Finding the unit Normal to a surface using the metric tensor.

Click For Summary

Discussion Overview

The discussion revolves around finding the unit normal vector to a surface defined by the equation $$\phi(x^1,x^2...,x^n) = c$$ using the metric tensor. Participants explore the relationship between the gradient of the surface and the metric tensor in deriving the unit normal vector, addressing both theoretical and technical aspects of the topic.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant presents the formula for the unit normal vector as $$\hat{e_{n}}=\frac{\nabla\phi}{|\nabla\phi|}$$ and seeks to understand its derivation using the metric tensor.
  • Another participant explains that the unit normal vector is defined as a vector of length one, perpendicular to the tangent plane, and relates this to the gradient vector.
  • A participant elaborates on the gradient $$\nabla \phi$$ being the vector dual to the differential, providing a coordinate expression and discussing the norm squared in terms of the metric tensor.
  • One participant requests further elaboration on the explanation, indicating they are new to the topic of tensors.

Areas of Agreement / Disagreement

Participants generally agree on the relationship between the gradient and the unit normal vector, but there is no consensus on the derivation of the expression involving the metric tensor, and some participants seek further clarification.

Contextual Notes

Some assumptions regarding the definitions of the metric tensor and the properties of the gradient may not be fully articulated, and the discussion does not resolve the mathematical steps involved in deriving the unit normal vector using the metric tensor.

Abhishek11235
Messages
174
Reaction score
39
Let $$\phi(x^1,x^2...,x^n) =c$$ be a surface. What is unit Normal to the surface?

I know how to find equation of normal to a surface. It is given by:
$$\hat{e_{n}}=\frac{\nabla\phi}{|\nabla\phi|}$$However the answer is given using metric tensor which I am not able to derive. Here is the answer

$$({g^{\alpha\beta}}{\frac{\partial\phi}{\partial x^{u}}}{\frac{\partial\phi}{\partial x^{v}}})^{-1/2} {g^{r\alpha}}{\frac{\partial\phi}{\partial x^{\alpha}}}$$

How can I Derive this?
 
Physics news on Phys.org
The unit normal to the surface has a length of one. It is a vector that is perpendicular to vectors in the tangent plane at that chosen point. The gradient vector at that point is also perpendicular to the tangent plane and so by computing the gradient and dividing the gradient vector by its own length we get the unit normal vector to the surface.

It’s explained in more detail in this video lecture

 
The gradient ##\nabla \phi## is the vector dual to the differential. In coordinates you have ##d\phi=\frac{\partial\phi}{\partial x^\alpha}dx^\alpha##, so for the gradient you get ##\nabla \phi = g^{r\alpha}\frac{\partial\phi}{\partial x^\alpha}\frac{\partial}{\partial x^r}##. The norm squared will be ##g^{uv}\frac{\partial\phi}{\partial x^u}\frac{\partial\phi}{\partial x^v}##. So the two expressions are the same.
 
  • Like
Likes   Reactions: Abhishek11235
martinbn said:
The gradient ##\nabla \phi## is the vector dual to the differential. In coordinates you have ##d\phi=\frac{\partial\phi}{\partial x^\alpha}dx^\alpha##, so for the gradient you get ##\nabla \phi = g^{r\alpha}\frac{\partial\phi}{\partial x^\alpha}\frac{\partial}{\partial x^r}##. The norm squared will be ##g^{uv}\frac{\partial\phi}{\partial x^u}\frac{\partial\phi}{\partial x^v}##. So the two expressions are the same.

Thankyou. But can you elaborate it in more detail. Please. I started Tensors few days ago.
 

Similar threads

  • · Replies 36 ·
2
Replies
36
Views
6K
  • · Replies 6 ·
Replies
6
Views
3K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 10 ·
Replies
10
Views
1K
  • · Replies 1 ·
Replies
1
Views
3K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 7 ·
Replies
7
Views
4K
  • · Replies 2 ·
Replies
2
Views
6K
Replies
5
Views
4K
Replies
15
Views
2K