Boundary Value Problems. Lecture Objectives. Ch. 27

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

Numerical Methods. ME Mechanical Lab I. Mechanical Engineering ME Lab I

PART 8. Partial Differential Equations PDEs

ME 501A Seminar in Engineering Analysis Page 1

k p theory for bulk semiconductors

Lecture 21: Numerical methods for pricing American type derivatives

Outline. Review Numerical Approach. Schedule for April and May. Review Simple Methods. Review Notation and Order

Solution of Linear System of Equations and Matrix Inversion Gauss Seidel Iteration Method

Numerical Heat and Mass Transfer

6.3.4 Modified Euler s method of integration

Fermi-Dirac statistics

Lecture 13 APPROXIMATION OF SECOMD ORDER DERIVATIVES

What is LP? LP is an optimization technique that allocates limited resources among competing activities in the best possible manner.

Our focus will be on linear systems. A system is linear if it obeys the principle of superposition and homogenity, i.e.

Numerical integration in more dimensions part 2. Remo Minero

Chapter Newton s Method

( ) [ ( k) ( k) ( x) ( ) ( ) ( ) [ ] ξ [ ] [ ] [ ] ( )( ) i ( ) ( )( ) 2! ( ) = ( ) 3 Interpolation. Polynomial Approximation.

e i is a random error

Associative Memories

On Pfaff s solution of the Pfaff problem

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands

Appendix B. The Finite Difference Scheme

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Implicit Integration Henyey Method

Denote the function derivatives f(x) in given points. x a b. Using relationships (1.2), polynomials (1.1) are written in the form

Formal solvers of the RT equation

Least Squares Fitting of Data

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise.

NUMERICAL DIFFERENTIATION

Research on Complex Networks Control Based on Fuzzy Integral Sliding Theory

Calculation method of electrical conductivity, thermal conductivity and viscosity of a partially ionized gas. Ilona Lázniková

Lecture 12: Discrete Laplacian

The Fundamental Theorem of Algebra. Objective To use the Fundamental Theorem of Algebra to solve polynomial equations with complex solutions

Finite Difference Method

2.29 Numerical Fluid Mechanics

Note 2. Ling fong Li. 1 Klein Gordon Equation Probablity interpretation Solutions to Klein-Gordon Equation... 2

,..., k N. , k 2. ,..., k i. The derivative with respect to temperature T is calculated by using the chain rule: & ( (5) dj j dt = "J j. k i.

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:

PHYS 705: Classical Mechanics. Calculus of Variations II

The line method combined with spectral chebyshev for space-time fractional diffusion equation

MACHINE APPLIED MACHINE LEARNING LEARNING. Gaussian Mixture Regression

DEMO #8 - GAUSSIAN ELIMINATION USING MATHEMATICA. 1. Matrices in Mathematica

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

LAGRANGIAN MECHANICS

DUE: WEDS FEB 21ST 2018

Finite Element Modelling of truss/cable structures

Dynamic Analysis Of An Off-Road Vehicle Frame

Example: Suppose we want to build a classifier that recognizes WebPages of graduate students.

Lossy Compression. Compromise accuracy of reconstruction for increased compression.

AS-Level Maths: Statistics 1 for Edexcel

Lecture Notes on Linear Regression

1 GSW Iterative Techniques for y = Ax

Several generation methods of multinomial distributed random number Tian Lei 1, a,linxihe 1,b,Zhigang Zhang 1,c

SIMPLE LINEAR REGRESSION

A NOTE ON CES FUNCTIONS Drago Bergholt, BI Norwegian Business School 2011

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

A new Approach for Solving Linear Ordinary Differential Equations

Note: Please use the actual date you accessed this material in your citation.

Polynomial Regression Models

Rectilinear motion. Lecture 2: Kinematics of Particles. External motion is known, find force. External forces are known, find motion

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens

2 Finite difference basics

Lecture 20: Noether s Theorem

In this section is given an overview of the common elasticity models.

we have E Y x t ( ( xl)) 1 ( xl), e a in I( Λ ) are as follows:

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 13

1 Introduction We consider a class of singularly perturbed two point singular boundary value problems of the form: k x with boundary conditions

New Method for Solving Poisson Equation. on Irregular Domains

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

Relaxation Methods for Iterative Solution to Linear Systems of Equations

ONE DIMENSIONAL TRIANGULAR FIN EXPERIMENT. Technical Advisor: Dr. D.C. Look, Jr. Version: 11/03/00

System in Weibull Distribution

Description of the Force Method Procedure. Indeterminate Analysis Force Method 1. Force Method con t. Force Method con t

Digital Signal Processing

Solving Fuzzy Linear Programming Problem With Fuzzy Relational Equation Constraint

Aerodynamics. Finite Wings Lifting line theory Glauert s method

Asymptotics of the Solution of a Boundary Value. Problem for One-Characteristic Differential. Equation Degenerating into a Parabolic Equation

1. Statement of the problem

Integral Transforms and Dual Integral Equations to Solve Heat Equation with Mixed Conditions

Numerical Transient Heat Conduction Experiment

One-sided finite-difference approximations suitable for use with Richardson extrapolation

AERODYNAMICS I LECTURE 6 AERODYNAMICS OF A WING FUNDAMENTALS OF THE LIFTING-LINE THEORY

Ballot Paths Avoiding Depth Zero Patterns

Nice plotting of proteins II

THEOREMS OF QUANTUM MECHANICS

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS. Dariusz Biskup

Topic 5: Non-Linear Regression

Quantum Particle Motion in Physical Space

FE REVIEW OPERATIONAL AMPLIFIERS (OP-AMPS)( ) 8/25/2010

16 Reflection and transmission, TE mode

Complex Numbers, Signals, and Circuits

CHAPTER 6. LAGRANGE S EQUATIONS (Analytical Mechanics)

Solutions for Homework #9

The Feynman path integral

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Kernel Methods and SVMs Extension

Transcription:

Boundar Vaue Probes Ch. 7 Lecture Obectves o understand the dfference between an nta vaue and boundar vaue ODE o be abe to understand when and how to app the shootng ethod and FD ethod. o understand what an Egenvaue Probe s. Inta Vaue Probes hese are the tpes of probes we have been sovng wth K ethods d f dt d f dt Subect to: t ( t ) ( t ) Y ( t ) ( t ) Y Y Y t

Inta Vaue Probes hese are the tpes of probes we have been sovng wth K ethods d f dt d f dt Subect to: t ( t ) ( t ) Y ( t ) ( t ) Y A condtons are specfed at the sae vaue of the ndependent varabe! Y Y t Boundar Vaue Probes Auar condtons are specfed at the boundares (not ust a one pont ke n nta vaue probes) ( ) wo Methods: Shootng Method Fnte Dfference Method Boundar Vaue Probes Auar condtons are specfed at the boundares (not ust a one pont ke n nta vaue probes) condtons are specfed at dfferent vaues of the ndependent varabe! ( ) wo Methods: Shootng Method Fnte Dfference Method

Shootng Method Appcabe to both near & non-near Boundar Vaue (BV) probes. Eas to peent No guarantee of convergence Approach: Convert a BV probe nto an nta vaue probe Sove the resutng probe teratve (tra & error) Lnear ODEs aow a quck near nterpoaton Non-near ODEs w requre an teratve approach sar to our root fndng technques. Shootng Method Coong fn Eape h heat transfer coeffcent k thera conductvt P pereter of fn A cross sectona area of fn abent teperature d hp d ka ( ) ( ) ( L) Anatca Souton hp ka θ ( ) ( ) d θ θ d hp ka Shootng Method Coong fn Eape θ ( ) ( ) ( ) ( L) d θ θ d θ ( ) c e c e Boundar Condtons θ ( ) θ θ ( L) θ θ ( ) c c θ L L θ ( L) ce ce θ θ ( ) ( θ / θ ) snh snh ( L ) θ snh L

Shootng Method Basc Method - Coong fn Eape. ewrte as two frst order ODEs d d d hp d ka ( ) d hp d ka. We need an nta vaue for Guess: ( ) ( ) ( ) ( L) ( ) Shootng Method Basc Method - Coong fn Eape. Integrate the two equatons usng K and ; ths w ed a souton at.integrate the two equatons agan usng a nd guess guess for ().. Lnear nterpoate the resuts to obtan the correct nta condton (Note: ths on works for Lnear ODEs. Eape: () act ( ) act Eact Shootng Method Coong fn Eape hp. ka Matab peentaton (rung_fn_uteqn. ) ecast the probe: d d d d d d ( ).. ( ).6e 96.e ( ) ( ) G

Non-Lnear BV Probes - Shootng Method Lnear nterpoaton between soutons w not necessar resut n a good estate of the requred boundar condtons ecast the probe as a oot fndng probe he souton of a set of ODEs can be consdered a functon g( o ) where o s the nta condton that s unknown. g( o ) f ( o ) bc Drve g( o ) to get our souton. Iteratve adust our guess. Non-near Shootng ethod Secant Method Consder the foowng ODEs sste d d ( a) d f ( ) ( b) b d. Guess an nta vaue of (.e. (a) ) ust as was done wth the near ethod. Usng K or soe other ODE ethod we w obtan souton at (b). a b. Denote the dfference between the boundar condton and our resut fro the ntegraton as soe functon. ( ) g( ( b) ' ( b) ) true ( b) ( b) guess Fnd the ero of ths functon Non-near Shootng ethod Secant Method. Check to see f s wthn an acceptabe toerance. Have we satsfed the boundar condton (b)? ε ε ε s. If not then use the Secant Method to deterne our net guess. ( ) () oot '( )

Non-near Shootng ethod Secant Method ( a) ( b) b BV No ( a) Guess IV ( a) Sove wth K () () (b) true( b) guess ( b) Is sa enough? Yes wrte out souton Fnte Dfference Method Aternatve to the shootng ethod Substtute fnte dfference equatons for dervatves n the orgna ODE. hs w gve us a set of sutaneous agebrac equatons that are soved a nodes. eca usng centra dfferencng: d d d d () - Fnte Dfference Method d ( ) d ewrte n fnte (centra) dfference for: ( ) Mutp b and sove for ( ) ( ) ( ) () 6 6. 6

7 Fnte Dfference Method ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 6 ( ) ( ) ( ) ( )........ Genera Equaton: Wrte out for a nodes: App boundar condtons: Equatons unknowns. Fnte Dfference Method........ Put n Matr for: Sove usng one of our Sstes of near agebrac Equatons ethods Fast eas to peent technque for sovng ODEs Fnte Dfference Method Etended to PDEs Consder a spe Eptca Equaton: LaPace s Equaton hs coud descrbe the stead state teperature dstrbuton n D eta pate. Dscrete (wrte n fnte dfference for) our PDE usng Centra Dfference technque: ) (

8 Fnte Dfference Method Etended to PDEs Consder a spe Eptca Equaton: LaPace s Equaton - - Fnte Dfference Method Etended to PDEs Sove for If Unfor spacng If Fnte Dfference Method Etended to PDEs Suppose we have a heated pate wth Drchet boundar condtons 8 We can eas use Gauss-Sede to sove our sste of equatons unt: new od new ε ε ε s

Fnte Dfference Method Etended to PDEs Heated Pate Matab Eape 9