Non-linear regression of Curie's Law

In summary: Your data suggests that ferrite has a relative permeability of 1+a tanh(b/T), where T is the thermodynamic temperature. A is clearly the maximum relative permeability - 1 - and b is unknown, but I believe you can estimate it using stat-mech theory. First, determine the maximum relative permeability, mu_r, using the equation: mu_r=1+a tanh(b/T). Next, determine the condition under which the maximum relative permeability is achieved, max mu_r - 1. This condition is achieved when the external magnetic field is equal to the ferrite's Curie temperature, Tc. Finally, use stat-mech theory to estimate b
  • #1
jdstokes
523
1
Hi,

I've collected some data on the relative permeability of ferrite at various temperatures, subject to a constant external magnetic field and I'd like to fit a curve to the data.

I believe that stat-mech theory predicts that [itex]\mu_r = 1 + a\tanh(b/T)[/itex] where [itex]T[/itex] is thermodynamic temperature. The constant [itex]a[/itex] is clearly [itex]\max \mu_r -1[/itex], but I can't figure out what condition I should use to estimate [itex]b[/itex]?

Any help would be greatly appreciated.

James
 
Physics news on Phys.org
  • #2
I'm no statician, but I've come across this type of problem before. I'll tell you how I dealt with it and you can decide if its reasonable (which I think it is).

You want to choose b in a way that minimizes the "error". If you have N measurements of mu as [itex]\mu_1, \mu_2, ... \mu_n[/itex]at temperatures [itex]T_1, T_2, ... T_N[/itex] then you can define an error by:
[tex]E=\sum_{i=1}^{N}(\mu_i-\mu(T_i,b))^2[/tex]
Where:
[tex]\mu(T,b)=1 + a\tanh(b/T)[/tex]
You want to find the value of b that minimizes the error so you come out with:
[tex]\frac{\partial E}{\partial b}=0=2\sum_{i=1}^{N}(\mu_i-(1 + a\tanh(b/T_i)))(\frac{a}{T_i}sech^2{\frac{b}{T_i}})[/tex]
Now this equation should not be too difficult to solve numerically as long as the number of measurements is not gigantic. You could use goal seek in excel. One thing to whatch out for, though: hopefully the solution is unique. If it is not you have to find the minimum amoung all the solutions. Excel will not tell you if there are other solutions. Maybe you can show mathematically the solution is unique, I haven't thought too much about that.

Also, be warned: I didn't read about this in a book, I just had a similar problem and this method made sense to me. Use it at your own discretion.
 
Last edited:
  • #3
I think this is a good idea, numerical solutions to such things can be found easily using Mathematica.

Thanks

James
 

1. What is non-linear regression of Curie's Law?

Non-linear regression of Curie's Law is a statistical method used to model the relationship between temperature and magnetization in a material, as described by Curie's Law. This method allows for the determination of the Curie temperature, which is the temperature at which a material loses its magnetic properties.

2. How does non-linear regression of Curie's Law work?

In non-linear regression, the relationship between temperature and magnetization is modeled using a non-linear equation, such as Curie's Law. The method then uses statistical techniques to fit this equation to the data, finding the best fit parameters for the equation and estimating the Curie temperature.

3. What are the assumptions behind non-linear regression of Curie's Law?

The main assumption is that the data follows Curie's Law, meaning that there is a linear relationship between the inverse of temperature and the magnetization. Additionally, the data should be normally distributed, and there should be minimal influence from outliers.

4. What are the benefits of using non-linear regression of Curie's Law?

Non-linear regression of Curie's Law allows for a more accurate determination of the Curie temperature compared to other methods. It also provides information about the strength of the relationship between temperature and magnetization, as well as any potential errors in the data.

5. Can non-linear regression of Curie's Law be applied to all materials?

No, non-linear regression of Curie's Law is typically only applicable to materials that exhibit magnetic properties, such as ferromagnetic, paramagnetic, or antiferromagnetic materials. It may also not be accurate for materials with complex magnetic behavior or at extremely low temperatures.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
4
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
30
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
470
Replies
8
Views
2K
  • Advanced Physics Homework Help
Replies
9
Views
3K
  • Advanced Physics Homework Help
Replies
3
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
4
Views
1K
  • Engineering and Comp Sci Homework Help
Replies
10
Views
8K
  • General Math
Replies
5
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
2K
Back
Top