Chapter 15 : Non newtonian phenomena

Example 15.1 - Page No :760

In [2]:
%matplotlib inline

import math 
from numpy import *
from matplotlib.pyplot import *

# Variables
# given
r = array([10, 20, 50, 100, 200, 400, 600, 1000, 2000])
tau = array([2.2, 3.1 ,4.4, 5.8, 7.4, 9.8, 11.1, 13.9, 17.0])

# Calculation and Results
#tau = tau*(10**-4);
plot(r,tau);
plot(r,tau,'ro');
suptitle("asic shear diagram for the fluid")
xlabel("Shear rate, S**-1 ")
ylabel("Shear streets, Nm**-2 ")

# the data falls nearly on a straight line
# from the graph the slope and the intercept are
slope = 0.3841;
intercept = 9.17046;
# from the relation tau = K*(-r)**n;
K = math.exp(intercept);
n = slope
print "K = ",K
print "n = ",n
print " The fluid is pseudo plastic, since the slope is less than 1 "
Populating the interactive namespace from numpy and matplotlib
K =  9609.04383369
n =  0.3841
 The fluid is pseudo plastic, since the slope is less than 1 
WARNING: pylab import has clobbered these variables: ['draw_if_interactive']
`%pylab --no-import-all` prevents importing * from pylab and numpy

Example 15.2 - Page No :774

In [3]:
# Variables
a = array([651, 1361, 2086, 5089, 7575, 11140, 19270, 25030])
tau = array([3.71, 7.49, 11.41, 24.08, -35.21, 46.25, 77.50, 96.68])

# from the graph
betao = -4.3790154;
beta1 = 0.8851;

# Calculations
K = math.exp(betao);
n = beta1;
plot(a,tau);
suptitle("Capillary shear diagram for polyisobutylene L-80 in cyclohexane.")
xlabel("Pseudoshear rate")
ylabel("Wall shear stress ")

# Results
print " The final rheological model_ is  tauw  =  %f*8*Uz,avg/do)**%f"%(K,n);
 The final rheological model_ is  tauw  =  0.012538*8*Uz,avg/do)**0.885100

Example 15.3 - Page No :774

In [9]:
# Variables
# from Example  15.2 
n = 0.8851;
K = 0.01254;
n = n;

# Calculations
K = K/((3*n+1)/(4*n));

# Results
print "n = ",n
print "K  =  %f N/m**2"%(K);
n =  0.8851
K  =  0.012146 N/m**2

Example 15.4 - Page No :775

In [24]:
# Variables
a = array([10, 20, 50, 100, 200, 400, 600, 1000, 2000])
tau = array([2.24, 3.10, 4.35, 5.77, 7.50, 9.13, 11.0, 13.52, 16.40])
tau = tau*10**-4;
betao = 8.96694;
beta1 = 0.48452520;
beta2 = 0.010923041;

# Calculations
# such a plot suggests a second order polynomila  of the type y = betao+beta1*x+beta2*x**2;
# where y = ln(tauw) and x = ln(8*Uz,avg/do) = ln(a);
# from the graph
n = beta1+2.*beta2*a;
phiw = ((3.*n+1.)/(4.*n))*(a);
mu = tau/phiw;

# Results

print " 8*Uz,avg/do     n         ((3*n+1)/4*n)    phiw            mu"
for i in range(9):
    print " %6.0f       %8.4f      %8.4f      %8.4f       %6.6f"%(a[i],n[i],3*n[i]+1/4*n[i],phiw[i],mu[i])


# Answer in book is wrong. Please calculate manually.
 8*Uz,avg/do     n         ((3*n+1)/4*n)    phiw            mu
     10         0.7030        2.1090       11.0563       0.000020
     20         0.9214        2.7643       20.4262       0.000015
     50         1.5768        4.7305       45.4273       0.000010
    100         2.6691        8.0074       84.3663       0.000007
    200         4.8537       14.5612      160.3013       0.000005
    400         9.2230       27.6689      310.8425       0.000003
    600        13.5922       40.7765      461.0358       0.000002
   1000        22.3306       66.9918      761.1954       0.000002
   2000        44.1767      132.5301      1511.3182       0.000001