Gradient, Divergence, Curl and Related Formulae
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Gradient, Divergence, Curl and Related Formulae The gradient, the divergence, and the curl are first-order differential operators acting on fields. The easiest way to describe them is via a vector nabla whose components are partial derivatives WRT Cartesian coordinates (x, y, z): ∂ ∂ ∂ = ˆx + ˆy + ˆz . (1) ∇ ∂x ∂y ∂z The operator is a true vector in the following sense: Consider any two Cartesian coordinate ∇ systems (x, y, z) and (x′,y′, z′) at some angles to each other; in general, they are related to each other as ′ x = T ′ x + T ′ y + T ′ z, x x × x y × x z × ′ y = T ′ x + T ′ y + T ′ z, (2) y x × y y × y z × ′ z = T ′ x + T ′ y + T ′ z, z x × z y × z z × for some orthogonal 3 3 matrix T . Then the components of the operator in the (x′,y′, z′) × ∇ system — i.e., the partial derivatives ∂/∂x′, ∂/∂y′, and ∂/∂z′ — are related to the partial derivatives WRT x, y, z precisely as components of any other vector: ∂ ∂ ∂ ∂ = T ′ + T ′ + T ′ , ∂x′ x x × ∂x x y × ∂y x z × ∂z ∂ ∂ ∂ ∂ = T ′ + T ′ + T ′ , (3) ∂y′ y x × ∂x y y × ∂y y z × ∂z ∂ ∂ ∂ ∂ = T ′ + T ′ + T ′ . ∂z′ z x × ∂x z y × ∂y z z × ∂z Consequently, just like a regular vector — say, A — has different components in the two coordinate systems but they assemble to the same vector as ′ ′ ′ Axˆx + Ayˆy + Azˆz = Ax′ ˆx + Ay′ ˆy + Az′ˆz = same A, (4) for the operator we also have ∇ ∂ ∂ ∂ ′ ∂ ′ ∂ ′ ∂ ˆx + ˆy + ˆz = ˆx + ˆy + ˆz = same . (5) ∂x ∂y ∂z ∂x′ ∂y′ ∂z′ ∇ 1 The Gradient: Acting with the operator on a scalar field S(x, y, z) produces a vector field ∇ ∂S ∂S ∂S S = ˆx + ˆy + ˆz = grad S(x, y, z) (6) ∇ ∂x ∂y ∂z called the gradient of S. Physically, the gradient measures how much S is changing with the location; it points in the direction of steepest increase of S while its magnitude is the rate of that increase. At the local maxima, local minima, or other stationary points of S, the gradient vanishes. Leibniz rule for the gradient of a product of two scalar fields S1(x, y, z) and S2(x, y, z): S S ) = S S + S S . (7) ∇ 1 2 1∇ 2 2∇ 1 Chain Rule for the gradient of a function of another function of the 3 coordinates: dP If P (x, y, z) = f S(x, y, z) , then P = S. (8) ∇ dS ∇ As an example, let’s apply this rule to a spherically symmetric field P (x, y, z) = P (r only), r = r = x2 + y2 + z2. (9) | | p By the chain rule, the gradient of any such field is dP dP P (r) = r = ˆr (10) ∇ dr ∇ dr where the second equality follows from the gradient of the radius r being the unit vector ˆr in the radial direction. This should be obvious from the physical meaning of the gradient, but let’s prove this mathematically. Let’s start with the gradient of r2 = x2 + y2 + z2: ∂ ∂ ∂ (r2) = ˆx + ˆy + ˆz x2 + y2 + z2 = 2x ˆx + 2y ˆy + 2z ˆz = 2r. (11) ∇ ∂x ∂y ∂z At the same time, by the chain rule, dr2 (r2) = r = 2r r, (12) ∇ dr ∇ ∇ hence 1 1 r = (r2) = (2r) = ˆr, (13) ∇ 2r ∇ 2r quod erat demonstrandum. 2 As an example of eq. (10), the gradient of any power of the radius is − rn = nrn 1 ˆr. (14) ∇ The Divergence of a vector field V(x, y, z) is a scalar field div V(x, y, z) which measures how much V spreads out at each point (x, y, z) (or for a negative divergence, how much V converges to the point (x, y, z)). Algebraically, the divergence is the scalar product (dot product) of the operator and the vector field on which it acts: ∇ ∂ ∂ ∂ div V(x, y, z) = V = V + V + V . (15) ∇· ∂x x ∂y y ∂z z Example: A vector field parallel to the x axis spreading out in x direction, V(x, y, z) = cx ˆx (for a constant c) The divergence of this field is ∂V ∂V ∂V ∂cx ∂0 ∂0 V = x + y + z = + + = c +0+0= c. (16) ∇· ∂x ∂y ∂z ∂x ∂y ∂z Another example: A field pointing and spreading out in the radial direction, V = cr, (17) 3 The divergence of this field is ∂V ∂V ∂V ∂cx ∂cy ∂cz V = x + y + z = + + = c + c + c = 3c. (18) ∇· ∂x ∂y ∂z ∂x ∂y ∂z Similar to the Leibniz rule for the gradient, there is a Leibniz rule for the divergence of a product of a scalar field S(x, y, z) and a vector field V(x, y, z): SV = S V + S V . (19) ∇· ∇ · ∇· This rule is particularly useful for calculating divergences of spherically symmetric radial fields V (r) V(x, y, z) = V (r only) ˆr = r (20) r Indeed, by the Leibniz rule (19), V (r) V (r) V (r) V = r = r + r (21) ∇· r ∇ r · r ∇· where in the second term on the RHS r = 3, cf. the example (18). As to the first term on ∇· the RHS, V (r) d(V/r) V ′ V = ˆr = ˆr (22) ∇ r dr r − r2 and hence V (r) V ′ V dV V r = ˆr r = r = . (23) ∇ r · r − r2 × · dr − r Plugging this formula and r = 3 into eq. (21), we arrive at ∇· dV V V dV V (V = V (r) ˆr) = + 3 = + 2 . (24) ∇· dr − r r × dr r In particular, for V (r)= krn (for a constant k), − − − krn ˆr = nkrn 1 + 2krn 1 = (n + 2)krn 1 . (25) ∇· Note: the Coulomb electric field of a point charge has this form for n = 2. Consequently, − Q ˆr Q 1 E E for = 2 , = (n = 2)+2=0 3 = 0, (26) 4πǫ0 r ∇· − × 4πǫ0 × r the Coulomb electric field seems to be divergence-less! However, this formula is valid only outside the charge, at r = 0, but right on top of the charge, at r = 0, the electric field itself is 6 4 singular and its divergence is actually infinite. As we shall see later in class, Q ˆr Q E E r (3) r for = 2 , ( ) = δ ( ). (27) 4πǫ0 r ∇· ǫ0 × The Curl or rotor of a vector field V(x, y, z) is another vector field curlV(x, y, z) which measures the vorticity of the V field. Specifically, the x component of the curlV measure the vorticity of the V field in the yz plane (and thus around the x axis), and likewise for the y and z components. Algebraically, curlV(x, y, z) obtains as a vector product of the and the vector field V ∇ on which it acts: curlV = rotV = V ∇ × ∂ ∂ ∂ = ˆx + ˆy + ˆz ˆxV + ˆyV + ˆzV ∂x ∂y ∂z × z y z ˆx ˆy ˆz ∂ ∂ ∂ (28) = det ∂x ∂y ∂z Vx Vy Vz ∂V ∂V ∂V ∂V ∂V ∂V = ˆx z y + ˆy x z + ˆz y x . ∂y − ∂z ∂z − ∂x ∂x − ∂y Example: the radially symmetric spreading out field (17) V(r)= cr has zero curl. Indeed, the x component of the curl evaluates to ∂(cr) ∂(cr) ∂cz ∂cy (cr) = z y = = 0 0 = 0, (29) ∇ × x ∂y − ∂z ∂y − ∂z − and likewise, the y component and the z component of (cr) also vanish. ∇ × 5 Another example: a vector field V = cxˆy cyˆx swirling in the xy plane like this − (30) has a non-trivial curl in the z direction: ∂(Vy = cx) ∂(V = cy) (cxˆy cyˆx) = x − = (+c) ( c) = 2c, (31) ∇ × − z ∂x − ∂y − − while the x and y components of the curl vanish: ∂(V = 0) ∂(V =+cx) (cxˆy cyˆx) = z y = 0 0 = 0, ∇ × − x ∂y − ∂z − (32) ∂(V = cy) ∂(V = 0) (cxˆy cyˆx) = x − z = 0 0 = 0. ∇ × − y ∂z − ∂x − Leibniz rule for the curl of a product of a scalar field S(x, y, z) and a vector field V(x, y, z): SV = S V + S V , (33) ∇ × ∇ × ∇ × Using this rule, it is easy to show that any spherically symmetric radial field V(x, y, z)= V (r)ˆr 6 has zero curl. Indeed, V (r) V = r, r V V V = r + r ∇ × ∇ r × r ∇ × where r = 0, ∇ × V d(V/r) and = ˆr, (34) ∇ r dr d(V/r) hence V = ˆr r + 0 ∇ × dr × d(V/r) = ˆr r = 0 + 0 dr × = 0. Other Leibniz rules: (a) Divergence of a vector product of two vector fields A(x, y, z) and B(x, y, z), (A B) = B ( A) A ( B). (35) ∇· × · ∇ × − · ∇ × (b) Curl of a cross product of two vector fields, (A B) = (B )A (A )B + A( B) B( A). (36) ∇ × × ·∇ − ·∇ ∇· − ∇· (c) Gradient of a dot product of two vector fields: (A B) = (A )B + (B )A + A ( B) + B ( A). (37) ∇ · ·∇ ·∇ × ∇ × × ∇ × In the last two formulae here (A ) and (B ) are not the divergences but rather the · ∇ · ∇ differential operators ∂ ∂ ∂ (A ) = A + A + A , ·∇ x ∂x y ∂y z ∂z (38) ∂ ∂ ∂ (B ) = B + B + B , ·∇ x ∂x y ∂y z ∂z 7 which then act on other fields. For example, ∂S ∂S ∂S (A )S = A + A + A , ·∇ x ∂x y ∂y z ∂z ∂B ∂B ∂B ∂B ∂B ∂B (A )B = A x + A x + A x ˆx + A y + A y + A y ˆy ·∇ x ∂x y ∂y z ∂z x ∂x y ∂y z ∂z ∂B ∂B ∂B + A z + A z + A z ˆz . x ∂x y ∂y z ∂z (39) Second Derivatives The gradient, the divergence, and the curl are first-order differential operators for the fields.