Curvilinear coordinates question

In summary, the conversation discusses the well-known identity for reciprocal frames in a system of curvilinear coordinates. The mistake in the calculation of this identity is found to be due to a misunderstanding of the chain rule for partial derivatives. The conversation also highlights the importance of understanding the actual function being manipulated when working with notation involving partial derivatives.
  • #1
mnb96
715
5
Hello,
given a system of curvilinear coordinates [itex]x_i=x_i(u_1,\ldots,u_n)[/itex]; [itex]u_i=u_i(x_1,\ldots,x_n)[/itex] and considering the position vector [itex]\mathbf{r}=x_1\mathbf{e}_1+\ldots+x_n\mathbf{e}_n[/itex] there is the well-known identity that defines the reciprocal frame:

[tex]\frac{\partial \mathbf{r}}{\partial u_i }\cdot \nabla u_j = \delta^i_j[/tex]

I tried to verify it by myself but I cannot see where is the mistake:

[tex]\frac{\partial \mathbf{r}}{\partial u_i }\cdot \nabla u_j=[/tex]

[tex]=(\frac{\partial x_1}{\partial u_i }\mathbf{e}_1+ \ldots + \frac{\partial x_n}{\partial u_i }\mathbf{e}_n )\cdot (\frac{\partial u_j}{\partial x_1 }\mathbf{e}_1+ \ldots + \frac{\partial u_j}{\partial x_n }\mathbf{e}_n ) = [/tex]

[tex]=n\frac{\partial u_j}{\partial u_i} = [/tex]

[tex]=n\delta^i_j[/tex]

Why am I getting that wrong multiplication by n ?

------------------------------------------------------
(Funny) EDIT: since there is a well-known proof in every book which correctly shows that the aforementioned inner-product equals [tex]\delta^i_j[/tex], if no-one manages to find my mistake, our world will have an "amazing" proof that 1=2=...=n for any integer n :)
 
Last edited:
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  • #2
Why? [tex]n\frac{\partial u_j}{\partial u_i}[/tex]

where does one get n?


What's with the c in [tex]\frac{\partial \mathbf{r}}{\partial u_i }\cdot \nabla u_jc=[/tex] ?
 
  • #3
ops...ignore that 'c': it was a typo and I removed it.
Back to the main question, let's assume i=j, we have:

[tex]
(\frac{\partial x_1}{\partial u_i }\mathbf{e}_1+ \ldots + \frac{\partial x_n}{\partial u_i }\mathbf{e}_n )\cdot (\frac{\partial u_j}{\partial x_1 }\mathbf{e}_1+ \ldots + \frac{\partial u_j}{\partial x_n }\mathbf{e}_n ) =
[/tex]

[tex]=\frac{\partial x_1}{\partial u_i }\frac{\partial u_i}{\partial x_1} + \frac{\partial x_2}{\partial u_i }\frac{\partial u_i}{\partial x_2 }+\ldots+\frac{\partial x_n}{\partial u_i} \frac{\partial u_i}{\partial x_n }=[/tex]

[tex]=\frac{\partial x_1}{\partial x_1}+\ldots+\frac{\partial x_n}{\partial x_n}=[/tex]

[tex]=1+1+1+\ldots+1=[/tex]

[tex]=n[/tex]


I know for sure there is a very trivial mistake I should be ashamed of, but I would like to realize where and why it is.
 
  • #4
You have a problem when you apply the chain rule. When you expand:

[tex]

(\frac{\partial x_1}{\partial u_i }\mathbf{e}_1+ \ldots + \frac{\partial x_n}{\partial u_i }\mathbf{e}_n )\cdot (\frac{\partial u_j}{\partial x_1 }\mathbf{e}_1+ \ldots + \frac{\partial u_j}{\partial x_n }\mathbf{e}_n )

[/tex]

You do get:

[tex]
\frac{\partial x_1}{\partial u_i }\frac{\partial u_i}{\partial x_1} + \frac{\partial x_2}{\partial u_i }\frac{\partial u_i}{\partial x_2 }+\ldots+\frac{\partial x_n}{\partial u_i} \frac{\partial u_i}{\partial x_n }
[/tex]

But:

[tex]\frac{\partial x_j}{\partial u_i}\frac{\partial u_i}{\partial x_j}\neq\frac{\partial x_j}{\partial x_j}[/tex]

Because you have:

[tex] u_i\left(x_1\left(u_1,...,u_n\right),...,x_n\left(u_1,...,u_n\right)\right)=u_i[/tex]

Then:

[tex]

\frac{\partial u_i}{\partial u_j} = \sum^{n}_{k=1}\frac{\partial u_i}{\partial x_k}\frac{\partial x_k}{\partial u_j} = \delta^{i}_{j}

[/tex]

But that sum, when [tex]i=j[/tex], is exactly what you obtain when you expand the inner product, and there's no [tex]n[/tex]. Sometimes, the Chain rule is tricky.
 
Last edited:
  • #5
Thanks a lot JSuarez!
actually once you realize that the situation we had was like [tex]u_i\left(x_1\left(u_1,...,u_n\right),...,x_n\left( u_1,...,u_n\right)\right)=u_i[/itex] everything makes perfectly sense.

In this case the lesson to learn for the novice (me) is probably that when making calculations with partial derivatives, one should always have very clear in mind the actual function he/she is manipulating. Naively relying on notation apparently leads to wrong conclusions.
 

What are curvilinear coordinates?

Curvilinear coordinates are a type of coordinate system used in mathematics and physics to describe points in a space or surface. They involve using a set of three coordinates to represent a point, with each coordinate representing a distance from a reference point along a specific direction or curve.

What are the advantages of using curvilinear coordinates?

Using curvilinear coordinates can simplify the mathematical representation of a problem by taking into account the natural curvature of a surface or space. They also allow for a more intuitive understanding of certain physical phenomena and can make certain calculations more straightforward.

How do curvilinear coordinates differ from Cartesian coordinates?

Cartesian coordinates, also known as rectangular coordinates, use three perpendicular axes to represent a point in a three-dimensional space. Curvilinear coordinates, on the other hand, use non-perpendicular axes that follow the curvature of the surface or space being described.

What are some common types of curvilinear coordinates?

Some common types of curvilinear coordinates include cylindrical coordinates, spherical coordinates, and ellipsoidal coordinates. These coordinate systems are often used to describe points on surfaces or in spaces with specific shapes or symmetries.

What are some applications of curvilinear coordinates?

Curvilinear coordinates are used in a wide range of fields, including physics, engineering, and computer graphics. They are particularly useful in problems involving curved surfaces or objects, such as celestial mechanics, fluid mechanics, and electromagnetism.

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