Gradient theorem for time-dependent vector field

In summary, the two sides of the equation are evaluated at different times, and the equation results in the value of \phi at t=t_2.
  • #1
dEdt
288
2
Let's say we have some time-independent scalar field [itex]\phi[/itex]. Obviously [tex]\phi\left(\mathbf{q}\right)-\phi\left(\mathbf{p}\right) = \int_{\gamma[\mathbf{p},\,\mathbf{q}]} \nabla\phi(\mathbf{x})\cdot d\mathbf{x}.[/tex]
This is of course still true if the path [itex]\gamma[/itex] is the trajectory of a particle moving through space. But let's say we have a time-dependent field instead, with [itex]\gamma[/itex] still being the trajectory of the particle. Will
[tex]\phi(\mathbf{q},t_2)-\phi(\mathbf{p},t_1)=\int_{\gamma[\mathbf{p},\,\mathbf{q},t]} \nabla\phi(\mathbf{x} (t))\cdot d\mathbf{x}?[/tex]
 
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  • #2
dEdt said:
Let's say we have some time-independent scalar field [itex]\phi[/itex]. Obviously [tex]\phi\left(\mathbf{q}\right)-\phi\left(\mathbf{p}\right) = \int_{\gamma[\mathbf{p},\,\mathbf{q}]} \nabla\phi(\mathbf{x})\cdot d\mathbf{x}.[/tex]
This is of course still true if the path [itex]\gamma[/itex] is the trajectory of a particle moving through space. But let's say we have a time-dependent field instead, with [itex]\gamma[/itex] still being the trajectory of the particle. Will
[tex]\phi(\mathbf{q},t_2)-\phi(\mathbf{p},t_1)=\int_{\gamma[\mathbf{p},\,\mathbf{q},t]} \nabla\phi(\mathbf{x} (t))\cdot d\mathbf{x}?[/tex]

Consider that the gradient theorem is basically a thinly disguised form of Stokes' Theorem, id est,

$$\int\limits_{\partial\gamma} \phi = \int\limits_{\gamma} d\phi$$

So, let's consider evaluating the exterior derivative of ##\phi(\vec{x}(t))##.

$$d\left(\phi(\vec{x}(t))\right) = \sum_{i=1}^{n}\frac{\partial\phi}{\partial x^i}dx^i = \langle\nabla\phi(\vec{x}(t)), \cdot\rangle$$
where the angle brackets denote the inner product of phi with something.

Do you agree with this? There are other ways to do this, but this method seems easiest.

(I'm fairly sure I did this right, but if someone from the upper math-y places wants to correct me, they should.)

If this works how I thinks it does,

$$\int\limits_{\partial\gamma}\phi(\vec{x}(t)) = \phi(\vec{q}(t))-\phi(\vec{p}(t)) = \int\limits_{\gamma} \langle\nabla\phi(\vec{x}(t)), d\vec{x}\rangle$$

where ##\vec{q}(t)## is the vector q at time t.
 
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  • #3
I'm sorry, but you're using a lot of terminology and notation that I'm unfamiliar with. What does [tex]\int_{\partial \gamma}\phi[/tex] mean? What's an exterior derivative?
 
  • #4
dEdt said:
I'm sorry, but you're using a lot of terminology and notation that I'm unfamiliar with. What does [tex]\int_{\partial \gamma}\phi[/tex] mean? What's an exterior derivative?
Oh...sorry. That may be out of what you've learned before. My apologies.

##\int\limits_{\partial\gamma}\phi## would be the oriented sum of phi at the endpoints of ##\gamma##. Because ##\gamma## is 1-dimensional (it's a curve), its boundary is a finite set of points (namely, its endpoints).

The exterior derivative is a a generalization of the differential. See here.
 
  • #5
dEdt said:
Let's say we have some time-independent scalar field [itex]\phi[/itex]. Obviously [tex]\phi\left(\mathbf{q}\right)-\phi\left(\mathbf{p}\right) = \int_{\gamma[\mathbf{p},\,\mathbf{q}]} \nabla\phi(\mathbf{x})\cdot d\mathbf{x}.[/tex]
This is of course still true if the path [itex]\gamma[/itex] is the trajectory of a particle moving through space. But let's say we have a time-dependent field instead, with [itex]\gamma[/itex] still being the trajectory of the particle. Will
[tex]\phi(\mathbf{q},t_2)-\phi(\mathbf{p},t_1)=\int_{\gamma[\mathbf{p},\,\mathbf{q},t]} \nabla\phi(\mathbf{x} (t))\cdot d\mathbf{x}?[/tex]
The left side is evaluated at two different values of t. What value of t are you using on the right?
 

Related to Gradient theorem for time-dependent vector field

1. What is the Gradient Theorem for time-dependent vector fields?

The Gradient Theorem for time-dependent vector fields is a fundamental concept in vector calculus that relates the line integral of a vector field to the scalar field that it is derived from. It states that the line integral of a time-dependent vector field along a curve is equal to the difference in the scalar field between the endpoints of the curve.

2. How is the Gradient Theorem applied in real-world scenarios?

The Gradient Theorem has numerous applications in physics, engineering, and other scientific fields. For example, it is used to calculate the work done by a force on a moving object, the flux of a varying magnetic field through a surface, and the electric potential difference between two points in an electric field.

3. What is the relationship between the Gradient Theorem and the Fundamental Theorem of Calculus?

The Gradient Theorem is essentially an extension of the Fundamental Theorem of Calculus to vector fields. The Fundamental Theorem of Calculus states that the integral of a function over an interval can be calculated by evaluating the antiderivative of the function at the endpoints of the interval. The Gradient Theorem is a generalization of this concept to vector fields and time-dependent functions.

4. How is the Gradient Theorem derived?

The Gradient Theorem can be derived using the fundamental theorem of calculus and the chain rule. By considering a small segment of the curve and taking the limit as the number of segments approaches infinity, we can arrive at the Gradient Theorem as a generalization of the fundamental theorem of calculus.

5. What are the limitations of the Gradient Theorem for time-dependent vector fields?

While the Gradient Theorem is a powerful tool, it does have limitations. It only applies to conservative vector fields, meaning that the line integral of the vector field must be independent of the path taken. It also does not consider the direction of the curve, only the start and endpoints, so it cannot be applied to closed curves. Additionally, the Gradient Theorem is only applicable in two or three dimensions and does not extend to higher dimensions.

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