 3. Formal procedure for dimensional analysis 3.1 Dimensional homogeneity 3.2 Buckingham’s Pi theorem 3.3 Applications 4. Physical modelling 4.1 Method 4.2 Incomplete similarity (“scale effects”) 4.3 Froude-number scaling 5. Non-dimensional groups in fluid mechanics References White (2011) – Chapter 5 Hamill (2011) – Chapter 10 Chadwick and Morfett (2013) – Chapter 11 Massey (2011) – Chapter 5 Hydraulics 2 T3-1 David Apsley 1. WHAT IS DIMENSIONAL ANALYSIS? Dimensional analysis is a means of simplifying a physical problem by appealing to dimensional homogeneity to reduce the number of relevant variables. It is particularly useful for: presenting and interpreting experimental data; attacking problems not amenable to a direct theoretical solution; checking equations; establishing the relative importance of particular physical phenomena; physical modelling. Example. The drag force F per unit length on a long smooth cylinder is a function of air speed U, density ρ, diameter D and viscosity μ. However, instead of having to draw hundreds of graphs portraying its variation with all combinations of these parameters, dimensional analysis tells us that the problem can be reduced to a single dimensionless relationship cD f (Re) where cD is the drag coefficient and Re is the Reynolds number. -
I. Power Number 1
I. Power Number 1. Definition How does power consumption relate to operating variables? The key dimensionless group is the Power Number (Po). _____________________External Force Exerted ______Power Po = = Inertial Force Imparted rN3d5 Power = External Power to Agitator r = Liquid Density N = Impeller Rotational Speed (rpm) d = Impeller Diameter Other important dimensionless groups: 2 ______________Inertial Forces ______rd N Osborne Reynolds Re = = Viscous Forces m (no apostrophe) 2 __________________Inertial Forces ____N d Fr = = William “Frew-d” Gravitational Forces g g = gravitational acceleration 2. Correlation with other Dimensionless Groups In general, the Power Number for ungassed systems is related to the Reynolds Number and the Froude Number: Po = c (Re)x (Fr)y In a well-baffled vessel, the gravitational forces are minimal, and the Power Number for ungassed systems is related only to the Reynolds Number: Po = c (Re)x Bioreactors are typically well-baffled. a) For laminar flow (Re<10), Po decreases linearly with the logarithm of Re. Thus, x = -1 and the power absorbed is a function of the fluid viscosity: Po = c (Re)-1 Power ______m or, ______ = c rN3d5 ( r d2 N ) Power = cd3N2m b) For transition flow (10<Re<10000), Po is a complex function of Re. c) For turbulent flow (10000<Re), Po is a constant (independent of Re). Thus, x = 0, and the power absorbed is not a function of the fluid viscosity. Po = c Power ______ = c rN3d5 Power = crN3d5 Notes: Axial-flow impellers usually have lower values of Po compared to radial-flow impellers. These relationships are for ungassed systems (i.e., no aeration).