Difference between revisions of "Koding Optimasi"
(Created page with "import numpy as np from scipy.optimize import minimize def calc_drag(x):#drag x1 = x[0] drag = 0.000000001*x1**6-0.0000003*x1**5+0.00003*x1**4-0.0008*x1**3-0.0002*x1...") |
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| Line 5: | Line 5: | ||
def calc_drag(x):#drag | def calc_drag(x):#drag | ||
x1 = x[0] | x1 = x[0] | ||
| − | drag = | + | drag = 21.185*x1**2-125.62*x1 + 143.69 |
return drag | return drag | ||
def calc_lift(x): #lift | def calc_lift(x): #lift | ||
x1 = x[0] | x1 = x[0] | ||
| − | lift = - | + | lift = -3.5089*x1**2 + 34.626*x1 - 43.619 |
return lift | return lift | ||
def objective(x): #sudut yang diminimalkan | def objective(x): #sudut yang diminimalkan | ||
| − | return | + | return calc_drag(x) |
def constraint1(x): #variable SUDUT yang meminimalkan persamaan garis drag | def constraint1(x): #variable SUDUT yang meminimalkan persamaan garis drag | ||
| Line 37: | Line 37: | ||
liftopt = calc_lift(xopt) # lift optimal | liftopt = calc_lift(xopt) # lift optimal | ||
| − | print ('sudut optimal = '+str(xopt[0])) | + | print ('sudut optimal = '+str(-xopt[0])) |
| − | print ('total force optimal = '+str( | + | print ('total force optimal = '+str(forceopt)) |
| − | print ('drag force optimal = '+str(dragopt)) | + | print ('drag force optimal = '+str(-dragopt)) |
print ('lift force optimal = '+str(liftopt)) | print ('lift force optimal = '+str(liftopt)) | ||
Latest revision as of 13:26, 4 December 2019
import numpy as np from scipy.optimize import minimize
def calc_drag(x):#drag
x1 = x[0] drag = 21.185*x1**2-125.62*x1 + 143.69 return drag
def calc_lift(x): #lift
x1 = x[0] lift = -3.5089*x1**2 + 34.626*x1 - 43.619 return lift
def objective(x): #sudut yang diminimalkan
return calc_drag(x)
def constraint1(x): #variable SUDUT yang meminimalkan persamaan garis drag
return 90 - calc_drag(x)
def constraint2(x): #variable SUDUT yang meminimalkan persamaan garis lift
return 90 - calc_lift(x)
con1=({'type':'ineq','fun':constraint1}) con2=({'type':'ineq','fun':constraint2}) cons = (con1,con2)
x1_guess = 50
x0 = np.array([x1_guess])
sol = minimize(objective,x0, method='SLSQP',constraints=cons, options={'disp':True})
xopt = sol.x forceopt = -sol.fun
dragopt = calc_drag(xopt) # drag optimal liftopt = calc_lift(xopt) # lift optimal
print ('sudut optimal = '+str(-xopt[0])) print ('total force optimal = '+str(forceopt)) print ('drag force optimal = '+str(-dragopt)) print ('lift force optimal = '+str(liftopt))
