Problem Set #1
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Problem Set #1
Problem 1
Why are biologic tissues viscoelastic? Pick a tissue (e.g., tendon, cartilage, blood vessel, heart wall, skin, muscle) and describe why it is advantageous for it to be viscoelastic.
Problem 2
Using the MATLAB simulation, a) compare the creep and stress relaxation responses of the Maxwell model to the Kelvin model, and b) describe why one model does a better job describing a fluid and one model does a better job describing a solid.
[Notes: (1) To run the simulation you need to download two files (Visco_Sim.m and Visco_Sim.fig). The ‘.fig’ file contains the graphics for the simulation and the ‘.m’ file contains the actual simulation code. (2) To run the simulation, change the directory within MATLAB to the one where both files are saved and type ‘Visco_Sim’. The simulation will NOT work if you simply double-click on the ‘.fig’ file (this will just load the graphics without the simulation code). (3) In order for the plots to be generated, you need to give each of the variables values (by sliding the bars).]
Problem 3
What are the effects on stress relaxation and creep of
changing each of the parameters (k1 , k2 , m) in the Three
Parameter Model? By examining the mechanical analog
for this model (two springs and a damper), describe for
what parameter values the model should behave like the
Maxwell model, the Kelvin model, an elastic solid, or a
classic fluid. Does this hold true in the simulation?
Problem 4
Perform a curve fit in Matlab to the three parameter model using the data in the table on the right. List the curve fit results for k1 , k2 , and m. Plot your results along with the original data. How well does the model fit the data? Would either the Kelvin model or the Maxwell model do just as well? What does that tell us about the behavior of the material; is it solid-like or fluid-like?
You must first define a function for the three parameter model:
function y=fuction_name(parameters, xdata)
m=parameters(1);
k1=parameters(2);
k2=parameters(3);
y=equation;
‘Parameters’ must be a matrix with values of the parameters k1 , k2 , and m. ‘Equation’ must be the equation representing the three parameter model. ‘xdata’ must be your x-axis data, i.e. time.
Save this function as ‘function_name.m’ . You should now be able to call this function from the Matlab command window to evaluate it for any given parameters and xdata. Make sure to change directories in Matlab so that the program can find your new function.
Use the function ‘lsqcurvefit’ to fit the data. This function returns the optimal parameters to fit the equation to the data. Your command may look something like this:
>>fit=lsqcurvefit(‘function_name',[1 1 1],data(:,1), data(:,2))
The inputs for lsqcurvefit are the function (‘function_name’), an initial guess (‘[1 1 1]’), column one of the data (‘data(:,1)’), and column two of the data (‘data(:,2)’). Type ‘help lsqcurvefit’ for more information. Note that the initial guess may have a great influence on your results, as the optimization procedure may get stuck around local minima which are nowhere near the correct results. You can also define upper and lower bounds to help the optimization routine (e.g., you know that the three parameters cannot be negative, so a possible lower bound might be 0).
Tips:
• For this data set, you can assume σ0 = 1.
• To import data from Excel, save your data in Excel in the first two columns of the first Excel sheet. Then try ‘data=xlsread('filename.xls')’ at the command prompt.
• To plot both the data and the fit at the same time, try the following: ‘plot(data(:,1), data(:,2),’o’, data(:,1), function_name(fit,data(:,1)))’ .
2022-09-18