Lecture 20 - Wave Propagation Response

Size: px
Start display at page:

Download "Lecture 20 - Wave Propagation Response"

Transcription

1 .09/.093 Fiite Eleet Aalysis of Solids & Fluids I Fall 09 Lecture 0 - Wave Propagatio Respose Prof. K. J. Bathe MIT OpeCourseWare Quiz #: Closed book, 6 pages of otes, o calculators. Covers all aterials icludig this week s lectures. L w C = tw (C depeds o aterial properties) For this syste, C is the wave speed (give), L w is the critical wavelegth to be represeted, t w is the total tie for this wave to travel past a poit, L e is the effective legth of a fiite eleet, ad is equivalet to Lw (give). To solve, we should use = L e C. Mesh L e should be saller tha the shortest wave legth we wat to pick up. To establish a esh, we use low-order eleets (4-ode eleets i D, 8-ode eleets i 3D). We use the cetral differece ethod, which requires stability. We eed to esure that cr = ω. Recall that i oliear aalysis, the wave speed chages. We kow that { } () ω ax ω { } () where ω is the largest frequecy of a assebled fiite eleet esh ad ax ω is the largest eleet frequecy of all eleets i the esh. The we ca use ad coservatively, we use a slightly saller value. = { } () ax ω 1

2 Lecture 0 Wave Propagatio Respose.09/.093, Fall 09 () How to Fid ω () For lower-order eleets, we have bouds for ω. (See Sectios 9.3/9.4 ad Table 9.5 that gives forulae for ω ().) Proof: We kow ( ) φ T ΣK () φ ω = ( ) (a) φ T ΣM () φ ω Σ () = U (b) Σ J () U () = φ T K () φ J () = φ T M () φ Note that K () is of the sae size as K. Also, for a eleet (), sice φ is ot a eigevector for K () : φ T K () φ ( ) ω () φ T M () φ ( ) U () J () ω () (c) Plug (c) ito (b) ad we have ( ) () () Σ ω { ( ) } ω J J ax ω () () Σ I practice, wave propagatio probles are very difficult to solve, due to reflectios, absorptios, ad the ay differet wave types (shear waves, etc.). As etioed, the cetral differece ethod is alost always used for wave propagatio solutios, because wave propagatio odelig eeds fie eshes. I beas, rotatioal DOFs result i high frequecies, so rotatioal ass is frequetly set to zero. If we have zero asses i the odel, we eed to use static codesatio. [ ] [ ] [ ] [ ] K aa K ab φ a = ω M a 0 φ a K ba K bb φ b 0 0 φ b No ass o b-dofs Use the d equatio to eliiate φ b fro the 1st equatio. K ba φ a + K bb φ b = 0 φ b = K 1 bb K baφ a We obtai ( ) K aa K ab K 1 K ba φ a = ω M a φ a bb It is the sae as Gauss eliiatio o b-dofs. Hece, if there are zero asses, we use static codesatio prior to the use of the cetral differece ethod. All we have discussed regardig trasiet/dyaic aalysis is also applicable (with odificatios!) i heat trasfer & fluid flow aalysis.

3 Lecture 0 Wave Propagatio Respose.09/.093, Fall 09 Trasiet Heat Trasfer Aalysis The goverig fiite eleet equatio is Cθ + Kθ = Q (give 0 θ) (A) where C is the heat capacity atrix, K is the coductivity atrix, ad Q is the heat flow iput vector. I. Mode Superpositio Frequetly, heat trasfer solutios ca be obtaied with coarser eshes. Let s exaie the syste Assue Cθ + Kθ = 0 (o heat flow iput) θ = e λt φ λ e λt Cφ + K e λt φ = 0 K φ = λcφ The eigevalues are 0 λ 1 λ... λ, ad the eigevectors are φ 1, φ,..., φ. For costat teperature over the esh, we have λ 1 = 0. Also: φ T i Cφ j = δ ij (B) We obtai the decoupled equatios. φ T i Kφ j = λ i δ ij η i + λ i η i = q i (i = 1,..., ) ; 0 η i (C) θ = Σ φ i η i i=1 [ ] Φ T CΦ = I ; Φ = φ 1... φ θ = Φη 0 η = Φ T C 0 θ Φ T KΦ =... λ i... q = Φ T Q q i = φ i T Q To solve Eq. (C) or perfor direct itegratio o Eq. (A), we ca use the Euler backward ethod or the Euler forward ethod. II. The Euler Backward Method Readig assiget: Sectio 9.6 ( ) t+ θ t+ θ t θ = Use Eq. (A) at tie t + to solve for t+ θ. This is a iplicit ethod, ad is ucoditioally stable. oly eeds to be selected for accuracy. 3

4 Lecture 0 Wave Propagatio Respose.09/.093, Fall 09 III. The Euler Forward Method The, use Eq. (A) at tie t. t θ = t+ θ t θ C t θ + K t θ = t Q 1 ( ) 1 C t+ θ = C t θ K t θ + t Q If C is a diagoal atrix, the o factorizatio is ivolved. The Euler forward ethod results i: 1 C t+ θ = t Qˆ The ethod is coditioally stable, ad ust satisfy Recall: Hece, Kφ = ω Mφ is the eigevalue proble. cr = λ MU + KU = 0 U = φ si ω (t τ) U = ω φ si ω (t τ ) Mω φ si ω (t τ ) + Kφ si ω (t τ ) = 0 η i + λ i η i = q i cr = λ ẍ i + ω i x i = r i cr = ω The explicit ethod uses the goverig equatios at tie t to obtai the solutio at t +. The iplicit ethod uses the goverig equatios at tie t + to obtai the solutio at t +. 4

5 MIT OpeCourseWare /.093 Fiite Eleet Aalysis of Solids ad Fluids I Fall 009 For iforatio about citig these aterials or our Ters of Use, visit:

5.6 Binomial Multi-section Matching Transformer

5.6 Binomial Multi-section Matching Transformer 4/14/21 5_6 Bioial Multisectio Matchig Trasforers 1/1 5.6 Bioial Multi-sectio Matchig Trasforer Readig Assiget: pp. 246-25 Oe way to axiize badwidth is to costruct a ultisectio Γ f that is axially flat.

More information

Student Supplement for Optoelectronics and Photonics: Principles and Practices Second Edition A Complete Course in Power Point.

Student Supplement for Optoelectronics and Photonics: Principles and Practices Second Edition A Complete Course in Power Point. Studet Suppleet for Optoelectroics ad Photoics: Priciples ad Practices Secod Editio A Coplete Course i Power Poit Chapter [ February 04] Copyright Iforatio ad Perissio: Part I This Power Poit presetatio

More information

ECE Spring Prof. David R. Jackson ECE Dept. Notes 20

ECE Spring Prof. David R. Jackson ECE Dept. Notes 20 ECE 6341 Sprig 016 Prof. David R. Jackso ECE Dept. Notes 0 1 Spherical Wave Fuctios Cosider solvig ψ + k ψ = 0 i spherical coordiates z φ θ r y x Spherical Wave Fuctios (cot.) I spherical coordiates we

More information

Transfer Function Analysis

Transfer Function Analysis Trasfer Fuctio Aalysis Free & Forced Resposes Trasfer Fuctio Syste Stability ME375 Trasfer Fuctios - Free & Forced Resposes Ex: Let s s look at a stable first order syste: τ y + y = Ku Take LT of the I/O

More information

The Binomial Multi-Section Transformer

The Binomial Multi-Section Transformer 4/15/2010 The Bioial Multisectio Matchig Trasforer preset.doc 1/24 The Bioial Multi-Sectio Trasforer Recall that a ulti-sectio atchig etwork ca be described usig the theory of sall reflectios as: where:

More information

Lecture Outline. 2 Separating Hyperplanes. 3 Banach Mazur Distance An Algorithmist s Toolkit October 22, 2009

Lecture Outline. 2 Separating Hyperplanes. 3 Banach Mazur Distance An Algorithmist s Toolkit October 22, 2009 18.409 A Algorithist s Toolkit October, 009 Lecture 1 Lecturer: Joatha Keler Scribes: Alex Levi (009) 1 Outlie Today we ll go over soe of the details fro last class ad ake precise ay details that were

More information

5.6 Binomial Multi-section Matching Transformer

5.6 Binomial Multi-section Matching Transformer 4/14/2010 5_6 Bioial Multisectio Matchig Trasforers 1/1 5.6 Bioial Multi-sectio Matchig Trasforer Readig Assiget: pp. 246-250 Oe way to axiize badwidth is to costruct a ultisectio Γ f that is axially flat.

More information

The Binomial Multi- Section Transformer

The Binomial Multi- Section Transformer 4/4/26 The Bioial Multisectio Matchig Trasforer /2 The Bioial Multi- Sectio Trasforer Recall that a ulti-sectio atchig etwork ca be described usig the theory of sall reflectios as: where: ( ω ) = + e +

More information

Massachusetts Institute of Technology

Massachusetts Institute of Technology 6.0/6.3: Probabilistic Systems Aalysis (Fall 00) Problem Set 8: Solutios. (a) We cosider a Markov chai with states 0,,, 3,, 5, where state i idicates that there are i shoes available at the frot door i

More information

Ma/CS 6a Class 22: Power Series

Ma/CS 6a Class 22: Power Series Ma/CS 6a Class 22: Power Series By Ada Sheffer Power Series Mooial: ax i. Polyoial: a 0 + a 1 x + a 2 x 2 + + a x. Power series: A x = a 0 + a 1 x + a 2 x 2 + Also called foral power series, because we

More information

Chapter 2. Asymptotic Notation

Chapter 2. Asymptotic Notation Asyptotic Notatio 3 Chapter Asyptotic Notatio Goal : To siplify the aalysis of ruig tie by gettig rid of details which ay be affected by specific ipleetatio ad hardware. [1] The Big Oh (O-Notatio) : It

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

Define a Markov chain on {1,..., 6} with transition probability matrix P =

Define a Markov chain on {1,..., 6} with transition probability matrix P = Pla Group Work 0. The title says it all Next Tie: MCMC ad Geeral-state Markov Chais Midter Exa: Tuesday 8 March i class Hoework 4 due Thursday Uless otherwise oted, let X be a irreducible, aperiodic Markov

More information

School of Mechanical Engineering Purdue University. ME375 Transfer Functions - 1

School of Mechanical Engineering Purdue University. ME375 Transfer Functions - 1 Trasfer Fuctio Aalysis Free & Forced Resposes Trasfer Fuctio Syste Stability ME375 Trasfer Fuctios - 1 Free & Forced Resposes Ex: Let s look at a stable first order syste: y y Ku Take LT of the I/O odel

More information

Uncertainty Principle of Mathematics

Uncertainty Principle of Mathematics Septeber 27 Ucertaity Priciple of Matheatics Shachter Mourici Israel, Holo ourici@walla.co.il Preface This short paper prove that atheatically, Reality is ot real. This short paper is ot about Heiseberg's

More information

8.3 Perturbation theory

8.3 Perturbation theory 8.3 Perturbatio theory Slides: Video 8.3.1 Costructig erturbatio theory Text referece: Quatu Mechaics for Scietists ad gieers Sectio 6.3 (u to First order erturbatio theory ) Perturbatio theory Costructig

More information

19.1 The dictionary problem

19.1 The dictionary problem CS125 Lecture 19 Fall 2016 19.1 The dictioary proble Cosider the followig data structural proble, usually called the dictioary proble. We have a set of ites. Each ite is a (key, value pair. Keys are i

More information

Lecture 19. Curve fitting I. 1 Introduction. 2 Fitting a constant to measured data

Lecture 19. Curve fitting I. 1 Introduction. 2 Fitting a constant to measured data Lecture 9 Curve fittig I Itroductio Suppose we are preseted with eight poits of easured data (x i, y j ). As show i Fig. o the left, we could represet the uderlyig fuctio of which these data are saples

More information

Lecture 8: October 20, Applications of SVD: least squares approximation

Lecture 8: October 20, Applications of SVD: least squares approximation Mathematical Toolkit Autum 2016 Lecturer: Madhur Tulsiai Lecture 8: October 20, 2016 1 Applicatios of SVD: least squares approximatio We discuss aother applicatio of sigular value decompositio (SVD) of

More information

Supplementary Material

Supplementary Material Suppleetary Material Wezhuo Ya a0096049@us.edu.s Departet of Mechaical Eieeri, Natioal Uiversity of Siapore, Siapore 117576 Hua Xu pexuh@us.edu.s Departet of Mechaical Eieeri, Natioal Uiversity of Siapore,

More information

(s)h(s) = K( s + 8 ) = 5 and one finite zero is located at z 1

(s)h(s) = K( s + 8 ) = 5 and one finite zero is located at z 1 ROOT LOCUS TECHNIQUE 93 should be desiged differetly to eet differet specificatios depedig o its area of applicatio. We have observed i Sectio 6.4 of Chapter 6, how the variatio of a sigle paraeter like

More information

EECE 301 Signals & Systems

EECE 301 Signals & Systems EECE 301 Sigals & Systems Prof. Mark Fowler Note Set #8 D-T Covolutio: The Tool for Fidig the Zero-State Respose Readig Assigmet: Sectio 2.1-2.2 of Kame ad Heck 1/14 Course Flow Diagram The arrows here

More information

The state space model needs 5 parameters, so it is not as convenient to use in this control study.

The state space model needs 5 parameters, so it is not as convenient to use in this control study. Trasfer fuctio for of the odel G θ K ω 2 θ / v θ / v ( s) = = 2 2 vi s + 2ζωs + ω The followig slides detail a derivatio of this aalog eter odel both as state space odel ad trasfer fuctio (TF) as show

More information

EE Midterm Test 1 - Solutions

EE Midterm Test 1 - Solutions EE35 - Midterm Test - Solutios Total Poits: 5+ 6 Bous Poits Time: hour. ( poits) Cosider the parallel itercoectio of the two causal systems, System ad System 2, show below. System x[] + y[] System 2 The

More information

Signal Processing. Lecture 02: Discrete Time Signals and Systems. Ahmet Taha Koru, Ph. D. Yildiz Technical University.

Signal Processing. Lecture 02: Discrete Time Signals and Systems. Ahmet Taha Koru, Ph. D. Yildiz Technical University. Sigal Processig Lecture 02: Discrete Time Sigals ad Systems Ahmet Taha Koru, Ph. D. Yildiz Techical Uiversity 2017-2018 Fall ATK (YTU) Sigal Processig 2017-2018 Fall 1 / 51 Discrete Time Sigals Discrete

More information

Supplementary Information

Supplementary Information Suppleetary Iforatio -Breakdow of cotiuu fracture echaics at the aoscale- Takahiro Shiada,,* Keji Ouchi, Yuu Chihara, ad Takayuki Kitaura Departet of echaical Egieerig ad Sciece, Kyoto Uiversity, Nishikyo-ku,

More information

Engineering Mechanics Dynamics & Vibrations. Engineering Mechanics Dynamics & Vibrations Plane Motion of a Rigid Body: Equations of Motion

Engineering Mechanics Dynamics & Vibrations. Engineering Mechanics Dynamics & Vibrations Plane Motion of a Rigid Body: Equations of Motion 1/5/013 Egieerig Mechaics Dyaics ad Vibratios Egieerig Mechaics Dyaics & Vibratios Egieerig Mechaics Dyaics & Vibratios Plae Motio of a Rigid Body: Equatios of Motio Motio of a rigid body i plae otio is

More information

+ au n+1 + bu n = 0.)

+ au n+1 + bu n = 0.) Lecture 6 Recurreces - kth order: u +k + a u +k +... a k u k 0 where a... a k are give costats, u 0... u k are startig coditios. (Simple case: u + au + + bu 0.) How to solve explicitly - first, write characteristic

More information

ADVANCED DIGITAL SIGNAL PROCESSING

ADVANCED DIGITAL SIGNAL PROCESSING ADVANCED DIGITAL SIGNAL PROCESSING PROF. S. C. CHAN (email : sccha@eee.hku.hk, Rm. CYC-702) DISCRETE-TIME SIGNALS AND SYSTEMS MULTI-DIMENSIONAL SIGNALS AND SYSTEMS RANDOM PROCESSES AND APPLICATIONS ADAPTIVE

More information

Chapter 4 : Laplace Transform

Chapter 4 : Laplace Transform 4. Itroductio Laplace trasform is a alterative to solve the differetial equatio by the complex frequecy domai ( s = σ + jω), istead of the usual time domai. The DE ca be easily trasformed ito a algebraic

More information

ECE 901 Lecture 4: Estimation of Lipschitz smooth functions

ECE 901 Lecture 4: Estimation of Lipschitz smooth functions ECE 9 Lecture 4: Estiatio of Lipschitz sooth fuctios R. Nowak 5/7/29 Cosider the followig settig. Let Y f (X) + W, where X is a rado variable (r.v.) o X [, ], W is a r.v. o Y R, idepedet of X ad satisfyig

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

Numerical Methods for Ordinary Differential Equations

Numerical Methods for Ordinary Differential Equations Numerical Methods for Ordiary Differetial Equatios Braislav K. Nikolić Departmet of Physics ad Astroomy, Uiversity of Delaware, U.S.A. PHYS 460/660: Computatioal Methods of Physics http://www.physics.udel.edu/~bikolic/teachig/phys660/phys660.html

More information

Summer MA Lesson 13 Section 1.6, Section 1.7 (part 1)

Summer MA Lesson 13 Section 1.6, Section 1.7 (part 1) Suer MA 1500 Lesso 1 Sectio 1.6, Sectio 1.7 (part 1) I Solvig Polyoial Equatios Liear equatio ad quadratic equatios of 1 variable are specific types of polyoial equatios. Soe polyoial equatios of a higher

More information

TMA4205 Numerical Linear Algebra. The Poisson problem in R 2 : diagonalization methods

TMA4205 Numerical Linear Algebra. The Poisson problem in R 2 : diagonalization methods TMA4205 Numerical Liear Algebra The Poisso problem i R 2 : diagoalizatio methods September 3, 2007 c Eiar M Røquist Departmet of Mathematical Scieces NTNU, N-749 Trodheim, Norway All rights reserved A

More information

Signal Processing in Mechatronics. Lecture 3, Convolution, Fourier Series and Fourier Transform

Signal Processing in Mechatronics. Lecture 3, Convolution, Fourier Series and Fourier Transform Sigal Processig i Mechatroics Summer semester, 1 Lecture 3, Covolutio, Fourier Series ad Fourier rasform Dr. Zhu K.P. AIS, UM 1 1. Covolutio Covolutio Descriptio of LI Systems he mai premise is that the

More information

ECE 308 Discrete-Time Signals and Systems

ECE 308 Discrete-Time Signals and Systems ECE 38-5 ECE 38 Discrete-Time Sigals ad Systems Z. Aliyazicioglu Electrical ad Computer Egieerig Departmet Cal Poly Pomoa ECE 38-5 1 Additio, Multiplicatio, ad Scalig of Sequeces Amplitude Scalig: (A Costat

More information

Numerical Methods in Fourier Series Applications

Numerical Methods in Fourier Series Applications Numerical Methods i Fourier Series Applicatios Recall that the basic relatios i usig the Trigoometric Fourier Series represetatio were give by f ( x) a o ( a x cos b x si ) () where the Fourier coefficiets

More information

2D DSP Basics: Systems Stability, 2D Sampling

2D DSP Basics: Systems Stability, 2D Sampling - Digital Iage Processig ad Copressio D DSP Basics: Systes Stability D Saplig Stability ty Syste is stable if a bouded iput always results i a bouded output BIBO For LSI syste a sufficiet coditio for stability:

More information

Integrals of Functions of Several Variables

Integrals of Functions of Several Variables Itegrals of Fuctios of Several Variables We ofte resort to itegratios i order to deterie the exact value I of soe quatity which we are uable to evaluate by perforig a fiite uber of additio or ultiplicatio

More information

Chapter 7: The z-transform. Chih-Wei Liu

Chapter 7: The z-transform. Chih-Wei Liu Chapter 7: The -Trasform Chih-Wei Liu Outlie Itroductio The -Trasform Properties of the Regio of Covergece Properties of the -Trasform Iversio of the -Trasform The Trasfer Fuctio Causality ad Stability

More information

x[0] x[1] x[2] Figure 2.1 Graphical representation of a discrete-time signal.

x[0] x[1] x[2] Figure 2.1 Graphical representation of a discrete-time signal. x[ ] x[ ] x[] x[] x[] x[] 9 8 7 6 5 4 3 3 4 5 6 7 8 9 Figure. Graphical represetatio of a discrete-time sigal. From Discrete-Time Sigal Processig, e by Oppeheim, Schafer, ad Buck 999- Pretice Hall, Ic.

More information

10.6 ALTERNATING SERIES

10.6 ALTERNATING SERIES 0.6 Alteratig Series Cotemporary Calculus 0.6 ALTERNATING SERIES I the last two sectios we cosidered tests for the covergece of series whose terms were all positive. I this sectio we examie series whose

More information

Describing Function: An Approximate Analysis Method

Describing Function: An Approximate Analysis Method Describig Fuctio: A Approximate Aalysis Method his chapter presets a method for approximately aalyzig oliear dyamical systems A closed-form aalytical solutio of a oliear dyamical system (eg, a oliear differetial

More information

Math 257: Finite difference methods

Math 257: Finite difference methods Math 257: Fiite differece methods 1 Fiite Differeces Remember the defiitio of a derivative f f(x + ) f(x) (x) = lim 0 Also recall Taylor s formula: (1) f(x + ) = f(x) + f (x) + 2 f (x) + 3 f (3) (x) +...

More information

FIR Filters. Lecture #7 Chapter 5. BME 310 Biomedical Computing - J.Schesser

FIR Filters. Lecture #7 Chapter 5. BME 310 Biomedical Computing - J.Schesser FIR Filters Lecture #7 Chapter 5 8 What Is this Course All About? To Gai a Appreciatio of the Various Types of Sigals ad Systems To Aalyze The Various Types of Systems To Lear the Skills ad Tools eeded

More information

Problem 1. Problem Engineering Dynamics Problem Set 9--Solution. Find the equation of motion for the system shown with respect to:

Problem 1. Problem Engineering Dynamics Problem Set 9--Solution. Find the equation of motion for the system shown with respect to: 2.003 Egieerig Dyamics Problem Set 9--Solutio Problem 1 Fid the equatio of motio for the system show with respect to: a) Zero sprig force positio. Draw the appropriate free body diagram. b) Static equilibrium

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

Signals and Systems. Problem Set: From Continuous-Time to Discrete-Time

Signals and Systems. Problem Set: From Continuous-Time to Discrete-Time Sigals ad Systems Problem Set: From Cotiuous-Time to Discrete-Time Updated: October 5, 2017 Problem Set Problem 1 - Liearity ad Time-Ivariace Cosider the followig systems ad determie whether liearity ad

More information

Properties and Tests of Zeros of Polynomial Functions

Properties and Tests of Zeros of Polynomial Functions Properties ad Tests of Zeros of Polyomial Fuctios The Remaider ad Factor Theorems: Sythetic divisio ca be used to fid the values of polyomials i a sometimes easier way tha substitutio. This is show by

More information

Definition of z-transform.

Definition of z-transform. - Trasforms Frequecy domai represetatios of discretetime sigals ad LTI discrete-time systems are made possible with the use of DTFT. However ot all discrete-time sigals e.g. uit step sequece are guarateed

More information

Acoustic Field inside a Rigid Cylinder with a Point Source

Acoustic Field inside a Rigid Cylinder with a Point Source Acoustic Field iside a Rigid Cylider with a Poit Source 1 Itroductio The ai objectives of this Deo Model are to Deostrate the ability of Coustyx to odel a rigid cylider with a poit source usig Coustyx

More information

Question 1: The magnetic case

Question 1: The magnetic case September 6, 018 Corell Uiversity, Departmet of Physics PHYS 337, Advace E&M, HW # 4, due: 9/19/018, 11:15 AM Questio 1: The magetic case I class, we skipped over some details, so here you are asked to

More information

MAT136H1F - Calculus I (B) Long Quiz 1. T0101 (M3) Time: 20 minutes. The quiz consists of four questions. Each question is worth 2 points. Good Luck!

MAT136H1F - Calculus I (B) Long Quiz 1. T0101 (M3) Time: 20 minutes. The quiz consists of four questions. Each question is worth 2 points. Good Luck! MAT36HF - Calculus I (B) Log Quiz. T (M3) Time: 2 miutes Last Name: Studet ID: First Name: Please mark your tutorial sectio: T (M3) T2 (R4) T3 (T4) T5 (T5) T52 (R5) The quiz cosists of four questios. Each

More information

Complex Numbers Solutions

Complex Numbers Solutions Complex Numbers Solutios Joseph Zoller February 7, 06 Solutios. (009 AIME I Problem ) There is a complex umber with imagiary part 64 ad a positive iteger such that Fid. [Solutio: 697] 4i + + 4i. 4i 4i

More information

Probability Theory. Exercise Sheet 4. ETH Zurich HS 2017

Probability Theory. Exercise Sheet 4. ETH Zurich HS 2017 ETH Zurich HS 2017 D-MATH, D-PHYS Prof. A.-S. Szita Coordiator Yili Wag Probability Theory Exercise Sheet 4 Exercise 4.1 Let X ) N be a sequece of i.i.d. rado variables i a probability space Ω, A, P ).

More information

Mechanical Vibrations

Mechanical Vibrations Mechaical Vibratios Cotets Itroductio Free Vibratios o Particles. Siple Haroic Motio Siple Pedulu (Approxiate Solutio) Siple Pedulu (Exact Solutio) Saple Proble 9. Free Vibratios o Rigid Bodies Saple Proble

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

State Space Representation

State Space Representation Optimal Cotrol, Guidace ad Estimatio Lecture 2 Overview of SS Approach ad Matrix heory Prof. Radhakat Padhi Dept. of Aerospace Egieerig Idia Istitute of Sciece - Bagalore State Space Represetatio Prof.

More information

Chapter 9: Numerical Differentiation

Chapter 9: Numerical Differentiation 178 Chapter 9: Numerical Differetiatio Numerical Differetiatio Formulatio of equatios for physical problems ofte ivolve derivatives (rate-of-chage quatities, such as velocity ad acceleratio). Numerical

More information

The z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j

The z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j The -Trasform 7. Itroductio Geeralie the complex siusoidal represetatio offered by DTFT to a represetatio of complex expoetial sigals. Obtai more geeral characteristics for discrete-time LTI systems. 7.

More information

6.4 Binomial Coefficients

6.4 Binomial Coefficients 64 Bioial Coefficiets Pascal s Forula Pascal s forula, aed after the seveteeth-cetury Frech atheaticia ad philosopher Blaise Pascal, is oe of the ost faous ad useful i cobiatorics (which is the foral ter

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

Lecture 10: Bounded Linear Operators and Orthogonality in Hilbert Spaces

Lecture 10: Bounded Linear Operators and Orthogonality in Hilbert Spaces Lecture : Bouded Liear Operators ad Orthogoality i Hilbert Spaces 34 Bouded Liear Operator Let ( X, ), ( Y, ) i i be ored liear vector spaces ad { } X Y The, T is said to be bouded if a real uber c such

More information

Lecture 25 (Dec. 6, 2017)

Lecture 25 (Dec. 6, 2017) Lecture 5 8.31 Quatum Theory I, Fall 017 106 Lecture 5 (Dec. 6, 017) 5.1 Degeerate Perturbatio Theory Previously, whe discussig perturbatio theory, we restricted ourselves to the case where the uperturbed

More information

Chapter 9 Computation of the Discrete. Fourier Transform

Chapter 9 Computation of the Discrete. Fourier Transform Chapter 9 Coputatio of the Discrete Fourier Trasfor Itroductio Efficiet Coputatio of the Discrete Fourier Trasfor Goertzel Algorith Deciatio-I-Tie FFT Algoriths Deciatio-I-Frequecy FFT Algoriths Ipleetatio

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

Statistics for Applications Fall Problem Set 7

Statistics for Applications Fall Problem Set 7 18.650. Statistics for Applicatios Fall 016. Proble Set 7 Due Friday, Oct. 8 at 1 oo Proble 1 QQ-plots Recall that the Laplace distributio with paraeter λ > 0 is the cotiuous probaλ bility easure with

More information

6.003 Homework #3 Solutions

6.003 Homework #3 Solutions 6.00 Homework # Solutios Problems. Complex umbers a. Evaluate the real ad imagiary parts of j j. π/ Real part = Imagiary part = 0 e Euler s formula says that j = e jπ/, so jπ/ j π/ j j = e = e. Thus the

More information

Bertrand s postulate Chapter 2

Bertrand s postulate Chapter 2 Bertrad s postulate Chapter We have see that the sequece of prie ubers, 3, 5, 7,... is ifiite. To see that the size of its gaps is ot bouded, let N := 3 5 p deote the product of all prie ubers that are

More information

Formula List for College Algebra Sullivan 10 th ed. DO NOT WRITE ON THIS COPY.

Formula List for College Algebra Sullivan 10 th ed. DO NOT WRITE ON THIS COPY. Forula List for College Algera Sulliva 10 th ed. DO NOT WRITE ON THIS COPY. Itercepts: Lear how to fid the x ad y itercepts. Syetry: Lear how test for syetry with respect to the x-axis, y-axis ad origi.

More information

DIGITAL SIGNAL PROCESSING LECTURE 3

DIGITAL SIGNAL PROCESSING LECTURE 3 DIGITAL SIGNAL PROCESSING LECTURE 3 Fall 2 2K8-5 th Semester Tahir Muhammad tmuhammad_7@yahoo.com Cotet ad Figures are from Discrete-Time Sigal Processig, 2e by Oppeheim, Shafer, ad Buc, 999-2 Pretice

More information

The Jordan Normal Form: A General Approach to Solving Homogeneous Linear Systems. Mike Raugh. March 20, 2005

The Jordan Normal Form: A General Approach to Solving Homogeneous Linear Systems. Mike Raugh. March 20, 2005 The Jorda Normal Form: A Geeral Approach to Solvig Homogeeous Liear Sstems Mike Raugh March 2, 25 What are we doig here? I this ote, we describe the Jorda ormal form of a matrix ad show how it ma be used

More information

Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors 5 Eigevalues ad Eigevectors 5.3 DIAGONALIZATION DIAGONALIZATION Example 1: Let. Fid a formula for A k, give that P 1 1 = 1 2 ad, where Solutio: The stadard formula for the iverse of a 2 2 matrix yields

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

Exercise 8 CRITICAL SPEEDS OF THE ROTATING SHAFT

Exercise 8 CRITICAL SPEEDS OF THE ROTATING SHAFT Exercise 8 CRITICA SEEDS OF TE ROTATING SAFT. Ai of the exercise Observatio ad easureet of three cosecutive critical speeds ad correspodig odes of the actual rotatig shaft. Copariso of aalytically coputed

More information

ECE4270 Fundamentals of DSP. Lecture 2 Discrete-Time Signals and Systems & Difference Equations. Overview of Lecture 2. More Discrete-Time Systems

ECE4270 Fundamentals of DSP. Lecture 2 Discrete-Time Signals and Systems & Difference Equations. Overview of Lecture 2. More Discrete-Time Systems ECE4270 Fudametals of DSP Lecture 2 Discrete-Time Sigals ad Systems & Differece Equatios School of ECE Ceter for Sigal ad Iformatio Processig Georgia Istitute of Techology Overview of Lecture 2 Aoucemet

More information

COMM 602: Digital Signal Processing

COMM 602: Digital Signal Processing COMM 60: Digital Sigal Processig Lecture 4 -Properties of LTIS Usig Z-Trasform -Iverse Z-Trasform Properties of LTIS Usig Z-Trasform Properties of LTIS Usig Z-Trasform -ve +ve Properties of LTIS Usig Z-Trasform

More information

Physical Chemistry I for Biochemists. Lecture 2 (1/12/11) Yoshitaka Ishii. Gas Ch. 1 Non-Ideal Gas (Ch 1 & Raff p21-41) Announcement

Physical Chemistry I for Biochemists. Lecture 2 (1/12/11) Yoshitaka Ishii. Gas Ch. 1 Non-Ideal Gas (Ch 1 & Raff p21-41) Announcement Physical Cheistry I for Biocheists Che340 Lecture (1/1/11) Yoshitaka Ishii Gas Ch. 1 No-Ideal Gas (Ch 1 & Raff p1-41) Aouceet HW 1 is due et Wedesday before the class (Fid HW1 at the web site) Attedace

More information

Perturbation Theory, Zeeman Effect, Stark Effect

Perturbation Theory, Zeeman Effect, Stark Effect Chapter 8 Perturbatio Theory, Zeea Effect, Stark Effect Ufortuately, apart fro a few siple exaples, the Schrödiger equatio is geerally ot exactly solvable ad we therefore have to rely upo approxiative

More information

Chapter 7 z-transform

Chapter 7 z-transform Chapter 7 -Trasform Itroductio Trasform Uilateral Trasform Properties Uilateral Trasform Iversio of Uilateral Trasform Determiig the Frequecy Respose from Poles ad Zeros Itroductio Role i Discrete-Time

More information

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations

ECE-S352 Introduction to Digital Signal Processing Lecture 3A Direct Solution of Difference Equations ECE-S352 Itroductio to Digital Sigal Processig Lecture 3A Direct Solutio of Differece Equatios Discrete Time Systems Described by Differece Equatios Uit impulse (sample) respose h() of a DT system allows

More information

Discrete-Time Systems, LTI Systems, and Discrete-Time Convolution

Discrete-Time Systems, LTI Systems, and Discrete-Time Convolution EEL5: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we begi our mathematical treatmet of discrete-time s. As show i Figure, a discrete-time operates or trasforms some iput sequece x [

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

Types of Waves Transverse Shear. Waves. The Wave Equation

Types of Waves Transverse Shear. Waves. The Wave Equation Waves Waves trasfer eergy from oe poit to aother. For mechaical waves the disturbace propagates without ay of the particles of the medium beig displaced permaetly. There is o associated mass trasport.

More information

Math 113 Exam 3 Practice

Math 113 Exam 3 Practice Math Exam Practice Exam will cover.-.9. This sheet has three sectios. The first sectio will remid you about techiques ad formulas that you should kow. The secod gives a umber of practice questios for you

More information

PARTIAL DIFFERENTIAL EQUATIONS SEPARATION OF VARIABLES

PARTIAL DIFFERENTIAL EQUATIONS SEPARATION OF VARIABLES Diola Bagayoko (0 PARTAL DFFERENTAL EQUATONS SEPARATON OF ARABLES. troductio As discussed i previous lectures, partial differetial equatios arise whe the depedet variale, i.e., the fuctio, varies with

More information

15.081J/6.251J Introduction to Mathematical Programming. Lecture 21: Primal Barrier Interior Point Algorithm

15.081J/6.251J Introduction to Mathematical Programming. Lecture 21: Primal Barrier Interior Point Algorithm 508J/65J Itroductio to Mathematical Programmig Lecture : Primal Barrier Iterior Poit Algorithm Outlie Barrier Methods Slide The Cetral Path 3 Approximatig the Cetral Path 4 The Primal Barrier Algorithm

More information

Inverse Matrix. A meaning that matrix B is an inverse of matrix A.

Inverse Matrix. A meaning that matrix B is an inverse of matrix A. Iverse Matrix Two square matrices A ad B of dimesios are called iverses to oe aother if the followig holds, AB BA I (11) The otio is dual but we ofte write 1 B A meaig that matrix B is a iverse of matrix

More information

Lecture #5: Begin Quantum Mechanics: Free Particle and Particle in a 1D Box

Lecture #5: Begin Quantum Mechanics: Free Particle and Particle in a 1D Box 561 Fall 013 Lecture #5 page 1 Last time: Lecture #5: Begi Quatum Mechaics: Free Particle ad Particle i a 1D Box u 1 u 1-D Wave equatio = x v t * u(x,t): displacemets as fuctio of x,t * d -order: solutio

More information

Lesson 03 Heat Equation with Different BCs

Lesson 03 Heat Equation with Different BCs PDE & Complex Variables P3- esso 3 Heat Equatio with Differet BCs ( ) Physical meaig (SJF ) et u(x, represet the temperature of a thi rod govered by the (coductio) heat equatio: u t =α u xx (3.) where

More information

MATH 31B: MIDTERM 2 REVIEW

MATH 31B: MIDTERM 2 REVIEW MATH 3B: MIDTERM REVIEW JOE HUGHES. Evaluate x (x ) (x 3).. Partial Fractios Solutio: The umerator has degree less tha the deomiator, so we ca use partial fractios. Write x (x ) (x 3) = A x + A (x ) +

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

AVERAGE MARKS SCALING

AVERAGE MARKS SCALING TERTIARY INSTITUTIONS SERVICE CENTRE Level 1, 100 Royal Street East Perth, Wester Australia 6004 Telephoe (08) 9318 8000 Facsiile (08) 95 7050 http://wwwtisceduau/ 1 Itroductio AVERAGE MARKS SCALING I

More information

j=1 dz Res(f, z j ) = 1 d k 1 dz k 1 (z c)k f(z) Res(f, c) = lim z c (k 1)! Res g, c = f(c) g (c)

j=1 dz Res(f, z j ) = 1 d k 1 dz k 1 (z c)k f(z) Res(f, c) = lim z c (k 1)! Res g, c = f(c) g (c) Problem. Compute the itegrals C r d for Z, where C r = ad r >. Recall that C r has the couter-clockwise orietatio. Solutio: We will use the idue Theorem to solve this oe. We could istead use other (perhaps

More information

x x x 2x x N ( ) p NUMERICAL METHODS UNIT-I-SOLUTION OF EQUATIONS AND EIGENVALUE PROBLEMS By Newton-Raphson formula

x x x 2x x N ( ) p NUMERICAL METHODS UNIT-I-SOLUTION OF EQUATIONS AND EIGENVALUE PROBLEMS By Newton-Raphson formula NUMERICAL METHODS UNIT-I-SOLUTION OF EQUATIONS AND EIGENVALUE PROBLEMS. If g( is cotiuous i [a,b], te uder wat coditio te iterative (or iteratio metod = g( as a uique solutio i [a,b]? '( i [a,b].. Wat

More information

Jacobi symbols. p 1. Note: The Jacobi symbol does not necessarily distinguish between quadratic residues and nonresidues. That is, we could have ( a

Jacobi symbols. p 1. Note: The Jacobi symbol does not necessarily distinguish between quadratic residues and nonresidues. That is, we could have ( a Jacobi sybols efiitio Let be a odd positive iteger If 1, the Jacobi sybol : Z C is the costat fuctio 1 1 If > 1, it has a decopositio ( as ) a product of (ot ecessarily distict) pries p 1 p r The Jacobi

More information

The time evolution of the state of a quantum system is described by the time-dependent Schrödinger equation (TDSE): ( ) ( ) 2m "2 + V ( r,t) (1.

The time evolution of the state of a quantum system is described by the time-dependent Schrödinger equation (TDSE): ( ) ( ) 2m 2 + V ( r,t) (1. Adrei Tokmakoff, MIT Departmet of Chemistry, 2/13/2007 1-1 574 TIME-DEPENDENT QUANTUM MECHANICS 1 INTRODUCTION 11 Time-evolutio for time-idepedet Hamiltoias The time evolutio of the state of a quatum system

More information

Løsningsførslag i 4M

Løsningsførslag i 4M Norges tekisk aturviteskapelige uiversitet Istitutt for matematiske fag Side 1 av 6 Løsigsførslag i 4M Oppgave 1 a) A sketch of the graph of the give f o the iterval [ 3, 3) is as follows: The Fourier

More information

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled

The picture in figure 1.1 helps us to see that the area represents the distance traveled. Figure 1: Area represents distance travelled 1 Lecture : Area Area ad distace traveled Approximatig area by rectagles Summatio The area uder a parabola 1.1 Area ad distace Suppose we have the followig iformatio about the velocity of a particle, how

More information