How to deduct the gradient in spherical coordinates?

In summary, the gradient in spherical coordinates can be found using the formula: \nabla f(r, \theta, \phi) = \frac{\partial f}{\partial r}\mathbf{e}_r+ \frac{1}{r}\frac{\partial f}{\partial \theta}\mathbf{e}_\theta+ \frac{1}{r \sin\theta}\frac{\partial f}{\partial \phi}\mathbf{e}_\phi. This is derived by using the normalized coordinate basis and comparing coefficients of the variations in r, theta, and phi.
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  • #2
The one given in Wikipedia is correct, or what is your question?
 
  • #3
Should be this one:

[tex] \nabla f(r, \theta, \phi) = \frac{\partial f}{\partial r}\mathbf{e}_r+ \frac{1}{r}\frac{\partial f}{\partial \theta}\mathbf{e}_\theta+ \frac{1}{r \sin\theta}\frac{\partial f}{\partial \phi}\mathbf{e}_\phi [/tex]
 
  • #4
That's correct. Maybe you want to know, how to derive it?

The point is to write
[tex]\mathrm{d} \phi=\mathrm{d} \vec{r} \cdot \vec{\nabla} \phi=\mathrm{d} r \partial_r \phi + \mathrm{d} \vartheta \partial_{\vartheta} \phi + \mathrm{d} \varphi \partial_{\varphi}\phi
[/tex]
in terms of the normalized coordinate basis [itex](\vec{e}_r,\vec{e}_{\vartheta},\vec{e}_{\varphi})[/itex]. The term from the variation of [itex]r[/itex] is
[tex]\mathrm{d} r \frac{\partial \vec{r}}{\partial r} \cdot \vec{\nabla} \phi=\mathrm{d} r \vec{e}_r \cdot \vec{\nabla} \phi.[/tex]
Comparing the coefficients of [itex]\mathrm{d} r[/itex] gives
[tex]\vec{e}_r \cdot \vec{\nabla} \phi=\partial_r \phi.[/tex]
For the [itex]\vartheta[/itex] component
[tex]\mathrm{d} \vartheta \frac{\partial \vec{r}}{\partial \vartheta} \cdot \vec{\nabla} \phi = r \vec{e}_{\vartheta} \cdot \vec{\nabla} \phi, \;\Rightarrow \; \vec{e}_{\vartheta} \cdot \vec{\nabla} \phi=\frac{1}{r} \partial_{\vartheta} \phi,[/tex]
and for [itex]\mathrm{d} \varphi[/itex]
[tex]\mathrm{d} \varphi \frac{\partial \vec{r}}{\partial \varphi} \cdot \vec{\nabla} \phi = r \sin \vartheta \vec{e}_{\vartheta} \cdot \vec{\nabla} \phi, \; \Rightarrow \; \vec{e}_{\varphi} \cdot \vec{\nabla} \phi=\frac{1}{r \sin \varphi} \partial_{\vartheta} \phi,[/tex]
 
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  • #5
Now clear, thanks.
 

Related to How to deduct the gradient in spherical coordinates?

1. What is the formula for finding the gradient in spherical coordinates?

The formula for finding the gradient in spherical coordinates is:
∇f = (∂f/∂r)er + (1/r)(∂f/∂θ)eθ + (1/rsinθ)(∂f/∂φ)eφ
where er, eθ, and eφ are unit vectors in the radial, polar, and azimuthal directions respectively.

2. How do you calculate the partial derivatives in spherical coordinates?

The partial derivatives in spherical coordinates can be calculated using the chain rule.
For example, to find ∂f/∂r, we would use the following formula:
∂f/∂r = (∂f/∂x)(∂x/∂r) + (∂f/∂y)(∂y/∂r) + (∂f/∂z)(∂z/∂r)
where x, y, and z are the Cartesian coordinates converted from spherical coordinates.

3. Can the gradient in spherical coordinates be written in terms of only two variables?

Yes, the gradient in spherical coordinates can be written in terms of only two variables by using the relationship between the partial derivatives and the unit vectors.
For example, we can write the gradient as:
∇f = (∂f/∂r)er + (1/r)(∂f/∂θ)(er + eθ) + (1/rsinθ)(∂f/∂φ)eφ
This form only involves the variables r and θ.

4. How does the gradient in spherical coordinates relate to the gradient in Cartesian coordinates?

The gradient in spherical coordinates and Cartesian coordinates are related by the Jacobian matrix.
The Jacobian matrix converts between the two coordinate systems, allowing us to relate the partial derivatives in spherical coordinates to those in Cartesian coordinates.
The formula for the gradient in Cartesian coordinates is:
∇f = (∂f/∂x)ex + (∂f/∂y)ey + (∂f/∂z)ez
where ex, ey, and ez are the unit vectors in the x, y, and z directions respectively.

5. What are the applications of calculating the gradient in spherical coordinates?

One of the main applications of calculating the gradient in spherical coordinates is in physics, specifically in solving problems involving spherical symmetry.
It is also useful in engineering and mathematics, for example in solving problems involving fluid flow or electromagnetism.
Furthermore, understanding the gradient in spherical coordinates is essential in many scientific fields, as it allows for a deeper understanding of multivariate calculus and vector calculus concepts.

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