P ROJECT : N UMERICAL S OLUTIONS TO O RDINARY D IFFERENTIAL E QUATIONS IN H ARDWARE Joseph Schneider EE 800 March 30, 2010.

Slides:



Advertisements
Similar presentations
Formal Computational Skills
Advertisements

Ordinary Differential Equations
Chapter 6 Differential Equations
MATLAB EXAMPLES Initial-value problems
SE301: Numerical Methods Topic 8 Ordinary Differential Equations (ODEs) Lecture KFUPM Read , 26-2, 27-1 CISE301_Topic8L8&9 KFUPM.
CS 282.  Any question about… ◦ SVN  Permissions?  General Usage? ◦ Doxygen  Remember that Project 1 will require it  However, Assignment 2 is good.
Numeriska beräkningar i Naturvetenskap och Teknik 1. Numerical differentiation and quadrature Discrete differentiation and integration Trapezoidal and.
Ordinary Differential Equations
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.
11 September 2007 KKKQ 3013 PENGIRAAN BERANGKA Week 10 – Ordinary Differential Equations 11 September am – 9.00 am.
ECIV 301 Programming & Graphics Numerical Methods for Engineers Lecture 31 Ordinary Differential Equations.
Initial-Value Problems
Efficient Simulation of Physical System Models Using Inlined Implicit Runge-Kutta Algorithms Vicha Treeaporn Department of Electrical & Computer Engineering.
Ordinary Differential Equations (ODEs) 1Daniel Baur / Numerical Methods for Chemical Engineers / Implicit ODE Solvers Daniel Baur ETH Zurich, Institut.
Numerical Solutions of Ordinary Differential Equations
Ordinary Differential Equations (ODEs) 1Daniel Baur / Numerical Methods for Chemical Engineers / Explicit ODE Solvers Daniel Baur ETH Zurich, Institut.
MATH 685/ CSI 700/ OR 682 Lecture Notes Lecture 10. Ordinary differential equations. Initial value problems.
CSE 330 : Numerical Methods Lecture 17: Solution of Ordinary Differential Equations (a) Euler’s Method (b) Runge-Kutta Method Dr. S. M. Lutful Kabir Visiting.
PART 7 Ordinary Differential Equations ODEs
Section 6.1: Euler’s Method. Local Linearity and Differential Equations Slope at (2,0): Tangent line at (2,0): Not a good approximation. Consider smaller.
Engineering Computation and Simulation Conor Brennan Dublin City University EE317.
9/22/ Runge 2 nd Order Method Major: All Engineering Majors Authors: Autar Kaw, Charlie Barker
EE3561_Unit 8Al-Dhaifallah14351 EE 3561 : Computational Methods Unit 8 Solution of Ordinary Differential Equations Lesson 3: Midpoint and Heun’s Predictor.
C OMPUTATION AND S IMULATION EE A SSIGNMENT T WO By: Shimiao Cheng, Femi Adeleke, Hanieh Alirezaeeabyaneh.
Computational Method in Chemical Engineering (TKK-2109)
10/20/ Runge 2 nd Order Method Chemical Engineering Majors Authors: Autar Kaw, Charlie Barker
Modelling & Simulation of Chemical Engineering Systems Department of Chemical Engineering King Saud University 501 هعم : تمثيل الأنظمة الهندسية على الحاسب.
5/30/ Runge 4 th Order Method Chemical Engineering Majors Authors: Autar Kaw, Charlie Barker
11/17/ Shooting Method Major: All Engineering Majors Authors: Autar Kaw, Charlie Barker
Numerical Solutions of ODE
Boundary Value Problems l Up to this point we have solved differential equations that have all of their initial conditions specified. l There is another.
Ordinary Differential Equations (ODEs) 1Michael Sokolov / Numerical Methods for Chemical Engineers / Explicit ODE Solvers Michael Sokolov ETH Zurich, Institut.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Part 7 - Chapter 25.
Numerical Analysis – Differential Equation
Announcements Read Chapters 11 and 12 (sections 12.1 to 12.3)
1/16/ Runge 4 th Order Method Civil Engineering Majors Authors: Autar Kaw, Charlie Barker
1/19/ Runge 4 th Order Method Major: All Engineering Majors Authors: Autar Kaw, Charlie Barker
Dr. Mujahed AlDhaifallah ( Term 342)
Today’s class Ordinary Differential Equations Runge-Kutta Methods
ECE 576 – Power System Dynamics and Stability Prof. Tom Overbye Dept. of Electrical and Computer Engineering University of Illinois at Urbana-Champaign.
Y=3x+1 y 5x + 2 =13 Solution: (, ) Solve: Do you have an equation already solved for y or x?
Chapter 21 Exact Differential Equation Chapter 2 Exact Differential Equation.
Ordinary Differential Equations
ECE 576 – Power System Dynamics and Stability Prof. Tom Overbye Dept. of Electrical and Computer Engineering University of Illinois at Urbana-Champaign.
2/28/ Runge 4 th Order Method Computer Engineering Majors Authors: Autar Kaw, Charlie Barker
ACSL, POSTECH1 MATLAB 입문 CHAPTER 8 Numerical Calculus and Differential Equations.
Lecture 39 Numerical Analysis. Chapter 7 Ordinary Differential Equations.
Dr. Mubashir Alam King Saud University. Outline Ordinary Differential Equations (ODE) ODE: An Introduction (8.1) ODE Solution: Euler’s Method (8.2) ODE.
Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Part 6 - Chapters 22 and 23.
CISE301_Topic8L71 CISE301: Numerical Methods Topic 8 Ordinary Differential Equations (ODEs) Lecture KFUPM (Term 101) Section 04 Read , 26-2,
Announcements Please read Chapters 11 and 12
Engineering Problem Solution
Part 7 - Chapter 25.
ECE 576 – Power System Dynamics and Stability
MATH 2140 Numerical Methods
Civil Engineering Majors Authors: Autar Kaw, Charlie Barker
Part 7 - Chapter 25.
Industrial Engineering Majors Authors: Autar Kaw, Charlie Barker
Measuring Errors Major: All Engineering Majors
SE301: Numerical Methods Topic 8 Ordinary Differential Equations (ODEs) Lecture KFUPM (Term 101) Section 04 Read , 26-2, 27-1 CISE301_Topic8L2.
Performance What hardware accelerators are you using/evaluating?
Numerical solution of first-order ordinary differential equations
Electrical Engineering Majors Authors: Autar Kaw, Charlie Barker
Numerical Computation and Optimization
Differential equations
Chemical Engineering Majors Authors: Autar Kaw, Charlie Barker
MATH 2140 Numerical Methods
CISE301: Numerical Methods Topic 8 Ordinary Differential Equations (ODEs) Lecture KFUPM Read , 26-2, 27-1 CISE301_Topic8L7 KFUPM.
Numerical solution of first-order ordinary differential equations 1. First order Runge-Kutta method (Euler’s method) Let’s start with the Taylor series.
Presentation transcript:

P ROJECT : N UMERICAL S OLUTIONS TO O RDINARY D IFFERENTIAL E QUATIONS IN H ARDWARE Joseph Schneider EE 800 March 30, 2010

O RDINARY D IFFERENTIAL E QUATIONS Function with one independent variable and derivatives of the dependent variable Eg. y’ = 1 + y/x Requires some initial condition in order to be solved Eg. y(1) = 2

O RDINARY D IFFERENTIAL E QUATIONS Found in many areas of engineering Radioactive decay, heat equation, motion… In electrical engineering, charge, flux, voltage, current, all intertwined by differential equations

O RDINARY D IFFERENTIAL E QUATIONS In some cases, original equation can be derived with relative ease; Exact solutions are then available In other cases, we will only have the ODE to work with Numerical solutions have been developed to deal with these cases

O RDINARY D IFFERENTIAL E QUATIONS Most basic case: Euler’s Method Selecting a step size h, iterate from initial value to desired value using the derivative function

O RDINARY D IFFERENTIAL E QUATION Euler’s Method most basic case – Simple, but inaccurate Variety of other methods that have been developed

O RDINARY D IFFERENTIAL E QUATIONS

Error directly linked with step size As step size decreases, error decreases; However, takes longer for process to complete Implemented in software (eg. Matlab), more accurate methods take several seconds to complete for smaller scale cases; Several minutes for larger cases

O RDINARY D IFFERENTIAL E QUATIONS Original project goal: Implement the Runge- Kutta 4 th order method in hardware for improved speed

P ROJECT Euler’s method also implemented for comparisons on area, timing, and accuracy. Current implementation: Uses fixed-point number representation, state machine Further steps on this implementation Variable-point number representation to improve accuracy Modify for parallelism to examine impacts on area, timing

P ROJECT Second implementation: Error-controlled design of Runge-Kutta method Error is specified at beginning of process, step size is varied to ensure final result meets error specifications

M ATLAB I MPLEMENTATIONS

P ROJECT For second implementation, desired to use a hardware design to improve on bottleneck of software design Comparisons to software on time vs. error threshold