Curl of Gradient of a Scalar Field

In summary, the conversation discusses the concept of the curl of a gradient of a scalar field and how it can be a null vector. The determinant of the cross derivatives is used to show that the individual components of the curl can only be zero if the mixed partial derivatives are equal. However, this is not always the case and there can be counter-examples. It is mentioned that in physics, functions are typically "nice" and have continuous second order mixed partials. The conversation concludes with the understanding that the thread will remain open for the benefit of others.
  • #1
Nishant Garg
4
0
Hello, new to this website, but one question that's been killing me is how can curl of a gradient of a scalar field be null vector when mixed partial derivatives are not always equal??

consider Φ(x,y,z) a scalar function
consider the determinant [(i,j,k),(∂/∂x,∂/∂y,∂/∂z),(∂Φ/∂x, ∂Φ/∂y, ∂Φ/∂z)] (this is from ∇×(∇Φ))
When you expand this you will get
[(∂^2Φ/∂y∂z)-(∂^2Φ/∂z∂y)]i-[(∂^2Φ/∂x∂z)-(∂^2Φ/∂z∂x)]j+[(∂^2Φ/∂x∂y)-(∂^2Φ/∂y∂x)]k
Now this can only be null vector when individual components are 0, and that's only when mixed partials are equal, but they are not always equal now are they?
 
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  • #2
Hello Nishant, welcome to PF :smile: !

If I (like you probably also did) simply google
proof that curl of a gradient is zero
then I find (e.g. here , but in many other places) that cross derivatives are equal.
You place a question mark there, though. Perhaps the definition of derivative -- writing out the lot in limit terms -- can help you see they really are equal. Or do you know a counter-example ?:wink:

Wiki on this subject.
 
  • #3
The last line of that post, like I said

If f is twice continuously differentiable, then its second derivatives are independent of the order in which the derivatives are applied. All the terms cancel in the expression for curl∇f, and we conclude that curl∇f=0.

So it must satisfy that condition above for curl of gradient to be null vector? It's not always null vector right, it must satisfy that condition?

Counter example, I try hard but can't think of a function whose second partial derivatives are different depending on respect to which you took derivative first
 
  • #4
Point is that in physics, functions are always "nice". But (see Wolfram ) seemingly nice functions can be pathological in this respect.

[edit] more goodies: Clairaut[/PLAIN] theorem and this thread with our colleagues
 
Last edited by a moderator:
  • #5
So is it safe to assume that our scalar function has continuous second order mixed partials?
 
  • #6
Ok, thank you. How do I close this thread?
 
  • #7
If we stop posting, that's closing the thread. It stays on the forum for the benefit of all !
 

1. What is the Curl of Gradient of a Scalar Field?

The Curl of Gradient of a Scalar Field, also known as the Laplacian Operator, is a mathematical operation that is used to determine the rate of change of a scalar function in a vector field. It is a combination of the gradient operator and the curl operator, and is commonly used in physics and engineering to describe the behavior of fluids and other physical systems.

2. How is the Curl of Gradient of a Scalar Field calculated?

The Curl of Gradient of a Scalar Field is calculated by taking the cross product of the gradient of the scalar field with the cross product of the del operator with the gradient of the scalar field. This can be represented mathematically as curl(∇φ), where φ is the scalar function and ∇ is the gradient operator.

3. What are the properties of the Curl of Gradient of a Scalar Field?

The Curl of Gradient of a Scalar Field has a few important properties, including linearity, commutativity, and associativity. It also has the identity property, which states that the curl of the gradient of a scalar field is always equal to zero.

4. What is the significance of the Curl of Gradient of a Scalar Field?

The Curl of Gradient of a Scalar Field is significant because it can be used to describe the behavior of physical systems, such as fluid flow, electromagnetism, and heat transfer. It also plays a crucial role in the study of vector calculus and is used in many mathematical models and equations.

5. How is the Curl of Gradient of a Scalar Field used in real-world applications?

The Curl of Gradient of a Scalar Field has numerous real-world applications, including fluid dynamics, electromagnetism, and heat transfer. It is also used in fields such as computer graphics, image processing, and machine learning to analyze and manipulate data. Additionally, it is used in physics and engineering to understand the behavior of physical systems and make predictions about their behavior.

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