0x030 Foundation
This note covers the classical differential geometry over regular curves and surfaces
1. Foundation
The vector differential operator del, written in \(\nabla\), is defined as follows:
Definition (gradient) Gradient acting on a scalar field to produce vector field.
The gradient \(\nabla T\) points in the direction of maximum increase of the function \(T\), its slop along this direction is given by \(|\nabla T|\)
Definition (divergence) Divergence acting on a vector field to produce scalar field.
Divergence is a measure of how much the vector \(v\) spreads out from each point (therefore a scalar field). In fluid dynamics, if there is no loss of fluid anywhere, then \(\nabla \cdot \mathbf{b} = 0\). This is called the continuity equation for an incompressible fluid. It is said to have no sources or sinks. A vector field whose divergence is zero is sometimes called solenoidal.
Lemma (properties of divergence) Divergence is a linear operator
Product rule
Definion (curl) curl acting on a vector field to produce another vector field.
curl is a measure of how much the vector \(v\) swirls around the point in question. A vector field wihtout rotation (i.e.: \(\nabla \times \mathbf{v}=0\)) is called irrotational, a field that is not irrotational is sometimes called a vortex field.
Furthermore, if the vector field is irrotational \(\nabla \times \mathbf{v} = 0\), then it is a conservative vector field, which means there exists a scalar field \(\phi\) such that
In conservative vector field, the line integral is path independent.
Lemma (properties of curl)
- \(\nabla \times (\phi \mathbf{A}) = (\nabla \phi) \times \mathbf{A} + \phi (\nabla \times \mathbf{A})\)
- \(\nabla \cdot (\mathbf{A} \times \mathbf{B}) = \mathbf{B} \cdot (\nabla \times \mathbf{A}) - \mathbf{A} \cdot (\nabla \times \mathbf{B})\)
- \(\nabla \times (A \times B) = (B \cdot \nabla) A - B(\nabla \cdot A) - (A \cdot \nabla)B + A(\nabla \cdot B)\)
- \(\nabla(A \cdot B) = (B \cdot \nabla) A + (A \cdot \nabla) B + B \times (\nabla \times A) + A \times (\nabla \times B)\)
2. Curve
2.1. Parameterized Curve
Definition (parametrized curve) A parametrized curve in \(R^n\) is a smooth function: \(\gamma: I \to R^n\), where \(I \subset R\) is an interval
Speed at time \(t\) is defined to be \(\gamma'(t)\), arc length between \(t_1, t_2\) is \(\int_{t_1}^{t_2} |\gamma'(t)|dt\)
A curve is called regular if its speed is always nonzero. It is called unit-speed or parametrized by arc length if its speed is always equal to 1
The image of a curve is called trace, which only represents the path of the moving object and it contains no time information. In many cases, we only care about the trace (e.g: curvature). It means we want those properties to be unchanged by a reparametrization
2.2. Curvature
Definition (curvature function) Let \(\gamma: I \to R^n\) be a regular curve. Its curvature function, \(\kappa: I \to [0, \infty)\), is defined as
This function is invariant wrt the parametrization
Proposition If \(\gamma\) is parametrized by arc length, then
because \(v(t)=1\) unit speed and \(a(t) = a^{\perp}(t)\) because \(a \perp v\) for constant speed.
Definition (osculating plane) The plane which contains the tangent and principal normal
Definition (normal plane) The plane contains principal normal and binormal, it is the plane perpendicular to the tangent vector
Definition (rectifying plane) The plane contains the tangent and binormal, perp to the principal normal.
Theorem (Frenet-Serret) \(\(\frac{d}{ds}\left(\begin{array}{c} \mathbf{T} \\ \mathbf{N} \\ \mathbf{B} \end{array}\right) = \left( \begin{array}{ccc} 0 & \kappa & 0 \\ -\kappa & 0 & \tau \\ 0 & -\tau & 0 \end{array} \right) \left(\begin{array}{c} \mathbf{T} \\ \mathbf{N} \\ \mathbf{B} \end{array}\right)\)\)
where \(\mathbf{T}\) is the unit tangent vector, \(\mathbf{N}\) is the principal normal vector and \(\mathbf{B}\) is the binormal vector, \(\kappa\) is the curvature and \(\tau\) is the torsion, torsion measures how sharply it is twisting out of the plane of curvature. (curve up?)
2.3. Line Integral
2.3.1. Line Integral over Scalar Field
Definition (arc length) Let \(\mathbf{x} \in C^1: [a,b] \to R^3\) where \(\mathbf{x}(t) = (x(t), y(t), z(t))\)with \(\mathbf{x}'(t) \neq 0\). The \(\mathbf{x}\) is called a smooth parametrization of \(C\). The length of the curve is
Definition (line integral of a scalar field) Let \(f\) be a continuous function on a smooth curve \(C\) parameterized by \(\mathbf{x}(t)\), the integral of \(f\) over \(C\) with respect to arc length is
The illustration on Wikipedia is pretty good
The line integral over
2.3.2. Line Integral over Vector Field
The vector field line integral has two versions:
- measuring the sum of tangent component (work)
- meausring the sum of perpendicular component (flux)
Definition (line integral of a vector field, work) For a vector field \(\mathbf{F}: R^n \to R^n\), the line integral along a piecewise smooth curve \(C\), in the direction of \(r\), is defined as
Theorem (fundamental theorem of calculus for line integrals, gradient theorem) Let \(\varphi: U \subseteq R^n \to R^n\) be a continuously differentiable function and \(\gamma\) any curve in \(U\) which starts at \(\mathbf{p}\) and ends at \(\mathbf{q}\). then
It implies the line integrals through a gradient field are path independent. In Physics, \(\varphi\) is a potential and \(\nabla \varphi\) is a conservative field: Work done by the conservative forces does not depend on the path, but only on the end points.
Criterion (conservative field is irrotational) A necessary and sufficient condition that a field \(\mathbf{F}\) to be conservative is that
Theorem (Green) Let \(\mathbf{F}\) be a continuously differentiable vector field, and \(D\) a domain in \(R^2\), then
Definition (line integral of a vector field, flux) For a vector field \(\mathbf{F}(x,y) = (P(x,y), Q(x,y))\), the line integral across a curve \(C\) is defined in terms of a piecewise smooth parametrization \(\mathbf{r}(t) = (x(t), y(t))\) as
3. Surface
3.1. Parameterized Surface
Definition (smooth surface) Let \(\mathbf{x}\) be a smoothly bounded set \(D: \mathbb{R}^2 \to \mathbb{R}^3\), denoted \(\mathbf{x}(u, v) = (x(u,v), y(u,v), z(u,v))\), Suppose \(\mathbf{x}\) satisfies the following conditions on the interior of \(D\)
- \(\mathbf{X}\) is 1-to-1
- the partial deriavtives are bounded
- partial derivatives are linearly independent, \(\mathbf{x}_u(u,v) \times \mathbf{x}_v(u,v) \neq 0\).
Then the range of \(\mathbf{x}\) is called a smooth surface, parametrized by \(\mathbf{x}\)
3.2. Surface Integral
3.2.1. Surface Integral over a Scalar field
Definition (surface integral over a scalar field) If a surface \(S\) is parametrized by the continuously differentiable function \(x(u,v)\) over the domain \(D\) in the uv-plane, then the scalar surface integral of the function \(f(x)\) over \(S\) is
3.2.2. Surface Integral over a Vector Field
Definition (surface integral over a vector field, flux)
Theorem (divergence, Gauss) Let \(F\) be a \(C^1\) vector field on regular set \(D\) and let \(N\) be the unit normals to \(\partial D\) that point out of \(D\), then
Theorem (curl, Stokes) Let \(G\) be a vector field that is \(C^1\) on a piece-wise smooth oriented surface \(S\) whose boundary \(\partial S\) is a piecewise smooth curve
4. Curvature of a Surface
4.1. Gauss Map
5. Curvilinear Coordinates
Suppose we change from the Cartesian coordinate \((x_1, x_2, x_3)\) to the curvilinear coordinate \((u_1, u_2, u_3)\)
The reverse transformation can also be mae
5.1. Orthogonal Curvilinear Coordinates
Suppose the point \(p\) has position \(\mathbf{r} = \mathbf{r}(u_1, u_2, u_3)\), consider the tangent vector to \(u_1\) curve is
where \(e_1\) is the unit tangent vector in this direction: \(e_1 = \frac{\partial \mathbf{r}}{\partial u_1}/|\frac{\partial \mathbf{r}}{\partial u_1}|\) and \(h_1 = |\frac{\partial \mathbf{r}}{\partial u_1}|\)
then
The arc length is determined by \(ds^2 = d\mathbf{r} \cdot d\mathbf{r}\)
volumene element is
6. Reference
[1] Tapp, Kristopher. Differential geometry of curves and surfaces. Berlin: Springer, 2016.
[2] Lax, Peter D., and Maria Shea Terrell. Multivariable Calculus with Applications. Springer, 2017.