UNIFORM FLOW. U x. U t
|
|
- Jean Gordon
- 5 years ago
- Views:
Transcription
1 UNIFORM FLOW if : 1) there are o appreciable variatios i the chael geometry (width, slope, roughess/grai size), for a certai legth of a river reach ) flow discharge does ot vary the, UNIFORM FLOW coditios are established U x U t 0 Give the cross sectio characteristics the locatio of the free surface / flow depth will result from the balace betwee gravity ad the flow resistace/frictio
2 Dimesioal aalysis: (,,, g, V, R hydraulicradius, k rough, C geometry ) V VR kr f,, R V gr Reyolds relative roughess Froude I a ope chael flow the flow is certaily turbulet ad the regime certaily rough ; thus at a scale of a river, we - eglect viscous effect ad - lose the depedecy o the Reyolds umber (Re high eough! ) BLACKBOARD Mometum equatio 1+,+ Hydraulic radius R=A/P
3 because the flow is uiform: the bed topography // free surface water slope // eergy grade lie Thus the eergy losses h f i a L reach ca be estimated by : assumig: V fuctio k R f 8 with r f frictio factor Note that L=4R is the key legth scale to cosider whe extedig the Moody diagram from pipe to ope hael flow (D pipe 4R hydraulic radious )
4 solvig for the shear stres VR k V f r,,,... V R gr from the uiform coditio : if γ we fv 8g V R C R 4Rg f Chezy coefficiet (coductace, ot resistace) for C R e solve slope of the eergy grade lie for the velocity icreasig, the cross -sect velocity is icreasig 1 f 8 1 V c f V c f = roughess coeff. we eed a empirical closure providig f or C or h f for a give cross sectioal geometry
5 The mea velocity profile i the smooth wall turbulet boudary layer : 1) viscous sublayer u = τ 0 y μ τ = μ du dy the velocity varies liearly, as a Couette flow (movig upper wall). Thus, the shear stress is costat: τ 0
6 scalig ear wall turbulece We ca defie a velocity scale u* = τ ρ [m/s] characteristic of ear wall turbulece u* = shear velocity or frictio velocity we ca rewrite the liear profile i the viscous sublayer as υ u u = yu υ where is a legth scale (very small, remember υ u =O( ) m /s, while u* is a fractio (~5-10%) of the udisturbed velocity U 0 δ boudary layer height we already have velocity scales: 1) u* ) U 0 How may legth scale? 1) υ u ) δ
7 viscous sublayer cotiued How thick is the viscous sublayer? it depeds o the boudary layer... as u* ad υ defie the viscous legth scale, we ca represet the extesio of the viscous sublayer i terms of multiples of the viscous scale (viscous wall uits) δ υ = 5 υ u Note that as u* δ υ : the viscous sublayer becomes thier Note: roughess protrusio (fixed physical scale) may emerge from the viscous sublayer ad chage the ear wall structure of the flow δ υ
8 above the viscous/roughess sublayer Logarithmic regio u u = 1 k l yu υ +C with u* = τ ρ where u* depeds o the flow ad the surface k is the vo Karma costat(?)= (k=0.41 is a good umber) C is the smooth wall costat(?) of itegratio (C=5.5 is a good umber) ote that is a rough wall boudary layer = 1 l u k where y 0 is the aerodyamic roughess legth: it is a measure of aerodyamic roughess, ot geometrical (surface) roughess u y y0 relatig to y 0 is complicate
9 The mea velocity profile: where is it valid? from about 60 viscous wall uits to about 15% of he boudary layer height it makes sese that the extesio of the log layer has to be determied by both ier scalig ad outer scalig
10 urface roughess 1) affect the mea velocity profile (through the aerodyamic roughess legth y 0 ) ) is related o-trivially with aerodyamic/hydraulic roughess coefficiets 3) affect the mea cross sectioal velocity ( through C, c f, f) 4) requires a empirical closures (Moody diagram, Maig, Gauckler trickler coeffs) VR kr V f,, V R gr uiform flow coditios : γ R e,... with a empirical closure estimatig (f, C, c f ) from cross sectioal geometry ad surface roughess obtai R Y ormal flow depth BLACKBOARD geeric formulatio: bis+tris+ BB 4BI7+
11 frictio factor i turbulet flows Resistace coefficiet from experimets (Nikuradse) k s /D relative roughess k s is the sad roughess height Keulega trapezoidal chael fully rough 1 f =.03 log R/k s +.1 ote that f is a measure of aerodyamic roughess while k s is a measure of geometric roughess 1 f =.0 log 10 Re f 0.8
12 Moody diagram costat Re f 1/ curves (give D, L, K s fid losses h f ) k s / 4R explicit formula f log ks 3.7D Re figure_09_1 Re= 4R V/ν
13 tabulated pipe roughess
14 Empirical closures: roughess ad frictio factors Maig proposed a dimesioal coeff.: V C R 1 origial (old) Maig V K R Gauckler-Maig is the dimesioless Maig coefficiet: while [K ]= m 1/3 /s is dimesioal (K dimesioal is the price we pay to have a umber) K =1 m 1/3 /s (I m, m/s) K =1.49 ft 1/3 /s (Eglish Uits ft, ft/s) large : low velocity, large roughess : e.g. =0.05 for high grass or cobbles small : high velocity, low roughess : e.g. =0.01 clea straight cocrete
15 Note that by itegratig the mea velocity profile o rough walls we should obtai a relatioship betwee the frictio factor ad the maig coeff (for the mea cross sectioal velocity) U=u * /k log (y/y 0 ) Also, we should be able to relate all the roughess coefficiets, e.g. i uiform flow γ R solvig for the velocity K (8g) f R 1/ 6 fv 4Rg V 8g f R Importat lik betwee rough wall boudary layer turbulece (f) ad hydraulics () V K R Ks strickler = 1/ maig. The coefficiet Ks strickler varies from 0 (rough stoe ad rough surface) to 80 (smooth cocrete ad cast iro).
16 ome cosideratios based o the roughess iduced by specific bed material the chael frictio factor is proportioal to the size of the larger grais, as compared to the hydraulic radius equivalet to the term z 0 / i rough wall TBL FLOW
17 Aother attampt to relate k s (geometry) with (fluid mechaics drag)
18 UNIFORM FLOW COMPUTATION Give the geometric characteristics of the cross sectio, we ca determie the roughess coefficiet ad calculate the uiform flow depth (or ormal depth) K V Q K A AR R A P Q K A R P A - - P 5/3 Q K Q VA the wetted perimeter P is a fuctio of y, kow oly if the cross sectio is give slope, discharge are boudary coditios defies the roughess Rectagular cross sectio: V Q K K R by( by) ( b y) P b y A by ( y / b) - - y 1 b 5/3 Q Vby Q K b 8/3 solve for y
19 for a trapezoidal chael Oce we compute the uiform flow depth, we ca determie if the flow is sub- or super- critical b y m 1 Note: P b y(1 m ) 1/ A y( b my) What happes if roughess icreases?
20 Complex cross sectios f frictio factor fo rthe cross sectio c f c ks (Re, R P B,, ) B B supposed to accout for: large scale rouhess, supercritical istabilities fairly costat with depth b ) m ( meaderig amplifyig term b surf roughess, 1 bak roughess,, slope variability i x (pools ad riffles) 3, chael obstructio ad 4 vegetatio p 1 1 Compoud chaels U 1 U U 3 p p 3 3 Eistei ad Chie Horto method (p i wetted perimeters) P i c P P i P 3/ i i Imposig U=U 1 =U = U 3 c Imposig R=R 1 =R = R 3
21
22 lope classificatio V AR c K c c Q K A R R Q K c 4/3 c R A P F>1, supercritical coditios F<1, subcritical coditios Q VA Rectagular sectio BB
Channel design. riprap-lined channels
Chael desig Vegetatio ad cobbles are used to cotrol erosio, dissipate eergy ad provide a evirometally sustaiable way to desig differet size chaels riprap-lied chaels Smooth chaels i circular coduits: PVC
More information4.1 Introduction. 4. Uniform Flow and its Computations
4. Uiform Flow ad its Computatios 4. Itroductio A flow is said to be uiform if its properties remai costat with respect to distace. As metioed i chapter oe of the hadout, the term ope chael flow i ope
More informationFree Surface Hydrodynamics
Water Sciece ad Egieerig Free Surface Hydrodyamics y A part of Module : Hydraulics ad Hydrology Water Sciece ad Egieerig Dr. Shreedhar Maskey Seior Lecturer UNESCO-IHE Istitute for Water Educatio S. Maskey
More informationModule 3d: Flow in Pipes Manning s Equation
Module d: Flow i Pipes Maig's Equatio for velocity ad flow applicable to both pipe (closed-coduit) flow ad ope chael flow. Robert Pitt Uiversity of Alabama ad hirley Clark Pe tate - Harrisburg It is typically
More informationOn the Blasius correlation for friction factors
O the Blasius correlatio for frictio factors Trih, Khah Tuoc Istitute of Food Nutritio ad Huma Health Massey Uiversity, New Zealad K.T.Trih@massey.ac.z Abstract The Blasius empirical correlatio for turbulet
More informationChemical Engineering 374
Chemical Egieerig 374 Fluid Mechaics NoNewtoia Fluids Outlie 2 Types ad properties of o-newtoia Fluids Pipe flows for o-newtoia fluids Velocity profile / flow rate Pressure op Frictio factor Pump power
More informationUsing vegetation properties to predict flow resistance and erosion rates. Nick Kouwen University of Waterloo Waterloo, Canada
1/37 INTERNATIONAL WORKSHOP o RIParia FORest Vegetated Chaels: Hydraulic Morphological ad Ecological Aspects Treto, Italy, 20-22 February 2003 Usig vegetatio properties to predict flow resistace ad erosio
More information11 Correlation and Regression
11 Correlatio Regressio 11.1 Multivariate Data Ofte we look at data where several variables are recorded for the same idividuals or samplig uits. For example, at a coastal weather statio, we might record
More informationSECTION 2 Electrostatics
SECTION Electrostatics This sectio, based o Chapter of Griffiths, covers effects of electric fields ad forces i static (timeidepedet) situatios. The topics are: Electric field Gauss s Law Electric potetial
More information17 Phonons and conduction electrons in solids (Hiroshi Matsuoka)
7 Phoos ad coductio electros i solids Hiroshi Matsuoa I this chapter we will discuss a miimal microscopic model for phoos i a solid ad a miimal microscopic model for coductio electros i a simple metal.
More informationSalmon: Lectures on partial differential equations. 3. First-order linear equations as the limiting case of second-order equations
3. First-order liear equatios as the limitig case of secod-order equatios We cosider the advectio-diffusio equatio (1) v = 2 o a bouded domai, with boudary coditios of prescribed. The coefficiets ( ) (2)
More informationTime-Domain Representations of LTI Systems
2.1 Itroductio Objectives: 1. Impulse resposes of LTI systems 2. Liear costat-coefficiets differetial or differece equatios of LTI systems 3. Bloc diagram represetatios of LTI systems 4. State-variable
More informationThe axial dispersion model for tubular reactors at steady state can be described by the following equations: dc dz R n cn = 0 (1) (2) 1 d 2 c.
5.4 Applicatio of Perturbatio Methods to the Dispersio Model for Tubular Reactors The axial dispersio model for tubular reactors at steady state ca be described by the followig equatios: d c Pe dz z =
More informationMost text will write ordinary derivatives using either Leibniz notation 2 3. y + 5y= e and y y. xx tt t
Itroductio to Differetial Equatios Defiitios ad Termiolog Differetial Equatio: A equatio cotaiig the derivatives of oe or more depedet variables, with respect to oe or more idepedet variables, is said
More informationMaximum and Minimum Values
Sec 4.1 Maimum ad Miimum Values A. Absolute Maimum or Miimum / Etreme Values A fuctio Similarly, f has a Absolute Maimum at c if c f f has a Absolute Miimum at c if c f f for every poit i the domai. f
More informationEXPERIMENTAL INVESTIGATION ON LAMINAR HIGHLY CONCENTRATED FLOW MODELED BY A PLASTIC LAW Session 5
EXPERIMENTAL INVESTIGATION ON LAMINAR HIGHLY CONCENTRATED FLOW MODELED BY A PLASTIC LAW Sessio 5 Sergio Brambilla, Ramo Pacheco, Fabrizio Sala ENEL.HYDRO S.p.a. Polo Idraulico Strutturale, Milao; Seior
More information3. Z Transform. Recall that the Fourier transform (FT) of a DT signal xn [ ] is ( ) [ ] = In order for the FT to exist in the finite magnitude sense,
3. Z Trasform Referece: Etire Chapter 3 of text. Recall that the Fourier trasform (FT) of a DT sigal x [ ] is ω ( ) [ ] X e = j jω k = xe I order for the FT to exist i the fiite magitude sese, S = x [
More informationf(x) dx as we do. 2x dx x also diverges. Solution: We compute 2x dx lim
Math 3, Sectio 2. (25 poits) Why we defie f(x) dx as we do. (a) Show that the improper itegral diverges. Hece the improper itegral x 2 + x 2 + b also diverges. Solutio: We compute x 2 + = lim b x 2 + =
More informationFluid Physics 8.292J/12.330J % (1)
Fluid Physics 89J/133J Problem Set 5 Solutios 1 Cosider the flow of a Euler fluid i the x directio give by for y > d U = U y 1 d for y d U + y 1 d for y < This flow does ot vary i x or i z Determie the
More informationSeries: Infinite Sums
Series: Ifiite Sums Series are a way to mae sese of certai types of ifiitely log sums. We will eed to be able to do this if we are to attai our goal of approximatig trascedetal fuctios by usig ifiite degree
More informationChapter Vectors
Chapter 4. Vectors fter readig this chapter you should be able to:. defie a vector. add ad subtract vectors. fid liear combiatios of vectors ad their relatioship to a set of equatios 4. explai what it
More informationMechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter
Time Respose & Frequecy Respose d -Order Dyamic System -Pole, Low-Pass, Active Filter R 4 R 7 C 5 e i R 1 C R 3 - + R 6 - + e out Assigmet: Perform a Complete Dyamic System Ivestigatio of the Two-Pole,
More informationTOTAL AIR PRESSURE LOSS CALCULATION IN VENTILATION DUCT SYSTEMS USING THE EQUAL FRICTION METHOD
TOTAL AIR PRESSURE LOSS CALCULATION IN VENTILATION DUCT SYSTEMS USING THE EQUAL FRICTION METHOD DOMNIA Flori*, HOUPAN Aca, POPOVICI Tudor Techical Uiversity of Cluj-Napoca, *e-mail: floridomita@istautclujro
More informationFall 2013 MTH431/531 Real analysis Section Notes
Fall 013 MTH431/531 Real aalysis Sectio 8.1-8. Notes Yi Su 013.11.1 1. Defiitio of uiform covergece. We look at a sequece of fuctios f (x) ad study the coverget property. Notice we have two parameters
More informationUNIFORM FLOW CRITICAL FLOW GRADUALLY VARIED FLOW
UNIFORM FLOW CRITICAL FLOW GRADUALLY VARIED FLOW Derivation of uniform flow equation Dimensional analysis Computation of normal depth UNIFORM FLOW 1. Uniform flow is the flow condition obtained from a
More informationDAY 19: Boundary Layer
DAY 19: Boundary Layer flat plate : let us neglect the shape of the leading edge for now flat plate boundary layer: in blue we highlight the region of the flow where velocity is influenced by the presence
More informationSimple Linear Regression
Chapter 2 Simple Liear Regressio 2.1 Simple liear model The simple liear regressio model shows how oe kow depedet variable is determied by a sigle explaatory variable (regressor). Is is writte as: Y i
More informationFinally, we show how to determine the moments of an impulse response based on the example of the dispersion model.
5.3 Determiatio of Momets Fially, we show how to determie the momets of a impulse respose based o the example of the dispersio model. For the dispersio model we have that E θ (θ ) curve is give by eq (4).
More informationU8L1: Sec Equations of Lines in R 2
MCVU U8L: Sec. 8.9. Equatios of Lies i R Review of Equatios of a Straight Lie (-D) Cosider the lie passig through A (-,) with slope, as show i the diagram below. I poit slope form, the equatio of the lie
More informationPolynomial Functions and Their Graphs
Polyomial Fuctios ad Their Graphs I this sectio we begi the study of fuctios defied by polyomial expressios. Polyomial ad ratioal fuctios are the most commo fuctios used to model data, ad are used extesively
More information6 BRANCHING PIPES. One tank to another 1- Q 1 = Q 2 +Q 3. One tank to two or more tanks 1- Q 1 = Q 2 + Q 3 or Q 3 =Q 1 + Q 2
6 RNHING PIPES Oe tak to aother - Q Q Q - pply eroulli s equatio betwee & through s & as well as & Oe tak to two or more taks - Q Q Q or Q Q Q - pply eroulli s equatio betwee each two taks 6. ischarge
More informationPrecalculus MATH Sections 3.1, 3.2, 3.3. Exponential, Logistic and Logarithmic Functions
Precalculus MATH 2412 Sectios 3.1, 3.2, 3.3 Epoetial, Logistic ad Logarithmic Fuctios Epoetial fuctios are used i umerous applicatios coverig may fields of study. They are probably the most importat group
More informationHonors Calculus Homework 13 Solutions, due 12/8/5
Hoors Calculus Homework Solutios, due /8/5 Questio Let a regio R i the plae be bouded by the curves y = 5 ad = 5y y. Sketch the regio R. The two curves meet where both equatios hold at oce, so where: y
More informationOlli Simula T / Chapter 1 3. Olli Simula T / Chapter 1 5
Sigals ad Systems Sigals ad Systems Sigals are variables that carry iformatio Systemstake sigals as iputs ad produce sigals as outputs The course deals with the passage of sigals through systems T-6.4
More informationU8L1: Sec Equations of Lines in R 2
MCVU Thursda Ma, Review of Equatios of a Straight Lie (-D) U8L Sec. 8.9. Equatios of Lies i R Cosider the lie passig through A (-,) with slope, as show i the diagram below. I poit slope form, the equatio
More informationFINALTERM EXAMINATION Fall 9 Calculus & Aalytical Geometry-I Questio No: ( Mars: ) - Please choose oe Let f ( x) is a fuctio such that as x approaches a real umber a, either from left or right-had-side,
More informationPAPER : IIT-JAM 2010
MATHEMATICS-MA (CODE A) Q.-Q.5: Oly oe optio is correct for each questio. Each questio carries (+6) marks for correct aswer ad ( ) marks for icorrect aswer.. Which of the followig coditios does NOT esure
More informationDesign Data 16. Partial Flow Conditions For Culverts. x S o Q F = C 1 = (3) A F V F Q = x A x R2/3 x S o n 1/2 (2) 1 DD 16 (07/09)
Desig Data 16 Partial Flow Coditios For Culverts Sewers, both saitary ad storm, are desiged to carry a peak flow based o aticipated lad developmet. The hydraulic capacity of sewers or culverts costructed
More information62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +
62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of
More informationApply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j.
Eigevalue-Eigevector Istructor: Nam Su Wag eigemcd Ay vector i real Euclidea space of dimesio ca be uiquely epressed as a liear combiatio of liearly idepedet vectors (ie, basis) g j, j,,, α g α g α g α
More informationPhysics 324, Fall Dirac Notation. These notes were produced by David Kaplan for Phys. 324 in Autumn 2001.
Physics 324, Fall 2002 Dirac Notatio These otes were produced by David Kapla for Phys. 324 i Autum 2001. 1 Vectors 1.1 Ier product Recall from liear algebra: we ca represet a vector V as a colum vector;
More informationWe will conclude the chapter with the study a few methods and techniques which are useful
Chapter : Coordiate geometry: I this chapter we will lear about the mai priciples of graphig i a dimesioal (D) Cartesia system of coordiates. We will focus o drawig lies ad the characteristics of the graphs
More informationDetermination of Manning s Flow Resistance Coefficient for Rivers in Malaysia
Determiatio of Maig s Flow Resistace Coefficiet for Rivers i Malaysia AHMAD BAKRI ABDUL GHAFFAR, Research Studet, School of Civil Egieerig, Uiversiti Sais Malaysia, Egieerig Campus, Seri Ampaga, 143 Nibog
More informationSection 1.1. Calculus: Areas And Tangents. Difference Equations to Differential Equations
Differece Equatios to Differetial Equatios Sectio. Calculus: Areas Ad Tagets The study of calculus begis with questios about chage. What happes to the velocity of a swigig pedulum as its positio chages?
More informationCHAPTER 8 SYSTEMS OF PARTICLES
CHAPTER 8 SYSTES OF PARTICLES CHAPTER 8 COLLISIONS 45 8. CENTER OF ASS The ceter of mass of a system of particles or a rigid body is the poit at which all of the mass are cosidered to be cocetrated there
More informationLecture 7: Density Estimation: k-nearest Neighbor and Basis Approach
STAT 425: Itroductio to Noparametric Statistics Witer 28 Lecture 7: Desity Estimatio: k-nearest Neighbor ad Basis Approach Istructor: Ye-Chi Che Referece: Sectio 8.4 of All of Noparametric Statistics.
More informationSummary: CORRELATION & LINEAR REGRESSION. GC. Students are advised to refer to lecture notes for the GC operations to obtain scatter diagram.
Key Cocepts: 1) Sketchig of scatter diagram The scatter diagram of bivariate (i.e. cotaiig two variables) data ca be easily obtaied usig GC. Studets are advised to refer to lecture otes for the GC operatios
More informationChapter 4. Fourier Series
Chapter 4. Fourier Series At this poit we are ready to ow cosider the caoical equatios. Cosider, for eample the heat equatio u t = u, < (4.) subject to u(, ) = si, u(, t) = u(, t) =. (4.) Here,
More informationAPPLICATION OF ERGUN EQUATION TO COMPUTATION OF CRITICAL SHEAR VELOCITY SUBJECT TO SEEPAGE
Citatio: Cheg, N. S. (). Applicatio of Ergu equatio to computatio of critical shear velocity subject to seepage. Joural of Irrigatio ad Draiage Egieerig, ASCE. 9(4), 78-8. APPLICATION OF ERGUN EQUATION
More information1 of 7 7/16/2009 6:06 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 6. Order Statistics Defiitios Suppose agai that we have a basic radom experimet, ad that X is a real-valued radom variable
More informationCS322: Network Analysis. Problem Set 2 - Fall 2009
Due October 9 009 i class CS3: Network Aalysis Problem Set - Fall 009 If you have ay questios regardig the problems set, sed a email to the course assistats: simlac@staford.edu ad peleato@staford.edu.
More informationWind Energy Explained, 2 nd Edition Errata
Wid Eergy Explaied, d Editio Errata This summarizes the ko errata i Wid Eergy Explaied, d Editio. The errata ere origially compiled o July 6, 0. Where possible, the chage or locatio of the chage is oted
More informationMATH Exam 1 Solutions February 24, 2016
MATH 7.57 Exam Solutios February, 6. Evaluate (A) l(6) (B) l(7) (C) l(8) (D) l(9) (E) l() 6x x 3 + dx. Solutio: D We perform a substitutio. Let u = x 3 +, so du = 3x dx. Therefore, 6x u() x 3 + dx = [
More informationSeptember 2012 C1 Note. C1 Notes (Edexcel) Copyright - For AS, A2 notes and IGCSE / GCSE worksheets 1
September 0 s (Edecel) Copyright www.pgmaths.co.uk - For AS, A otes ad IGCSE / GCSE worksheets September 0 Copyright www.pgmaths.co.uk - For AS, A otes ad IGCSE / GCSE worksheets September 0 Copyright
More informationLecture 9: Diffusion, Electrostatics review, and Capacitors. Context
EECS 5 Sprig 4, Lecture 9 Lecture 9: Diffusio, Electrostatics review, ad Capacitors EECS 5 Sprig 4, Lecture 9 Cotext I the last lecture, we looked at the carriers i a eutral semicoductor, ad drift currets
More informationChE 471 Lecture 10 Fall 2005 SAFE OPERATION OF TUBULAR (PFR) ADIABATIC REACTORS
SAFE OPERATION OF TUBULAR (PFR) ADIABATIC REACTORS I a exothermic reactio the temperature will cotiue to rise as oe moves alog a plug flow reactor util all of the limitig reactat is exhausted. Schematically
More informationThe Method of Least Squares. To understand least squares fitting of data.
The Method of Least Squares KEY WORDS Curve fittig, least square GOAL To uderstad least squares fittig of data To uderstad the least squares solutio of icosistet systems of liear equatios 1 Motivatio Curve
More informationMATH 1080: Calculus of One Variable II Fall 2017 Textbook: Single Variable Calculus: Early Transcendentals, 7e, by James Stewart.
MATH 1080: Calculus of Oe Variable II Fall 2017 Textbook: Sigle Variable Calculus: Early Trascedetals, 7e, by James Stewart Uit 3 Skill Set Importat: Studets should expect test questios that require a
More informationSuggested solutions TEP4170 Heat and combustion technology 25 May 2016 by Ivar S. Ertesvåg. Revised 31 May 2016
Suggested solutios TEP470 Heat ad combustio techology 5 May 06 by Ivar S Ertesvåg Revised 3 May 06 ) Itroduce the Reyolds decompositio: ui ui u i (mea ad fluctuatio) ito the give equatio Average the equatio
More informationMATHEMATICS. 61. The differential equation representing the family of curves where c is a positive parameter, is of
MATHEMATICS 6 The differetial equatio represetig the family of curves where c is a positive parameter, is of Order Order Degree (d) Degree (a,c) Give curve is y c ( c) Differetiate wrt, y c c y Hece differetial
More information( a) ( ) 1 ( ) 2 ( ) ( ) 3 3 ( ) =!
.8,.9: Taylor ad Maclauri Series.8. Although we were able to fid power series represetatios for a limited group of fuctios i the previous sectio, it is ot immediately obvious whether ay give fuctio has
More informationThe Growth of Functions. Theoretical Supplement
The Growth of Fuctios Theoretical Supplemet The Triagle Iequality The triagle iequality is a algebraic tool that is ofte useful i maipulatig absolute values of fuctios. The triagle iequality says that
More informationPrice per tonne of sand ($) A B C
. Burkig would like to purchase cemet, iro ad sad eeded for his costructio project. He approached three suppliers A, B ad C to equire about their sellig prices for the materials. The total prices quoted
More informationSOLUTIONS: ECE 606 Homework Week 7 Mark Lundstrom Purdue University (revised 3/27/13) e E i E T
SOUIONS: ECE 606 Homework Week 7 Mark udstrom Purdue Uiversity (revised 3/27/13) 1) Cosider a - type semicoductor for which the oly states i the badgap are door levels (i.e. ( E = E D ). Begi with the
More information! = Chemical Engineering Class 35 page 1. I. Non-Newtonian Fluids - Overview A. Simple Classifications of Fluids 1. Shear-Dependent Fluids.
Chemical Egieerig 74 - Class 5 page I. No-Newtoia Fluids - Overview A. Simple Classificatios of Fluids. Shear-epedet Fluids a. Newtoia obeys Newtos Law of viscosity) µ!! = µ dv x dy dv /dy x dv /dy x c.
More informationRecursive Algorithms. Recurrences. Recursive Algorithms Analysis
Recursive Algorithms Recurreces Computer Sciece & Egieerig 35: Discrete Mathematics Christopher M Bourke cbourke@cseuledu A recursive algorithm is oe i which objects are defied i terms of other objects
More informationLinear Regression Models
Liear Regressio Models Dr. Joh Mellor-Crummey Departmet of Computer Sciece Rice Uiversity johmc@cs.rice.edu COMP 528 Lecture 9 15 February 2005 Goals for Today Uderstad how to Use scatter diagrams to ispect
More information1 Approximating Integrals using Taylor Polynomials
Seughee Ye Ma 8: Week 7 Nov Week 7 Summary This week, we will lear how we ca approximate itegrals usig Taylor series ad umerical methods. Topics Page Approximatig Itegrals usig Taylor Polyomials. Defiitios................................................
More informationUniform Channel Flow Basic Concepts Hydromechanics VVR090
Uniform Channel Flow Basic Concepts Hydromechanics VVR090 ppt by Magnus Larson; revised by Rolf L Feb 2014 SYNOPSIS 1. Definition of Uniform Flow 2. Momentum Equation for Uniform Flow 3. Resistance equations
More informationInfinite Sequences and Series
Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet
More informationDefinition 4.2. (a) A sequence {x n } in a Banach space X is a basis for X if. unique scalars a n (x) such that x = n. a n (x) x n. (4.
4. BASES I BAACH SPACES 39 4. BASES I BAACH SPACES Sice a Baach space X is a vector space, it must possess a Hamel, or vector space, basis, i.e., a subset {x γ } γ Γ whose fiite liear spa is all of X ad
More informationUniform Channel Flow Basic Concepts. Definition of Uniform Flow
Uniform Channel Flow Basic Concepts Hydromechanics VVR090 Uniform occurs when: Definition of Uniform Flow 1. The depth, flow area, and velocity at every cross section is constant 2. The energy grade line,
More informationb i u x i U a i j u x i u x j
M ath 5 2 7 Fall 2 0 0 9 L ecture 1 9 N ov. 1 6, 2 0 0 9 ) S ecod- Order Elliptic Equatios: Weak S olutios 1. Defiitios. I this ad the followig two lectures we will study the boudary value problem Here
More informationMath 112 Fall 2018 Lab 8
Ma Fall 08 Lab 8 Sums of Coverget Series I Itroductio Ofte e fuctios used i e scieces are defied as ifiite series Determiig e covergece or divergece of a series becomes importat ad it is helpful if e sum
More informationEECS564 Estimation, Filtering, and Detection Hwk 2 Solns. Winter p θ (z) = (2θz + 1 θ), 0 z 1
EECS564 Estimatio, Filterig, ad Detectio Hwk 2 Sols. Witer 25 4. Let Z be a sigle observatio havig desity fuctio where. p (z) = (2z + ), z (a) Assumig that is a oradom parameter, fid ad plot the maximum
More informationWhat is Uniform flow (normal flow)? Uniform flow means that depth (and velocity) remain constant over a certain reach of the channel.
Hydraulic Lecture # CWR 4 age () Lecture # Outlie: Uiform flow i rectagular cael (age 7-7) Review for tet Aoucemet: Wat i Uiform flow (ormal flow)? Uiform flow mea tat det (ad velocity) remai cotat over
More information1.3 Convergence Theorems of Fourier Series. k k k k. N N k 1. With this in mind, we state (without proof) the convergence of Fourier series.
.3 Covergece Theorems of Fourier Series I this sectio, we preset the covergece of Fourier series. A ifiite sum is, by defiitio, a limit of partial sums, that is, a cos( kx) b si( kx) lim a cos( kx) b si(
More informationis also known as the general term of the sequence
Lesso : Sequeces ad Series Outlie Objectives: I ca determie whether a sequece has a patter. I ca determie whether a sequece ca be geeralized to fid a formula for the geeral term i the sequece. I ca determie
More informationOffice: JILA A709; Phone ;
Office: JILA A709; Phoe 303-49-7841; email: weberjm@jila.colorado.edu Problem Set 5 To be retured before the ed of class o Wedesday, September 3, 015 (give to me i perso or slide uder office door). 1.
More informationCHAPTER 10 INFINITE SEQUENCES AND SERIES
CHAPTER 10 INFINITE SEQUENCES AND SERIES 10.1 Sequeces 10.2 Ifiite Series 10.3 The Itegral Tests 10.4 Compariso Tests 10.5 The Ratio ad Root Tests 10.6 Alteratig Series: Absolute ad Coditioal Covergece
More informationPeriod #8: Fluid Flow in Soils (II)
Period #8: Fluid Flow i Soils (II) 53:030 Class Notes; C.C. Swa, Uiversity of Iowa A. Measurig Permeabilities i Soils 1. The Costat Head Test (For Coarse Graied Soils): Upstream ad dowstream head elevatios
More informationMathematics: Paper 1
GRADE 1 EXAMINATION JULY 013 Mathematics: Paper 1 EXAMINER: Combied Paper MODERATORS: JE; RN; SS; AVDB TIME: 3 Hours TOTAL: 150 PLEASE READ THE FOLLOWING INSTRUCTIONS CAREFULLY 1. This questio paper cosists
More informationCHAPTER 5. Theory and Solution Using Matrix Techniques
A SERIES OF CLASS NOTES FOR 2005-2006 TO INTRODUCE LINEAR AND NONLINEAR PROBLEMS TO ENGINEERS, SCIENTISTS, AND APPLIED MATHEMATICIANS DE CLASS NOTES 3 A COLLECTION OF HANDOUTS ON SYSTEMS OF ORDINARY DIFFERENTIAL
More informationCastiel, Supernatural, Season 6, Episode 18
13 Differetial Equatios the aswer to your questio ca best be epressed as a series of partial differetial equatios... Castiel, Superatural, Seaso 6, Episode 18 A differetial equatio is a mathematical equatio
More informationMath 113, Calculus II Winter 2007 Final Exam Solutions
Math, Calculus II Witer 7 Fial Exam Solutios (5 poits) Use the limit defiitio of the defiite itegral ad the sum formulas to compute x x + dx The check your aswer usig the Evaluatio Theorem Solutio: I this
More informationProperties and Hypothesis Testing
Chapter 3 Properties ad Hypothesis Testig 3.1 Types of data The regressio techiques developed i previous chapters ca be applied to three differet kids of data. 1. Cross-sectioal data. 2. Time series data.
More informationMath 21B-B - Homework Set 2
Math B-B - Homework Set Sectio 5.:. a) lim P k= c k c k ) x k, where P is a partitio of [, 5. x x ) dx b) lim P k= 4 ck x k, where P is a partitio of [,. 4 x dx c) lim P k= ta c k ) x k, where P is a partitio
More informationPhysics 116A Solutions to Homework Set #1 Winter Boas, problem Use equation 1.8 to find a fraction describing
Physics 6A Solutios to Homework Set # Witer 0. Boas, problem. 8 Use equatio.8 to fid a fractio describig 0.694444444... Start with the formula S = a, ad otice that we ca remove ay umber of r fiite decimals
More informationCalculus 2 Test File Fall 2013
Calculus Test File Fall 013 Test #1 1.) Without usig your calculator, fid the eact area betwee the curves f() = 4 - ad g() = si(), -1 < < 1..) Cosider the followig solid. Triagle ABC is perpedicular to
More informationQuiz. Use either the RATIO or ROOT TEST to determine whether the series is convergent or not.
Quiz. Use either the RATIO or ROOT TEST to determie whether the series is coverget or ot. e .6 POWER SERIES Defiitio. A power series i about is a series of the form c 0 c a c a... c a... a 0 c a where
More informationSection 1.4. Power Series
Sectio.4. Power Series De itio. The fuctio de ed by f (x) (x a) () c 0 + c (x a) + c 2 (x a) 2 + c (x a) + ::: is called a power series cetered at x a with coe ciet sequece f g :The domai of this fuctio
More informationResponse Variable denoted by y it is the variable that is to be predicted measure of the outcome of an experiment also called the dependent variable
Statistics Chapter 4 Correlatio ad Regressio If we have two (or more) variables we are usually iterested i the relatioship betwee the variables. Associatio betwee Variables Two variables are associated
More informationMECHANICAL INTEGRITY DESIGN FOR FLARE SYSTEM ON FLOW INDUCED VIBRATION
MECHANICAL INTEGRITY DESIGN FOR FLARE SYSTEM ON FLOW INDUCED VIBRATION Hisao Izuchi, Pricipal Egieerig Cosultat, Egieerig Solutio Uit, ChAS Project Operatios Masato Nishiguchi, Egieerig Solutio Uit, ChAS
More informationProblem Cosider the curve give parametrically as x = si t ad y = + cos t for» t» ß: (a) Describe the path this traverses: Where does it start (whe t =
Mathematics Summer Wilso Fial Exam August 8, ANSWERS Problem 1 (a) Fid the solutio to y +x y = e x x that satisfies y() = 5 : This is already i the form we used for a first order liear differetial equatio,
More informationBACKMIXING IN SCREW EXTRUDERS
BACKMIXING IN SCREW EXTRUDERS Chris Rauwedaal, Rauwedaal Extrusio Egieerig, Ic. Paul Grama, The Madiso Group Abstract Mixig is a critical fuctio i most extrusio operatios. Oe of the most difficult mixig
More informationECE 8527: Introduction to Machine Learning and Pattern Recognition Midterm # 1. Vaishali Amin Fall, 2015
ECE 8527: Itroductio to Machie Learig ad Patter Recogitio Midterm # 1 Vaishali Ami Fall, 2015 tue39624@temple.edu Problem No. 1: Cosider a two-class discrete distributio problem: ω 1 :{[0,0], [2,0], [2,2],
More informationOutline. Linear regression. Regularization functions. Polynomial curve fitting. Stochastic gradient descent for regression. MLE for regression
REGRESSION 1 Outlie Liear regressio Regularizatio fuctios Polyomial curve fittig Stochastic gradiet descet for regressio MLE for regressio Step-wise forward regressio Regressio methods Statistical techiques
More informationSequences and Series of Functions
Chapter 6 Sequeces ad Series of Fuctios 6.1. Covergece of a Sequece of Fuctios Poitwise Covergece. Defiitio 6.1. Let, for each N, fuctio f : A R be defied. If, for each x A, the sequece (f (x)) coverges
More informationA sequence of numbers is a function whose domain is the positive integers. We can see that the sequence
Sequeces A sequece of umbers is a fuctio whose domai is the positive itegers. We ca see that the sequece,, 2, 2, 3, 3,... is a fuctio from the positive itegers whe we write the first sequece elemet as
More information1 Introduction to reducing variance in Monte Carlo simulations
Copyright c 010 by Karl Sigma 1 Itroductio to reducig variace i Mote Carlo simulatios 11 Review of cofidece itervals for estimatig a mea I statistics, we estimate a ukow mea µ = E(X) of a distributio by
More information