Skip to main content

Section 7.7 Scalar Fields, Potentials, and the Direction Nature Chooses

A scalar field is a rule that assigns a single number to every point of a region: the temperature \(T(x,y)\) in a city, the pressure \(P(x,y)\) over an ocean, the elevation \(h(x,y)\) on a hillside, the concentration \(c(x,y)\) of a pollutant in a corridor. A scalar field carries no direction of its own. It is only a number at each location.
Physics, by contrast, is full of quantities that point somewhere: heat flows, forces push, wind blows. These are vector fields. The gradient is what ties them back to scalar fields β€” at least for the conservative fields taken up in MATH 14 at SCU, where the whole vector field is recovered from a single scalar potential by differentiation. Because such fields are so common in nature, the pattern in the table below appears again and again across physics.
When a scalar field governs a physical force or flux, we call the field a potential, and the associated vector quantity is, up to a constant, the negative gradient of that potential.
Setting Scalar field Vector quantity Level curves are
Heat conduction temperature \(T\) heat flux \(\mathbf q = -k\nabla T\) (Fourier’s law) isotherms
Electrostatics potential \(V\) electric field \(\mathbf E = -\nabla V\) equipotentials
Mechanics potential energy \(U\) conservative force \(\mathbf F = -\nabla U\) contours of constant energy
Meteorology pressure \(P\) pressure-gradient force \(-\nabla P\) per unit volume isobars
The minus sign in each of these laws encodes one physical fact: nature moves downhill. Heat drains from hot to cold, charges move toward low electric potential, a ball rolls toward low gravitational potential energy, air accelerates toward low pressure. So the direction the physics actually selects is the direction of most rapid decrease, which is exactly the case \(\theta = \pi\) of \(D_{\mathbf u}f = |\nabla f|\cos\theta\text{.}\)
Three consequences carry over unchanged from SectionΒ 7.5, and they are worth restating in physical language.
  • \(\theta = 0\text{:}\) fastest increase, at the rate \(|\nabla f|\text{.}\) This is the direction heat comes from.
  • \(\theta = \pi\text{:}\) fastest decrease, at the rate \(-|\nabla f|\text{.}\) This is the direction the flux, the force, or the wind actually points.
  • \(\theta = \pi/2\text{:}\) no change at all. These directions trace the level curves of the field β€” isotherms, equipotentials, isobars, the contour lines on a topographic map. Since \(\nabla f\) is orthogonal to the level curve through a point, the flux, the force, or the wind always crosses the contours at right angles.

Example 7.23. Two Runners in a City Heat Island.

On a still summer afternoon the pavement downtown is far hotter than the outlying neighborhoods. Model the surface air temperature, in degrees Fahrenheit, by
\begin{equation*} T(x,y) = 74 + 14\,e^{-(x^2 + 3y^2)/40}, \end{equation*}
where \(x\) and \(y\) are measured in miles east and north of downtown. The core reaches \(88^\circ\)F, and the outskirts settle near \(74^\circ\)F. Two runners meet at the point \(P_0(1,1)\text{.}\)
  1. The first runner is overheating and wants cooler air as quickly as possible. Which direction should she take, and how fast does the temperature fall along it?
  2. The second runner has settled into the temperature at \(P_0\) and wants a route on which it never changes at all. What path should she run?
Solution.
Differentiating the exponential with the chain rule,
\begin{align*} \nabla T \amp = -\frac{14}{40}\,e^{-(x^2+3y^2)/40}\left(2x\,\mathbf i + 6y\,\mathbf j\right),\\ (\nabla T)_{(1,1)} \amp = -\frac{14\,e^{-1/10}}{40}\left(2\,\mathbf i + 6\,\mathbf j\right)\\ \amp \approx -0.633\,\mathbf i - 1.900\,\mathbf j . \end{align*}
At \(P_0\) the temperature itself is \(T(1,1) = 74 + 14e^{-1/10} \approx 86.7^\circ\)F.
Part 1. Temperature falls fastest in the direction of \(-\nabla T\text{,}\) which is a positive multiple of \(\mathbf i + 3\mathbf j\text{.}\) Normalizing, the heading is north-northeast:
\begin{equation*} \mathbf u = \frac{1}{\sqrt{10}}\,\mathbf i + \frac{3}{\sqrt{10}}\,\mathbf j, \end{equation*}
and the rate of change along it is
\begin{align*} D_{\mathbf u} T \amp = -|\nabla T| = -\frac{14\,e^{-1/10}}{40}\sqrt{4 + 36}\\ \amp \approx -2.0 \ \frac{{}^\circ\text{F}}{\text{mi}} . \end{align*}
Notice how modest this is. The gradient hands her the best available direction, not a dramatic one: even running the optimal heading, she must cover a full mile to shed about two degrees. Any other heading does worse.
Part 2. The second runner needs \(D_{\mathbf u}T = 0\text{,}\) that is, \(\mathbf u\) orthogonal to \(\nabla T\text{:}\)
\begin{equation*} \mathbf u = \pm\frac{1}{\sqrt{10}}\left(3\,\mathbf i - \mathbf j\right). \end{equation*}
Following that condition continuously, rather than for a single step, means never leaving the level curve of \(T\) through \(P_0\text{.}\) Since \(T\) is constant exactly when \(x^2 + 3y^2\) is constant, her route is
\begin{equation*} x^2 + 3y^2 = 4, \end{equation*}
an ellipse with semi-axes \(2\) miles east-west and \(2/\sqrt{3} \approx 1.15\) miles north-south β€” a closed loop of roughly ten miles, every step of it at \(86.7^\circ\)F.
In the animation below, switch between the two runners. On the loop the thermometer holds at \(86.7^\circ\)F while the blue gradient arrow stays stubbornly perpendicular to her heading; on the escape route the same reading falls steadily, and the path cuts across every isotherm at a right angle.
Use the interactive figure to explore the problem further geometrically, switching between the two routes and watching how the readings respond as the runner moves.
Instructions.
Press β€œRun the isotherm” to send the runner around the level curve \(x^2+3y^2=4\text{,}\) or β€œDash for cool air” to send her along the path of steepest cooling. The readout shows her temperature, position, directional derivative \(D_{\mathbf u}T\text{,}\) and distance travelled; the blue arrow is \(\nabla T\) at her position. Use β€œPace” to change her speed, and β€œPause” or β€œReset” to control the animation.
Figure 7.24. Two routes from \(P_0(1,1)\) in the field \(T(x,y) = 74 + 14e^{-(x^2+3y^2)/40}\text{.}\) The highlighted ellipse is the level curve \(x^2+3y^2=4\text{,}\) on which \(D_{\mathbf u}T = 0\text{.}\)
You have attempted of activities on this page.