Review of accuracy analysis Euler: Local error = O(h 2 ) Global error = O(h) Runge-Kutta Order 4: Local error = O(h 5 ) Global error = O(h 4 ) But there’s.

Slides:



Advertisements
Similar presentations
Finite Difference Discretization of Hyperbolic Equations: Linear Problems Lectures 8, 9 and 10.
Advertisements

Chapter 13 Partial differential equations
PHY 042: Electricity and Magnetism Laplace’s equation Prof. Hugo Beauchemin 1.
Lecture 13 2 nd order partial differential equations Remember Phils Problems and your notes = everything Only.
Chapter 3 Steady-State Conduction Multiple Dimensions
1cs542g-term Notes  No extra class tomorrow.
Total Recall Math, Part 2 Ordinary diff. equations First order ODE, one boundary/initial condition: Second order ODE.
PART 7 Ordinary Differential Equations ODEs
Dr. Jie Zou PHY Chapter 9 Ordinary Differential Equations: Initial-Value Problems Lecture (I) 1 1 Besides the main textbook, also see Ref.: “Applied.
ECE602 BME I Partial Differential Equations in Biomedical Engineering.
CSE245: Computer-Aided Circuit Simulation and Verification Lecture Note 5 Numerical Integration Spring 2010 Prof. Chung-Kuan Cheng 1.
AE/ME 339 Computational Fluid Dynamics (CFD) K. M. Isaac Professor of Aerospace Engineering.
Computer-Aided Analysis on Energy and Thermofluid Sciences Y.C. Shih Fall 2011 Chapter 6: Basics of Finite Difference Chapter 6 Basics of Finite Difference.
Partial Differential Equations
Lecture 34 - Ordinary Differential Equations - BVP CVEN 302 November 28, 2001.
Chapter 9 Differential equations
Introduction to Numerical Methods I
PDEs: General classification
Partial differential equations Function depends on two or more independent variables This is a very simple one - there are many more complicated ones.
Numerical Methods for Partial Differential Equations
Chapter 16 Integration of Ordinary Differential Equations.
Mat-F February 21, 2005 Separation of variables Åke Nordlund Niels Obers, Sigfus Johnsen / Anders Svensson Kristoffer Hauskov Andersen Peter Browne Rønne.
Types of Governing equations
CISE301_Topic8L31 SE301: Numerical Methods Topic 8 Ordinary Differential Equations (ODEs) Lecture KFUPM (Term 101) Section 04 Read , 26-2,
CISE301: Numerical Methods Topic 9 Partial Differential Equations (PDEs) Lectures KFUPM (Term 101) Section 04 Read & CISE301_Topic9.
SE301: Numerical Methods Topic 9 Partial Differential Equations (PDEs) Lectures KFUPM Read & CISE301_Topic9 KFUPM.
MATH 685/ CSI 700/ OR 682 Lecture Notes Lecture 10. Ordinary differential equations. Initial value problems.
Numerical methods for PDEs PDEs are mathematical models for –Physical Phenomena Heat transfer Wave motion.
Hyperbolic PDEs Numerical Methods for PDEs Spring 2007 Jim E. Jones.
Introduction to Numerical Methods for ODEs and PDEs Methods of Approximation Lecture 3: finite differences Lecture 4: finite elements.
Lecture 16 Solving the Laplace equation in 2-D Remember Phils Problems and your notes = everything Only 6 lectures.
Lecture 3.
1 EEE 431 Computational Methods in Electrodynamics Lecture 4 By Dr. Rasime Uyguroglu
Integration of 3-body encounter. Figure taken from
Lecture 20 Spherical Harmonics – not examined
Programming assignment #2 Solving a parabolic PDE using finite differences Numerical Methods for PDEs Spring 2007 Jim E. Jones.
7. Introduction to the numerical integration of PDE. As an example, we consider the following PDE with one variable; Finite difference method is one of.
Solving an elliptic PDE using finite differences Numerical Methods for PDEs Spring 2007 Jim E. Jones.
Introduction to Numerical Methods for ODEs and PDEs Lectures 1 and 2: Some representative problems from engineering science and classification of equation.
Engineering Analysis – Computational Fluid Dynamics –
1 Spring 2003 Prof. Tim Warburton MA557/MA578/CS557 Lecture 24.
Numerical Analysis – Differential Equation
MA354 An Introduction to Math Models (more or less corresponding to 1.0 in your book)
Numerical methods 1 An Introduction to Numerical Methods For Weather Prediction by Mariano Hortal office 122.
AMS 691 Special Topics in Applied Mathematics Lecture 8
§ Separation of Cartesian variables: Orthogonal functions Christopher Crawford PHY
Partial Derivatives bounded domain Its boundary denoted by
Partial Derivatives Example: Find If solution: Partial Derivatives Example: Find If solution: gradient grad(u) = gradient.
Math 445: Applied PDEs: models, problems, methods D. Gurarie.
Chapter 1 Partial Differential Equations
Dr. Mujahed AlDhaifallah ( Term 342)
Today’s class Ordinary Differential Equations Runge-Kutta Methods
Differential Equations
MA354 Math Modeling Introduction. Outline A. Three Course Objectives 1. Model literacy: understanding a typical model description 2. Model Analysis 3.
Dr. Mubashir Alam King Saud University. Outline Ordinary Differential Equations (ODE) ODE: An Introduction (8.1) ODE Solution: Euler’s Method (8.2) ODE.
The story so far… ATM 562 Fovell Fall, Convergence We will deploy finite difference (FD) approximations to our model partial differential equations.
Towards Future Navier-Stokes Schemes Uniform Accuracy, O(h) Time Step, Accurate Viscous/Heat Fluxes Hiroaki Nishikawa National Institute of Aerospace.
This chapter is concerned with the problem in the form Chapter 6 focuses on how to find the numerical solutions of the given initial-value problems. Main.
Solving Partial Differential Equation Numerically Pertemuan 13 Matakuliah: S0262-Analisis Numerik Tahun: 2010.
1 Spring 2003 Prof. Tim Warburton MA557/MA578/CS557 Lecture 28.
Ch. 12 Partial Differential Equations
MA2213 Lecture 10 ODE. Topics Importance p Introduction to the theory p Numerical methods Forward Euler p. 383 Richardson’s extrapolation.
Keywords (ordinary/partial) differencial equation ( 常 / 偏 ) 微分方程 difference equation 差分方程 initial-value problem 初值问题 convex 凸的 concave 凹的 perturbed problem.
Lecture 18 3D Cartesian Systems
EEE 431 Computational Methods in Electrodynamics
Finite Difference Methods
Sec 21: Analysis of the Euler Method
Partial Differential Equations
Presentation transcript:

Review of accuracy analysis Euler: Local error = O(h 2 ) Global error = O(h) Runge-Kutta Order 4: Local error = O(h 5 ) Global error = O(h 4 ) But there’s more to worry about: stability and convergence

Stability & Convergence Stability: Suppose we perturb initial condition by ε. Then 1) effect  0 as ε  0, and 2) effect grows only polynomially fast as h  0. Convergence: Solution of discrete problem  solution of continuous problem as h  0.

Stability & Convergence For Ordinary Differential Equations (ODEs), Stability ↔ Convergence But for Partial Differential Equations (PDEs), where there are more than one variable--- time and space, for example--- Stability and convergence are not equivalent. We require an additional condition.

Lax’s Theorem Consistent: A finite-difference scheme is consistent if the local truncation error  0 as the grid size  0. (Not always true for PDEs, as we shall see.) Lax’s Theorem: If a finite-difference scheme for an initial-value PDE is consistent, then Stability ↔ Convergence

PDEs Partial differential equations are at the very heart of many sciences, and provide our best understanding of the way the world works. Some examples: Quantum mechanics; propagation of waves of all kinds; elasticity; diffusion of particles, population, prices, information; spread of heat; electrostatic field; magnetic fields, fluid flow; etc., etc., etc.

To see the power… Suppose you work for the government and your job is to worry about the possibility of terrorist nuclear weapons. What is the critical mass of U (“25”)? The following material was classified, but is now public: see The Los Alamos Primer: The first lectures on how to build an atomic bomb, R. Serber, Univ. of Calif. Press, Berkeley, QC773.A1S47

Simplest model of neutron diffusion Laplace operator: In spherical coordinates: So for spherically symmetric systems:

Consider a sphere of “25” Let N(t,x,y,z) be the number of neutrons in a tiny cube and consider the net growth of N at any given point in space and any particular time: Rate of change of neutron flux Diffusion influxfission

Consider a sphere of “25” where = mean time between fissions = avg. no. of neutrons produced per fission D = diffusion constant

Separation of variables: an important technique where = effective neutron number Leads to

Separation of variables: an important technique For sphere of radius R, can check solution With the boundary condition So critical mass is determined by

Answers For Uranium: More accurate boundary condition gives 56 kg, and thick U tamper gives 15 kg

Little Boy