def run_test3():
xmin, xmax = -5.0, 5.0
ymin, ymax = -5.0, 5.0
func = stybtang
xDOE = mt.read_tabulated_data_without_header('LHS10.txt')
xDOE = np.vstack([xDOE, [-5,-5]])
yDOE = np.array([func(xi) for xi in xDOE])
X1 = np.arange(xmin,xmax,0.25)
X2 = np.arange(ymin,ymax,0.25)
X1, X2 = np.meshgrid(X1,X2)
X = np.vstack([X1.flatten(),X2.flatten()])
X = np.transpose(X)
Y = np.array([t.jackknifing(xDOE,yDOE,xi) for xi in X])
Y = np.reshape(Y,X1.shape)
Y2 = np.array([t.jackknifing(xDOE,yDOE,xi) for xi in xDOE])
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111, projection='3d')
ax1.hold(True)
ax1.plot_surface(X1,X2,Y,cmap=cm.jet,rstride=1, cstride=1)
ax1.plot(xDOE[:,0],xDOE[:,1],Y2,'ro')
plt.show()
def __init__(self, path):
#self.data = mt.read_tabulated_data_without_header('ClarkY-C81-lowfiCFD.txt',1)
self.data = mt.read_tabulated_data_without_header(path,1)
self.xl = np.array([0.2, -20.0]) #Mach, alpha
self.xu = np.array([0.8, 20.0])
self.norm = mt.Normalization(self.xl, self.xu,-1.0,1.0)
#self.norm = mt.Normalization([-10,-10],[10,10])
x = self.norm.normalize(self.data[:,:2])
#x = (self.data[:,:2] + np.array([-0.2, 20.0])) / np.array([0.3, 20.0]) - np.array([1.0, 1.0])
self.modelCL = mt.RbfMod(x, self.data[:,2],f='thin_plate')
self.modelCD = mt.RbfMod(x, self.data[:,3],f='thin_plate')
self.modelCM = mt.RbfMod(x, self.data[:,4],f='thin_plate')
self.x = x
def run_test2():
xmin, xmax = -2.0, 2.0
ymin, ymax = -2.0, 2.0
func = mt.NormalizedFunction(tf.ackley,[xmin,ymin],[xmax,ymax])
npop = 3
spread = 0.5
ndim = 2
itrMax = 10
errFilterRatio = 0.0
# --- func evaluation ---
xLHS = mt.read_tabulated_data_without_header('LHS20.txt')
xFFD = np.array([[-5.0,-5],[5,5],[-5,5],[5,-5]])
xDOE = np.vstack([xFFD,xLHS])
yDOE = np.array([func(xi) for xi in xDOE])
nskip = len(xFFD)
err, SE, MSE = t.cross_validation(xDOE,yDOE,nskip)
for i in range(itrMax):
print len(xDOE), MSE, np.max(SE)
model = mt.RbfMod(xDOE,SE)
fig = plt.figure(1)
ax1 = fig.add_subplot(111,projection='3d')
ax1.hold(True)
ax1.set_xlabel('X1')
ax1.set_ylabel('X2')
x,y,z = t.get_points_for_2d_plot(model,xmin,xmax,ymin,ymax)
ax1.plot_surface(x,y,z,cmap=cm.jet,rstride=1, cstride=1)
ax1.plot(xDOE[:,0],xDOE[:,1],SE,'ro')
fig2 = plt.figure(2)
ax2 = fig2.add_subplot(111,projection='3d')
ax2.hold(True)
model2 = mt.RbfMod(xDOE,yDOE)
x1,y1,z1 = t.get_points_for_2d_plot(func,xmin,xmax,ymin,ymax)
x2,y2,z2 = t.get_points_for_2d_plot(model2,xmin,xmax,ymin,ymax)
ax2.plot_wireframe(x1,y1,z1,color='b')
ax2.plot_wireframe(x2,y2,z2,color='r')
ax2.plot(xDOE[:,0],xDOE[:,1],yDOE,'go')
plt.show()
xDOEadd = get_new_samples(xDOE, SE, npop,spread)
xDOE = np.vstack([xDOE, xDOEadd])
yDOE = np.array([stybtang(xi) for xi in xDOE])
err, SE, MSE = t.cross_validation(xDOE,yDOE,nskip)
def run_test1():
# styblinski-tang function
X1 = np.arange(-5,5,0.25)
X2 = np.arange(-5,5,0.25)
X1, X2 = np.meshgrid(X1,X2)
Y = stybtang([X1,X2])
# DOE points
xLHS = mt.read_tabulated_data_without_header('LHS10.txt')
xFFD = np.array([[-5.0,-5],[5,5],[-5,5],[5,-5]])
xDOE = np.vstack([xFFD,xLHS])
yDOE = np.array([stybtang(xi) for xi in xDOE])
rbf = mt.RbfMod(xDOE,yDOE)
X = np.vstack([X1.flatten(),X2.flatten()])
yRbf = rbf(X)
Y2 = np.reshape(yRbf,X1.shape)
err = t.cross_validation(xDOE,yDOE,4)
err = err*err
err[0:4] = np.zeros(4)
rbfErr = mt.RbfMod(xDOE,err)
errRbf = rbfErr(X)
# plot area
fig = plt.figure(1)
ax = fig.add_subplot(111, projection='3d')
ax.hold = True
ax.plot_wireframe(X1,X2,Y)
ax.plot_wireframe(X1,X2,Y2,color='r')
fig2 = plt.figure(2)
ax2 = fig2.add_subplot(111)
ax2.plot(xDOE[:,0],xDOE[:,1],'ro')
fig3 = plt.figure(3)
ax3 = fig3.add_subplot(111, projection='3d')
ax3.plot_surface(X1,X2,np.reshape(errRbf,X1.shape),cmap=cm.jet,rstride=1, cstride=1)
#ax3.plot_wireframe(X1,X2,(Y-Y2)**2)
ax3.plot(xDOE[:,0],xDOE[:,1],err,'ro')
plt.show()
def get_DOE_data():
xDOE = mt.read_tabulated_data_without_header('LHS20.txt')
xDOE += 5.0
xDOE *= 0.1
return xDOE
def results():
path1 = 'ClarkY-C81.txt'
path2 = 'ClarkY-ad.txt'
path3 = 'aDOE_results.txt'
n = 50
data1 = mt.read_tabulated_data_without_header(path1,1)
data2 = mt.read_tabulated_data_without_header(path2)
data3 = mt.read_tabulated_data_without_header(path3)
xl = np.array([0.2, -20.0]) #Mach, alpha
xu = np.array([0.8, 20.0])
norm = mt.Normalization(xl, xu,-1.0,1.0)
x1 = norm.normalize(data1[:,:2])
modelCL1 = mt.RbfMod(x1, data1[:,2],f='cubic')
modelCD1 = mt.RbfMod(x1, data1[:,3],f='cubic')
modelCM1 = mt.RbfMod(x1, data1[:,4],f='cubic')
x2 = norm.normalize(data2[:,:2])
modelCL2 = mt.RbfMod(x2, data2[:,2],f='cubic')
modelCD2 = mt.RbfMod(x2, data2[:,3],f='cubic')
modelCM2 = mt.RbfMod(x2, data2[:,4],f='cubic')
X1, X2 = t.get_2d_grid(n, [-1.0, -1.0], [1.0, 1.0])
X1r,X2r = t.get_2d_grid(n, xl, xu)
X = t.get_ffd_2d(n, [-1.0, -1.0], [1.0, 1.0])
Ym11d = np.array([modelCL1(xi) for xi in X])
Ym11 = np.reshape(Ym11d,X1.shape)
Ym21d = np.array([modelCD1(xi) for xi in X])
Ym21 = np.reshape(Ym21d,X1.shape)
Ym31d = np.array([modelCM1(xi) for xi in X])
Ym31 = np.reshape(Ym31d,X1.shape)
Ym12d = np.array([modelCL2(xi) for xi in X])
Ym12 = np.reshape(Ym12d,X1.shape)
Ym22d = np.array([modelCD2(xi) for xi in X])
Ym22 = np.reshape(Ym22d,X1.shape)
Ym32d = np.array([modelCM2(xi) for xi in X])
Ym32 = np.reshape(Ym32d,X1.shape)
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111, projection='3d')
ax1.hold(True)
#ax1.plot_wireframe(X1,X2,Ym11,color='b')
ax1.plot_surface(X1r,X2r,Ym12,cmap=cm.jet, rstride=1, cstride=1)
ax1.plot(data2[:,0],data2[:,1],data2[:,2],'rs')
ax1.set_xlabel('Mach')
ax1.set_ylabel('Angle of attack, deg')
ax1.set_zlabel('Lift coefficient')
#ax1.legend(['Sample points','Exact function','Approximation'])
fig2 = plt.figure(2)
ax2 = fig2.add_subplot(111, projection='3d')
ax2.hold(True)
#ax2.plot_wireframe(X1,X2,Ym21,color='b')
ax2.plot_surface(X1r,X2r,Ym22,cmap=cm.jet, rstride=1, cstride=1)
ax2.plot(data2[:,0],data2[:,1],data2[:,3],'rs')
ax2.set_xlabel('Mach')
ax2.set_ylabel('Angle of attack, deg')
ax2.set_zlabel('Drag coefficient')
fig3 = plt.figure(3)
ax3 = fig3.add_subplot(111, projection='3d')
ax3.hold(True)
#ax3.plot_wireframe(X1,X2,Ym31,color='b')
ax3.plot_surface(X1r,X2r,Ym32,cmap=cm.jet, rstride=1, cstride=1)
ax3.plot(data2[:,0],data2[:,1],data2[:,4],'rs')
ax3.set_xlabel('Mach')
ax3.set_ylabel('Angle of attack, deg')
ax3.set_zlabel('Moment coefficient')
fig4 = plt.figure(4)
ax4 = fig4.add_subplot(111)
ax4.plot(data2[:,0],data2[:,1],'ro')
ax4.set_xlabel('Mach')
ax4.set_ylabel('Angle of attack, deg')
plt.show()
def get_DOE_data(path='LHS10.txt'):
xDOE = mt.read_tabulated_data_without_header(path)
xDOE += 5.0
xDOE *= 0.1
return xDOE
def run_test1():
fhigh = tf.ForresterND(0.40, 0.9, 2.0)
flow = tf.ForresterND(0.45, 1.0, 0.0)
tolLow = 0.1
tolHigh = 0.1
minDistance = 0.05
errHist = list()
xDOE = mt.read_tabulated_data_without_header('LHS30.txt')
xDOE += 5.0
xDOE *= 0.1
yl = np.array([flow(xi) for xi in xDOE])
xl = np.array([0.0, 0.0])
xu = np.array([1.0, 1.0])
bnds = np.transpose(np.vstack([xl,xu]))
X1 = np.linspace(xl[0],xu[0],50)
X2 = np.linspace(xl[1],xu[1],50)
X1, X2 = np.meshgrid(X1,X2)
X = np.vstack([X1.flatten(),X2.flatten()])
X = np.transpose(X)
X1v = np.linspace(xl[0],xu[0],3)
X2v = np.linspace(xl[1],xu[1],3)
X1v, X2v = np.meshgrid(X1v,X2v)
Xv = np.vstack([X1v.flatten(),X2v.flatten()])
Xv = np.transpose(Xv)
err = tolLow+1
while err>tolLow:
jn = np.array([t.jackknifing(xi, xDOE,yl) for xi in Xv])
jnsortedIdx = np.argsort(jn)
jnsortedIdx = np.flipud(jnsortedIdx)
match = True
idx = 0
while match:
x0 = Xv[jnsortedIdx[0]]
#cnstr = ({'type':'ineq','fun':constraint,'args':(X,)})
rslt = minimize(objective,x0,method='SLSQP',bounds=bnds,args=(xDOE,yl,))
# constraints=cnstr)
xnew = rslt.x
err = -rslt.fun
dist = np.array([np.linalg.norm(xi-xnew) for xi in xDOE])
dist = np.min(dist)
if dist <= minDistance:
match = True
idx += 1
else:
match = False
print err, xnew, x0, idx
errHist.append(err)
xDOE = np.vstack([xDOE,xnew])
yl = np.array([flow(xi) for xi in xDOE])
print len(xDOE)
print get_avg_jackknife(xDOE,yl)
Yl = np.array([flow(xi) for xi in X])
model = mt.RbfMod(xDOE, yl)
Ym = np.array([model(xi) for xi in X])
Yl = np.reshape(Yl,X1.shape)
Ym = np.reshape(Ym,X1.shape)
# fig1 = plt.figure(1)
# ax1 = fig1.add_subplot(111, projection='3d')
# ax1.hold(True)
# ax1.plot_wireframe(X1,X2,Yl,color='r')
# ax1.plot_wireframe(X1,X2,Ym,color='b')
# ax1.plot(xDOE[:,0],xDOE[:,1],yl,'ro')
# ax1.set_xlabel('X1')
#
# fig2 = plt.figure(2)
# ax2 = fig2.add_subplot(111)
# ax2.plot(range(len(errHist)),errHist,'rs-')
# ax2.set_yscale('log')
# plt.show()
#
# variable fidelity model construction
flowApprox = mt.RbfMod(xDOE,yl)
xDOEh = mt.read_tabulated_data_without_header('LHS20.txt')
xDOEh += 5.0
xDOEh *= 0.1
#xDOEh = np.array([[0.0,0.0], [0, 1], [1,1], [1,0], [0.5,0.5]])
yh = np.array([fhigh(xi) for xi in xDOEh])
yl = np.array([flowApprox(xi) for xi in xDOEh])
gamma = yh - yl
fscaling = HybridScaling(xDOEh,yh,yl,flowApprox,0.0)
# --- vf DOE ---
err = tolHigh+1.0
while err>tolHigh:
jn = np.array([t.jackknifing(xi, xDOEh,gamma) for xi in Xv])
jnsortedIdx = np.argsort(jn)
jnsortedIdx = np.flipud(jnsortedIdx)
match = True
idx = 0
while match:
x0 = Xv[jnsortedIdx[idx]]
rslt = minimize(objective,x0,method='SLSQP',bounds=bnds,args=(xDOEh,gamma))
xnew = rslt.x
err = -rslt.fun
dist = np.array([np.linalg.norm(xi-xnew) for xi in xDOEh])
dist = np.min(dist)
if dist <= minDistance:
match = True
idx += 1
else:
match = False
print err, xnew, x0, idx
errHist.append(err)
xDOEh = np.vstack([xDOEh,xnew])
yl = np.array([flow(xi) for xi in xDOEh])
yh = np.array([fhigh(xi) for xi in xDOEh])
gamma = yh-yl
fscaling = HybridScaling(xDOEh,yh,yl,flowApprox,1.0)
print len(xDOEh)
# --- plot ---
Yl = np.array([fhigh(xi) for xi in X])
Ym = np.array([fscaling(xi) for xi in X])
Yl = np.reshape(Yl,X1.shape)
Ym = np.reshape(Ym,X1.shape)
print get_avg_jackknife(xDOEh,gamma)
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111, projection='3d')
ax1.hold(True)
ax1.plot_wireframe(X1,X2,Yl,color='r')
ax1.plot_wireframe(X1,X2,Ym,color='b')
ax1.plot(xDOEh[:,0],xDOEh[:,1],yh,'ro')
ax1.legend(['high','scaled','samples-high'])
ax1.set_xlim(0,1)
ax1.set_ylim(0,1)
ax1.set_zlim(0,5)
ax1.set_xlabel('X1')
ax1.set_ylabel('X2')
plt.show()
def create_aero_db():
af = MyTools.airfoil.airfoil.Airfoil()
af.read_txt('Clark-Y.txt')
#af.create_naca4()
#
flow = AeroModel('ClarkY-adaptive.txt')
flow.plot()
# fhigh = AeroModel('aero_data_jfoil.txt')
#flow.plot()
#fhigh.plot()
#fhigh = CFDrun(af)
#fhigh.modelCL.rbf.epsilon = 1e-8
#flow.plot()
#fhigh.plot()
# def func(x):
# cl0, cd0, cm0 = flow(x)
# cl1, cd1, cm1 = fhigh(x)
# return cl1-cl0, cd1-cd0, cm1-cm0
func = CFDrun(af)
func.path = 'ClarkY-adaptive.txt'
# func = AeroModel('ClarkY-C81.txt')
# func.plot()
# fhigh = AeroModel('ClarkY-adaptive.txt')
# fhigh.plot()
Mach = np.array([0.2, 0.8])
alpha = np.array([-20.0, 20.0])
DOEpath = 'LHS30.txt'
tol = np.array([0.05, 0.001, 0.01])
runBudget = 100
minDistance = 0.025
kFold = 1
pOut = 5
xDOE0 = t.get_ffd_2d(51, [-1.0, -1], [1.,1.]) # trial initial points
nptsPlot = 50
# --- ---
bnds = np.array([[-0.0001,-0.0001],[1.0001,1.0001]])
_xl = np.array([-1.0, -1.0])
_xu = np.array([1.0, 1.0])
xl = np.array([Mach[0], alpha[0]])
xu = np.array([Mach[1], alpha[1]])
norm = mt.Normalization(xl, xu)
xDOE = mt.read_tabulated_data_without_header(DOEpath)
#xDOE = t.get_ffd_2d(3,[-1.0, -1], [1.,1.])
#get_aero_data(xDOE[10], af, norm)
data = np.zeros([len(xDOE),3])
dataNew = np.zeros(3)
for i,xx in enumerate(xDOE):
data[i,0], data[i,1], data[i,2] = func(xx)
xreal = func.norm.denormalize(xx)
out2 = '%.6f\t%.6f\t%.8f\t%.8f\t%.8f\n'%(xreal[0],xreal[1],data[i,0],data[i,1],data[i,2])
fid2 = open('ClarkY-ad.txt','at')
fid2.write(out2)
fid2.close()
# data = mt.read_tabulated_data_without_header(func.path)[:,2:]
itr = 0
err = tol+1.0
npts = len(xDOE)
while np.all(err>tol) and runBudget>npts:
itr += 1
dataSetIdx = itr%3
ds = data[:,dataSetIdx]
jn0 = np.array([objective(xi, xDOE, ds, None, pOut, kFold) for xi in xDOE0])
xsort = xDOE0[np.argsort(jn0)]
#xopt = np.zeros([nTrial,xDOE.shape[1]])
pointFound = False
i = 0
while not pointFound:
x0 = xsort[i]
i += 0
rslt = minimize(objective,x0,method='SLSQP',bounds=bnds,
args=(xDOE,ds,None,pOut, kFold,minDistance,))
xopt = t.move_pt_to_bounds(rslt.x,_xl,_xu) #SLSQP bugfix
pointFound = check_point(xopt, xDOE, minDistance)
xDOE = mt.remove_duplicates(np.vstack([xopt,xDOE]))
npts = len(xDOE)
dataNew[0], dataNew[1], dataNew[2] = func(xopt)
data = np.vstack([dataNew, data])
err1 = t.RRMSE(xDOE,data[:,0])
err2 = t.RRMSE(xDOE,data[:,1])
err3 = t.RRMSE(xDOE,data[:,2])
# err1 = np.average(np.array([get_jn(xi, xDOE, data[:,0],None, pOut, kFold) for xi in xDOE]))
# err2 = np.average(np.array([get_jn(xi, xDOE, data[:,1],None, pOut, kFold) for xi in xDOE]))
# err3 = np.average(np.array([get_jn(xi, xDOE, data[:,2],None, pOut, kFold) for xi in xDOE]))
err = np.array([err1,err2,err3])
out = '%d\t%d\t%.4e\t%.4e\t%.4e\n'%(itr, len(xDOE), err[0], err[1], err[2])
xreal = func.norm.denormalize(xDOE[0])
out2 = '%.6f\t%.6f\t%.8f\t%.8f\t%.8f\n'%(xreal[0],xreal[1],dataNew[0],dataNew[1],dataNew[2])
print out
fid1 = open('aDOE_results.txt','at')
fid1.write(out)
fid1.close()
fid2 = open('ClarkY-ad.txt','at')
fid2.write(out2)
fid2.close()
#--- plot area
iz = 0
X1, X2 = t.get_2d_grid(nptsPlot, [-1.0, -1.0], [1.0, 1.0])
X = t.get_ffd_2d(nptsPlot, [-1.0, -1.0], [1.0, 1.0])
X1r, X2r = t.get_2d_grid(nptsPlot, xl, xu)
#Yl = np.array([func(xi)[iz] for xi in X])
#Yl = np.reshape(Yl,X1.shape)
xDOEreal = np.zeros(xDOE.shape)
for i,xd in enumerate(xDOE):
xDOEreal[i] = norm.denormalize(xd)
Yobj = np.array([objective(xi, xDOE, data[:,iz],None,1, kFold) for xi in X])
Yobj = np.reshape(Yobj,X1.shape)
yobj = np.array([objective(xi, xDOE, data[:,iz],None,1, kFold) for xi in xDOE])
model = mt.RbfMod(xDOE, data[:,iz])
Ym1 = np.array([model(xi) for xi in X])
Ym1 = np.reshape(Ym1,X1.shape)
fig1 = plt.figure(1)
ax1 = fig1.add_subplot(111, projection='3d')
ax1.hold(True)
#.plot_wireframe(X1r,X2r,Yl,color='r')
#ax1.plot_wireframe(X1,X2,Ym0,color='b')
ax1.plot_wireframe(X1r,X2r,Ym1,color='b')
ax1.plot(xDOEreal[:,0],xDOEreal[:,1],data[:,iz],'ro')
#ax1.plot(xDOEinit[:,0],xDOEinit[:,1],ylinit,'bs')
ax1.set_xlabel('X1')
ax1.set_ylabel('X2')
ax1.set_zlim([-1,1])
fig2 = plt.figure(2)
ax2 = fig2.add_subplot(111, projection='3d')
ax2.hold(True)
ax2.plot_wireframe(X1,X2,Yobj,color='r')
ax2.plot(xDOE[:,0],xDOE[:,1],yobj,'ro')
ax2.set_xlabel('X1')
#lvls = np.linspace(-30,20,20)
fig3 = plt.figure(3)
ax3 = fig3.add_subplot(111)
ax3.hold(True)
ax3.plot(xDOE[:,0],xDOE[:,1],'ro')
#ax3.plot(xDOEinit[:,0],xDOEinit[:,1],'bs')
#CS = ax3.contourf(X1,X2, Yl, levels=lvls)
#plt.colorbar(CS, ticks=lvls, format='%.4f')
ax3.set_xlabel('X1')
ax3.set_ylabel('X2')
fig4 = plt.figure(4)
ax4 = fig4.add_subplot(111)
ax4.hold(True)
ax4.plot(xDOE[:,0],xDOE[:,1],'ro')
#ax4.plot(xDOEinit[:,0],xDOEinit[:,1],'bs')
#CS1 = ax4.contourf(X1,X2, Ym1, levels=lvls)
#plt.colorbar(CS1, ticks=lvls, format='%.4f')
ax4.set_xlabel('X1')
ax4.set_ylabel('X2')
#ax3.clabel(CS, inline=1, fontsize=10)
plt.show()
def get_DOE_data():
xDOE = mt.read_tabulated_data_without_header('LHS20.txt')
xDOE += 5.0
xDOE *= 0.1
xDOE = np.vstack([xDOE,[1,1]])
return xDOE
def main():
Mach = 0.85
alpha = 0.0
variance = 0.025
MachLow = Mach*(1.0-variance)
MachUp = Mach*(1.0+variance)
alphaLow = alpha-0.25
alphaUp = alpha+0.25
inFileDOE = 'LHS9_100.txt'
doe = mt.read_tabulated_data_without_header(inFileDOE)
lb = np.array([MachLow, alphaLow, 0.04343078, 0.0900427, 0.0570076, 0.08289899, -0.06762751, -0.13041535, -0.02843321])
ub = np.array([MachUp, alphaUp, 0.08343078, 0.1900427, 0.1570076, 0.18289899, 0.03237249, -0.03041535, 0.07156679])
zTE = 0.005
alt = 10000
yplus = 1.0
gridScaleL = 1.0
gridScaleH = 4.0
outputFile = inFileDOE[:-4] + '_CFD.txt'
norm = mt.Normalization(lb,ub)
xDOE = norm.denormalize(doe)
case = 0
fid = open(outputFile, 'wt')
fid.write('Mach\talpha\t')
for i in range(len(lb)-2):
fid.write('A%d\t'%(i+1))
#fid.write('clHigh\tcdHigh\tcmHigh\tclLow\tcdLow\tcmLow\tLDhigh\tLDlow\tthickness\n')
fid.write('cl\tcd\tcm\ttc\n')
fid.close()
for i, xx in enumerate(xDOE):
M = xx[0]
a = xx[1]
fc = cfd.flight_conditions.FlightConditions(M, alt)
af = MyTools.airfoil.airfoil.cst_x(xx[2:], 101)
af.set_trailing_edge(zTE)
tc = af.thickness
try:
# case += 1
# clH,cdH,cmH, gs = cfd.run_analysis(af, fc, a, yplus, gridScaleH, case)
case += 1
clL,cdL,cmL, gs = cfd.run_analysis(af, fc, a, yplus, gridScaleL, case)
fid = open(outputFile, 'at')
fid.write('%.6f\t%.6f\t'%(M,a))
for _x in xx[2:]:
fid.write('%.8f\t'%_x)
#fid.write('%.8f\t%.8f\t%.8f\t%.8f\t%.8f\t%.8f\t%.8f\t%.8f\t%.6f\n'%(clH,cdH,cmH,clL,cdL,cmL,clH/cdH,clL/cdL,tc))
fid.write('%.8f\t%.8f\t%.8f\t%.8f\n'%(clL,cdL,cmL,tc))
fid.close()
except IOError:
case += 1
fid = open(outputFile, 'at')
fid.write('%.6f\t%.6f\t'%(M,a))
for _x in xx[2:]:
fid.write('%.8f\t'%_x)
fid.write('calculation failed\n')
fid.close()
