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Numerical Integration with MATLAB

Module by: Serhat Beyenir. E-mail the author

Summary: Basic Numerical Integration with MATLAB

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Plot

This chapter essentially deals with the problem of computing the area under a curve. First, we will employ a basic approach and form trapezoids under a curve. From these trapezoids, we can calculate the total area under a given curve. This method can be tedious and is prone to errors, so in the second half of the chapter, we will utilize a built-in MATLAB function to carry out numerical integration.

A Basic Approach

There are various methods to calculating the area under a curve, for example, Rectangle Method, Trapezoidal Rule and Simpson's Rule. The following procedure is a simplified method.

Consider the curve below:

Figure 1: Numerical integration
integration

Each segment under the curve can be calculated as follows:

12( y 0 + y 1 )Δx+12( y 1 + y 2 )Δx+12( y 2 + y 3 )Δx 1 2 y 0 y 1 Δx 1 2 y 1 y 2 Δx 1 2 y 2 y 3 Δx
(1)

Therefore, if we take the sum of the area of each trapezoid, given the limits, we calculate the total area under a curve. Consider the following example.

Example 1

Given the following data, plot an x-y graph and determine the area under a curve between x=3 and x=30

Table 1: Data Set
Index x [m] y [N]
1 3 27.00
2 10 14.50
3 15 9.40
4 20 6.70
5 25 5.30
6 30 4.50

First, let us enter the data set. For x, issue the following command x=[3,10,15,20,25,30];. And for y, y=[27,14.5,9.4,6.7,5.3,4.5];. If yu type in [x',y'], you will see the following tabulated result. Here we transpose row vectors with ' and displaying them as columns:

ans =

    3.0000   27.0000
   10.0000   14.5000
   15.0000    9.4000
   20.0000    6.7000
   25.0000    5.3000
   30.0000    4.5000

Compare the data set above with the given information in the question.

To plot the data type the following:

plot(x,y),title('Distance-Force Graph'),xlabel('Distance[m]'),ylabel('Force[N]'),grid

The following figure is generated:

Figure 2: Distance-Force Graph
integration

To compute dx for consecutive x values, we will use the index for each x value, see the given data in the question.:

dx=[x(2)-x(1),x(3)-x(2),x(4)-x(3),x(5)-x(4),x(6)-x(5)];

dy is computed by the following command:

dy=[0.5*(y(2)+y(1)),0.5*(y(3)+y(2)),0.5*(y(4)+y(3)),0.5*(y(5)+y(4)),0.5*(y(6)+y(5))];

dx and dy can be displayed with the following command: [dx',dy']. The result will look like this:

[dx',dy']

ans =

    7.0000   20.7500
    5.0000   11.9500
    5.0000    8.0500
    5.0000    6.0000
    5.0000    4.9000

Our results so far are shown below

Table 2: x, y and corresponding differential elements
x [m] y [N] dx [m] dy [N]
3 27.00    
10 14.50 7.00 20.75
15 9.40 5.00 11.95
20 6.70 5.00 8.05
25 5.30 5.00 6.00
30 4.50 5.00 4.90

If we multiply dx by dy, we find da for each element under the curve. The differential area da=dx*dy, can be computed using the 'term by term multiplication' technique in MATLAB as follows:

da=dx.*dy

da =

  145.2500   59.7500   40.2500   30.0000   24.5000

Each value above represents an element under the curve or the area of trapezoid. By taking the sum of array elements, we find the total area under the curve.

sum(da)

ans =

  299.7500

Table x illustrates all the steps and results of our MATLAB computation.

Table 3: Computation of the approximate area under a curve
x [m] y [N] dx [m] dy [N] dA [Nm]
3 27.00      
10 14.50 7.00 20.75 145.25
15 9.40 5.00 11.95 59.75
20 6.70 5.00 8.05 40.25
25 5.30 5.00 6.00 30.00
30 4.50 5.00 4.90 24.50
        299.75

The Trapezoidal Rule

Sometimes it is rather convenient to use a numerical approach to solve a definite integral. The trapezoid rule allows us to approximate a definite integral using trapezoids.

The trapz Command

Z = trapz(Y) computes an approximation of the integral of Y using the trapezoidal method.

Now, let us see a typical problem.

Example 2

Given Area=25x2d x Area x 2 5 x 2 , an analytical solution would produce 39. Use trapz command and solve it

  1. Initialize variable x as a row vector, from 2 with increments of 0.1 to 5: x=2:.1:5;
  2. Declare variable y as y=x^2;. Note the following error prompt: ??? Error using ==> mpower Inputs must be a scalar and a square matrix. This is because x is a vector quantity and MATLAB is expecting a scalar input for y. Because of that, we need to compute y as a vector and to do that we will use the dot operator as follows: y=x.^2;. This tells MATLAB to create vector y by taking each x value and raising its power to 2.
  3. Now we can issue the following command to calculate the first area, the output will be as follows:
area1=trapz(x,y)

area1 =

   39.0050

Notice that this numerical value is slightly off. So let us increase the number of increments and calculate the area again:

x=2:.01:5;
y=x.^2;
area2=trapz(x,y)

area2 =

   39.0001

Yet another increase in the number of increments:

x=2:.001:5;
y=x.^2;
area3=trapz(x,y)

area3 =

   39.0000

Example 3

Determine the value of the following integral:

0πsinxd x x 0 x

  1. Initialize variable x as a row vector, from 0 with increments of pi/100 to pi: x=0:pi/100:pi;
  2. Declare variable y as y=sin(x);
  3. Issue the following command to calculate the first area, the output will be as follows:
area1=trapz(x,y)

area1 =

    1.9998

let us increase the increments as above:

x=0:pi/1000:pi;
y=sin(x);
area2=trapz(x,y)

area2 =

    2.0000

Summary of Key Points

  1. In its simplest form, numerical integration involves calculating the areas of segments that make up the area under a curve,
  2. MATLAB has built-in functions to perform numerical integration,
  3. Z = trapz(Y) computes an approximation of the integral of Y using the trapezoidal method.

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