Vector Calculus - gradient geometry

In summary, the gradient vector and velocity vector are unrelated to each other. The velocity vector can be in any direction and is not affected by the scalar field F or its gradient. The normal vector to the tangent plane, which is tangent to the level sets of F, is also unrelated to the velocity and can be in any direction. The gradient vector, while still arbitrary, is in the same direction as the normal vector and can be changed by altering the scalar field F.
  • #1
DFeng25
5
0
Hello. I can't seem to wrap my head around the geometry of the gradient vector in ℝ3

So for F=f(x(t),y(t)), [tex]\frac{dF}{dt}=\frac{dF}{dx}\frac{dx}{dt}+\frac{dF}{dy}\frac{dy}{dt}[/tex]
This just boils down to
[tex]\frac{dF}{dt}=∇F \cdot v[/tex]

Along a level set, the dot product of the gradient vector and velocity vector equals zero. But going uphill/downhill, the dot product no longer equals zero.

To my understanding, the velocity vector lies on the tangent plane, which is perpendicular to the gradient vector. By this logic, the dot product should always equal zero.

What am I missing here? What is the geometry of the gradient vector and velocity vector in relation to one another?

Edit: Is it possible that I'm just confusing the gradient vector with the normal vector? In that case, the dot product of the normal vector and velocity vector will always equal zero, right?
 
Last edited:
Physics news on Phys.org
  • #2
The gradient vector and the velocity vector have nothing to do with each other. The velocity can be in any direction, and so can the gradient.

The velocity is tangent to the curve t → (x(t), y(t)), not to the level surface of F
 
  • #3
Thank you. So just to clarify, the normal and velocity vectors are always perpendicular, but the gradient and normal vectors are not necessarily the same?
 
  • #4
Normal to what? Normal to the level sets? The velocity can be in any direction, so it does not have to be perpendicular to anything.

The normal to the level sets is in the same direction as the gradient, but does not have the same magnitude. The normal is defined to have magnitude 1.
 
  • #5
Oops, I meant the normal to the tangent plane. Sorry about that.
 
  • #6
Say you have a scalar field F, and a particle moving through space.

Now how the particle moves does not depend at all on what F is. It can move any way it wants to. So the velocity of the particle is not related to what F is, or what the gradient of F is. Depending on what F is, the gradient of F can be perpendicular to the velocity, or it can be in the same direction as the velocity, or any other direction.

The normal vector is the unit vector perpendicular to the tangent plane (I assume you're talking about the plane tangent to the level sets), which again depends on what F is. It too can be in any direction, unrelated to the velocity. Without changing how the particle moves, you can change F to make the normal vector point in any direction you want.
 
Last edited:
  • #7
Would all of this be the same if the particle is moving along a parametric surface in ℝ3 defined by [tex]r(x,y)= <x,y,f(x,y)>[/tex]

I thought that [tex]n=\frac{\partial r}{\partial x} \times \frac{\partial r}{\partial y}[/tex]

And the partial derivatives represent the x- and y-components of the velocity vector. Would this mean that the velocity vector lies on the tangent plane and is therefore perpendicular to the normal vector to the plane?

Regarding the gradient vector for a parametric surface, is it's geometry still arbitrary for a parametric surface?

(I apologize if I was using the incorrect terminology before, and if I did not give proper context to my question)
 
  • #8
Ah I see, so you were talking about a particle constrained to move on a particular surface, and by 'tangent plane' you meant a plane tangent to the constraint surface.

In that case, yes the normal to the tangent plane is always perpendicular to the velocity.

The gradient however is still arbitrary. It does not have any special relationship with the velocity.
 
  • #9
Thank you very much! Those were the answers I needed, sorry it took so long for me to correctly phrase my initial question.
 

1. What is the definition of gradient in vector calculus?

The gradient is a vector operator that represents the rate of change of a scalar field in a particular direction. It is calculated by taking the partial derivatives of the scalar field with respect to each coordinate direction.

2. How is the gradient used in geometry?

The gradient is used in geometry to find the direction of steepest ascent or descent of a scalar field. It is also used to find the normal vector to a surface at a given point.

3. What is the relationship between gradient and directional derivative?

The directional derivative is a measure of the rate of change of a function in a particular direction. The gradient is the vector that gives the direction of maximum change, and its magnitude is equal to the directional derivative in that direction.

4. Can the gradient be negative?

Yes, the gradient can be negative. The gradient is a vector, so it has both magnitude and direction. The magnitude of the gradient represents the rate of change, so if the function is decreasing, the gradient will be negative.

5. How is the gradient used in real-world applications?

The gradient is used in many real-world applications, such as in physics, engineering, and economics. It is used to calculate the force or acceleration of a system, determine the optimal path for a robot, and find the direction of maximum profit in a business model, to name a few examples.

Similar threads

Replies
4
Views
1K
Replies
2
Views
718
  • Special and General Relativity
2
Replies
38
Views
4K
  • Differential Geometry
Replies
2
Views
579
Replies
3
Views
1K
  • Differential Geometry
Replies
21
Views
631
Replies
4
Views
3K
  • Calculus
Replies
7
Views
2K
Replies
1
Views
1K
  • Calculus and Beyond Homework Help
Replies
9
Views
749
Back
Top