Question about the vector cross product in spherical or cylindrical coordinates

Click For Summary
SUMMARY

The discussion focuses on calculating the vector cross product in spherical and cylindrical coordinates using the determinant method. It confirms that the determinant approach is valid for orthogonal unit basis vectors, such as \(\mathbf{e}_r\), \(\mathbf{e}_\theta\), and \(\mathbf{e}_\phi\). The cross product can be expressed as a determinant involving these basis vectors and their respective components. Additionally, the discussion highlights the significance of the Levi-Civita symbol in simplifying the computation of the cross product without being dependent on the coordinate system used.

PREREQUISITES
  • Understanding of vector calculus, specifically cross products
  • Familiarity with spherical and cylindrical coordinate systems
  • Knowledge of the Levi-Civita symbol and its application in tensor analysis
  • Proficiency in using determinants for vector operations
NEXT STEPS
  • Study the properties of the Levi-Civita symbol in detail
  • Learn how to compute vector operations in spherical coordinates
  • Explore the implications of scale factors in vector calculus
  • Investigate the relationship between tensor analysis and vector calculus
USEFUL FOR

Students and professionals in physics and engineering, particularly those working with vector calculus in spherical and cylindrical coordinates, as well as anyone interested in advanced mathematical concepts such as tensor analysis.

dyn
Messages
774
Reaction score
63
Hi
If i calculate the vector product of a and b in cartesian coordinates i write it as a determinant with i , j , k in the top row. The 2nd row is the 3 components of a and the 3rd row is the components of b.
Does this work for sphericals or cylindricals eg . can i put er , eθ , eφ in the top row with the relevant components in the 2nd and 3rd rows ?
I know that for the formula for grad , div and curl in sphericals or cylindricals then scale factors appear but is the cross product of 2 vectors independent of scale factors ?
Thanks
 
Physics news on Phys.org
The simplest solution is to convert both vectors to cartesian, do the cross product and convert backup to spherical or cylindrical.

However, doing the cross product spherically or cylindrically directly boils down to find a vector that is perpendicular to both vectors following the right hand rule convention and recalling that the magnitude of the resultant vector is:

##\vec A \times \vec B = |A||B|sin(\theta)##

where ##\theta## is the angle between ##\vec A## and ##\vec B## and |A| and |B| are the magnitudes of the respective vectors ##\vec A## and ##\vec B##.

There is an old thread on it here:

https://www.physicsforums.com/threa...ame-way-i-do-in-cartesian-coordinates.772165/

A more detailed answer may be found here:

https://math.stackexchange.com/questions/1358590/cross-product-spherical-coordinates
 
dyn said:
Hi
If i calculate the vector product of a and b in cartesian coordinates i write it as a determinant with i , j , k in the top row. The 2nd row is the 3 components of a and the 3rd row is the components of b.
Does this work for sphericals or cylindricals eg . can i put er , eθ , eφ in the top row with the relevant components in the 2nd and 3rd rows ?
I know that for the formula for grad , div and curl in sphericals or cylindricals then scale factors appear but is the cross product of 2 vectors independent of scale factors ?
Thanks

THe determinant method works for any set of orthogonal unit basis vectors which form a right-handed triple, so if in your convention \mathbf{e}_r \times \mathbf{e}_\theta = \mathbf{e}_{\phi} then <br /> \mathbf{a} \times \mathbf{b} = \left| \begin{matrix}<br /> \mathbf{e}_{r} &amp; \mathbf{e}_{\theta} &amp; \mathbf{e}_{\phi} \\<br /> a_r &amp; a_\theta &amp; a_\phi \\<br /> b_r &amp; b_\theta &amp; b_\phi<br /> \end{matrix}\right|. Curl doesn't work this way because in polar coordinates the basis vectors are functions of position and not constants.
 
  • Like
Likes   Reactions: jedishrfu, Delta2 and dyn
Thank you
 
Since I've seen you post in some physics sections before, here is an excerpt on how to use the levi-cievta sign from notes i wrote years ago (since physicists love the following two ideas, it's worth getting use to it):

We will now introducte the Levi-Cievta symbol as follows: $$\epsilon_{ijk} = \begin{cases}
1 & 123, 312, 231 \\
-1 & 321, 132, 213 \\
0 & i=j=k
\end{cases} $$

Where the numbers represent the subscripts. So that means ##\epsilon_{123} = 1##. So how do you keep track of which is which? When I was younger, it was introduced to be a permutation, and a bunch of other nomenclature that I couldn't quite grasp until I was older. So I'll give you the secret I used! I simply start with 123, and then bump up the numbers to get 312, then again to get 231. Then, to get the -1, you just start with 321, and do the same. This is essentially what a permutation is, but when you're seeing all this for the first time, and a new symbol, even the simple things seem hard! So, how exactly does this relate to cross product? Well, let me throw out this equation (Note I am using Einstein summation): $$\vec{A} \times \vec{B} = \epsilon_{ijk}A^jB^k\hat{e_i}$$ Pretty obvious now, right? Okay, if it isn't obvious how they're related, don't worry! ##A^j## ##B^k## are the components of our vector, and ##\hat{e_i}##is our basis vector. Let's explicitly write out the sum to see if it will give us the same result as the cross product. \\

$$
\epsilon_{ijk}A^jB^k\hat{e_i} = $$
\begin{align*}
\epsilon_{111}A^1B^1\hat{e_1} + \epsilon_{112}A^1B^2\hat{e_1} + \epsilon_{113}A^1B^3\hat{e_1} + \\ \epsilon_{123}A^2B^3\hat{e_1} + \epsilon_{122}A^2B^2\hat{e_1} + \epsilon_{121}A^2B^1\hat{e_1} +\\
\epsilon_{131}A^3B^1\hat{e_1}+\epsilon_{132}A^3B^2\hat{e_1}+\epsilon_{133}A^3B^3\hat{e_1} +\\ \epsilon_{211}A^1B^1\hat{e_2}+ \epsilon_{212}A^1B^2\hat{e_2} + \epsilon_{213}A^1B^3\hat{e_2} + \\
\epsilon_{223}A^2B^3\hat{e_2} + \epsilon_{222}A^2B^2\hat{e_2} + \epsilon_{221}A^2B^1\hat{e_2} + \\
\epsilon_{231}A^3B^1\hat{e_2} + \epsilon_{232}A^3B^2\hat{e_2} + \epsilon_{233}A^3B^3\hat{e_2} +\\
\epsilon_{311}A^1B^1\hat{e_3} + \epsilon_{312}A^1B^2\hat{e_3} + \epsilon_{313}A^1B^3\hat{e_3} + \\
\epsilon_{323}A^2B^3\hat{e_3} + \epsilon_{322}A^2B^2\hat{e_3} + \epsilon_{321}A^2B^1\hat{e_3} +\\
\epsilon_{331}A^3B^1\hat{e_3} + \epsilon_{332}A^3B^2\hat{e_3} + \epsilon_{333}A^3B^3\hat{e_3}
\end{align*}

So, I know what you're thinking... 27 terms from such a simple line?! That's the power of the Einstein summation convention! However, I only wrote this out so you can get a feel for that power. In reality, we just use what the Levi-Civita symbol tells, that is any ##i=j=k## just go to zero, so our 27 equations zoom down to 6.
So we're left with
\begin{align*}
\epsilon_{ijk}A^jB^k\hat{e_i} =
\epsilon_{123}A^2B^3\hat{e_1} + \epsilon_{132}A^3B^2\hat{e_1} \\
\epsilon_{231}A^3B^1\hat{e_2} + \epsilon_{213}A^1B^3\hat{e_2} \\
\epsilon_{312}A^1B^2\hat{e_3} + \epsilon_{321}A^2B^1\hat{e_3}
\end{align*}
I've specifically put them in this order for a reason, we can now factor out the specific basis vector on each line and let's see what we get.
We now see that our equation becomes
\begin{align*}
\epsilon_{ijk}A^jB^k\hat{e_i} =
\hat{e_1}(A^2B^3 - A^3B^2) + \\
\hat{e_2}(A^3B^1 - A^1B^3)+ \\
\hat{e_3}(A^1B^2 - A^2B^1)
\end{align*}Which, as you can see, makes no mention of the particular coordinate system you're in. I do go into more details in my notes, but I think this is long enough for you to get the idea!
 
  • Like
Likes   Reactions: dyn and jedishrfu
Nice tensor exposition! However, it may be overkill for the OP as Tensor Analysis is seldom taught until needed in grad school for either Differential Geometry or General Relativity.
 

Similar threads

  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 1 ·
Replies
1
Views
3K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 3 ·
Replies
3
Views
11K
  • · Replies 8 ·
Replies
8
Views
3K
  • · Replies 1 ·
Replies
1
Views
4K
Replies
3
Views
3K
  • · Replies 6 ·
Replies
6
Views
3K
  • · Replies 7 ·
Replies
7
Views
3K
  • · Replies 3 ·
Replies
3
Views
2K