from __future__ import division
#Variable declaration:
fm = 30/100 #Mole fraction of methane
fe = 50/100 #Mole fraction of ethane
fp = 20/100 #Mole fraction of pentane
LFLm = 0.046 #Lower flammability limit for methane
LFLe = 0.035 #Lower flammability limit for ethane
LFLp = 0.014 #Lower flammability limit for propane
UFLm = 0.142 #Upper flammability limit for methane
UFLe = 0.151 #Upper flammability limit for ethane
UFLp = 0.078 #Upper flammability limit for propane
#Calculation:
LFLmix = 1/((fm/LFLm)+(fe/LFLe)+(fp/LFLp)) #Lower flammability limit of gas mixture
UFLmix = 1/((fm/UFLm)+(fe/UFLe)+(fp/UFLp)) #Upper flammability limit of gas mixture
#Result:
print "The upper flammability limit (UFL) of the gas mixture is :",round(UFLmix*100,2)," % ."
print "The lower flammability limit (LFL) of the gas mixture is :",round(LFLmix*100,2)," % ."
print "There is a printing mistake in book."
from __future__ import division
from sympy import nsimplify as changeToFraction
#Variable declaration:
P_A = 10/100 #Probability that the first tube is defective if the first is replaced
P_B = 10/100 #Probability that the second tube is defective if the first is replaced
#Calculation:
P_AB = P_A*P_B #Probability that the two tubes are defective if the first is replaced
P_B_A = 9/99 #Probability that the second tube is defective if the first tube is not replaced
Pd_AB = P_A*P_B_A #Probability that both tubes are defective if the first tube is not replaced
#Result:
print "The probability that both tubes are defective if :"
print "(a) the first is replaced before the second is drawn is :",changeToFraction(P_AB)," ."
print "(b) the first is not replaced before the second is drawn is :",changeToFraction(Pd_AB)," ."
from sympy import symbols, integrate, exp
#Variable declaration:
X = symbols('X') #Range of X
Px = 1.7*(exp(-1.7*X)) #Probability distribution function
#Calculation:
P = integrate(Px, (X,2,6)) #Probability that X will have a value between 2 and 6
#Result:
print "The probability that X will have a value between 2 and 6 is :",round(P,4)," ."
from __future__ import division
from math import factorial
#Variable Declaration:
n = 20 #Total number of components
p = 0.1 #Probability of success
#Calculations:
def binomial(n,p,x):
P=0
for x in range(0,x,1):
P = P + p**x*(1-p)**(n-x)*factorial(n)/(factorial(x)*factorial(n-x))
return P
#Results:
print "Probability that the sprinkler system fails :",round((1-binomial(n,p,4))*100,2),"%"
from sympy import symbols, integrate, exp
#Variable declaration:
a = 1.3*10**-3 #Constant a
B = 0.77 #Constant B
t = symbols('t') #Time (h)
Ft = a*B*t**(B-1)*(exp(-a*t**B)) #Pdf for heat exchanger tube
Pt = integrate(Ft, (t,0,1000)) #Probability that a heat exchanger will fail within 100 hours
#Result:
print "The probability that a tube in a heat exchanger will fail in 1000 hours is :",round(Pt,2)," ."
from __future__ import division
from scipy.stats import norm as f
#Variable declaration:
m = 0.4008 #Mean(inch)
s = 0.0004 #Standard Deviation(inch)
UL = 0.4000+0.001 #Upper Limit
LL = 0.4000-0.001 #Upper Limit
#Calculation:
Ps = f.cdf(UL,m,s)-f.cdf(LL,m,s)#Probability of meeting specs
Pd = 1-Ps #Probability of defect
#Results:
print 'Probability of meeting specifications:',round(Ps*100,2),'%'
print 'Probability of Defect:',round(Pd*100,2),'%'
from __future__ import division
from numpy import array,minimum
from math import sqrt
#variable Declaration:
mTa = array([100]*10) #Mean weeks for thermometer failure(A)
mTb = array([90]*10) #Mean weeks for thermometer failure(B)
mTc = array([80]*10) #Mean weeks for thermometer failure(C)
sTa = 30 #Standard deviation (weeks) for thermometer failure(A)
sTb = 20 #Standard deviation (weeks) for thermometer failure(B)
sTc = 10 #Standard deviation (weeks) for thermometer failure(C)
Ra = array([0.52,0.80,0.45,0.68,0.59,0.01,0.50,0.29,0.34,0.46]) #Random No corrosponding to A
Rb = array([0.77,0.54,0.96,0.02,0.73,0.67,0.31,0.34,0.00,0.48]) #Random No corrosponding to B
Rc = array([0.14,0.39,0.06,0.86,0.87,0.90,0.28,0.51,0.56,0.82]) #Random No corrosponding to B
Za = array([0.05,0.84,-0.13,0.47,0.23,-2.33,0.00,-0.55,-0.41,-0.10]) #Normal variable corrosponding to random No for A
Zb = array([0.74,0.10,1.75,-2.05,0.61,0.44,-0.50,-0.41,-3.90,-0.05]) #Normal variable corrosponding to random No for B
Zc = array([-1.08,-0.28,-1.56,1.08,1.13,1.28,-0.58,0.03,0.15,0.92]) #Normal variable corrosponding to random No for C
#Calculations:
Ta = mTa+sTa*Za
Tb = mTb+sTb*Zb
Tc = mTc+sTc*Zc
Ts = minimum(Ta,Tb)
Ts = minimum(Ts,Tc)
m = array([sum(Ts)/len(Ts)]*10)
s = sqrt(sum((Ts-m)**2)/(len(Ts)-1))
#Results:
print 'Standard deviation :',round(s,1)," Weeks"
from __future__ import division
#Variable declaration:
t = 273 #Standard temperature (K)
v = 0.0224 #Volume of air occupied by 1 gmol of ideal gas (m^3)
V = 1100 #Volume of heat exchanger (m^3)
T = 22+273 #Temperature of heat exchanger (K)
x1 = 0.75 #gmols of hydrocarbon leaking from the exchanger (gmol)
#Calculation:
n = V*(1/v)*(t/T) #Total number of gmols of air in the room (gmol)
xHC = (x1/(n+x1))*10**6 #The mole fraction of hydrocarbon in the room (ppm)
#Result:
print "1. The mole fraction of hydrocarbon in the room is :",round(xHC*1000,-1)," ppb ."