Understanding Parameterization in Divergence-Free Vector Fields

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In summary: An integral curve is a graph that shows the change in function over time. In this problem, you are looking for the curve that represents the path a particle takes as it moves around the container. You will need to find the velocity, v, at different points on the curve, and then use that information to find the function, x, that represents the path. The relationship between <cos \theta, sin \theta> and <-sin \theta, cos \theta> is that <cos \theta, sin \theta> = -1.
  • #1
JD571
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Hi, I am having a lot of trouble on the parameterization part of this problem:

Suppose there is a cylindrical container of liquid with circular flow velocity given in cylindrical coordinates by v(r,[tex]\theta[/tex],z) = <-f(r,z)sin[tex]\theta[/tex], f(r,z)cos[tex]\theta[/tex],0> for some function f(r,z) which we will determine. The vector field is divergence free.

Find a curve, x(t), that represents the path followed by a particle of liquid at radius r. So, x’(t) = v(x(t)) and x(0) = (r,0,z). Use this to find[tex]\theta[/tex](t), v(t) and a(t) = v’(t). Then write a in therms of r,[tex]\theta[/tex], z.

This is a long problem and I understand how to do the rest of it but this section is really confusing to me and I can't think of anything that would work. Thanks for any help.
 
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  • #2
I'm going to assume that <-f(r,z)sin[tex]\theta[/tex], f(r,z)cos[tex]\theta[/tex],0> is in rectangular coordinates because the problem is much easier that way.

To solve this problem you have to find the integral curve of a particle at radius r. Look at the velocity field, v.

--What if v were <f(r,z)cos [tex]\theta[/tex], f(r,z)sin [tex]\theta[/tex],0> instead? Could you picture what that would look like?
--What is the relationship between <cos [tex]\theta[/tex], sin [tex]\theta[/tex]> and <-sin [tex]\theta[/tex], cos [tex]\theta[/tex]>? (try the dot product)
--From those two you should be able to get an idea of what v looks like, and then a good guess at what the integral curves are.
 
  • #3
Thanks for the help but I have never heard of an integral curve before, I looked around online and I couldn't get a solid definition of what one was in terms that I understood. This is for a multivariable calc class where we just finished up the divergence theorem. I don't know if you can think of another way to do it or try to explain it to me but thanks for the effort.
 
  • #4
If you haven't covered integral curves yet, which would surprise me, you will soon so you might as well learn what they are.
 

1. What is the parameterization problem?

The parameterization problem refers to the issue of finding appropriate values for the parameters in a given mathematical model or algorithm. These parameters can greatly affect the performance and accuracy of the model, but it can be difficult to determine the optimal values.

2. Why is the parameterization problem important?

The parameterization problem is important because accurate and effective parameter values are crucial for producing reliable results from a model or algorithm. If parameters are not properly chosen, it can lead to inaccurate or unrealistic outcomes.

3. How do scientists approach the parameterization problem?

Scientists approach the parameterization problem through a combination of theoretical analysis and empirical testing. They may use mathematical techniques, such as sensitivity analysis, to determine the sensitivity of the model to different parameter values. They may also conduct experiments or simulations to test the model's performance with different parameter values.

4. What are some common challenges in parameterization?

Some common challenges in parameterization include the high dimensionality of parameter spaces, nonlinearity of relationships between parameters and model outputs, and the presence of multiple local optima (where different parameter values can produce similar results).

5. How can the parameterization problem be addressed?

The parameterization problem can be addressed through careful consideration of the model's purpose and objectives, as well as thorough testing and validation of different parameter values. Collaboration and communication among scientists with different expertise can also help to address the parameterization problem more effectively.

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