Lecture 20 - Wave Propagation Response

Similar documents
5.6 Binomial Multi-section Matching Transformer

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

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

Transfer Function Analysis

The Binomial Multi-Section Transformer

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

5.6 Binomial Multi-section Matching Transformer

The Binomial Multi- Section Transformer

Massachusetts Institute of Technology

Ma/CS 6a Class 22: Power Series

Chapter 2. Asymptotic Notation

Chapter 4. Fourier Series

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

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

Uncertainty Principle of Mathematics

8.3 Perturbation theory

19.1 The dictionary problem

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

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

Supplementary Material

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

EECE 301 Signals & Systems

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

EE Midterm Test 1 - Solutions

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

Supplementary Information

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

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

ADVANCED DIGITAL SIGNAL PROCESSING

Chapter 4 : Laplace Transform

ECE 901 Lecture 4: Estimation of Lipschitz smooth functions

Fall 2013 MTH431/531 Real analysis Section Notes

Numerical Methods for Ordinary Differential Equations

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

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

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

ECE 308 Discrete-Time Signals and Systems

Numerical Methods in Fourier Series Applications

2D DSP Basics: Systems Stability, 2D Sampling

Integrals of Functions of Several Variables

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

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

10.6 ALTERNATING SERIES

Describing Function: An Approximate Analysis Method

Math 257: Finite difference methods

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

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

Quiz. Use either the RATIO or ROOT TEST to determine whether the series is convergent or not.

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

Properties and Tests of Zeros of Polynomial Functions

Definition of z-transform.

Acoustic Field inside a Rigid Cylinder with a Point Source

Question 1: The magnetic case

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!

Complex Numbers Solutions

Probability Theory. Exercise Sheet 4. ETH Zurich HS 2017

Mechanical Vibrations

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

State Space Representation

Chapter 9: Numerical Differentiation

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

6.4 Binomial Coefficients

Recursive Algorithms. Recurrences. Recursive Algorithms Analysis

Lecture 10: Bounded Linear Operators and Orthogonality in Hilbert Spaces

Lecture 25 (Dec. 6, 2017)

Chapter 9 Computation of the Discrete. Fourier Transform

CHAPTER 10 INFINITE SEQUENCES AND SERIES

Statistics for Applications Fall Problem Set 7

6.003 Homework #3 Solutions

Bertrand s postulate Chapter 2

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

DIGITAL SIGNAL PROCESSING LECTURE 3

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

Eigenvalues and Eigenvectors

Time-Domain Representations of LTI Systems

Exercise 8 CRITICAL SPEEDS OF THE ROTATING SHAFT

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

COMM 602: Digital Signal Processing

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

Perturbation Theory, Zeeman Effect, Stark Effect

Chapter 7 z-transform

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

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

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

Types of Waves Transverse Shear. Waves. The Wave Equation

Math 113 Exam 3 Practice

PARTIAL DIFFERENTIAL EQUATIONS SEPARATION OF VARIABLES

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

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

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

Lesson 03 Heat Equation with Different BCs

MATH 31B: MIDTERM 2 REVIEW

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

AVERAGE MARKS SCALING

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)

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

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

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.

Løsningsførslag i 4M

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

Transcription:

.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

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.

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

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

MIT OpeCourseWare http://ocw.it.edu.09 /.093 Fiite Eleet Aalysis of Solids ad Fluids I Fall 009 For iforatio about citig these aterials or our Ters of Use, visit: http://ocw.it.edu/ters.