Exploring the Properties of $(\vec A \cdot \nabla)$

In summary, the operator (\vec A \cdot \nabla) is well defined and appears in many vector calculus identities, but cannot be written generally in curvilinear coordinates. It has different forms depending on whether it is applied to vectors or scalars. This operator is often glossed over in vector calculus classes and explicit curvilinear formulas are never given. It is sometimes referred to as the del operator and is extremely useful in operator algebra. When applied to a scalar function, it is equivalent to \vec A \cdot \nabla (f), but when applied to a vector function, it behaves as a scalar operator. The result of applying this operator to a vector function can be found by expressing it as a linear combination
  • #1
StatusX
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[tex](\vec A \cdot \nabla) [/tex]

Is this operator well defined? It appears in many vector calculs identites, and it has an easy enough explicit formula in cartesian coordinates. But I've heard it cannot be written generally in the curvilinear coordinates. I assume this is because this operator can be applied to vectors or scalars, and has different forms depending on which. Is this the case? Why is this operator always glossed over in vector calculus classes? Why are explicit curvilinear formulas for it never given? Does it have a name?

For example:

[tex]( (\vec v \cdot \nabla) \vec v)_r \neq (\vec v \cdot \nabla) v_r [/tex]

although I was never told this, and still don't completely understand it.
 
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  • #2
Ok, so now I see that [itex](\vec A \cdot \nabla) f[/itex] for a scalar f is just equal to [itex]\vec A \cdot \nabla (f)[/itex]. I'm not sure why the former notation would ever be used. But the application of this operator to vector fields is still mysterious to me.
 
  • #3
I'm not sure why the former notation would ever be used.
(1) Because operator algebra is extremely useful.
(2) Would you really rather write:

[tex]
\vec{A} \cdot \nabla( \vec{A} \cdot \nabla( \vec{A} \cdot \nabla(f)))
[/tex]

instead of

[tex]
(\vec{A} \cdot \nabla)^3 f
[/tex]

?

(such things do arise. For example, in multivariable Taylor series)
 
  • #4
StatusX said:
Ok, so now I see that [itex](\vec A \cdot \nabla) f[/itex] for a scalar f is just equal to [itex]\vec A \cdot \nabla (f)[/itex]. I'm not sure why the former notation would ever be used. But the application of this operator to vector fields is still mysterious to me.



May i know what if f is a vector? [itex](\vec A \cdot \nabla) \vec F[/itex] is this equivalent to [itex]\vec A (\nabla \cdot (vec F))[/itex].
 
  • #5
boarie said:
May i know what if f is a vector? [itex](\vec A \cdot \nabla) \vec F[/itex] is this equivalent to [itex]\vec A (\nabla \cdot (vec F))[/itex].
Nope. Everything in [itex](\vec{A} \cdot \nabla) \vec{F}[/itex] is as written. You take the dot product, then apply the operator to [itex]\vec{F}[/itex].

If you want to associate it, then you have to take a tour through tensors -- [itex]\nabla \vec{F}[/itex] is a bivector, and then you contract it with [itex]\vec{A}[/itex].
 
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  • #6
Hurkyl said:
Nope. Everything in [itex](\vec{A} \cdot \nabla) \vec{F}[/itex] is as written. You take the dot product, then apply the operator to [itex]\vec{F}[/itex].

If you want to associate it, then you have to take a tour through tensors -- [itex]\nabla \vec{F}[/itex] is a bivector, and then you contract it with [itex]\vec{A}[/itex].

hi Hurkyl

Thx for your advice.. thus may i know, if we are to take the del operator with A and then apply to F, the resultant will be: [itex](\vec{A1} \frac{\partial \vec{F1}}{\partial x}... [/itex]?
 
  • #7
Well, since you didn't write down what you think it is, I can only guess. I suspect you did not do what I said, and instead simply component-wise multipled all three parts.
 
  • #8
Hurkyl said:
Well, since you didn't write down what you think it is, I can only guess. I suspect you did not do what I said, and instead simply component-wise multipled all three parts.

hi Hurkyl

My apology for not being clear.. is it wrong to state the result as:

[itex](\vec{A_{x}} \frac{\partial \vec{F_{x}}}{\partial x} + \vec{A_{y}} \frac{\partial \vec{F_{y}}}{\partial y} + \vec{A_{z}} \frac{\partial \vec{F_{z}}}{\partial z}) [/itex]
 
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  • #9
Yes, it is wrong.
You should have a vector as the result, not a scalar.
 
  • #10
arildno said:
Yes, it is wrong.
You should have a vector as the result, not a scalar.

Roger on that...

Thus I shld perfom A dot del:

[itex](\vec{A_{x}} \frac{\partial }{\partial x} + \vec{A_{y}} \frac{\partial }{\partial y} + \vec{A_{z}} \frac{\partial }{\partial z}) [/itex]

which gives me a scalar.

And multiply with vector F?

[itex]((\vec{A_{x}} \frac{\partial }{\partial x} + \vec{A_{y}} \frac{\partial }{\partial y} + \vec{A_{z}} \frac{\partial }{\partial z})) (\vec F)[/itex]

Resulting in:

[itex][\vec{A_{x}} \frac{\partial \vec{F_{x}}}{\partial x} + \vec{A_{y}} \frac{\partial \vec{F_{x}}}{\partial y} + \vec{A_{z}} \frac{\partial \vec{F_{x}}}{\partial z}, \vec{A_{x}} \frac{\partial \vec{F_{y}}}{\partial x} + \vec{A_{y}} \frac{\partial \vec{F_{y}}}{\partial y} + \vec{A_{z}} \frac{\partial \vec{F_{y}}}{\partial z}, \vec{A_{x}} \frac{\partial \vec{F_{z}}}{\partial x} + \vec{A_{y}} \frac{\partial \vec{F_{z}}}{\partial y} + \vec{A_{z}} \frac{\partial \vec{F_{z}}}{\partial z}, ] [/itex]

Is this correct?
 
  • #11
Here's how I think it works. When you have a vector function [itex]\vec A(\vec r)[/tex], then for a given coordinate system, you can associate to it three scalar functions. For example, in cartesian coordinates, these are Ax, Ay, and Az.

Now, when you have an operator involving del, you can classify it as behaving as a vector or a scalar. For example, del itself acts like a vector, and so can be dotted or crossed with a vector function to give a scalar or vector respectively. The laplacian operator (del squared) acts like a scalar, so can act on scalars or vectors, also to give a scalar or vector respectively. The operator that is the subject of this thread falls into the scalar operator category.

Now, these operators are all linear, so:

[tex] (\vec A \cdot \nabla) (\vec B + \vec C) = (\vec A \cdot \nabla) \vec B + (\vec A \cdot \nabla) \vec C [/tex]

[tex] (\vec A \cdot \nabla) (b+c) = (\vec A \cdot \nabla) b + (\vec A \cdot \nabla) c [/tex]

There are also analoques of the product rule, which I don't feel like completely working out, but these would include expressions for [itex] (\vec A \cdot \nabla) (b \vec C) [/itex], [itex] (\vec A \cdot \nabla) (b c) [/itex], [itex] (\vec A \cdot \nabla) (\vec B \cdot \vec C) [/itex], and [itex] (\vec A \cdot \nabla) (\vec B \times \vec C) [/itex]. The first one is the important one, and I believe the rule is:

[tex] (\vec A \cdot \nabla) (b \vec C) = \vec C (\nabla(b) \cdot \vec A) + b (\vec A \cdot \nabla) \vec C [/tex]

Now you can combine these two rule to find the result of applying this operator to a vector function by expressing it as a linear combination of the basis vectors in your particular coordinate system. So if the basis vectors are [itex]\vec u [/itex],[itex]\vec v [/itex],[itex]\vec w [/itex], then:

[tex](\vec A \cdot \nabla) \vec B = (\vec A \cdot \nabla) (B_u \vec u + B_v \vec v + B_w \vec w ) = B_u (\vec A \cdot \nabla) \vec u + \vec u (\vec A \cdot \nabla(B_u) ) +B_v (\vec A \cdot \nabla) \vec v + \vec v (\vec A \cdot \nabla(B_v) ) +B_w (\vec A \cdot \nabla) \vec w + \vec w (\vec A \cdot \nabla(B_w) ) [/tex]

This is the equation relating the result of this operator applied to vectors to the result of applying it componentwise to the associated scalar fields described in the first paragraph. In cartesian coordinates, [itex](\vec A \cdot \nabla) \hat x=(\vec A \cdot \nabla) \hat y= (\vec A \cdot \nabla) \hat z=0[/itex], so the above reduces to the simple relation:

[tex](\vec A \cdot \nabla) \vec B = \hat x(\vec A \cdot \nabla(B_x) ) + \hat y (\vec A \cdot \nabla(B_y) ) + \hat z (\vec A \cdot \nabla(B_z) ) [/tex]

In other coordinate systems, this does not happen (eg, in cylindrical coordinates [itex](\vec A \cdot \nabla) \hat r \neq 0[/itex]), so there will be extra terms involving the derivatives of the basis vectors. This was the main source of my confusion, but I think I'm satisfied now. I'm just a little confused why these important points are never (at least in my experience) explicitly talked about.
 
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Related to Exploring the Properties of $(\vec A \cdot \nabla)$

What is $(\vec A \cdot \nabla)$?

$(\vec A \cdot \nabla)$ is a mathematical operation known as the dot product. It involves taking the components of a vector $\vec A$ and multiplying them by the corresponding partial derivatives in the direction of the gradient vector $\nabla$. In other words, it measures the rate of change of a function in a specific direction.

How is $(\vec A \cdot \nabla)$ used in science?

$(\vec A \cdot \nabla)$ is commonly used in physics and engineering to describe the behavior of vector fields, such as electromagnetic fields. It is also used in fluid dynamics to calculate the flow of fluids and in thermodynamics to study heat transfer.

What are the properties of $(\vec A \cdot \nabla)$?

Some important properties of $(\vec A \cdot \nabla)$ include linearity, meaning that it can be distributed over addition and scalar multiplication, and the product rule, which states that the dot product of two vectors can be rewritten as the dot product of their individual components. It also follows the commutative and associative properties.

How does $(\vec A \cdot \nabla)$ relate to the gradient vector?

The gradient vector $\nabla$ is a mathematical operator that represents the direction and magnitude of the greatest rate of change of a function. When combined with the dot product, $(\vec A \cdot \nabla)$ can be used to calculate the directional derivative of a function, which measures how much the function changes in the direction of the gradient vector.

Can $(\vec A \cdot \nabla)$ be extended to higher dimensions?

Yes, the dot product and the gradient vector can be extended to higher dimensions. In three-dimensional space, the gradient vector is represented by $\nabla = \frac{\partial}{\partial x} \hat i + \frac{\partial}{\partial y} \hat j + \frac{\partial}{\partial z} \hat k$, and the dot product becomes $(\vec A \cdot \nabla) = \frac{\partial A_x}{\partial x} + \frac{\partial A_y}{\partial y} + \frac{\partial A_z}{\partial z}$. This can be extended to higher dimensions by adding more partial derivatives and components.

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