Gradient Divergence of Nabla Operator Defined

In summary, the Nabla operator is defined as a summation of partial derivatives with respect to generalized coordinates and Lame coefficients. Two different definitions are presented, one using unit vectors as functions of position and the other using the notation for cartesian coordinates. The latter clarifies that the derivative does not operate on the unit vectors or Lame coefficients. In order to calculate the expression for divergence, it is necessary to consider the product of the vector field and the derivative of the unit vectors, which can be written as a summation of terms with the Lame coefficients and derivatives.
  • #1
LagrangeEuler
717
20
Nabla operator is defined by

[tex]\nabla = \sum^3_{i=1} \frac{1}{h_i}\frac{\partial}{\partial q_i}\vec{e}_{q_i}[/tex]
where ##q_i## are generalized coordinates (spherical polar, cylindrical...) and ##h_i## are Lame coefficients. Why then
[tex]div(\vec{A})=\sum^3_{i=1} \frac{1}{h_i}\frac{\partial}{\partial q_i}\vec{e}_{q_i} \cdot \sum_j A_j\vec{e}_{q_j}=\sum_i\frac{1}{h_i}\frac{\partial}{\partial q_i}A_i[/tex]
where I am making the mistake?
here is different definition.
https://www.jfoadi.me.uk/documents/lecture_mathphys2_05.pdf
 
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  • #2
The unit vectors are generally functions of position; only in cartesian coordinates are they constants. This is why we generally write [itex]
\nabla = \sum_i \mathbf{e}_i h_i^{-1} \partial_i[/itex] to make it clear that the derivative does not operate on [itex]\mathbf{e}_i[/itex] or [itex]h_i[/itex]. However for divergence we get [tex]
\sum_i \mathbf{e}_i h_i^{-1} \partial_i \cdot \left( \sum_j A_j \mathbf{e}_j \right) =
\sum_i \sum_j \mathbf{e}_i h_i^{-1} \cdot \left( A_j \partial_i\mathbf{e}_j + \mathbf{e}_j \partial_i A_j \right)[/tex]
 
  • #3
Thanks a lot. It makes sense of course. What is the easiest way to calculate ##A_j \partial_i \vec{e}_j##? How to write that in order to get real expression of divergence with the Lame coefficients and derivatives.
 

FAQ: Gradient Divergence of Nabla Operator Defined

What is the gradient divergence of the Nabla operator?

The gradient divergence of the Nabla operator is a mathematical concept used in vector calculus to measure the rate of change of a vector field at a given point. It is represented by the symbol ∇ · F, where ∇ is the Nabla operator and F is the vector field. Essentially, it measures how much the vector field is spreading out or converging at a particular point.

How is the gradient divergence of the Nabla operator calculated?

The gradient divergence of the Nabla operator is calculated using the partial derivative of each component of the vector field with respect to its corresponding variable. This is then summed together to obtain the total divergence at a given point. The formula for calculating the gradient divergence is ∇ · F = ∂Fx/∂x + ∂Fy/∂y + ∂Fz/∂z.

What is the significance of the gradient divergence of the Nabla operator?

The gradient divergence of the Nabla operator is an important concept in physics and engineering, as it helps to understand the behavior of vector fields in three-dimensional space. It is used to study fluid flow, electromagnetism, and other phenomena involving vector fields. It also has applications in computer graphics and image processing.

How is the gradient divergence of the Nabla operator related to the gradient and the divergence?

The gradient divergence of the Nabla operator is related to the gradient and the divergence through the vector identity ∇ · (∇f) = ∇2f, where f is a scalar function. This means that the gradient divergence of a scalar function is equal to the Laplacian of that function, which measures the rate of change of the function at a given point.

Can the gradient divergence of the Nabla operator be negative?

Yes, the gradient divergence of the Nabla operator can be negative. This indicates that the vector field is converging at a particular point. Conversely, a positive gradient divergence means that the vector field is spreading out. A zero gradient divergence means that the vector field is neither converging nor spreading out, but is instead circulating or circulating with constant strength at that point.

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