Copyright © 2003 jsd
Let’s start by considering Ferris Bueller’s car problem: Suppose you borrow a car, drive it around a complicated path, and then return it to exactly where you found it. Some things will return to their original state, but some will not:
|The X-coordinate (longitude) returns to its original value. The Y-coordinate (latitude) returns to its original value.||The distance traveled is nonzero. During the trip, the odometer reading advances by some nonzero amount. It does not return to its original value.|
A possible path is shown in figure 1. The total length of the loop is about 5.4 units. Along the path there are hash marks every 0.2 units.
We can understand this mathematically as follows: The element of arc length (ds) is defined via:
Now if we integrate around the loop, from start to finish, we find
We can even draw a picture of dx and dy.
We see that dx and dy are very different from ds.
In contrast, s is not a potential. There is no way you can join up the hash marks in figure 1 to create a well-behaved contour map. The s-value cannot be interpreted as a height.
Forsooth, if we consider the (x,y) plane to be our state space, s is not even a function of state. Now s is a perfectly good function of s as we move along the path, but s is not a function of location in the (x,y) plane. This is particularly obvious at the point where the path crosses itself.
The same goes for ds: We know the magnitude and direction of ds as a function of s as we go along the given path, but ds not a function of location in the (x,y) plane. This is particularly obvious at the point where the path crosses itself.
Let’s be clear: s and ds exist only in the low-dimensional space defined by the path. They are not functions of state in the higher-dimensional (x,y) state space.
The moral of the story is:
An intermediate case is shown in figure 4. The hash marks represent the vector field x dy. This is intermediate in the sense that x dy is a perfectly good function of state in the (x,y) plane, so it is not as wacky as the element of arc length (ds) that we saw in figure 1. On the other hand, x dy is not the gradient of any potential, so understanding figure 4 is more work than understanding figure 2 or figure 3.
In figure 4 the hash marks are partially like the contour lines on a topo map, insofar as the closeness of the hash marks indicates the steepness of the local slope. On the other hand, there is no way you can join up the hash marks to make a well-behaved contour map. At every point, there is a notion of local slope, but there is no valid notion of height. Because it is not a potential, you cannot integrate the slope to find the height in any consistent way, because the integral strongly depends on the choice of path. In particular:
This is important, because in thermodynamics it is exceedingly common to encounter vector fields of the form x dy i.e. things that are not the gradient of any potential.
Similarly, in electromagnetism, it is exceedingly common to encounter things like x dy − y dx, i.e. things that are not the gradient of any potential. In particular, sometimes people who ought to know better pretend that the voltage is a potential, even when it is not. For more about the definition of voltage, including its dependence on the choice of path, see reference 1.
The moral of the story is:
The diagrams in this section were created using the spreadsheet in reference 2.
In the real world, there are lots of things that locally have a notion of “uphill” and “downhill”, but don’t have a definite height. Examples abound in electromagnetism and in thermodynamics. Figure 5 may help you visualize what’s going on. In the figure, clockwise = locally downward, everywhere.
Non-experts tend to think that every force-field that is a function of position must be derived from some potential, but it’s just not true.
If you want a real-world example, imagine you are standing near a whirlwind. If you walk clockwise around the core of the whirlwind, the force of the wind is assisting you the whole way. If you walk counterclockwise, the force of the wind is opposing you the whole way. (Walk slowly, so that any dependence on your velocity is negligible.)
In the simplest case, a whirlwind can be described as a vortex, which has a particularly simple distribution of velocities, as shown in figure 6.
It is amusing to note that in an ideal vortex, if you care only about the velocity, the integral of velocity is the same along any simply-connected path that encompasses the core, such as the red path in figure 7. In particular, the integral is 20 steps, as you can verify by counting it yourself. Meanwhile, the integral is zero for any simply-connected path that does not encompass the center, such as the blue path in figure 7.
As another example, when talking about voltage, the recommended term is simply voltage. Beware that some people use the term “electric potential” as if it were synonymous with (or preferable to) the term “voltage”, but this is a horrible misnomer and misconception. There are innumerable cases where you have a voltage but you don’t have a potential. The notorious “ground loops” and other wiring loops are a case in point. If the voltage were a potential, you wouldn’t care whether your wiring had loops or not.
While ground loops are unhelpful, many helpful devices exploit the same physics, i.e. the fact that you can pick up a voltage by going around a closed loop. Examples include transformers, generators, and betatrons. As described in reference 3, a betatron uses a magnetic field that steadily increases as a function of time. For simplicity let’s consider a simplified betatron in which the magnetic field is spatially uniform, in which case the induced electric field is shown in figure 8.
In figure 8 you can see that the magnitude as well as the direction of the force is changing from place to place. In contrast, in figure 5 the magnitude is the same everywhere and only the direction is changing.
The electric field inside an ordinary transformer is definitely not the gradient of any potential. It is qualitatively similar to the betatron field.
(By the way: An unsuitable example would be the frictional force due to sliding on a stationary table-top. This force is not derived from any potential. It is a function of velocity, not a function of position. We aren’t discussing such forces here.)
In figure 5 or figure 8, pick two points A and B, and a definite path from one to the other. The height-change along the path is the number of upward steps you take going along the path, minus the number of downward steps. The answer depends on your choice of path, not just on the choice of endpoints. (For a potential, such as figure 10, the answer depends only on the endpoints, independent of path.)
If we want to get fancy, we can say each of these figures represents something called a one-form, as discussed in reference 4 and references therein. A one-form that is the gradient of some potential is called grady. A one-form that is not grady is called ungrady or non-grady.
Additional terminology: A grady force field is commonly called a conservative force field. Alas, this terminology is just begging to be misunderstood. In electrodynamics, for example, both grady and non-grady force fields uphold conservation of energy, conservation of momentum, conservation of charge, et cetera, so it seems very strange to call the non-grady fields “non-conservative”.
Also: Many books refer to a grady one-form as an exact differential, and an ungrady one-form as an inexact differential. Alas, this terminology is also begging to be misunderstood. Beware that in this context, inexact does not mean imprecise or approximate. Not at all. Also beware that an inexact differential is really not a differential at all.
Compared to the concept of “inexact differential”, the concept of ungrady one-form is more modern, more powerful, and easier to visualize.
To be explicit: The force at each point in the field depicted by figure 5 has components given by:
In this case the magnitude of F is constant everywhere, and F points clockwise everywhere.
The corresponding equations for the non-conservative force acting on the electron in the betatron are actually simpler, even though figure 8 appears more complicated:
In this case, the magnitude of F is proportional to the radius. Again, F points clockwise everywhere. This field has the interesting property that it has the came amount of curl everywhere. That is, if you go clockwise around a closed loop anywhere in the betatron electric field (figure 8), the net number of downward steps that you take is proportional to the area of the loop, independent of the shape of the loop. Try it.
You can easily verify that neither of these force-fields can be the gradient of any potential. (Hint: d ∧ d Φ = 0 for any potential Φ. If you insist on writing this hint in terms of cross products, which I don’t recommend, the expression is ∇ × ∇ Φ = 0.)
A more aesthetic non-potential is presented in figure 9 but it isn’t quite as precise or generalizable.
Let’s discuss the technique used to represent one-forms in figures such as figure 8. I call this the fish-scale representation.
These fish-scale diagrams are a big help, because they are a convenient way to portray ungrady one-forms.
When talking about the forces and about the energy, we must be careful to keep track of which is which.
I don’t consider this technique an optical illusion. I consider it a non-deceptive way of portraying one-forms such as the electric field in a betatron.
The portrayal has various minor imperfections, including:
Also we must be careful when using the word "downward" (as in "clockwise = locally downward everywhere").
The boundary of a scale is meaningful. The question of whether one scale is above another scale is not meaningful.
The fish-scale representation works fine for grady as well as ungrady one-forms. Figure 10 shows something that is a potential, namely a conical bump. The bump has a slope, which is a grady one-form.
Other representations are sometimes possible. In any space where there is a metric (a dot product) so we have a notion of length and a notion of angles, then every one-form has a dual representation in terms of vectors. A vector field can be represented by placing, at selected points, a symbol representing the magnitude and direction of the vector at that point. Magnitude can be encoded either by the length of the symbol or by barbs as discussed in reference 5.
On the other hand, there are cases of interest (notably thermodynamics) where we lack a metric, and one-forms are not readily representable in terms of vectors. In such cases, the fish-scale representation is particularly valuable.
The fish-scale technique can be considered a modification of conventional mapmakers’ techniques for depicting relief, including contour lines, shading, et cetera.
On a map drawn in two dimensions, the contours are contour lines. In three dimensions the contours are surfaces, also called shells. More generally, in a space of dimension D, the contours are hypersurfaces of dimension D−1. We note in passing that the fish-scale technique can be generalized to higher dimensions. In the D=2 fish-scale diagrams, you see lots of 3/4-complete circles. In D=3, the corresponding elements will be 3/4-complete spheres, each nestled against its neighbors. The techniques of shading to portray the orientation of the contour also generalizes. For now, though, we will concentrate on the two-dimensional case.
Plain old contour lines are problematic even for grady one-forms. For one thing, they don’t “grab” the viewer. A related problem is that contour lines don’t distinguish an upslope from a downslope. You can’t tell a pit from a peak. When cartographers want to create a map with visually perceptible relief, they must supplement (or replace) contour lines with various shading and/or color-coding schemes.
For ungrady one-forms, contours are even more problematic.
People often speak of contours as equipotentials, although technically you can’t have equipotentials if you don’t have a potential. Still, you can have the next best thing, namely constant-energy contours. Start at an arbitrary point. Shoot out “trial move” vectors in every direction. Make a note of the directions in which the trial move causes zero change in energy. Move a small distance in that direction, and iterate. This maps out the constant-energy contour. As long as you stay on this contour, your energy doesn’t change. (One thing you cannot do is label this contour as to energy, because if you leave the contour and come back to it, you will in general have a different energy.)
These constant-energy contours do a good job of portraying the orientation of a one-form even for an ungrady one-form (i.e. a non-conservative force field). However, portraying the orientation is only half the battle. Recall that we have multiple objectives:
With grady one-forms, both objectives can be achieved with high accuracy. You choose contour lines that are evenly spaced in energy.
With ungrady one-forms, there is a terrible dilemma. Suppose you map out the constant-energy contours using the method described above. When you attempt to represent the magnitude of the one-form, you will find that if the spacing between contours is correct in one region it will be incorrect in neighboring regions. You will have to start new contours here and there. There is no way to do it perfectly.
Just to rub salt in the wound, the shading techniques that work for a grady one-form look hideous when applied to the discontinuous contour lines that are characteristic of an ungrady one-form.
Copyright © 2003 jsd