EGR 103/Spring 2021/Lab 10

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Assignment

You will be using nonlinear regression as well as function minimization to solve several engineering problems. The main Python methods you will be using are:

Note that generally we will bring in scipy.optimize with 

import scipy.optimize as opt

so the function calls will look like 

opt.curve_fit()
opt.fminbound()
opt.fmin()

In the documentation on Scipy, they bring in the entire optimize package with 

from scipy import optimize

so their function calls look like 

optimize.curve_fit()
optimize.fminbound()
optimize.fmin()

Chapra 15.11

  • See Python:Fitting#Nonlinear_Regression
  • For the initial guesses, make sure you understand the subscripts for the parameters and then figure out how to approximate their values from the information provided in the problem.

Chapra 15.22

Chapra 15.29

  • See Python:Fitting#Nonlinear_Regression
  • Note that the independent variable will be the temperature in K and the dependent value will be the natural log of the pressure. That is also what you will be graphing.

Chapra 7.16

  • Note that you will be using both fmin and fminbound just to compare and contrast open and closed methods. The two values you get should be close and either one should work in Connect.

Chapra 7.25, 7.26, 7.27

  • For the initial guesses, you can use [0, 0]

Chapra 7.31

  • Note that you will be using both fmin and fminbound just to compare and contrast open and closed methods. The two values you get should be close and either one should work in Connect.

Chapra 7.34

  • Note that you will be using both fmin and fminbound just to compare and contrast open and closed methods. The two values you get should be close and either one should work in Connect.

Chapra 7.36

  • Note that you will be using both fmin and fminbound just to compare and contrast open and closed methods. The two values you get should be close and either one should work in Connect.
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