Chapter Finite Difference Method for Ordinary Differential Equations

Similar documents
Chapter Finite Difference Method for Ordinary Differential Equations

I-POLYA PROCESS AND APPLICATIONS Leda D. Minkova

Suppose we have observed values t 1, t 2, t n of a random variable T.

5-1. We apply Newton s second law (specifically, Eq. 5-2). F = ma = ma sin 20.0 = 1.0 kg 2.00 m/s sin 20.0 = 0.684N. ( ) ( )

Lecture 5. Plane Wave Reflection and Transmission

( ) ( ) Weibull Distribution: k ti. u u. Suppose t 1, t 2, t n are times to failure of a group of n mechanisms. The likelihood function is

Chapter 3: Vectors and Two-Dimensional Motion

Name of the Student:

2 shear strain / L for small angle

CONSISTENT EARTHQUAKE ACCELERATION AND DISPLACEMENT RECORDS

1 Constant Real Rate C 1

Backcalculation Analysis of Pavement-layer Moduli Using Pattern Search Algorithms

3. A Review of Some Existing AW (BT, CT) Algorithms

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

Different kind of oscillation

Available online Journal of Scientific and Engineering Research, 2017, 4(2): Research Article

s = rθ Chapter 10: Rotation 10.1: What is physics?

, t 1. Transitions - this one was easy, but in general the hardest part is choosing the which variables are state and control variables

ON TOTAL TIME ON TEST TRANSFORM ORDER ABSTRACT

CHAPTER 10: LINEAR DISCRIMINATION

p E p E d ( ) , we have: [ ] [ ] [ ] Using the law of iterated expectations, we have:

(8) Gain Stage and Simple Output Stage

Using DP for hierarchical discretization of continuous attributes. Amit Goyal (31 st March 2008)

by Lauren DeDieu Advisor: George Chen

L4:4. motion from the accelerometer. to recover the simple flutter. Later, we will work out how. readings L4:3

Outline. GW approximation. Electrons in solids. The Green Function. Total energy---well solved Single particle excitation---under developing

The sound field of moving sources

08.06 Shooting Method for Ordinary Differential Equations

SCIENCE CHINA Technological Sciences

( ) α is determined to be a solution of the one-dimensional minimization problem: = 2. min = 2

Mass-Spring Systems Surface Reconstruction

calculating electromagnetic

Reflection and Refraction

( ) ( )) ' j, k. These restrictions in turn imply a corresponding set of sample moment conditions:

Lecture 2 M/G/1 queues. M/G/1-queue

CptS 570 Machine Learning School of EECS Washington State University. CptS Machine Learning 1

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5

Variants of Pegasos. December 11, 2009

Notes on the stability of dynamic systems and the use of Eigen Values.

TRANSIENTS. Lecture 5 ELEC-E8409 High Voltage Engineering

Determination of residual stresses and material properties by an energy-based method using artificial neural networks

EMA5001 Lecture 3 Steady State & Nonsteady State Diffusion - Fick s 2 nd Law & Solutions

Numerical, Experimental and Theoretical Studies on Mechanism of K-H Instability and Ring Generation bhids behind Supersonic MVG

COMPLEMENTARY ENERGY METHOD FOR CURVED COMPOSITE BEAMS

Chapters 2 Kinematics. Position, Distance, Displacement

UNIT10 PLANE OF REGRESSION

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are

Chapter Lagrangian Interpolation

European Option Pricing for a Stochastic Volatility Lévy Model with Stochastic Interest Rates

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

Solution in semi infinite diffusion couples (error function analysis)

BAR & TRUSS FINITE ELEMENT. Direct Stiffness Method

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy

Chapter 12. Ordinary Differential Equation Boundary Value (BV) Problems

If there are k binding constraints at x then re-label these constraints so that they are the first k constraints.

Physics Exam II Chapters 25-29

Complex Numbers. x = B B 2 4AC 2A. or x = x = 2 ± 4 4 (1) (5) 2 (1)

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

Pendulum Dynamics. = Ft tangential direction (2) radial direction (1)

University of California, Davis Date: June xx, PRELIMINARY EXAMINATION FOR THE Ph.D. DEGREE ANSWER KEY

Professor Wei Zhu. 1. Sampling from the Normal Population

Modern Energy Functional for Nuclei and Nuclear Matter. By: Alberto Hinojosa, Texas A&M University REU Cyclotron 2008 Mentor: Dr.

LINEAR MOMENTUM. product of the mass m and the velocity v r of an object r r

ScienceDirect. Behavior of Integral Curves of the Quasilinear Second Order Differential Equations. Alma Omerspahic *

Shooting Method for Ordinary Differential Equations Autar Kaw

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim

HERMITE SERIES SOLUTIONS OF LINEAR FREDHOLM INTEGRAL EQUATIONS

Go over vector and vector algebra Displacement and position in 2-D Average and instantaneous velocity in 2-D Average and instantaneous acceleration

8 Baire Category Theorem and Uniform Boundedness

EMPORIUM H O W I T W O R K S F I R S T T H I N G S F I R S T, Y O U N E E D T O R E G I S T E R.

January Examinations 2012

DUE: WEDS FEB 21ST 2018

Sections 3.1 and 3.4 Exponential Functions (Growth and Decay)

Field due to a collection of N discrete point charges: r is in the direction from

Dynamic Model of the Axially Moving Viscoelastic Belt System with Tensioner Pulley Yanqi Liu1, a, Hongyu Wang2, b, Dongxing Cao3, c, Xiaoling Gai1, d

Integral Vector Operations and Related Theorems Applications in Mechanics and E&M

A VISCOPLASTIC MODEL OF ASYMMETRICAL COLD ROLLING

Density Matrix Description of NMR BCMB/CHEM 8190

Volatility Interpolation

Physics 201 Lecture 15

8.5 Circles and Lengths of Segments

Set of square-integrable function 2 L : function space F

P 365. r r r )...(1 365

ajanuary't I11 F or,'.

Scattering at an Interface: Oblique Incidence

( ) () we define the interaction representation by the unitary transformation () = ()

A. Thicknesses and Densities

Basic molecular dynamics

Today - Lecture 13. Today s lecture continue with rotations, torque, Note that chapters 11, 12, 13 all involve rotations

19 The Born-Oppenheimer Approximation

Lecture-V Stochastic Processes and the Basic Term-Structure Equation 1 Stochastic Processes Any variable whose value changes over time in an uncertain

Displacement, Velocity, and Acceleration. (WHERE and WHEN?)

ˆ SSE SSE q SST R SST R q R R q R R q

Observer Design for Nonlinear Systems using Linear Approximations

Physics 2A Chapter 11 - Universal Gravitation Fall 2017

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data

2/20/2013. EE 101 Midterm 2 Review

ECSE Partial fraction expansion (m<n) 3 types of poles Simple Real poles Real Equal poles

On One Analytic Method of. Constructing Program Controls

Density Matrix Description of NMR BCMB/CHEM 8190

Transcription:

Chape 8.7 Fne Dffeence Mehod fo Odnay Dffeenal Eqaons Afe eadng hs chape, yo shold be able o. Undesand wha he fne dffeence mehod s and how o se o solve poblems. Wha s he fne dffeence mehod? The fne dffeence mehod s sed o solve odnay dffeenal eqaons ha have condons mposed on he bonday ahe han a he nal pon. These poblems ae called bonday-vale poblems. In hs chape, we solve second-ode odnay dffeenal eqaons of he fom d y f ( x, y, y' ), a x b, () dx wh bonday condons y ( a) y a and y ( b) yb () Many academcs efe o bonday vale poblems as poson-dependen and nal vale poblems as me-dependen. Tha s no necessaly he case as llsaed by he followng examples. The dffeenal eqaon ha govens he deflecon y of a smply sppoed beam nde nfomly dsbed load (Fge ) s gven by d y qx( L x) () dx EI whee x locaon along he beam (n) E Yong s modls of elascy of he beam (ps) I second momen of aea (n ) q nfom loadng nensy (lb/n) L lengh of beam (n) The condons mposed o solve he dffeenal eqaon ae y ( x ) () y ( x L) Clealy, hese ae bonday vales and hence he poblem s consdeed a bonday-vale poblem. 8.7.

8.7. Chape 8.7 y q x L Fge Smply sppoed beam wh nfom dsbed load. Now consde he case of a canleveed beam wh a nfomly dsbed load (Fge ). The dffeenal eqaon ha govens he deflecon y of he beam s gven by d y q( L x) () dx EI whee x locaon along he beam (n) E Yong s modls of elascy of he beam (ps) I second momen of aea (n ) q nfom loadng nensy (lb/n) L lengh of beam (n) The condons mposed o solve he dffeenal eqaon ae y ( x ) (6) dy ( x ) dx Clealy, hese ae nal vales and hence he poblem needs o be consdeed as an nal vale poblem. y q x L Fge Canleveed beam wh a nfomly dsbed load.

Fne Dffeence Mehod 8.7. Example The deflecon y n a smply sppoed beam wh a nfom load q and a ensle axal load T s gven by d y Ty qx( L x) (E.) dx EI EI whee x locaon along he beam (n) T enson appled (lbs) E Yong s modls of elascy of he beam (ps) I second momen of aea (n ) q nfom loadng nensy (lb/n) L lengh of beam (n) y q T x T L Fge Smply sppoed beam fo Example. Gven, T 7 lbs, q lbs/n, L 7 n, E Ms, and I n, a) Fnd he deflecon of he beam a x ". Use a sep sze of x " and appoxmae he devaves by cenal dvded dffeence appoxmaon. b) Fnd he elave e eo n he calclaon of y (). Solon a) Sbsng he gven vales, d y 7y () x(7 x) 6 6 dx ( )() ( )() d y y 7. x(7 x) (E.) dx d y Appoxmang he devave a node by he cenal dvded dffeence dx appoxmaon,

8.7. Chape 8.7 Fge Illsaon of fne dffeence nodes sng cenal dvded dffeence mehod. d y y y y dx ( x) We can ewe he eqaon as y y y y 7. (7 ) x x ( x) Snce x, we have nodes as gven n Fge (E.) (E.) x x x x 7 Fge Fne dffeence mehod fom x o x 7 wh x. The locaon of he nodes hen s x x x x x x x x x x 7 Wng he eqaon a each node, we ge Node : Fom he smply sppoed bonday condon a x, we oban y (E.) Node : Rewng eqaon (E.) fo node gves y y y y 7. x (7 x ) ().6y.y.6y 7. ()(7 ).6y.y.6y 9.7 (E.6) Node : Rewng eqaon (E.) fo node gves y y y y 7. x(7 x ) ().6y.y.6y 7. ()(7 ).6y.y.6y 9.7 (E.7) Node : Fom he smply sppoed bonday condon a x 7, we oban y (E.8)

Fne Dffeence Mehod 8.7. Eqaons (E.-E.8) ae smlaneos eqaons wh nknowns and can be wen n max fom as y.6..6 y 9.7.6..6 y 9.7 y The above eqaons have a coeffcen max ha s dagonal (we can se Thomas algohm o solve he eqaons) and s also scly dagonally domnan (convegence s gaaneed f we se eave mehods sch as he Gass-Sedel mehod). Solvng he eqaons we ge, y y.8 y.8 y y ) y( x ) y.8" ( The exac solon of he odnay dffeenal eqaon s deved as follows. The homogeneos pa of he solon s gven by solvng he chaacesc eqaon m m ±. Theefoe,.x.x yh Ke K e The pacla pa of he solon s gven by y p Ax Bx C Sbsng he dffeenal eqaon (E.) gves d y p y 7. x(7 x) p dx d ( Ax Bx C) ( Ax Bx C) 7. x(7 x) dx A ( Ax Bx C) 7. x(7 x) Ax Bx (A C).6 x 7. x Eqang ems gves A. B.6 6 A C Solvng he above eqaon gves A.7 B 8. C.7

8.7.6 Chape 8.7 The pacla solon hen s.7x 8.x.7 y p The complee solon s hen gven by.x.x y.7x 8.x.7 Ke K e Applyng he followng bonday condons y ( x ) y ( x 7) we oban he followng sysem of eqaons K K.7.9K.8997K.7 These eqaons ae epesened n max fom by K.7.9.8997 K.7 A nmbe of dffeen nmecal mehods may be lzed o solve hs sysem of eqaons sch as he Gassan elmnaon. Usng any of hese mehods yelds K.77666 K.9777 Sbsng hese vales back no he eqaon gves.x.x y.7x 8.x.7.776666 e.9777 e Unlke ohe examples n hs chape and n he book, he above expesson fo he deflecon of he beam s dsplayed wh a lage nmbe of sgnfcan dgs. Ths s done o mnmze he ond-off eo becase he above expesson nvolves sbacon of lage nmbes ha ae close o each ohe. b) To calclae he elave e eo, we ms fs calclae he vale of he exac solon a y..() y ( ).7() 8.().7.776666 e.().9777 e y ( ). The e eo s gven by E Exac Vale Appoxmae Vale E. (.8) E. The elave e eo s gven by Te Eo % Te Vale. %. %

Fne Dffeence Mehod 8.7.7 Example Take he case of a pesse vessel ha s beng esed n he laboaoy o check s ably o whsand pesse. Fo a hck pesse vessel of nne ads a and oe ads b, he dffeenal eqaon fo he adal dsplacemen of a pon along he hckness s gven by d d d d (E.) The nne ads a and he oe ads b 8, and he maeal of he pesse vessel s ASTM A6 seel. The yeld sengh of hs ype of seel s 6 ks. Two san gages ha ae bonded angenally a he nne and he oe ads mease nomal angenal san as / a.776 / b.86 (E.a,b) a he maxmm needed pesse. Snce he adal dsplacemen and angenal san ae elaed smply by, (E.) hen.776.87' ' a b.86 8.769' ' The maxmm nomal sess n he pesse vessel s a he nne ads E d σ max ν ν a d a whee E Yong s modls of seel (E Ms) ν Posson s ao ( ν.) The faco of safey, FS s gven by Yeld sengh of seel σ max a and s gven by (E.7) FS (E.8) a) Dvde he adal hckness of he pesse vessel no 6 eqdsan nodes, and fnd he adal dsplacemen pofle b) Fnd he maxmm nomal sess and faco of safey as gven by eqaon (E.8) c) Fnd he exac vale of he maxmm nomal sess as gven by eqaon (E.8) f s gven ha he exac expesson fo adal dsplacemen s of he fom C C. Calclae he elave e eo.

8.7.8 Chape 8.7 Solon a b - n a - b Fge Nodes along he adal decon. a) The adal locaons fom a o b ae dvded no n eqally spaced segmens, and hence eslng n n nodes. Ths wll allow s o fnd he dependen vaable nmecally a hese nodes. A node along he adal hckness of he pesse vessel, d (E.9) d ( ) d (E.) d Sch sbsons wll conve he odnay dffeenal eqaon no a lnea eqaon (b wh moe han one nknown). By wng he eslng lnea eqaon a dffeen pons a whch he odnay dffeenal eqaon s vald, we ge smlaneos lnea eqaons ha can be solved by sng echnqes sch as Gassan elmnaon, he Gass-Sedel mehod, ec. Sbsng hese appoxmaons fom Eqaons (E.9) and (E.) n Eqaon (E.) (E.) ( ) ( ) ( ) ( ) (E.) Le s beak he hckness, b a, of he pesse vessel no n nodes, ha s a s node and b s node n. Tha means we have n nknowns. We can we he above eqaon fo nodes,..., n. Ths wll gve s n eqaons. A he edge nodes, and n, we se he bonday condons of

Fne Dffeence Mehod 8.7.9 a n b Ths gves a oal of n eqaons. So we have n nknowns and n lnea eqaons. These can be solved by any of he nmecal mehods sed fo solvng smlaneos lnea eqaons. We have been asked o do he calclaons fo n, ha s a oal of 6 nodes. Ths gves b a n 8.6 " A node, a ",.87 " (E.) A node,.6.6" (E.).6.6 (.6)(.6) (.6).6 (.6)(.6).7778.88.7 (E.) A node,.6.6 6.".6.6 ( 6.)(.6) 6..6 ( 6.)(.6).7778.8.66 (E.6) A node, A node, A node, 6..6 6.8".6.6 ( 6.8)(.6) 6.8.6 ( 6.8)(.6).7778.8.9 (E.7) 6.8.6 7..6.6 ( 7.)(.6) ( 7.).6 ( 7.)(.6).7778.799. (E.8) 7..6 8.769 b (E.9) Wng Eqaon (E.) o (E.9) n max fom gves

8.7. Chape 8.7.7778.88.7778.7.8.7778.66.8.7778.9.799.87..769 The above eqaons ae a -dagonal sysem of eqaons and specal algohms sch as Thomas algohm can be sed o solve sch a sysem of eqaons..87.66..7.68.769 b) To fnd he maxmm sess, s gven by Eqaon (E.7) as E d σ max ν ν a d a 6 E ps ν..87 a d a d.66.87.6.767 The maxmm sess n he pesse vessel hen s 6.87 σmax.(. 767 )..7 ps So he faco of safey FS fom Eqaon (E.8) s 6 FS.6896.7 c) The dffeenal eqaon has an exac solon and s gven by he fom C C (E.) whee C and C ae fond by sng he bonday condons a a and b.

Fne Dffeence Mehod 8.7. C ( a) ( ).87 C() C ( b) ( 8).769 C(8) 8 gvng C.6 C.6 Ths.6.6 (E.) d.6.6 (E.) d E d σ max ν ν a d a.6 6.6( ).6..6..8 ps The e eo s E.8.7.689 The absole elave e eo s.8.7.8.7% Example The appoxmaon n Example d d s fs ode accae, ha s, he e eo s of O( ). The appoxmaon d (E.) d ( ) s second ode accae, ha s, he e eo s O( ) ) Mxng hese wo appoxmaons wll esl n he ode of accacy of O( ) and O( ) ), ha s O( ). So s bee o appoxmae

8.7. Chape 8.7 d (E.) d ( ) becase hs eqaon s second ode accae. Repea Example wh he moe accae appoxmaons. Solon a) Repeang he poblem wh hs appoxmaon, a node n he pesse vessel, d d ( ) d d Sbsng Eqaons (E.) and (E.) n Eqaon (E.) gves ( ) ( ) ( ) ( ) ( ) ( ) A node, a " (E.) (E.) (E.).87" (E.6) A node,.6.6" (.6)(.6) (.6) (.6) (.6).6 (.6)(.6).697.87.966 (E.7) A node,.6.6 6. ".6 A node, (E.8).86.9 ( 6.)(.6).6.6 6..6 ( 6.)(.6) 6..6 6. 8 ".6 A node, (E.9).77.9 ( 6.8)(.6).6.6 6.8.6 ( 6.8)(.6) 6.8.6 7. ".66 A node, (E.) 7..78.89 ( 7.)(.6).6.6 ( ).6 ( 7.)(.6) 7..6 8 " / b.769 " (E.) Wng Eqaons (E.6) h (E.) n max fom gves

Fne Dffeence Mehod 8.7..697.87.6.966.86.6.9.77.66.9.78.87.89.769 The above eqaons ae a -dagonal sysem of eqaons and specal algohms sch as Thomas algohm can be sed o solve sch eqaons..87".6".9 ".689".86 ".769 " d b) d a ( ).87.6.9 (.6).9 6.87 σ max.(.9 )..666 ps Theefoe, he faco of safey FS s 6 FS.666.7 c) The e eo n calclang he maxmm sess s E.8.666 8 ps The elave e eo n calclang he maxmm sess s 8.8.6% Table Compasons of adal dsplacemens fom wo mehods. exac s ode nd ode

8.7. Chape 8.7.87.87..87..6.6.66.6.6. 6....6.9.876 6.8.68.7.87.689.6 7..8.68.9.86 9.66 8.769.769..769. ORDINARY DIFFERENTIAL EQUATIONS Topc Fne Dffeence Mehods of Solvng Odnay Dffeenal Eqaons Smmay Texbook noes of Fne Dffeence Mehods of solvng odnay dffeenal eqaons Majo Geneal Engneeng Ahos Aa Kaw, Cong Ngyen, Lke Snyde Dae Jly 7, Web Se hp://nmecalmehods.eng.sf.ed