UNIFORM FLOW. U x. U t

Size: px
Start display at page:

Download "UNIFORM FLOW. U x. U t"

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

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 information

4.1 Introduction. 4. Uniform Flow and its Computations

4.1 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 information

Free Surface Hydrodynamics

Free 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 information

Module 3d: Flow in Pipes Manning s Equation

Module 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 information

On the Blasius correlation for friction factors

On 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 information

Chemical Engineering 374

Chemical 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 information

Using vegetation properties to predict flow resistance and erosion rates. Nick Kouwen University of Waterloo Waterloo, Canada

Using 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 information

11 Correlation and Regression

11 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 information

SECTION 2 Electrostatics

SECTION 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 information

17 Phonons and conduction electrons in solids (Hiroshi Matsuoka)

17 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 information

Salmon: Lectures on partial differential equations. 3. First-order linear equations as the limiting case of second-order equations

Salmon: 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 information

Time-Domain Representations of LTI Systems

Time-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 information

The 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.

The 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 information

Most text will write ordinary derivatives using either Leibniz notation 2 3. y + 5y= e and y y. xx tt t

Most 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 information

Maximum and Minimum Values

Maximum 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 information

EXPERIMENTAL 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 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 information

3. 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 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 information

f(x) dx as we do. 2x dx x also diverges. Solution: We compute 2x dx lim

f(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 information

Fluid Physics 8.292J/12.330J % (1)

Fluid 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 information

Series: Infinite Sums

Series: 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 information

Chapter Vectors

Chapter 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 information

Mechatronics. Time Response & Frequency Response 2 nd -Order Dynamic System 2-Pole, Low-Pass, Active Filter

Mechatronics. 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 information

TOTAL 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 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 information

Fall 2013 MTH431/531 Real analysis Section Notes

Fall 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 information

UNIFORM FLOW CRITICAL FLOW GRADUALLY VARIED FLOW

UNIFORM 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 information

DAY 19: Boundary Layer

DAY 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 information

Simple Linear Regression

Simple 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 information

Finally, we show how to determine the moments of an impulse response based on the example of the dispersion model.

Finally, 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 information

U8L1: Sec Equations of Lines in R 2

U8L1: 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 information

Polynomial Functions and Their Graphs

Polynomial 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 information

6 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 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 information

Precalculus MATH Sections 3.1, 3.2, 3.3. Exponential, Logistic and Logarithmic Functions

Precalculus 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 information

Honors Calculus Homework 13 Solutions, due 12/8/5

Honors 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 information

Olli Simula T / Chapter 1 3. Olli Simula T / Chapter 1 5

Olli 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 information

U8L1: Sec Equations of Lines in R 2

U8L1: 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 information

FINALTERM 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 information

PAPER : IIT-JAM 2010

PAPER : 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 information

Design 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)

Design 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 information

62. 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 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 information

Apply change-of-basis formula to rewrite x as a linear combination of eigenvectors v j.

Apply 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 information

Physics 324, Fall Dirac Notation. These notes were produced by David Kaplan for Phys. 324 in Autumn 2001.

Physics 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 information

We will conclude the chapter with the study a few methods and techniques which are useful

We 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 information

Determination of Manning s Flow Resistance Coefficient for Rivers in Malaysia

Determination 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 information

Section 1.1. Calculus: Areas And Tangents. Difference Equations to Differential Equations

Section 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 information

CHAPTER 8 SYSTEMS OF PARTICLES

CHAPTER 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 information

Lecture 7: Density Estimation: k-nearest Neighbor and Basis Approach

Lecture 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 information

Summary: CORRELATION & LINEAR REGRESSION. GC. Students are advised to refer to lecture notes for the GC operations to obtain scatter diagram.

Summary: 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 information

Chapter 4. Fourier Series

Chapter 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 information

APPLICATION OF ERGUN EQUATION TO COMPUTATION OF CRITICAL SHEAR VELOCITY SUBJECT TO SEEPAGE

APPLICATION 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 information

1 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 information

CS322: Network Analysis. Problem Set 2 - Fall 2009

CS322: 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 information

Wind Energy Explained, 2 nd Edition Errata

Wind 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 information

MATH Exam 1 Solutions February 24, 2016

MATH 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 information

September 2012 C1 Note. C1 Notes (Edexcel) Copyright - For AS, A2 notes and IGCSE / GCSE worksheets 1

September 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 information

Lecture 9: Diffusion, Electrostatics review, and Capacitors. Context

Lecture 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 information

ChE 471 Lecture 10 Fall 2005 SAFE OPERATION OF TUBULAR (PFR) ADIABATIC REACTORS

ChE 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 information

The Method of Least Squares. To understand least squares fitting of data.

The 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 information

MATH 1080: Calculus of One Variable II Fall 2017 Textbook: Single Variable Calculus: Early Transcendentals, 7e, by James Stewart.

MATH 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 information

Suggested solutions TEP4170 Heat and combustion technology 25 May 2016 by Ivar S. Ertesvåg. Revised 31 May 2016

Suggested 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 information

MATHEMATICS. 61. The differential equation representing the family of curves where c is a positive parameter, is of

MATHEMATICS. 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 ( ) =!

( 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 information

The Growth of Functions. Theoretical Supplement

The 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 information

Price per tonne of sand ($) A B C

Price 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 information

SOLUTIONS: ECE 606 Homework Week 7 Mark Lundstrom Purdue University (revised 3/27/13) e E i E T

SOLUTIONS: 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 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 information

Recursive Algorithms. Recurrences. Recursive Algorithms Analysis

Recursive 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 information

Linear Regression Models

Linear 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 information

1 Approximating Integrals using Taylor Polynomials

1 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 information

Uniform Channel Flow Basic Concepts Hydromechanics VVR090

Uniform 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 information

Infinite Sequences and Series

Infinite 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 information

Definition 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.

Definition 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 information

Uniform Channel Flow Basic Concepts. Definition of Uniform Flow

Uniform 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 information

b i u x i U a i j u x i u x j

b 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 information

Math 112 Fall 2018 Lab 8

Math 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 information

EECS564 Estimation, Filtering, and Detection Hwk 2 Solns. Winter p θ (z) = (2θz + 1 θ), 0 z 1

EECS564 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 information

What is Uniform flow (normal flow)? Uniform flow means that depth (and velocity) remain constant over a certain reach of the channel.

What 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 information

1.3 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.

1.3 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 information

is also known as the general term of the sequence

is 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 information

Office: JILA A709; Phone ;

Office: 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 information

CHAPTER 10 INFINITE SEQUENCES AND SERIES

CHAPTER 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 information

Period #8: Fluid Flow in Soils (II)

Period #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 information

Mathematics: Paper 1

Mathematics: 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 information

CHAPTER 5. Theory and Solution Using Matrix Techniques

CHAPTER 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 information

Castiel, Supernatural, Season 6, Episode 18

Castiel, 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 information

Math 113, Calculus II Winter 2007 Final Exam Solutions

Math 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 information

Properties and Hypothesis Testing

Properties 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 information

Math 21B-B - Homework Set 2

Math 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 information

Physics 116A Solutions to Homework Set #1 Winter Boas, problem Use equation 1.8 to find a fraction describing

Physics 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 information

Calculus 2 Test File Fall 2013

Calculus 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 information

Quiz. 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 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 information

Section 1.4. Power Series

Section 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 information

Response 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

Response 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 information

MECHANICAL INTEGRITY DESIGN FOR FLARE SYSTEM ON FLOW INDUCED VIBRATION

MECHANICAL 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 information

Problem 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 =

Problem 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 information

BACKMIXING IN SCREW EXTRUDERS

BACKMIXING 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 information

ECE 8527: Introduction to Machine Learning and Pattern Recognition Midterm # 1. Vaishali Amin Fall, 2015

ECE 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 information

Outline. Linear regression. Regularization functions. Polynomial curve fitting. Stochastic gradient descent for regression. MLE for regression

Outline. 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 information

Sequences and Series of Functions

Sequences 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 information

A sequence of numbers is a function whose domain is the positive integers. We can see that the sequence

A 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 information

1 Introduction to reducing variance in Monte Carlo simulations

1 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