Dawson Community College the Debye Model Python Exercise

I uploaded for you everything you need. Please make sure there is no PLAGIARISM.

Please don’t forget to do MEMO just 3 to 4 paragraphs explaining the coding and the final answer 🙂

Let me know if you have any questions.

The Debye model is often used to predict the heat capacity of a solid material (see:
https://en.wikipedia.org/wiki/Debye model). This is important in many industrial applications when
one wishes to predict how heat will be transferred through a solid material. It is also important in the
semiconductor industry because modern CPUs generate a significant amount of heat that must be
transported away from the CPU to avoid thermal damage. The most difficult step in predicting the heat
capacity is determining the value of g(u), which is defined by the formula:
1/4 x4e*
g(u) = u
DETTE
dx
(ex – 1)2
You have been hired by Intel to generate a plot showing the value of g(u) for values of u between 0.01
and 1.0 (note: Intel was able to determine that g(0) = 0, they’re smart). You should be using Gaussian
quadrature (i.e., scipy.integrate.quadrature) for evaluating the integral. I recommend that you start by
trying to obtain a code that can calculate g(u) for a given value of u (e.g., u = 0.5). Then, you have two
choices: (1) run the code for various values of u and record the resulting values for g in a spreadsheet –
using the spreadsheet to generate a plot of g vs. u, or (2) use numpy.inspace to create a vector of u
values between 0.01 and 1.0 and then create a for loop to iterate through the values in that vector, use
quadrature to determine g(u) for each value of u, store the result, and then plot g vs. U using
matplotlib.
Write a memo to Intel summarizing your approach, resulting figure, and include your code in the
appendix.