Optimal Function for Computational Mesh: Achieving Desired Shape and Flexibility

In summary, the person is seeking an analytical function for a computational mesh that has a similar shape as a figure provided. The function must satisfy certain conditions and allow for the variation of a parameter. They are open to suggestions other than polynomial functions.
  • #1
Clausius2
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I am seeking a function [tex]r=r(\eta)[/tex] for a computational mesh. It has to have the same shape as the one shown in the figure attached. This figure has been achieved via polinomical approximation, but it doesn't give me many chances to change parameters of contractions. Maybe there is an analytical function which can behave as it is shown in the figure.

It has to check:

[tex] r(\eta=0)=0[/tex]
[tex] r'(\eta=0)\sim 0[/tex]
[tex] r(\eta=\eta_o)=1[/tex]
[tex] r'(\eta=\eta_o)\sim 0[/tex]
[tex] r''(\eta=\eta_o)\sim 0[/tex]
[tex] r(\eta=\eta_{max})=r_{max}[/tex]

Do you know some function (apart of a polinomical one) which behaves as the one shown in the figure?. The data is [tex]r_{max},\eta_{max}[/tex] and I must be able to vary successfully [tex] \eta_o[/tex].

Please Help!
Thanks!
 

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  • #2
Except from the start, where one could use a trig function, it looks as if ##x^{2n+1}## for some ##n## and appropriately shifted would do the job.
 

FAQ: Optimal Function for Computational Mesh: Achieving Desired Shape and Flexibility

1. What is a fitting function?

A fitting function is a mathematical function that is used to model or approximate a set of data points. It is often used in data analysis and curve-fitting to find the best function that describes the relationship between variables in the data.

2. How do you determine the best fitting function?

The best fitting function is determined by analyzing the data and selecting a function that closely matches the trend of the data points. This can be done by using statistical methods such as least squares regression or by visually inspecting the data and selecting a function that fits well.

3. Can a fitting function accurately predict future data points?

No, a fitting function is used to describe and model existing data, but it cannot accurately predict future data points. It is important to note that a fitting function should not be used for extrapolation beyond the range of the original data.

4. Are there different types of fitting functions?

Yes, there are many different types of fitting functions, such as linear, exponential, logarithmic, and polynomial. The type of fitting function used depends on the type of data and the relationship between the variables.

5. How do you know if a fitting function is a good fit for the data?

A fitting function is a good fit for the data if it closely follows the trend of the data points and has a low error or residual value. This can be evaluated by calculating the coefficient of determination (R-squared) or by comparing the actual data points to the predicted values from the fitting function.

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