Calculus III - Curl and Divergence (2024)

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Section 17.1 : Curl and Divergence

Before we can get into surface integrals we need to get some introductory material out of the way. That is the purpose of the first two sections of this chapter.

In this section we are going to introduce the concepts of the curl and the divergence of a vector.

Let’s start with the curl. Given the vector field \(\vec F = P\,\vec i + Q\,\vec j + R\,\vec k\) the curl is defined to be,

\[{\mathop{\rm curl}\nolimits} \vec F = \left( {{R_y} - {Q_z}} \right)\vec i + \left( {{P_z} - {R_x}} \right)\vec j + \left( {{Q_x} - {P_y}} \right)\vec k\]

There is another (potentially) easier definition of the curl of a vector field. To use it we will first need to define the \(\nabla \) operator. This is defined to be,

\[\nabla = \frac{\partial }{{\partial x}}\,\,\vec i + \frac{\partial }{{\partial y}}\,\,\vec j + \frac{\partial }{{\partial z}}\,\,\vec k\]

We use this as if it’s a function in the following manner.

\[\nabla f = \frac{{\partial f}}{{\partial x}}\,\,\vec i + \frac{{\partial f}}{{\partial y}}\,\,\vec j + \frac{{\partial f}}{{\partial z}}\,\,\vec k\]

So, whatever function is listed after the \(\nabla \) is substituted into the partial derivatives. Note as well that when we look at it in this light we simply get the gradient vector.

Using the \(\nabla \) we can define the curl as the following cross product,

\[{\mathop{\rm curl}\nolimits} \vec F = \nabla \times \vec F = \left| {\begin{array}{*{20}{c}}{\vec i}&{\vec j}&{\vec k}\\{\displaystyle \frac{\partial }{{\partial x}}}&{\displaystyle \frac{\partial }{{\partial y}}}&{\displaystyle \frac{\partial }{{\partial z}}}\\P&Q&R\end{array}} \right|\]

We have a couple of nice facts that use the curl of a vector field.

Facts

  1. If \(f\left( {x,y,z} \right)\) has continuous second order partial derivatives then \({\mathop{\rm curl}\nolimits} \left( {\nabla f} \right) = \vec 0\). This is easy enough to check by plugging into the definition of the derivative so we’ll leave it to you to check.
  2. If \(\vec F\) is a conservative vector field then \({\mathop{\rm curl}\nolimits} \vec F = \vec 0\). This is a direct result of what it means to be a conservative vector field and the previous fact.
  3. If \(\vec F\) is defined on all of \({\mathbb{R}^3}\) whose components have continuous first order partial derivative and \({\mathop{\rm curl}\nolimits} \vec F = \vec 0\) then \(\vec F\) is a conservative vector field. This is not so easy to verify and so we won’t try.

Example 1 Determine if \(\vec F = {x^2}y\,\vec i + xyz\,\vec j - {x^2}{y^2}\,\vec k\) is a conservative vector field.

Show Solution

So, all that we need to do is compute the curl and see if we get the zero vector or not.

\[\begin{align*}{\mathop{\rm curl}\nolimits} \vec F & = \left| {\begin{array}{*{20}{c}}{\vec i}&{\vec j}&{\vec k}\\{\displaystyle \frac{\partial }{{\partial x}}}&{\displaystyle \frac{\partial }{{\partial y}}}&{\displaystyle \frac{\partial }{{\partial z}}}\\{{x^2}y}&{xyz}&{ - {x^2}{y^2}}\end{array}} \right|\\ & = - 2{x^2}y\,\vec i + yz\,\vec k - \left( { - 2x{y^2}\,\vec j} \right) - xy\,\vec i - {x^2}\vec k\\ & = - \left( {2{x^2}y + xy} \right)\vec i + 2x{y^2}\,\vec j + \left( {yz - {x^2}} \right)\vec k\\ & \ne \vec 0\end{align*}\]

So, the curl isn’t the zero vector and so this vector field is not conservative.

Next, we should talk about a physical interpretation of the curl. Suppose that \(\vec F\) is the velocity field of a flowing fluid. Then \({\mathop{\rm curl}\nolimits} \vec F\) represents the tendency of particles at the point \(\left( {x,y,z} \right)\) to rotate about the axis that points in the direction of \({\mathop{\rm curl}\nolimits} \vec F\). If \({\mathop{\rm curl}\nolimits} \vec F = \vec 0\) then the fluid is called irrotational.

Let’s now talk about the second new concept in this section. Given the vector field \(\vec F = P\,\vec i + Q\,\vec j + R\,\vec k\) the divergence is defined to be,

\[{\mathop{\rm div}\nolimits} \vec F = \frac{{\partial P}}{{\partial x}} + \frac{{\partial Q}}{{\partial y}} + \frac{{\partial R}}{{\partial z}}\]

There is also a definition of the divergence in terms of the \(\nabla \) operator. The divergence can be defined in terms of the following dot product.

\[{\mathop{\rm div}\nolimits} \vec F = \nabla \centerdot \vec F\]

Example 2 Compute \({\mathop{\rm div}\nolimits} \vec F\) for \(\vec F = {x^2}y\,\vec i + xyz\,\vec j - {x^2}{y^2}\,\vec k\)

Show Solution

There really isn’t much to do here other than compute the divergence.

\[{\mathop{\rm div}\nolimits} \vec F = \frac{\partial }{{\partial x}}\left( {{x^2}y} \right) + \frac{\partial }{{\partial y}}\left( {xyz} \right) + \frac{\partial }{{\partial z}}\left( { - {x^2}{y^2}} \right) = 2xy + xz\]

We also have the following fact about the relationship between the curl and the divergence.

\[{\mathop{\rm div}\nolimits} \left( {{\mathop{\rm curl}\nolimits} \vec F} \right) = 0\]

Example 3 Verify the above fact for the vector field \(\vec F = y{z^2}\,\vec i + xy\,\vec j + yz\,\vec k\).

Show Solution

Let’s first compute the curl.

\[\begin{align*}{\mathop{\rm curl}\nolimits} \vec F & = \left| {\begin{array}{*{20}{c}}{\vec i}&{\vec j}&{\vec k}\\{\displaystyle \frac{\partial }{{\partial x}}}&{\displaystyle \frac{\partial }{{\partial y}}}&{\displaystyle \frac{\partial }{{\partial z}}}\\{y{z^2}}&{xy}&{yz}\end{array}} \right|\\ & = z\,\vec i + 2yz\,\vec j + y\,\vec k - {z^2}\vec k\\ & = z\vec i + 2yz\,\vec j + \left( {y - {z^2}} \right)\vec k\end{align*}\]

Now compute the divergence of this.

\[{\mathop{\rm div}\nolimits} \left( {{\mathop{\rm curl}\nolimits} \vec F} \right) = \frac{\partial }{{\partial x}}\left( z \right) + \frac{\partial }{{\partial y}}\left( {2yz} \right) + \frac{\partial }{{\partial z}}\left( {y - {z^2}} \right) = 2z - 2z = 0\]

We also have a physical interpretation of the divergence. If we again think of \(\vec F\) as the velocity field of a flowing fluid then \({\mathop{\rm div}\nolimits} \vec F\) represents the net rate of change of the mass of the fluid flowing from the point \(\left( {x,y,z} \right)\) per unit volume. This can also be thought of as the tendency of a fluid to diverge from a point. If \({\mathop{\rm div}\nolimits} \vec F = 0\) then the \(\vec F\) is called incompressible.

The next topic that we want to briefly mention is the Laplace operator. Let’s first take a look at,

\[{\mathop{\rm div}\nolimits} \left( {\nabla f} \right) = \nabla \centerdot \nabla f = {f_{xx}} + {f_{yy}} + {f_{zz}}\]

The Laplace operator is then defined as,

\[{\nabla ^2} = \nabla \centerdot \nabla \]

The Laplace operator arises naturally in many fields including heat transfer and fluid flow.

The final topic in this section is to give two vector forms of Green’s Theorem. The first form uses the curl of the vector field and is,

\[\oint_{C}{{\vec F\centerdot d\,\vec r}} = \iint\limits_{D}{{\left( {{\mathop{\rm curl}\nolimits} \vec F} \right)\centerdot \vec k\,dA}}\]

where \(\vec k\) is the standard unit vector in the positive \(z\) direction.

The second form uses the divergence. In this case we also need the outward unit normal to the curve \(C\). If the curve is parameterized by

\[\vec r\left( t \right) = x\left( t \right)\vec i + y\left( t \right)\vec j\]

then the outward unit normal is given by,

\[\vec n = \frac{{y'\left( t \right)}}{{\left\| {\vec r'\left( t \right)} \right\|}}\vec i - \frac{{x'\left( t \right)}}{{\left\| {\vec r'\left( t \right)} \right\|}}\vec j\]

Here is a sketch illustrating the outward unit normal for some curve \(C\) at various points.

Calculus III - Curl and Divergence (1)

The vector form of Green’s Theorem that uses the divergence is given by,

\[\oint_{C}{{\vec F\centerdot \vec n\,ds}} = \iint\limits_{D}{{{\mathop{\rm div}\nolimits} \vec F\,dA}}\]

Calculus III - Curl and Divergence (2024)

FAQs

What is divergence and curl in calculus? ›

Roughly speaking, divergence measures the tendency of the fluid to collect or disperse at a point, and curl measures the tendency of the fluid to swirl around the point. Divergence is a scalar, that is, a single number, while curl is itself a vector.

What is curl in Calc 3? ›

Curl is an operator which takes in a function representing a three-dimensional vector field and gives another function representing a different three-dimensional vector field.

What is the formula for div and curl? ›

Formulas for divergence and curl

For F:R3→R3 (confused?), the formulas for the divergence and curl of a vector field are divF=∂F1∂x+∂F2∂y+∂F3∂zcurlF=(∂F3∂y−∂F2∂z,∂F1∂z−∂F3∂x,∂F2∂x−∂F1∂y).

What is the curl rule in calculus? ›

In vector calculus, the curl, also known as rotor, is a vector operator that describes the infinitesimal circulation of a vector field in three-dimensional Euclidean space. The curl at a point in the field is represented by a vector whose length and direction denote the magnitude and axis of the maximum circulation.

How to remember the curl formula? ›

So if you can use the rule that “multiplication” by ∂∂x is the same as taking the partial derivative with respect to x (and similar for the other derivatives), then you can remember the curl formula by curlF=∇×F.

What is an example of divergence? ›

Divergence describes how fast the area of your span is changing. For example, imagine that the river gets faster and faster the further you go downstream. Then your friends in front of you will keep getting further and further ahead, and your span stretches out. This is an example of a positive divergence.

How to calculate divergence? ›

The divergence is generally denoted by “div”. The divergence of a vector field can be calculated by taking the scalar product of the vector operator applied to the vector field. I.e., ∇ . F(x, y).

What does curl tell us? ›

The curl of a vector field captures the idea of how a fluid may rotate. Imagine that the below vector field F represents fluid flow. The vector field indicates that the fluid is circulating around a central axis.

Why is gradient divergence and curl important? ›

Learning about gradient, divergence and curl are important, especially in CFD. They help us calculate the flow of liquids and correct the disadvantages. For example, curl can help us predict the voracity, which is one of the causes of increased drag.

Is the divergence of the curl always zero? ›

Theorem 18.5. 1 ∇⋅(∇×F)=0. In words, this says that the divergence of the curl is zero.

What is the theorem of curl? ›

The Stoke's theorem states that “the surface integral of the curl of a function over a surface bounded by a closed surface is equal to the line integral of the particular vector function around that surface.” Where, C = A closed curve.

What is divergence in Calc 3? ›

Divergence measures the change in density of a fluid flowing according to a given vector field.

What is the rule for curl and divergence? ›

A positive divergence corresponds to fluid expansion, i.e. the fluid is generally moving away from the point, while a negative divergence corresponds to fluid compression, i.e. the fluid is generally moving toward the point. curl(cF) = c curl(F) and div(cF) = c div(F).

What is divergence and curl in maths? ›

The divergence of a vector field is a scalar function. Divergence measures the “outflowing-ness” of a vector field. If ⇀v is the velocity field of a fluid, then the divergence of ⇀v at a point is the outflow of the fluid less the inflow at the point. The curl of a vector field is a vector field.

What is the divergence theorem in calculus? ›

The divergence theorem relates a surface integral across closed surface S to a triple integral over the solid enclosed by S. The divergence theorem is a higher dimensional version of the flux form of Green's theorem, and is therefore a higher dimensional version of the Fundamental Theorem of Calculus.

What is convergence and divergence in calculus? ›

When the limit of a series approaches a real number (i.e., the limit exists), it displays convergent behavior. As a result, an approximation can be evaluated for that given series. However, if the limit does not exist or is equal to infinity, that series displays divergent behavior.

What is the difference between curl divergence and gradient? ›

To start with, the gradient is a differential operator that operates on a scalar field, while the divergence is a differential operator that operates on a vector field (just as the curl is also a differential operator that operates on a vector field).

What do you mean by divergence? ›

The point where two things split off from each other is called a divergence.

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