The ``kinematic equation'' of fluid flows

In summary, the equation \frac{d}{dt} \int_{S(t)} \mathbf{F} \cdot d\mathbf{S} = \int_{S(t)} \left(\frac{\partial \mathbf{F}}{\partial t}\right)_{\mathbf{x}} \cdot d\mathbf{S} + \int_{S(t)} \mathbf{n} \cdot (\nabla \times (\mathbf{u} \times \mathbf{F})) - \int_{\partial S(t)} (\mathbf{u} \times \mathbf{F}) \cdot d\mathbf{r} is a corollary of Reynold's transport theorem
  • #1
hunt_mat
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I saw this in a fluid book and I want to get a clean derivation
I saw this in a textbook and I thought it is a corollary of Reynold's transport theorem. Let [itex]\mathbf{F}[/itex] be a smooth vector field Consider the surface integral:
[tex]\int_{S}\mathbf{F}\cdot d\mathbf{S}[/tex] and now take the derivative of it, then the expression can be written as:
[tex]\frac{d}{dt}\int_{S}\mathbf{F}\cdot d\mathbf{S}=\int_{S}\frac{\partial\mathbf{F}}{\partial t}\cdot d\mathbf{S}-\int_{\partial S}(\mathbf{u}\times\mathbf{F})\cdot d\mathbf{r}[/tex].

Is anyone familiar with this equation? Does anyone know of a nice clear vector analysis way of deriving it? I've looked in Batchelor but couldn't find a derivation.
 
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  • #2
This equation will hold so long as ##\mathbf{F}(t,\mathbf{r})## is divergence-free, ##\nabla \cdot \mathbf{F} = 0##. Begin with the definition of the derivative, \begin{align*}
\dfrac{d}{dt} \int_{S(t)} \mathbf{F}(t) \cdot d\mathbf{S} = \lim_{h \rightarrow 0} \dfrac{1}{h} \left[ \int_{S(t+h)} \mathbf{F}(t+h) \cdot d\mathbf{S} - \int_{S(t)} \mathbf{F}(t) \cdot d\mathbf{S} \right]
\end{align*}Expand ##\mathbf{F}(t+h) = \mathbf{F}(t) + h \dfrac{\partial \mathbf{F}}{\partial t} + O(h^2)##, then\begin{align*}
\dfrac{d}{dt} \int_{S(t)} \mathbf{F}(t) \cdot d\mathbf{S} &= \int_{S(t)} \dfrac{\partial \mathbf{F}}{\partial t} \cdot d\mathbf{S} + \lim_{h \rightarrow 0} \dfrac{1}{h} \left[ \int_{S(t+h)} \mathbf{F}(t) \cdot d\mathbf{S} - \int_{S(t)} \mathbf{F}(t) \cdot d\mathbf{S} \right] \\ \\
&\equiv \int_{S(t)} \dfrac{\partial \mathbf{F}}{\partial t} \cdot d\mathbf{S} + \lim_{h \rightarrow 0} \dfrac{1}{h} \left( \int_{S(t+h)} - \int_{S(t)} \right) \mathbf{F}(t) \cdot d\mathbf{S}
\end{align*}Between times ##t## and ##t+h##, the boundary ##\partial S## of ##S## sweeps out a surface ##B(t)## such that ##S(t+h)##, ##B(t)## and ##S(t)## together make a closed surface. Given that ##\nabla \cdot \mathbf{F} = 0##, then keeping in mind the orientations of ##S(t)## and ##S(t+h)##:\begin{align*}
\left(\int_{S(t+h)} + \int_{B(t)} -\int_{S(t)} \right) \mathbf{F}(t) \cdot d\mathbf{S} &= 0 \\ \\
\left(\int_{S(t+h)} -\int_{S(t)} \right) \mathbf{F}(t) \cdot d\mathbf{S} &= -\int_{B(t)} \mathbf{F}(t) \cdot d\mathbf{S}
\end{align*}If the boundary ##\partial S## of ##S## moves with a velocity ##\mathbf{u}(t)##, then the surface element of ##B(t)## is nothing but the vector area ##d\mathbf{r} \times h\mathbf{u}(t)## where ##d\mathbf{r}## is the line element along ##\partial S##. Therefore\begin{align*}
\left(\int_{S(t+h)} -\int_{S(t)} \right) \mathbf{F}(t) \cdot d\mathbf{S} &= -\int_{B(t)} \mathbf{F}(t) \cdot d\mathbf{r} \times h\mathbf{u}(t) \\ \\
&= -h\int_{B(t)} \mathbf{u}(t) \times \mathbf{F}(t) \cdot d\mathbf{r}
\end{align*}which finishes the derivation.
 
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  • #3
Thanks for this but it wasn't quite the "clean" method I was asking. If I represent [itex]d\mathbf{S}=\hat{\mathbf{n}}(t)dS[/itex], then can't I use a change of variables to transform the surface into a "fixed" surface [itex]S_{0}[/itex] then we can take the derivative directly into the integral via the dominated convergence theorem as a partial derivative.
 
  • #4
hunt_mat said:
Thanks for this but it wasn't quite the "clean" method I was asking. If I represent [itex]d\mathbf{S}=\hat{\mathbf{n}}(t)dS[/itex], then can't I use a change of variables to transform the surface into a "fixed" surface [itex]S_{0}[/itex] then we can take the derivative directly into the integral via the dominated convergence theorem as a partial derivative.

Not quite. You can use the Lagrangian view of the fluid, in which [itex]\mathbf{X}[/itex] represents the fluid parcel which started at [itex]\mathbf{X}[/itex] at [itex]t = 0[/itex]. A surface which moves with the fluid is then fixed in this view, and if [itex]\mathbf{x}[/itex] is position in the Eulerian depiction (in which [itex]\mathbf{x}[/itex] is fixed and the fluid moves past it) then [tex]
\left( \frac{\partial \mathbf{F}}{\partial t}\right)_{\mathbf{X}} =\left(\frac{\partial \mathbf{F}}{\partial t}\right)_{\mathbf{x}} + (\mathbf{u} \cdot \mathbf{\nabla})\mathbf{F}[/tex] and [tex]
(\mathbf{x},t) = \left(\mathbf{X} + \int_0^t \mathbf{u}(\mathbf{X},t')\,dt',t\right).[/tex]
 
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  • #5
I've started the calculation but I've gotten stuck:
[tex]
\begin{gather*}
\frac{d}{dt}\int_{S_{t}}\mathbf{f}\cdot\hat{\mathbf{n}} = \frac{d}{dt}\int_{S_{0}}J\mathbf{F}\cdot\hat{\mathbf{N}}dS_{0} \\
= \int_{S_{0}}\frac{\partial J}{\partial t}\mathbf{F}\cdot\hat{\mathbf{N}}+J\frac{\partial\mathbf{F}}{\partial t}\cdot\hat{\mathbf{N}}+J\mathbf{F}\cdot\frac{\partial\hat{\mathbf{N}}}{\partial t}dS_{0} \\
= \int_{S_{t}}\mathbf{f}\cdot\hat{\mathbf{n}}\nabla\cdot\mathbf{u}+\frac{D\mathbf{f}}{Dt}\cdot\hat{\mathbf{n}}dS+\int_{S_{0}}J\mathbf{F}\cdot\frac{\partial\hat{\mathbf{N}}}{\partial t}dS_{0}
\end{gather*}
[/tex]

I think the first two terms combine to yield:
[tex]\frac{\partial\mathbf{f}}{\partial t}+\nabla\cdot((\mathbf{f}\cdot\hat{\mathbf{n}})\mathbf{u})[/tex]
I don't know what to do with the final term.
 
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  • #6
The general fact is as follows
$$\frac{d}{dt}\Big|_{t=0}\int_{g_v^t(S)}\omega =\int_SL_v\omega,$$
where ##g^t_v## is a flow of the vector field ##v(x)## and ##L_v## is the Lie derivative.
One can also employ the formula ##L_v\omega=i_vd\omega+di_v\omega## and then use the Stokes theorem
 
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  • #7
Parametrize [itex]S(t)[/itex] as [itex]\mathbf{x}(s_1,s_2,t)[/itex] for [itex](s_1,s_2) \in \Omega \subset \mathbb{R}^2[/itex]. Then [tex]
\frac{d}{dt} \int_{S(t)} \mathbf{F} \cdot d\mathbf{S} = \frac{d}{dt} \int_{\Omega} \mathbf{F} \cdot \mathbf{n}\,ds_1\,ds_2.[/tex] Now [itex]\Omega[/itex] is independent of [itex]t[/itex] so we can pull the derivative inside the integral, but it becomes a partial derivative at fixed [itex]s = (s_1,s_2)[/itex]. We then have [tex]
\left(\frac{\partial\mathbf{F}}{\partial t}\right)_{s} = \left(\frac{\partial\mathbf{F}}{\partial t}\right)_{\mathbf{x}} + (\mathbf{u} \cdot \nabla) \mathbf{F}.[/tex] We also have [tex]
\begin{split}
\left(\frac{\partial n_i}{\partial t}\right)_s &= \epsilon_{ijk} \frac{\partial}{\partial t} \left( \frac{\partial x_j}{\partial s_1}\frac{\partial x_k}{\partial s_2}\right) \\
&= \epsilon_{ijk} \left(\frac{\partial u_j}{\partial s_1} \frac{\partial x_k}{\partial s_2} + \frac{\partial u_k}{\partial s_2} \frac{\partial x_j}{\partial s_1}\right) \\
&= \epsilon_{ijk} \left(\frac{\partial u_j}{\partial s_1} \frac{\partial x_k}{\partial s_2} - \frac{\partial u_j}{\partial s_2} \frac{\partial x_k}{\partial s_1}\right) \\
&= \epsilon_{ijk} \frac{\partial u_j}{\partial x_l}\left(\frac{\partial x_l}{\partial s_1} \frac{\partial x_k}{\partial s_2} - \frac{\partial x_l}{\partial s_2} \frac{\partial x_k}{\partial s_1}\right) \\
&= \epsilon_{ijk} \frac{\partial u_j}{\partial x_l} \left(\delta_{lp}\delta_{kq} - \delta_{lq}\delta_{kp}\right)\frac{\partial x_p}{\partial s_1} \frac{\partial x_q}{\partial x_2} \\
&= \epsilon_{ijk} \frac{\partial u_j}{\partial x_l} \epsilon_{lkm}\epsilon_{mpq} \frac{\partial x_p}{\partial s_1} \frac{\partial x_q}{\partial x_2} \\
&= -\epsilon_{ijk} \epsilon_{klm} \frac{\partial u_j}{\partial x_l} n_m \\
&= \left(\delta_{im}\delta_{jl} - \delta_{il}\delta_{jm}\right) \frac{\partial u_j}{\partial x_l} n_m\\
&= n_i\frac{\partial u_j}{\partial x_j} - n_j \frac{\partial u_j}{\partial x_i}.
\end{split}[/tex] Hence [tex]
\left(\frac{\partial}{\partial t} (\mathbf{F} \cdot \mathbf{n}) \right)_s = \left(\frac{\partial \mathbf{F}}{\partial t}\right)_{\mathbf{x}} \cdot \mathbf{n} + \mathbf{n} \cdot \left(((\mathbf{u} \cdot \nabla)\mathbf{F}) +
\mathbf{F} (\nabla \cdot \mathbf{u}) - (\mathbf{F} \cdot \nabla)\mathbf{u}\right).[/tex] I think the last three terms can be rearranged into [itex]-\mathbf{n} \cdot (\nabla \times(\mathbf{u} \times \mathbf{F}))[/itex] on the assumption that [itex]\nabla \cdot \mathbf{F} = 0[/itex].
 
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  • #8
wrobel said:
The general fact is as follows
$$\frac{d}{dt}\Big|_{t=0}\int_{g_v^t(S)}\omega =\int_SL_v\omega,$$
where ##g^t_v## is a flow of the vector field ##v(x)## and ##L_v## is the Lie derivative.
One can also employ the formula ##L_v\omega=i_vd\omega+di_v\omega## and then use the Stokes theorem
This looks like a change of variables formula, am I right?
 
  • #10
wrobel said:
sure
It's been a while since I've done integration on manifolds (~20 years), so you'll need to walk through this, I'm guessing that I would use the reverse flow of the vector field as the change of variable and that would mean I would have to pull back and taking the derivative inside the integral means that I get the Lie derivative?
 
  • #11
yes by definition it will be the Lie derivative and all the formulas above are the special cases of this construction
 
  • #12
wrobel said:
yes by definition it will be the Lie derivative and all the formulas above are the special cases of this construction
I'm currently looking at the maths, I will want to write down a proof for this statement.
 
  • #13
pasmith said:
Parametrize [itex]S(t)[/itex] as [itex]\mathbf{x}(s_1,s_2,t)[/itex] for [itex](s_1,s_2) \in \Omega \subset \mathbb{R}^2[/itex]. Then [tex]
\frac{d}{dt} \int_{S(t)} \mathbf{F} \cdot d\mathbf{S} = \frac{d}{dt} \int_{\Omega} \mathbf{F} \cdot \mathbf{n}\,ds_1\,ds_2.[/tex] Now [itex]\Omega[/itex] is independent of [itex]t[/itex] so we can pull the derivative inside the integral, but it becomes a partial derivative at fixed [itex]s = (s_1,s_2)[/itex]. We then have [tex]
\left(\frac{\partial\mathbf{F}}{\partial t}\right)_{s} = \left(\frac{\partial\mathbf{F}}{\partial t}\right)_{\mathbf{x}} + (\mathbf{u} \cdot \nabla) \mathbf{F}.[/tex] We also have [tex]
\begin{split}
\left(\frac{\partial n_i}{\partial t}\right)_s &= \epsilon_{ijk} \frac{\partial}{\partial t} \left( \frac{\partial x_j}{\partial s_1}\frac{\partial x_k}{\partial s_2}\right) \\
&= \epsilon_{ijk} \left(\frac{\partial u_j}{\partial s_1} \frac{\partial x_k}{\partial s_2} + \frac{\partial u_k}{\partial s_2} \frac{\partial x_j}{\partial s_1}\right) \\
&= \epsilon_{ijk} \left(\frac{\partial u_j}{\partial s_1} \frac{\partial x_k}{\partial s_2} - \frac{\partial u_j}{\partial s_2} \frac{\partial x_k}{\partial s_1}\right) \\
&= \epsilon_{ijk} \frac{\partial u_j}{\partial x_l}\left(\frac{\partial x_l}{\partial s_1} \frac{\partial x_k}{\partial s_2} - \frac{\partial x_l}{\partial s_2} \frac{\partial x_k}{\partial s_1}\right) \\
&= \epsilon_{ijk} \frac{\partial u_j}{\partial x_l} \left(\delta_{lp}\delta_{kq} - \delta_{lq}\delta_{kp}\right)\frac{\partial x_p}{\partial s_1} \frac{\partial x_q}{\partial x_2} \\
&= \epsilon_{ijk} \frac{\partial u_j}{\partial x_l} \epsilon_{lkm}\epsilon_{mpq} \frac{\partial x_p}{\partial s_1} \frac{\partial x_q}{\partial x_2} \\
&= -\epsilon_{ijk} \epsilon_{klm} \frac{\partial u_j}{\partial x_l} n_m \\
&= \left(\delta_{im}\delta_{jl} - \delta_{il}\delta_{jm}\right) \frac{\partial u_j}{\partial x_l} n_m\\
&= n_i\frac{\partial u_j}{\partial x_j} - n_j \frac{\partial u_j}{\partial x_i}.
\end{split}[/tex] Hence [tex]
\left(\frac{\partial}{\partial t} (\mathbf{F} \cdot \mathbf{n}) \right)_s = \left(\frac{\partial \mathbf{F}}{\partial t}\right)_{\mathbf{x}} \cdot \mathbf{n} + \mathbf{n} \cdot \left(((\mathbf{u} \cdot \nabla)\mathbf{F}) +
\mathbf{F} (\nabla \cdot \mathbf{u}) - (\mathbf{F} \cdot \nabla)\mathbf{u}\right).[/tex] I think the last three terms can be rearranged into [itex]-\mathbf{n} \cdot (\nabla \times(\mathbf{u} \times \mathbf{F}))[/itex] on the assumption that [itex]\nabla \cdot \mathbf{F} = 0[/itex].
Hi, this is exactly what I wanted, thank you.
 
  • #14
wrobel said:
yes by definition it will be the Lie derivative and all the formulas above are the special cases of this construction
So we need a change of variables. Will this be the streamlines? This will lead to the pullback. The vector which is involved in the Lie derivative will be the velocity vector(tangent to the streamlines) at t=0. This is the basic calculation right?
 

What is the kinematic equation of fluid flows?

The kinematic equation of fluid flows is a mathematical equation that describes the motion of fluid particles in a fluid flow. It is derived from the fundamental principles of fluid mechanics and is used to study the behavior of fluids in motion.

What are the variables in the kinematic equation of fluid flows?

The kinematic equation of fluid flows typically includes variables such as velocity, pressure, density, and viscosity. These variables can vary depending on the specific type of fluid flow being studied.

How is the kinematic equation of fluid flows used in research?

The kinematic equation of fluid flows is used in research to analyze and predict the behavior of fluids in various situations. It is commonly used in fields such as aerodynamics, hydrodynamics, and meteorology to study the motion of fluids in air and water.

What are some real-world applications of the kinematic equation of fluid flows?

The kinematic equation of fluid flows has many practical applications, such as predicting the flow of air over an airplane wing, studying the flow of water in a river, and designing efficient water turbines. It is also used in the development of weather forecasting models.

Are there any limitations to the kinematic equation of fluid flows?

While the kinematic equation of fluid flows is a powerful tool for studying fluid motion, it does have some limitations. It assumes that the fluid is homogeneous, incompressible, and has a constant density, which may not always be the case in real-world scenarios. Additionally, it does not take into account external factors such as turbulence or boundary effects.

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