Green's Theorem

Divergence and Curl

In Lecture 4 we discussed the gradient function. In this lecture we discuss an alternate method of solving some line integrals through the use of two new concepts - divergence and curl.

First, an elaboration on last lecture:
Two functions, interpreted in terms of a surface:

The gradient of f is perpendicular to the surface.
The curves in the x-y plane are for g=constant.
The gradient of g is a projection of the gradient of f onto the x-y plane.

Green's Theorem in the Plane

Let us relate certain line integrals to area integrals.
Remember the equations for flux and circulation:

We will write these in a new way. Imagine a small chunk of the path of length ds. From this, we find a new way of expressing T and n.


If F=Mi + Nj, then we can conclude:

These equations are leading to divergence and curl.

Divergence and Curl

We will calculate the flux out of the box to find an expression for divergence.

We continue by simplifying these integrals and then grouping N terms and M terms together.

We see that the flux equals the area of the box times an expession of M and N. This expression is defined as the divergence. The notation used to show divergence is

Also note that divF=flux/area.
In terms of fluid flow, the divergence can be interpreted as the flux over an infinitely small loop.

We neglect to show the entire process for circulation, but through a similar method to the one shown above, it can be demonstrated that:

Once again, our result is the area of the box times a (somewhat different) expression of M and N. This expression is defined as the curl.

Note that the curl is equivalent to the circulation/area.
Also, curl is a vector quantity, but in 2 dimensions, this can be neglected since the direction of the vector will always be perpendicular to the 2 dimensions. A more complete definition will be given later in the course.

Green's Theorem

For circulation:

For flux:

R is the region enclosed by C.
Green's Theorem converts the line integral into an area integral, which is often easier to solve.
Examples will be given next lecture.

Lecture given by Prof. Alan Zehnder on 9/11/95
Lecture written by Aric Shafran and edited by Lawrence C. Weintraub