Statistical Mechanics Solutions: R K Pathria Book

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SUMMARY

This discussion focuses on finding solutions for the end-of-chapter problems in "Statistical Mechanics" by R K Pathria. Users seek resources for specific exercises, particularly problems 1.1 and 2.1. A solution link provided by a user leads to a PDF that addresses the ideal gas case, while another participant offers a detailed approach to solving the problems using Taylor series expansions and Gaussian distributions. The conversation emphasizes the importance of understanding the general case rather than just the ideal scenario.

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  • Understanding of statistical mechanics concepts, particularly microstates and macrostates.
  • Familiarity with Taylor series expansions in mathematical physics.
  • Knowledge of Gaussian distributions and their applications in statistical mechanics.
  • Basic proficiency in reading and interpreting physics problem statements and solutions.
NEXT STEPS
  • Research "Taylor series in statistical mechanics" for deeper mathematical insights.
  • Study "Gaussian distributions in statistical mechanics" to understand their significance.
  • Explore online resources or forums for "Statistical Mechanics R K Pathria solutions" for additional problem-solving strategies.
  • Review "Microstates and macrostates in statistical mechanics" to solidify foundational concepts.
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Students and educators in physics, particularly those studying statistical mechanics, as well as anyone seeking to enhance their problem-solving skills in this field.

rgshankar76
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I wish to know about web sites or other resourses from where i can get solutions for all the end of the chapter problems for the book on statistical Mechanics by R K Pathria
 
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there is' t...

:(
 
Why not post the problems here? Most statistical mechanics textbook problems are trivial...
 
i 'm coursing statistical mechanics and i need to do all of the excersices of pathria ...

i have do some of then , chapter 1,2 now.. .. but i can't understand the 1.1 and the 2.1

http://img228.imageshack.us/img228/3023/dibujojd7.png



well ...i found some kind of solution but i don't know it's really good ... www.mtholyoke.edu/~mktrias/physics/pathria_1.1.pdf[/URL]


i hope any help ... thanks
 
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In the last line of the PDF file, you got the expression proportional to:

W(x) = [x(E-x)]^M

with M = 3N/2 and x = E1

You should then proceed, not by saying: "this is a binomial distribution etc. etc." but by expanding around the maximum. The maximum is at x = E/2. Let's put x = E/2 + y, and call

W(x) = W(E/2 + y) = P(y)

and expand:

Log[P(y)] = M [Log(E/2 + y) + Log(E/2 - y)] =

M[2 Log(E/2) + Log(1 + 2y/E) + Log(1 - 2y/E)] =

Let's expand in powers of 2y/E


M [2 Log(E/2) - 4 y^2/E^2 + ...]

So, for small y we have:

P(y) = const. Exp(-4 y^2/E^2)

If you are careful and keep the constant terms, the Gaussian should automatically be correctly normalized.
 
ok .. thanks a lot.


and if you know any of the exercise 2.1 let me know...

thans .
 
Hi dukemaster !

I don't write english well, my language is spanish... but that's not important here, the important is the physic. The page that you checked isn't bad, but Margaret Trias solved the problem for the ideal gas case. I think that the book want the solution in the general case. I'll tell you the solution of the fisrt part, you can do the last part knowing the first.

Supose that the microestates number is function of E1 and E2 like: O(E1,E2) , where O is Omega (in spanish). The logarithm Ln(O(E1,E2)) decreases more quickly than the fuction O(E1,E2), then, we'll take an expansion in Taylor series about [E1] (averge value), this is:

Ln(O(E1,E2)) =Ln(O([E1],E2))+(E1-[E1])(d_1)Ln(O([E1],E2))+))+(E1-[E1])^2(d_2)Ln(O([E1],E2))+...

where (d_1) is the first derivate with respect E1 and (d_2) the second derivate with respect the same.

Check that the first term in the expansion is constant, the secod is zero (that's the equilibrium condition) and the third is differente to zero, then:

Ln(O(E1,E2)) =C+(E1-[E1])^2 (d_2)Ln(O([E1],E))

taking the exponetial in both sides:

O(E1,E2)) =Aexp{(E1-[E1])^2 (d_2)Ln(O([E1],E))}

that's is the Gaussian in the parameter E1. If you take the case of ideal classical gas O(E)=cte E^(3N/2), you'll find the solution of b).

Greetings to all!

I hope it will serve my comment.
 
thanks ALBERTO666 !

o como decimos en chile " Muchas Gracias" jejeje .. I'm Chilean and my english is't too good jeje ...

i saw your answer and i repeat ... thanks.

to do this excercises i need to expand my mind .. and thing more than usual..jeje. well ... Bye
 

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