4.4 divergence and curl of a Vector Field
Since \(\nabla \) has the character of a vector, it can appear as one factor of a dot or cross product. Let \[\mathbf F = F_1\, \mathbf i + F_2\, \mathbf j + F_3\,\mathbf k\] be a vector field whose components are differentiable functions of \(x\), \(y\) and \(z\).
- 1.
- The divergence of \(\mathbf F\) is the scalar function \[\dive \mathbf F = \nabla \cdot \mathbf F = \left (\mathbf i\frac {\partial } {\partial x} + \mathbf j\frac {\partial }{\partial y} + \mathbf k\frac {\partial } {\partial z}\right )\cdot (F_1\,\mathbf i + F_2\,\mathbf j + F_3\,\mathbf k) = \frac {\partial F_1}{\partial x} + \frac {\partial F_2}{\partial y} + \frac {\partial F_3}{\partial z} .\]
- 2.
- The curl of \(\mathbf F\) is the vector field \[\curl \mathbf F = \nabla \times \mathbf F = \begin {vmatrix} \mathbf i & \mathbf j & \mathbf k\\[1ex] \frac {\partial }{\partial x} & \frac {\partial }{\partial y} & \frac {\partial }{\partial z}\\[1ex] F_1 & F_2 & F_3 \end {vmatrix} = \left ( \frac {\partial F_3}{\partial y}-\frac {\partial F_2}{\partial z}\right )\mathbf i - \left (\frac {\partial F_3}{\partial x}-\frac {\partial F_1}{\partial z}\right ) \mathbf j + \left (\frac {\partial F_2 }{\partial x}-\frac {\partial F_1}{\partial y}\right ) \mathbf k .\]
- 3.
- If \(f(x,y,z)\) is twice differentiable, the divergence of its gradient is \[\dive (\grad f) = \nabla \cdot \nabla f = \frac {\partial ^2 f}{\partial x^2} + \frac {\partial ^2 f}{\partial y^2}+ \frac {\partial ^2f}{\partial z^2} = \nabla ^2f ,\] where \(\nabla ^2 = \frac {\partial ^2}{\partial x^2}+ \frac {\partial ^2}{\partial y^2} + \frac {\partial ^2}{\partial z^2}\) is the Laplacian. A function with \(\nabla ^2f=0\) is called harmonic, and the equation is Laplace’s equation.
For a fluid with velocity field \(\mathbf F\), the divergence at a point is the net rate at which fluid flows out of a small region around the point, per unit volume, and the curl is twice the angular velocity of a small paddle wheel placed there. The divergence theorem and Stokes’ theorem, at the end of the section, make both statements precise.
Properties
If \(\mathbf U\) and \(\mathbf V\) are vector fields and \(f\) a scalar function, all differentiable, then
- 1.
- \(\nabla \cdot (\mathbf U\pm \mathbf V) = \nabla \cdot \mathbf U \pm \nabla \cdot \mathbf V\);
- 2.
- \(\nabla \cdot (f\mathbf U) = f\,(\nabla \cdot \mathbf U) + \mathbf U\cdot \nabla f\);
- 3.
- \(\nabla \times (\mathbf U\pm \mathbf V) = \nabla \times \mathbf U \pm \nabla \times \mathbf V\);
- 4.
- \(\nabla \times (f\mathbf V) = f\,(\nabla \times \mathbf V) + (\nabla f)\times \mathbf V\);
- 5.
- \(\nabla \times (\nabla f) = \mathbf 0\) and \(\nabla \cdot (\nabla \times \mathbf V) = 0\), when \(f\) and the components of \(\mathbf V\) have continuous second partial derivatives.
Proof. Write \(\mathbf U = U_1\,\mathbf i + U_2\,\mathbf j + U_3\,\mathbf k\) and \(\mathbf V = V_1\,\mathbf i + V_2\,\mathbf j + V_3\,\mathbf k\). Properties 1 and 3 hold because differentiation is linear. For property 2, \[\nabla \cdot (f\mathbf U) = \sum _{i}\frac {\partial (fU_i)}{\partial x_i} = \sum _i f\frac {\partial U_i}{\partial x_i} + \sum _i U_i\frac {\partial f}{\partial x_i} = f\,\nabla \cdot \mathbf U + \mathbf U\cdot \nabla f ,\] writing \(x_1, x_2, x_3\) for \(x, y, z\). For property 4, the \(\mathbf i\)-component of \(\nabla \times (f\mathbf V)\) is \[\frac {\partial (fV_3)}{\partial y} - \frac {\partial (fV_2)}{\partial z} = f\left (\frac {\partial V_3}{\partial y} - \frac {\partial V_2}{\partial z}\right ) + \left (\frac {\partial f}{\partial y}V_3 - \frac {\partial f}{\partial z}V_2\right ),\] which is the \(\mathbf i\)-component of \(f\,\nabla \times \mathbf V + \nabla f\times \mathbf V\); the other components are similar. For property 5, the \(\mathbf i\)-component of \(\nabla \times \nabla f\) is \(f_{zy} - f_{yz} = 0\), and \[\nabla \cdot (\nabla \times \mathbf V) = (V_{3,yx} - V_{2,zx}) - (V_{3,xy} - V_{1,zy}) + (V_{2,xz} - V_{1,yz}) = 0 ,\] where \(V_{3,yx}\) denotes \(\frac {\partial ^2V_3}{\partial x\partial y}\); the terms cancel in pairs because mixed partial derivatives agree. □
- 1.
- Find the curl and divergence of \(\mathbf F = x^2y\,\mathbf i - xyz\,\mathbf j + yz^2\,\mathbf k\).
- 2.
- Prove that \(\nabla ^2\left (\frac {1}{r}\right ) = 0\) for \(r\neq 0\), where \(r^2 = x^2 + y^2 + z^2\).
Solution.
- 1.
- The divergence is \[\nabla \cdot \mathbf F = \frac {\partial }{\partial x}(x^2y) + \frac {\partial }{\partial y}(-xyz) + \frac {\partial }{\partial z}(yz^2) = 2xy - xz + 2yz ,\] and the curl is \begin {align*} \nabla \times \mathbf F & = \begin {vmatrix} \mathbf i & \mathbf j & \mathbf k\\[1ex] \frac {\partial }{\partial x} & \frac {\partial }{\partial y} & \frac {\partial } {\partial z}\\[1ex] x^2y & -xyz & yz^2 \end {vmatrix}\\[1ex] & = \left (\frac {\partial }{\partial y}(yz^2) - \frac {\partial }{\partial z}(-xyz)\right )\mathbf i - \left (\frac {\partial }{\partial x}(yz^2) - \frac {\partial }{\partial z}(x^2y) \right )\mathbf j\\ & \qquad + \left (\frac {\partial } {\partial x}(-xyz) - \frac {\partial }{\partial y}(x^2y)\right )\mathbf k\\[1ex] & = (z^2+xy)\,\mathbf i - 0\,\mathbf j - (yz + x^2)\,\mathbf k . \end {align*}
- 2.
- By Example 4.3.1 with \(n = -1\), \(\nabla \left (\frac 1r\right ) = -r^{-3}\mathbf r\). Then by property 2, with \(f = -r^{-3}\) and \(\mathbf U = \mathbf r\), \[\nabla ^2\left (\frac 1r\right ) = \nabla \cdot \left (-r^{-3}\mathbf r\right ) = -r^{-3}\,\nabla \cdot \mathbf r - \mathbf r\cdot \nabla (r^{-3}) .\] Here \(\nabla \cdot \mathbf r = 1 + 1 + 1 = 3\) and, again by Example 4.3.1, \(\nabla (r^{-3}) = -3r^{-5}\mathbf r\), so \[\nabla ^2\left (\frac 1r\right ) = -3r^{-3} + 3r^{-5}(\mathbf r\cdot \mathbf r) = -3r^{-3} + 3r^{-5}r^2 = 0 .\] So \(\frac 1r\) is harmonic away from the origin: the gravitational and electric potentials satisfy Laplace’s equation in empty space.
A vector field \(\mathbf F\) is irrotational if \(\curl \mathbf F = \mathbf 0\), and incompressible (or solenoidal) if \(\dive \mathbf F =0\). The three equations of Theorem 4.2.2 are exactly the vanishing of the three components of \(\curl \mathbf F\), which gives the following.
Theorem 4.4.3. In a simply connected region, a vector field \(\mathbf F\) with continuous first partial derivatives is the gradient of a function \(f\) if and only if \(\curl \mathbf F = \mathbf 0\).
Example 4.4.4. Show that the vector field \(\mathbf F = 2xy\,\mathbf i + (x^2 + z^2)\,\mathbf j + 2zy\,\mathbf k\) is the gradient of a function \(f\), and find a potential function for \(\mathbf F\).
Solution. \[\nabla \times \mathbf F = \begin {vmatrix} \mathbf i & \mathbf j & \mathbf k\\[1ex] \frac {\partial }{\partial x} & \frac {\partial }{\partial y} & \frac {\partial } {\partial z}\\[1ex] 2xy & x^2+ z^2 & 2zy \end {vmatrix} = (2z - 2z)\,\mathbf i - (0 - 0)\,\mathbf j + (2x - 2x)\,\mathbf k = \mathbf 0 ,\] and \(\mathbf F\) is defined on all of \(\mathbb {R}^3\), so by Theorem 4.4.3 it is conservative. A potential \(f\) satisfies \[\frac {\partial f}{\partial x} = 2xy ,\qquad \frac {\partial f}{\partial y}= x^2+z^2 ,\qquad \frac {\partial f}{\partial z} = 2yz .\] Integrating the first with respect to \(x\), \(f = x^2y + h(y,z)\), where the constant of integration may depend on \(y\) and \(z\). The second then gives \(x^2 + h_y = x^2 + z^2\), so \(h_y = z^2\) and \(h = yz^2 + g(z)\). Hence \(f = x^2y + yz^2 + g(z)\), and the third equation gives \(2yz + g'(z) = 2yz\), so \(g\) is a constant. Therefore \[f(x,y,z) = x^2y + yz^2 + C .\]
Example 4.4.5. Find the divergence and curl of the rotation field \(\mathbf F = -y\,\mathbf i + x\,\mathbf j\) of Example 4.1.3 and of the radial field \(\mathbf G = x\,\mathbf i + y\,\mathbf j\), and interpret them.
Solution. Regarding both as fields in space with zero \(\mathbf k\)-component, \(\nabla \cdot \mathbf F = 0\) and \(\nabla \times \mathbf F = (1 - (-1))\,\mathbf k = 2\,\mathbf k\), while \(\nabla \cdot \mathbf G = 2\) and \(\nabla \times \mathbf G = \mathbf 0\). The rotation spreads nothing out but rotates everything, with angular velocity \(1\) about the \(z\)-axis (half the curl); the radial field rotates nothing but spreads out from every point.
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