MATH 212 NE 217 Douglas Wilhelm Harder Department of Electrical and Computer Engineering University of Waterloo Waterloo, Ontario, Canada Copyright © 2011 by Douglas Wilhelm Harder. All rights reserved. Advanced Calculus 2 for Electrical Engineering Advanced Calculus 2 for Nanotechnology Engineering 1-D Finite-Element Methods with Poisson’s Equation

1-D Finite-element Methods with Poisson’s Equation 2 Outline This topic discusses an introduction to finite-element methods –Review of Poisson’s equation –Defining a new kernel V(x) –Approximate solutions using uniform test functions

1-D Finite-element Methods with Poisson’s Equation 3 Outcomes Based Learning Objectives By the end of this laboratory, you will: –Understand how to approximate the heat-conduction/diffusion and wave equations in two and three dimensions –You will understand the differences between insulated and Dirichlet boundary conditions

1-D Finite-element Methods with Poisson’s Equation 4 The Target Equation Recall the first of Maxwell’s equations (Gauss’s equation): If we are attempting to solve for the underlying potential function, under the assumption that it is a conservative field, we have This is in the form of Poisson’s equation

1-D Finite-element Methods with Poisson’s Equation 5 The Target Equation In one dimension, this simplifies to: Define: and thus we are solving for

1-D Finite-element Methods with Poisson’s Equation 6 The Integral If, it follows that for any test function  (x) and therefore Substituting the alterative definition of V(x) into this equation, we get

1-D Finite-element Methods with Poisson’s Equation 7 The Integral Consider the first test function  1 (x) :

1-D Finite-element Methods with Poisson’s Equation 8 Integration by Parts Again, take but before we apply integration by parts, expand the integral: Everything in the second integral is known: bring it to the right:

1-D Finite-element Methods with Poisson’s Equation 9 Integration by Parts The left-hand integral is no different from before, and performing integration by parts, we have

1-D Finite-element Methods with Poisson’s Equation 10 Integration by Parts First, substituting in the first test function: which yields 111

1-D Finite-element Methods with Poisson’s Equation 11 The System of Linear Equations Again, recall that we approximated the solution by unknown piecewise linear functions: where we define on

1-D Finite-element Methods with Poisson’s Equation 12 Unequally Spaced Points Recall that we approximated the left-hand integral by substituting the piecewise linear functions to get:

1-D Finite-element Methods with Poisson’s Equation 13 Integration by Parts That is, our linear equations are: This time, let’s take an actual example

1-D Finite-element Methods with Poisson’s Equation 14 Integration by Parts Consider the equation with the boundary conditions u(0) = 0 u(1) = 0

1-D Finite-element Methods with Poisson’s Equation 15 Integration by Parts The solution to the boundary value problem u(0) = 0 u(1) = 0 is the exact function

1-D Finite-element Methods with Poisson’s Equation 16 Integration by Parts We know in one dimension if the right-hand side is close to zero, the solution is a straight line Thus, choose 9 interior points with a focus on the centre: >> x_uneq = [ ];

1-D Finite-element Methods with Poisson’s Equation 17 Integration by Parts We will compare this approximation with the approximation found using 9 equally spaced interior points –The finite difference approximation >> x_eq = 0:0.1:1;

1-D Finite-element Methods with Poisson’s Equation 18 The Test Functions The function to find the approximations is straight-forward: function [ v ] = uniform1d( x, uab, rho ) n = length( x ) - 2; idx = 1./diff(x); M = diag( -(idx( 1:end - 1 ) + idx( 2: end )) ) +... diag( idx( 2:end - 1 ), -1 ) +... diag( idx( 2:end - 1 ), +1 ); b = zeros ( n, 1 ); for k = 1:n b(k) = 0.5*int( rho, x(k), x(k + 2) ); end b(1) = b(1) - idx(1)*uab(1); b(end) = b(end) - idx(end)*uab(end); v = [uab(1); M \ b; uab(2)]; end int( rho, a, b ) approximates

1-D Finite-element Methods with Poisson’s Equation 19 The Test Functions The right-hand function of is also straight-forward: function [ u ] = rho( x ) u = sin( pi*x ).^4; end

1-D Finite-element Methods with Poisson’s Equation 20 The Equations Thus, we can find our two approximations: >> x_eq = (0:0.1:1)'; >> plot( x_eq, uniform1d( x_eq, [0, ), 'b+' ); >> hold on >> x_uneq = [ ]'; >> plot( x_uneq, uniform1d( x_uneq, [0, ), 'rx' );

1-D Finite-element Methods with Poisson’s Equation 21 The Equations It is difficult to see which is the better function, therefore create a function storing the actual solution (as found in Maple): function u = u(x) u = -1/16*(... (cos(pi*x).^4 - 5*cos(pi*x).^2 + 4)/pi^ *x.*(x - 1)... ); end

1-D Finite-element Methods with Poisson’s Equation 22 The Equations Instead, plotting the errors: >> x_eq = 0:0.1:1; >> plot( x_eq, uniform1d( x_eq, [0, ) - u(x_eq), 'b+' ); >> hold on >> xnu = [ ]; >> plot( x_uneq, uniform1d( x_uneq, [0, ) - u(x_uneq), 'rx' );

1-D Finite-element Methods with Poisson’s Equation 23 Integration by Parts Understanding that the right-hand side has a greater influence in the centre, appropriately changing the sample points yielded a significantly better approximation

1-D Finite-element Methods with Poisson’s Equation 24 Summary In this topic, we have generalized Laplace’s equation to Poisson’s equation –Used the same uniform test functions –We looked at a problem for which there is an exact solution Changing the points allowed us to get better approximations

1-D Finite-element Methods with Poisson’s Equation 25 What’s Next? T he impulse function (the derivative of a step function) is difficult to deal with… We will next consider test functions that avoid this… –The test functions will be tents –This generalizes to higher dimensions

1-D Finite-element Methods with Poisson’s Equation 26 References [1]Glyn James, Advanced Modern Engineering Mathematics, 4 th Ed., Prentice Hall, 2011, §§9.2-3.

1-D Finite-element Methods with Poisson’s Equation 27 Usage Notes These slides are made publicly available on the web for anyone to use If you choose to use them, or a part thereof, for a course at another institution, I ask only three things: –that you inform me that you are using the slides, –that you acknowledge my work, and –that you alert me of any mistakes which I made or changes which you make, and allow me the option of incorporating such changes (with an acknowledgment) in my set of slides Sincerely, Douglas Wilhelm Harder, MMath