Vector Fields

Section 4.1 Vector Fields

Motivating Questions

What is a vector field?
What are some familiar contexts in which vector fields arise?
How do we draw a vector field?
How do gradients of functions with partial derivatives connect to vector fields?

Thus far vectors have played a central role in our study of multivariable calculus. We know how to do operations on vectors (addition, scalar multiplication, dot product, etc.), and we have seen how vectors can be used to describe curves in

R^{2}

and

R^{3} .

The examples of using vectors to describe curves was our first example of a vector-valued function. That is, a curve

r (t)

is a function that takes as input a real number and produces a vector in

R^{2}

R^{3} .

In this section, we will expand our understanding of vector-valued functions to take as input a point

(x, y)

R^{2}

or a point

(x, y, z)

R^{3}

and produce a vector (typically in

R^{2}

R^{3},

respectively).

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Preview Activity 4.1.1.

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It’s common for weather forecasters to discuss the wind speed, but as any student who has gotten this far in the text will know, this nomenclature is imprecise. It’s not terribly helpful to tell someone the wind is blowing at 10 ^km⁄_h without telling them the direction in which the wind is blowing. If you’re trying to make a decision based on what the wind is doing, you need to know about the direction as well. (Perhaps you are taking off in a hot air balloon and need to know which direction the chase team should head to keep track of you.) Because of the swirling nature of wind, it makes sense to give the wind velocity at every point in a region (two-dimensional or three-dimensional).

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(a)

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Suppose that given a point

(x, y)

in the plane, you know that the wind velocity at that point is given by the vector

F (x, y) = ⟨ y, x ⟩ .

For example, we’d then know that at the point

(1, - 1),

the wind velocity is

F (1, - 1) = ⟨ - 1, 1 ⟩ .

In the table below, fill in the wind velocity vectors for the given points.

$(x, y)$	$(2, 1)$	$(0, 0)$	$(- 1, 2)$	$(3, - 1)$	$(- 2, - 1)$
$F (x, y)$

Answer.

$(x, y)$	$(2, 1)$	$(0, 0)$	$(- 1, 2)$	$(3, - 1)$	$(- 2, - 1)$
$F (x, y)$	$⟨ 1, 2 ⟩$	$⟨ 0, 0 ⟩$	$⟨ 2, - 1 ⟩$	$⟨ - 1, 3 ⟩$	$⟨ - 1, - 2 ⟩$

Solution.

The vector field

F (x, y)

takes the coordinates of the point

(x, y)

and reverses them to make the components of the vector. Thus,

F (2, 1) = ⟨ 1, 2 ⟩ .

The remaining vectors are shown in the table below.

$(x, y)$	$(2, 1)$	$(0, 0)$	$(- 1, 2)$	$(3, - 1)$	$(- 2, - 1)$
$F (x, y)$	$⟨ 1, 2 ⟩$	$⟨ 0, 0 ⟩$	$⟨ 2, - 1 ⟩$	$⟨ - 1, 3 ⟩$	$⟨ - 1, - 2 ⟩$

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(b)

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Suppose that we associate the vector

G (x, y) = - x j

to a point

(x, y)

in the plane. Complete the table below by giving the vector associated to each of the given points.

$(x, y)$	$(- 2, 0)$	$(- 1, 2)$	$(0, - 2)$	$(1, 1)$	$(2, 3)$	$(3, 2)$	$(- 1, 0)$	$(1, 3)$
$G (x, y)$

Answer.

$(x, y)$	$(- 2, 0)$	$(- 1, 2)$	$(0, - 2)$	$(1, 1)$	$(2, 3)$	$(3, 2)$	$(- 1, 0)$	$(1, 3)$
$G (x, y)$	$⟨ 0, 2 ⟩$	$⟨ 0, 1 ⟩$	$⟨ 0, 0 ⟩$	$⟨ 0, - 1 ⟩$	$⟨ 0, - 2 ⟩$	$⟨ 0, - 3 ⟩$	$⟨ 0, 1 ⟩$	$⟨ 0, - 1 ⟩$

Solution.

Note that another way of writing

G (x, y)

⟨ 0 - x ⟩ .

Thus,

G (2, 3) = ⟨ 0, - 2 ⟩,

for example. The remaining answers are found in the table below.

$(x, y)$	$(- 2, 0)$	$(- 1, 2)$	$(0, - 2)$	$(1, 1)$	$(2, 3)$	$(3, 2)$	$(- 1, 0)$	$(1, 3)$
$G (x, y)$	$⟨ 0, 2 ⟩$	$⟨ 0, 1 ⟩$	$⟨ 0, 0 ⟩$	$⟨ 0, - 1 ⟩$	$⟨ 0, - 2 ⟩$	$⟨ 0, - 3 ⟩$	$⟨ 0, 1 ⟩$	$⟨ 0, - 1 ⟩$

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(c)

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A table of values of these vector-valued functions is useful, but perhaps even better is a method of visualizing the vectors. In keeping with our wind velocity analogy, if

F (2, 1) = ⟨ 1, 2 ⟩,

we draw the vector

⟨ 1, 2 ⟩

with its tail at the point

(2, 1) .

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Using the first set of axes in Figure 4.1.1, plot the vectors

F (x, y)

for the five points in the table in part a. The example

F (1, - 1) = ⟨ - 1, 1 ⟩

is drawn for you.

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(d)

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Using the second set of axes in Figure 4.1.1, plot the vectors

G (x, y)

for the eight points in the table in part b.

described in detail following the image — Figure 4.1.1. Axes for plotting some vectors from $F (x, y)$ and $G (x, y) .$

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Subsection 4.1.1 Examples of Vector Fields

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As Preview Activity 4.1.1 showed you, a velocity vector field is an example of a scenario where associating a vector to each point in a region is useful. We denote such a vector field by

F (x, y)

F (x, y, z),

where the vector associated to the point

(x, y)

(x, y, z)

is the velocity of something at that point. Wind velocity is one example, but another example would be the velocity of a flowing fluid. Figure 4.1.2 shows such a velocity vector field. Technically, it only shows some of the vectors in the vector field, since the figure would be unintelligible if all of the vectors were shown. This is illustrated by the inset in the upper left corner, which gives a better picture of what we would see if we zoomed in on the red square of the main figure.

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Force fields, such as those created by gravity, are also examples of vector fields. For example, the earth exerts a gravitational force on objects. The force is directed from the center of the object to the center of the earth, and its magnitude is determined by the distance between the object and the earth. An illustration of this vector field can be seen in Figure 4.1.3, where the earth is positioned at the origin, but not shown. Notice that the vectors get shorter as the distance from the origin increases, reflecting the fact that the gravitational force is weaker at that distance.

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Figure 4.1.3. Gravitational vector field.

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Subsection 4.1.2 Mathematical Vector Fields

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As suggested in the introduction and Preview Activity 4.1.1, vector fields can be given by formulas.

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Definition 4.1.4.

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A vector field in

2

-space is function whose value at a point

(x, y)

is a

2

-dimensional vector

F (x, y) .

Similarly, in

3

-space, a vector field is a function

F (x, y, z)

whose value at the point

(x, y, z)

is a

3

-dimensional vector.

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Since

F (x, y, z)

is a vector, it has

i,

j,

and

k

components. Each of these components is a scalar function of the point

(x, y, z),

and so we will often write

F (x, y, z) = F_{1} (x, y, z) i + F_{2} (x, y, z) j + F_{3} (x, y, z) k .

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For example, if

F (x, y, z) = ⟨ x^{2}, x y \sin (z), y^{3} ⟩,

then

F_{1} (x, y, z) = x^{2},

F_{2} (x, y, z) = x y \sin (z),

and

F_{3} (x, y, z) = y^{3} .

Any time we are considering a vector field

F (x, y, z),

the definitions of functions

F_{1},

F_{2},

and

F_{3}

should be assumed in this manner. (For a vector field

F (x, y)

2

-space, we only have the functions

F_{1}

and

F_{2},

defined analogously.)

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Subsection 4.1.3 Plotting Vector Fields

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Preview Activity 4.1.1 gave you a chance to plot some vectors in the vector fields

F (x, y) = ⟨ y, x ⟩

and

G (x, y) = ⟨ 0, - x ⟩ .

It would be impossible to sketch all of the vectors in these vector fields, since there is one for every point in the plane. In fact, even sketching by hand many more of the vectors than you were asked to in the preview activity rapidly becomes tedious. Fortunately, computer algebra systems can do a great job of making such sketches.

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One tool to plot 2D vector fields can be found here

www.geogebra.org/m/cXgNb58T

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One thing to keep in mind, however, is that the magnitudes of the vectors are typically scaled in these plots, including plots of vector fields we will encounter later in the text. To illustrate this, consider the two plots of the vector field

F (x, y) = y i + x j

in Figure 4.1.5.

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The left plot shows some of the vectors but accurately depicts all of their magnitudes, making the figure very hard to understand, especially along the lines

y = x

and

y = - x .

The plot on the right, however, uses a uniform scaling to make the figure easier to read. As before, each vector’s direction is completely accurate, but now the magnitudes are much smaller. However, the relative magnitudes are preserved, helping us to see that vectors farther from the origin have larger magnitude than those closer to the origin.

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Example 4.1.6.

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The plot in Figure 4.1.7 illustrates the vector field

F (x, y) = y i - x j .

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Starting with one of the vectors near the point $(2, 0),$ sketch a curve that follows the direction of the vector field $F .$ To help visualize what you are doing, it may be useful to think of the vector field as the velocity vector field for some flowing water and that you are imagining tracing the path that a tiny particle inserted into the water would follow as the water moved it around.
Repeat the previous step for at least two other starting points not on the curve you previously sketched.
What shape do the curves you sketched in the previous two steps form?
Verify that $F (x, y)$ is orthogonal to $⟨ x, y ⟩ .$
What is the relationship between the function $f (x, y) = x^{2} + y^{2}$ and the vector $x i + y j ?$
What does this tell you about the relationship between $F (x, y)$ and circles centered at the origin? What is the relationship between $| F (x, y) |$ and the radius of the appropriate circle?

Solution.

An animated vector field plotted in the plane with $x$ and $y$ both ranging from $- 5$ to $5 .$ The vectors have a counterclockwise rotation about the origin, with vectors getting progressively longer as they get farther from the origin. Starting at $(2, 0),$ the circle of radius $2$ centered at the origin gradually appears.
Circles centered at the origin.
$F (x, y) \cdot ⟨ x, y ⟩ = ⟨ y, - x ⟩ \cdot ⟨ x, y ⟩ = y x - x y = 0$
Notice that the gradient of $f (x, y) = x^{2} + y^{2}$ is $⟨ 2 x, 2 y ⟩,$ which is a scalar multiple of $⟨ x, y ⟩ .$ Therefore $⟨ x, y ⟩$ will be orthogonal to the level curves of $f$ (which are circles centered at the origin).

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Subsection 4.1.4 Gradient Vector Fields

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Without using the terminology, we’ve actually already encountered one very important family of vector fields a number of times. Given a function

f

of two or three variables, the gradient of

f

is a vector field, since for any point where

f

has first-order partial derivatives,

\nabla f

assigns a vector to that point.

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Activity 4.1.2.

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(a)

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In Figure 4.1.8 there are three sets of axes showing level curves for functions

f,

g,

and

h,

respectively. Sketch at least six vectors in the gradient vector field for each function. In making your sketches, you don’t have to worry about getting vector magnitudes precise, but you should ensure that the relative magnitudes (and directions) are correct for each function independently.

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(b)

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Verify that

F (x, y) = ⟨ 6 x y, 3 x^{2} + 9 \sqrt{y} ⟩

is a gradient vector field by finding a function

f

such that

\nabla f (x, y) = F (x, y) .

For reasons originating in physics, such a function

f

is called a potential function for the vector field

F .

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(c)

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Is the function

f

found in part b unique? That is, can you find another function

g

such that

\nabla g (x, y) = F (x, y)

but

f \neq g ?

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(d)

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Is the vector field

F (x, y) = 6 x y i + (2 x + 9 \sqrt{y}) j

a gradient vector field? Why or why not?

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Subsection 4.1.5 Summary

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A $2$ -dimensional vector field is a function defined on part of $R^{2}$ whose value is a $2$ -dimensional vector. A $3$ -dimensional vector field is a function defined on part of $R^{3}$ whose value is a $3$ -dimensional vector.
Vector fields arise in familiar contexts such as wind velocity, fluid velocity, and gravitational force.
Vector fields are generally plotted in ways that ensure the direction and relative magnitudes of the vectors sketched are correct instead of ensuring that each vector’s magnitude is depicted correctly.
The gradient of a function $f$ of two or three variables is a vector field defined wherever $f$ has partial derivatives.

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Exercises 4.1.6 Exercises

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1.

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Match the formulas with the corresponding vector field plots.

$i$
$j$
$x i$
$x j$
$y i$
$y j$

Hint.

In a vector field of the form

k i

where

k

is a scalar (e.g.

k = 1

k = x

k = y

), all the vectors must be horizontal. Likewise, in a vector field of the form

k j

all vectors must be vertical. Consider the lengths of the vectors, too.

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2.

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In the two images below you’ll see contour maps for two different functions. On each contour map, draw the gradient field of the function.

Hint.

The gradient vector is always orthogonal to the contour line, it points in the direction of greatest ascent, and its length is the directional derivative in the direction of greatest ascent. Don’t forget that the function is changing most rapidly where the contour lines are closest together.

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