Graduate Fluids and the no-penetration condition

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SUMMARY

The discussion focuses on the no-penetration condition in fluid dynamics, specifically how it relates to the gradient of a potential function, represented as $$\vec{u} = -\nabla \phi$$. Participants clarify that the expression $$\nabla \phi \cdot \hat{n} = 0$$ is equivalent to $$\frac{\partial \phi}{\partial n} = 0$$, where $$\hat{n}$$ is the outward normal vector. The conversation highlights the importance of understanding the dot product in this context and emphasizes the need for clarity when generalizing to non-Cartesian systems.

PREREQUISITES
  • Understanding of vector calculus, specifically gradient and dot product operations.
  • Familiarity with fluid dynamics concepts, particularly the no-penetration condition.
  • Knowledge of potential flow theory and its mathematical representations.
  • Basic understanding of Cartesian and non-Cartesian coordinate systems.
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  • Study the mathematical foundations of vector calculus, focusing on gradients and directional derivatives.
  • Explore potential flow theory in fluid dynamics to understand its applications and implications.
  • Learn about non-Cartesian coordinate systems and their relevance in fluid mechanics.
  • Investigate advanced topics in fluid dynamics, such as boundary layer theory and its mathematical formulations.
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Fluid dynamics students, researchers in applied mathematics, and engineers working on fluid flow simulations will benefit from this discussion, particularly those interested in boundary conditions and potential flow analysis.

member 428835
Hi PF!

Denote the velocity of a fluid ##\vec u## and define a potential ##\vec u = -\nabla \phi##. Let ##\hat n ## be an outward-oriented surface normal to a solid boundary. I would express no penetration at the boundary as $$ u \cdot \hat n = 0 \implies \nabla \phi \cdot \hat n = 0.$$
However, the text writes $$\frac{\partial \phi}{\partial n} = 0$$ where ##n## is the direction of the outward-oriented normal. Can someone explain this result?
 
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The gradient operator is defined in cartesian coordinates as
$$\nabla=\frac{\partial}{\partial x}\hat{x}+\frac{\partial}{\partial y}\hat{y}+\frac{\partial}{\partial z}\hat{z}$$
Looking at a particular example, if ##\hat{n}=\hat{x}## then
$$\nabla\phi\cdot\hat{n}=\nabla\phi\cdot\hat{x}=\frac{\partial\phi}{\partial x}\hat{x}\cdot\hat{x}+\frac{\partial\phi}{\partial y}\hat{y}\cdot\hat{x}+\frac{\partial\phi}{\partial z}\hat{z}\cdot\hat{x}=\frac{\partial\phi}{\partial x}=\frac{\partial\phi}{\partial n}$$
 
The above answer has some math errors when carrying out the dot product.

Really, though, you don't need any fancy math to figure this out if you just think about the meaning of a dot product. If you just remember that ##\vec{a}\cdot\vec{b}## is the projection of ##\vec{a}## onto ##\vec{b}## (i.e. the size of ##\vec{a}## in the direction of ##\vec{b}## if ##\vec{b} = \hat{b}## is of unit length), you can apply that here. In this case, you've got ##\nabla\phi \cdot \hat{n}##. Since ##\nabla \phi## is the gradient and ##\hat{n}## is a unit vector normal to the surface, the dot product represents the magnitude of the gradient in the direction of the unit normal, or ##\frac{\partial \phi}{\partial n}##.
 
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boneh3ad said:
In this case, you've got ##\nabla\phi \cdot \hat{n}##. Since ##\nabla \phi## is the gradient and ##\hat{n}## is a unit vector normal to the surface, the dot product represents the magnitude of the gradient in the direction of the unit normal, or ##\frac{\partial \phi}{\partial n}##.
Riiiiiight, just like the directional derivative! Got it!
 
boneh3ad said:
The above answer has some math errors when carrying out the dot product.
Can you be more specific?
 
Your assumed form of your normal vector had one nonzero component yet somehow it was magically distributed to all of the gradient components.
 
Yes, that's because a dot product distributes over addition.
$$\nabla\phi\cdot\hat{x}=\left(\frac{\partial\phi}{\partial x}\hat{x}+\frac{\partial\phi}{\partial y}\hat{y}+\frac{\partial\phi}{\partial z}\hat{z}\right)\cdot\hat{x}=\frac{\partial\phi}{\partial x}\hat{x}\cdot\hat{x}+\frac{\partial\phi}{\partial y}\hat{y}\cdot\hat{x}+\frac{\partial\phi}{\partial z}\hat{z}\cdot\hat{x}=\frac{\partial\phi}{\partial x}$$
 
It certainly is distributive over addition, and I suppose that, while the way you've done it is technically correct, you've turned one dot product into three dot products. It would be much simpler to just compute the first one directly. Otherwise, what happens when you have to generalize it to a situation where ##\hat{n}## is not parallel to one of the Cartesian directions?

In your example (which is a special case, not general), you would do it directly by:
\nabla\phi \cdot \hat{n} = \left( \hat{\imath}\dfrac{\partial \phi}{\partial x} + \hat{\jmath}\dfrac{\partial \phi}{\partial y} + \hat{k}\dfrac{\partial \phi}{\partial z} \right) \cdot (\hat{\imath}) = \left( \hat{\imath}\dfrac{\partial \phi}{\partial x} + \hat{\jmath}\dfrac{\partial \phi}{\partial y} + \hat{k}\dfrac{\partial \phi}{\partial z} \right) \cdot (1\hat{\imath} +0\hat{\jmath} + 0\hat{k})
= \left[(1) \left(\dfrac{\partial \phi}{\partial x} \right) + (0) \left(\dfrac{\partial \phi}{\partial y} \right) + (0) \left(\dfrac{\partial \phi}{\partial z}\right) \right] = \dfrac{\partial \phi}{\partial x}
 
boneh3ad said:
Otherwise, what happens when you have to generalize it to a situation where ^nn^\hat{n} is not parallel to one of the Cartesian directions?
The same logic still applies. Let's say ##\hat{n}=2\hat{x}+1\hat{y}## then
$$\nabla\phi\cdot\left(2\hat{x}+1\hat{y}\right)=\left(\frac{\partial\phi}{\partial x}\hat{x}+\frac{\partial\phi}{\partial y}\hat{y}+\frac{\partial\phi}{\partial z}\hat{z}\right)\cdot\left(2\hat{x}+1\hat{y}\right)=\frac{\partial\phi}{\partial x}\left(\hat{x}\cdot2\hat{x}+\hat{x}\cdot\hat{y}\right)+\frac{\partial\phi}{\partial y}\left(\hat{y}\cdot2\hat{x}+\hat{y}\cdot\hat{y}\right)+\frac{\partial\phi}{\partial z}\left(\hat{z}\cdot2\hat{x}+\hat{z}\cdot\hat{y}\right)=2\frac{\partial\phi}{\partial x}+\frac{\partial\phi}{\partial y}$$
This is certainly pedantic for the simple case here but this process is useful when working with a non-Cartesian system or when ##\hat{n}## is defined on an irregular boundary.
 

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