if optimize == True:
# optimization setup
optProb = Optimization('VAWT_Power', obj_func)
optProb.addObj('obj')
n = np.size(x0)
optProb.addVarGroup('xvars', n, 'c', lower=xlow, upper=xupp, value=x0)
optProb.addVarGroup('yvars', n, 'c', lower=ylow, upper=yupp, value=y0)
num_cons_sep = (n - 1) * n / 2
optProb.addConGroup('sep', num_cons_sep, lower=0, upper=None)
num_cons_obs = nobs * nwind
optProb.addConGroup('SPL', num_cons_obs, lower=0, upper=SPLlim / 10.)
opt = SNOPT()
opt.setOption('Scale option', 0)
if rotdir_spec == 'cn':
opt.setOption(
'Print file',
basepath + path.sep + 'optimization_results/SNOPT_print_SPL' +
str(SPLlim) + '_turb' + str(n) + '_counterrot.out')
opt.setOption(
'Summary file', basepath + path.sep +
'optimization_results/SNOPT_summary_SPL' + str(SPLlim) +
'_turb' + str(n) + '_counterrot.out')
elif rotdir_spec == 'co':
opt.setOption(
'Print file',
basepath + path.sep + 'optimization_results/SNOPT_print_SPL' +
str(SPLlim) + '_turb' + str(n) + '_corot.out')
opt.setOption(
def fit(s,t,length,plot,comp,read_data,opt_print):
global posdn
global poslt
global velod
t2 = t+'.0'
wfit = s+'_'+t2
wfit2 = s+'_'+t2
wfit3 = s+'_'+t2
wfit4 = s+'_'+t2
wfit5 = s+'_'+t2
wfit6 = s+'_'+t2
length2 = length
length3 = length
length4 = length
length5 = length
length6 = length
wind = 15.
wind2 = 14.
wind3 = 12.
wind4 = 16.
rad = 3.
dia = rad*2.
tsr = float(wfit[3]+'.'+wfit[4]+wfit[5])
rot = tsr*wind/rad
rot2 = tsr*wind2/rad
rot3 = tsr*wind3/rad
rot4 = tsr*wind4/rad
rot5 = 17.
rot6 = 18.
wind5 = rot5*rad/tsr
wind6 = rot6*rad/tsr
if comp == 'mac':
# fdata = '/Users/ning1/Documents/Flow Lab/STAR-CCM+/NACA0021/MoveForward/test.csv'
fdata = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/Velocity Sections/'+wfit+'.csv'
fdata2 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'fsl':
fdata = '/fslhome/ebtingey/compute/moveForward/Velocity/'+wfit+'.csv'
fdata2 = '/fslhome/ebtingey/compute/moveForward/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/fslhome/ebtingey/compute/moveForward/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/fslhome/ebtingey/compute/moveForward/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/fslhome/ebtingey/compute/moveForward/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/fslhome/ebtingey/compute/moveForward/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'win':
fdata = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//Velocity Sections//'+wfit+'.csv'
fdata2 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel14//Velocity//'+wfit2+'.csv'
fdata3 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel12//Velocity//'+wfit3+'.csv'
fdata4 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel16//Velocity//'+wfit4+'.csv'
fdata5 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot17//Velocity//'+wfit5+'.csv'
fdata6 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot18//Velocity//'+wfit6+'.csv'
if read_data ==1:
posdn,poslt,velod = starccm_read(np.array([fdata]),dia,np.array([wind]),length,opt_print)
if read_data ==2:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),length,opt_print)
if read_data ==3:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),length,opt_print)
if read_data ==4:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),length,opt_print)
if read_data ==5:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),length,opt_print)
if read_data ==6:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),length,opt_print)
if plot == True:
if read_data ==1:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata]),dia,np.array([wind]),opt_print)
if read_data ==2:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),opt_print)
if read_data ==3:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),opt_print)
if read_data ==4:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),opt_print)
if read_data ==5:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),opt_print)
if read_data ==6:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),opt_print)
start = length/30.
xd = np.linspace(start,length,30)/dia
## Optimization
optProb = Optimization('VAWTWake_Velo', obj_func)
optProb.addObj('obj')
men0 = 0.
sdv10 = 0.5
sdv20 = 0.1
sdv30 = 10.
sdv40 = 0.5
rat0 = 10.
wdt0 = 10.
spr10 = 0.5
spr20 = 0.1
spr30 = 20.
spr40 = 1.
scl10 = 0.5
scl20 = 0.1
scl30 = 40.
# men0 = 0.107980482
# sdv10 = 5.09E-01
# sdv20 = 0.056288195
# sdv30 = 50
# sdv40 = 0.5
# rat0 = 13.19127977
# wdt0 = 14.20436344
# spr10 = 1
# spr20 = 0.010825
# spr30 = 132.4282087
# spr40 = 1
# scl10 = 0.365635251
# scl20 = 0.082475724
# scl30 = 37.61946447
men0 = -0.0138439642406
sdv10 = 0.0
sdv20 = 0.17803796067
sdv30 = 9.69044107271
sdv40 = 0.50982003115
rat0 = 0.0
wdt0 = 10.0
spr10 = 0.998862596849
spr20 = 1.47011550439e-05
spr30 = 22.5386579407
spr40 = 1.0
scl10 = 0.380051328623
scl20 = 0.134712758388
scl30 = 45.7788575653
men0 = -0.0384248691061
sdv10 = 0.0
sdv20 = 0.17803796067
sdv30 = 9.69044107271
sdv40 = 0.81447171018
rat0 = 0.0
wdt0 = 10.0
spr10 = 0.0232236137751
spr20 = 0.0
spr30 = 12.6315271162
spr40 = 9.75322269238
scl10 = 0.357152390403
scl20 = 0.135756021609
scl30 = 33.8432403717
param0 = np.array([men0,sdv10,sdv20,sdv30,sdv40,rat0,wdt0,spr10,spr20,spr30,spr40,scl10,scl20,scl30])
param_l = np.array([None,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.])
param_u = np.array([None,10.,1.,50.,None,None,None,1.,1.,50.,None,1.,1.,None])
# param_l = np.array([None,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.])
# param_u = np.array([None,10.,1.,None,None,None,None,None,None,None,None,1.,1.,None])
# param_l = np.array([None,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.])
# param_u = np.array([None,None,None,None,None,None,None,None,None,None,None,1.,1.,None])
nparam = np.size(param0)
optProb.addVarGroup('param', nparam, 'c', lower=param_l, upper=param_u, value=param0)
opt = SNOPT()
opt.setOption('Scale option',2)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'win':
opt.setOption('Print file','C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//optVel//SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//OptVel//SNOPT_summary'+s+'_'+t+'.out')
res = opt(optProb, sens=None)
if opt_print == True:
print res
pow = res.fStar
paramf = res.xStar['param']
if opt_print == True:
print paramf[0]
print paramf[1]
print paramf[2]
print paramf[3]
print paramf[4]
print paramf[5]
print paramf[6]
print paramf[7]
print paramf[8]
print paramf[9]
print paramf[10]
print paramf[11]
print paramf[12]
print paramf[13]
men = paramf[0]
sdv1 = paramf[1]
sdv2 = paramf[2]
sdv3 = paramf[3]
sdv4 = paramf[4]
rat = paramf[5]
wdt = paramf[6]
spr1 = paramf[7]
spr2 = paramf[8]
spr3 = paramf[9]
spr4 = paramf[10]
scl1 = paramf[11]
scl2 = paramf[12]
scl3 = paramf[13]
paper = False
if plot == True:
if paper == True:
for i in range(30):
name = str(i+1)
ind = str(i)
plt.figure(1)
ax1 = plt.subplot(5,6,i+1)
color = 'bo'
color2 = 'r-'
fs = 15
lab = 'CFD'
lab2 = 'Trend'
tex = '$x/D$ = '+str("{0:.2f}".format(x[i]/dia))
exec('xfit = np.linspace(min(pos'+name+'/dia)-1.,max(pos'+name+'/dia)+1.,500)')
if i == 5:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color,label=lab)')
men_v,spr_v,scl_v,rat_v,spr_v = paramfit(xd[i],men,spr,scl,rat,spr)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,spr_v),xfit,'r-',linewidth=2,label=lab2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.legend(loc="upper left", bbox_to_anchor=(1,1),fontsize=fs)
else:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
men_v,spr_v,scl_v,rat_v,spr_v = paramfit(xd[i],men,spr,scl,rat,spr)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,spr_v),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.text(0.3,0.8,tex,fontsize=fs)
if i <= 23:
plt.setp(ax1.get_xticklabels(), visible=False)
else:
plt.xlabel('$y/D$',fontsize=fs)
plt.xticks(fontsize=fs)
if i == 0 or i == 6 or i == 12 or i == 18 or i ==24:
plt.ylabel(r'$u/U_\infty$',fontsize=fs)
plt.yticks(fontsize=fs)
else:
plt.setp(ax1.get_yticklabels(), visible=False)
elif paper == False:
for i in range(30):
name = str(i+1)
plt.figure(1)
plt.subplot(5,6,i+1)
color = 'bo'
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
plt.plot(veldist(xd[i],xfit,men,sdv1,sdv2,sdv3,sdv4,rat,wdt,spr1,spr2,spr3,spr4,scl1,scl2,scl3),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
# plt.legend(loc=1)
plt.xlabel('Normalized Velocity')
plt.ylabel('$y/D$')
return men,sdv1,sdv2,sdv3,sdv4,rat,wdt,spr1,spr2,spr3,spr4,scl1,scl2,scl3
funcs,_ = obj_func_grid(input)
AEPstart = funcs['obj']
print AEPstart
nCalls = 0
"""Optimization"""
optProb = Optimization('Wind_Farm_AEP', obj_func_grid)
optProb.addObj('obj')
optProb.addVar('dx', type='c', lower=0., upper=None, value=dx)
optProb.addVar('dy', type='c', lower=0., upper=None, value=dy)
optProb.addVar('offset', type='c', lower=None, upper=None, value=offset)
optProb.addVar('rotate', type='c', lower=None, upper=None, value=rotate)
num_cons_sep = (nTurbs-1)*nTurbs/2
optProb.addConGroup('sep', num_cons_sep, lower=0., upper=None)
optProb.addConGroup('bound', nTurbs, lower=0., upper=None)
opt = SNOPT()
opt.setOption('Scale option',0)
opt.setOption('Iterations limit',1000000)
opt.setOption('Summary file','summary_grid.out')
opt.setOption('Major optimality tolerance',1.e-5)
opt.setOption('Major feasibility tolerance',1.e-6)
res = opt(optProb)
dx_f = res.xStar['dx']
dy_f = res.xStar['dy']
o_f = res.xStar['offset']
r_f = res.xStar['rotate']
# print 'dx_f: ', dx_f
# print 'dy_f: ', dy_f
# print 'offset_f: ', o_f
# print 'rotate_f: ', r_f
print nCalls
input = {'dx':dx_f,'dy':dy_f,'offset':o_f,'rotate':r_f}
def fit(s,t,length,plot,comp,read_data,opt_print):
global posdn
global poslt
global velod
t2 = t+'.0'
wfit = s+'_'+t2
wfit2 = s+'_'+t2
wfit3 = s+'_'+t2
wfit4 = s+'_'+t2
wfit5 = s+'_'+t2
wfit6 = s+'_'+t2
length2 = length
length3 = length
length4 = length
length5 = length
length6 = length
wind = 15.
wind2 = 14.
wind3 = 12.
wind4 = 16.
rad = 3.
dia = rad*2.
tsr = float(wfit[3]+'.'+wfit[4]+wfit[5])
rot = tsr*wind/rad
rot2 = tsr*wind2/rad
rot3 = tsr*wind3/rad
rot4 = tsr*wind4/rad
rot5 = 17.
rot6 = 18.
wind5 = rot5*rad/tsr
wind6 = rot6*rad/tsr
if comp == 'mac':
# fdata = '/Users/ning1/Documents/Flow Lab/STAR-CCM+/NACA0021/MoveForward/test.csv'
fdata = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/Velocity Sections/'+wfit+'.csv'
fdata2 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'fsl':
fdata = '/fslhome/ebtingey/compute/moveForward/Velocity/'+wfit+'.csv'
fdata2 = '/fslhome/ebtingey/compute/moveForward/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/fslhome/ebtingey/compute/moveForward/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/fslhome/ebtingey/compute/moveForward/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/fslhome/ebtingey/compute/moveForward/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/fslhome/ebtingey/compute/moveForward/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'win':
fdata = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//Velocity Sections//'+wfit+'.csv'
fdata2 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel14//Velocity//'+wfit2+'.csv'
fdata3 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel12//Velocity//'+wfit3+'.csv'
fdata4 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel16//Velocity//'+wfit4+'.csv'
fdata5 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot17//Velocity//'+wfit5+'.csv'
fdata6 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot18//Velocity//'+wfit6+'.csv'
if read_data ==1:
posdn,poslt,velod = starccm_read(np.array([fdata]),dia,np.array([wind]),length,opt_print)
if read_data ==2:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),length,opt_print)
if read_data ==3:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),length,opt_print)
if read_data ==4:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),length,opt_print)
if read_data ==5:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),length,opt_print)
if read_data ==6:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),length,opt_print)
if plot == True:
if read_data ==1:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata]),dia,np.array([wind]),opt_print)
if read_data ==2:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),opt_print)
if read_data ==3:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),opt_print)
if read_data ==4:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),opt_print)
if read_data ==5:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),opt_print)
if read_data ==6:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),opt_print)
start = length/30.
xd = np.linspace(start,length,30)/dia
## Optimization
optProb = Optimization('VAWTWake_Velo', obj_func)
optProb.addObj('obj')
spr10 = 10.0
pow10 = 5.0
pow20 = 0.5
pow30 = 1.0
spr20 = 2.0
skw0 = 0.0
odr0 = 2.0
scl10 = 0.5
scl20 = 0.1
scl30 = 20.0
# spr10 = 213.8593169
# pow10 = 10.39210953
# pow20 = 2.086951239
# pow30 = 0.035659319
# spr20 = 0.007589688
# skw0 = 10.63462155
# odr0 = 2.0
# scl10 = 0.537566448
# scl20 = 0.041077603
# scl30 = 56.74689143
param0 = np.array([spr10,pow10,pow20,pow30,spr20,skw0,odr0,scl10,scl20,scl30])
param_l = np.array([0.,0.,0.,0.,0.,None,0.,0.,0.,0.])
param_u = np.array([None,None,None,None,None,None,None,1.,1.,None])
nparam = np.size(param0)
optProb.addVarGroup('param', nparam, 'c', lower=param_l, upper=param_u, value=param0)
opt = SNOPT()
opt.setOption('Scale option',2)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'win':
opt.setOption('Print file','C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//optVel//SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//OptVel//SNOPT_summary'+s+'_'+t+'.out')
res = opt(optProb, sens=None)
if opt_print == True:
print res
pow = res.fStar
paramf = res.xStar['param']
if opt_print == True:
print paramf[0]
print paramf[1]
print paramf[2]
print paramf[3]
print paramf[4]
print paramf[5]
print paramf[6]
print paramf[7]
print paramf[8]
print paramf[9]
spr1 = paramf[0]
pow1 = paramf[1]
pow2 = paramf[2]
pow3 = paramf[3]
spr2 = paramf[4]
skw = paramf[5]
odr = paramf[6]
scl1 = paramf[7]
scl2 = paramf[8]
scl3 = paramf[9]
paper = False
if plot == True:
if paper == True:
for i in range(30):
name = str(i+1)
ind = str(i)
plt.figure(1)
ax1 = plt.subplot(5,6,i+1)
color = 'bo'
color2 = 'r-'
fs = 15
lab = 'CFD'
lab2 = 'Trend'
tex = '$x/D$ = '+str("{0:.2f}".format(x[i]/dia))
exec('xfit = np.linspace(min(pos'+name+'/dia)-1.,max(pos'+name+'/dia)+1.,500)')
if i == 5:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color,label=lab)')
skw_v,spr_v,scl_v,rat_v,spr_v = paramfit(xd[i],skw,spr,scl,rat,spr)
plt.plot(veldist(xfit,skw_v,spr_v,scl_v,rat_v,spr_v),xfit,'r-',linewidth=2,label=lab2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.legend(loc="upper left", bbox_to_anchor=(1,1),fontsize=fs)
else:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
skw_v,spr_v,scl_v,rat_v,spr_v = paramfit(xd[i],skw,spr,scl,rat,spr)
plt.plot(veldist(xfit,skw_v,spr_v,scl_v,rat_v,spr_v),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.text(0.3,0.8,tex,fontsize=fs)
if i <= 23:
plt.setp(ax1.get_xticklabels(), visible=False)
else:
plt.xlabel('$y/D$',fontsize=fs)
plt.xticks(fontsize=fs)
if i == 0 or i == 6 or i == 12 or i == 18 or i ==24:
plt.ylabel(r'$u/U_\infty$',fontsize=fs)
plt.yticks(fontsize=fs)
else:
plt.setp(ax1.get_yticklabels(), visible=False)
elif paper == False:
for i in range(30):
name = str(i+1)
plt.figure(1)
plt.subplot(5,6,i+1)
color = 'bo'
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
plt.plot(veldist(xd[i],xfit,spr1,pow1,pow2,pow3,spr2,skw,odr,scl1,scl2,scl3),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
# plt.legend(loc=1)
plt.xlabel('Normalized Velocity')
plt.ylabel('$y/D$')
return spr1,pow1,pow2,pow3,spr2,skw,odr,scl1,scl2,scl3
def fit(s,t,length,plot,comp,read_data,opt_print):
global xd
global pos1d
global pos2d
global pos3d
global pos4d
global pos5d
global pos6d
global pos7d
global pos8d
global pos9d
global pos10d
global pos11d
global pos12d
global pos13d
global pos14d
global pos15d
global pos16d
global pos17d
global pos18d
global pos19d
global pos20d
global pos21d
global pos22d
global pos23d
global pos24d
global pos25d
global pos26d
global pos27d
global pos28d
global pos29d
global pos30d
global velo1d
global velo2d
global velo3d
global velo4d
global velo5d
global velo6d
global velo7d
global velo8d
global velo9d
global velo10d
global velo11d
global velo12d
global velo13d
global velo14d
global velo15d
global velo16d
global velo17d
global velo18d
global velo19d
global velo20d
global velo21d
global velo22d
global velo23d
global velo24d
global velo25d
global velo26d
global velo27d
global velo28d
global velo29d
global velo30d
t2 = t+'.0'
wfit = s+'_'+t2
wfit2 = s+'_'+t2
wfit3 = s+'_'+t2
wfit4 = s+'_'+t2
wfit5 = s+'_'+t2
wfit6 = s+'_'+t2
length2 = length
length3 = length
length4 = length
length5 = length
length6 = length
wind = 15.
wind2 = 14.
wind3 = 12.
wind4 = 16.
rad = 3.
dia = rad*2.
tsr = float(wfit[3]+'.'+wfit[4]+wfit[5])
rot = tsr*wind/rad
rot2 = tsr*wind2/rad
rot3 = tsr*wind3/rad
rot4 = tsr*wind4/rad
rot5 = 17.
rot6 = 18.
wind5 = rot5*rad/tsr
wind6 = rot6*rad/tsr
if comp == 'mac':
# fdata = '/Users/ning1/Documents/Flow Lab/STAR-CCM+/NACA0021/MoveForward/test.csv'
fdata = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/Velocity Sections/'+wfit+'.csv'
fdata2 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'fsl':
fdata = '/fslhome/ebtingey/compute/moveForward/Velocity/'+wfit+'.csv'
fdata2 = '/fslhome/ebtingey/compute/moveForward/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/fslhome/ebtingey/compute/moveForward/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/fslhome/ebtingey/compute/moveForward/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/fslhome/ebtingey/compute/moveForward/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/fslhome/ebtingey/compute/moveForward/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'win':
fdata = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//Velocity Sections//'+wfit+'.csv'
fdata2 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel14//Velocity//'+wfit2+'.csv'
fdata3 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel12//Velocity//'+wfit3+'.csv'
fdata4 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel16//Velocity//'+wfit4+'.csv'
fdata5 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot17//Velocity//'+wfit5+'.csv'
fdata6 = 'C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot18//Velocity//'+wfit6+'.csv'
if read_data ==1:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata]),dia,np.array([wind]),opt_print)
if read_data ==2:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),opt_print)
if read_data ==3:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),opt_print)
if read_data ==4:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),opt_print)
if read_data ==5:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),opt_print)
if read_data ==6:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),opt_print)
start = length/30.
xd = np.linspace(start,length,30)/dia
## Optimization
optProb = Optimization('VAWTWake_Velo', obj_func)
optProb.addObj('obj')
param0 = np.array([2.91638655e-04, -1.70286993e-03 , 2.38051673e-02 , 7.65610623e-01,6.40509205e-02 , 6.99046413e-01, 7.83484187e-01 , 4.55408268e-01, 1.18716383e-01 , 2.05484572e+01 , -2.67741935e+00 , 4.43022575e+01,-2.10925147e+00 , 3.30400554e+01])
# param0 = np.array([-0.000168794,0.005413905,-0.151372907,0.194333896,-29.52514866,-5.555943879,0.350540531,106.116331,0.000304188,17.4218187,30.06169144,47.3929253,32.97190468,33.4188042])
# param0 = np.array([-0.430957043,0.202977737,0.20361798,-0.818862214,-1.568705068,0.753568492,0.255557818,0.162385094,3.7137047,19.67995783,31.32756058,77.24259087,13.72492175,48.22628446])
# param0 = np.array([-0.0002347,0.0094285,-0.2262108,0.0847296,0.7634520,5.0331194,0.3964091,6.0135974,0.0052853,19.9466507,-2.0322239,47.3381111,-1.1426171,27.5907093])
param_l = np.array([-1.,-1,-1.,-1.,-1,-1.,0.,0.,0.,None,0.,None,0.])
param_u = np.array([1.,1.,1.,1.,1.,1.,None,None,None,0.,None,0.,None])
param_l = np.array([None,None,None,0.,0.,0.,0.,0.,0.,0.,None,0.,None,0.])
param_u = np.array([None,None,None,None,None,None,None,None,None,None,0.,None,0.,None])
nparam = np.size(param0)
optProb.addVarGroup('param', nparam, 'c', lower=param_l, upper=param_u, value=param0)
opt = SNOPT()
opt.setOption('Scale option',2)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'win':
opt.setOption('Print file','C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//optVel//SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//OptVel//SNOPT_summary'+s+'_'+t+'.out')
res = opt(optProb, sens=None)
if opt_print == True:
print res
pow = res.fStar
paramf = res.xStar['param']
if opt_print == True:
print paramf[0]
print paramf[1]
print paramf[2]
print paramf[3]
print paramf[4]
print paramf[5]
print paramf[6]
print paramf[7]
print paramf[8]
print paramf[9]
print paramf[10]
print paramf[11]
print paramf[12]
print paramf[13]
men = np.array([paramf[0],paramf[1],paramf[2]])
spr = np.array([paramf[3],paramf[4],paramf[5],paramf[6]])
scl = np.array([paramf[7],paramf[8],paramf[9]])
rat = np.array([paramf[10],paramf[11]])
tns = np.array([paramf[12],paramf[13]])
paper = False
if plot == True:
if paper == True:
for i in range(30):
name = str(i+1)
ind = str(i)
plt.figure(1)
ax1 = plt.subplot(5,6,i+1)
color = 'bo'
color2 = 'r-'
fs = 15
lab = 'CFD'
lab2 = 'Trend'
tex = '$x/D$ = '+str("{0:.2f}".format(x[i]/dia))
exec('xfit = np.linspace(min(pos'+name+'/dia)-1.,max(pos'+name+'/dia)+1.,500)')
if i == 5:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color,label=lab)')
men_v,spr_v,scl_v,rat_v,tns_v = paramfit(xd[i],men,spr,scl,rat,tns)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,tns_v),xfit,'r-',linewidth=2,label=lab2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.legend(loc="upper left", bbox_to_anchor=(1,1),fontsize=fs)
else:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
men_v,spr_v,scl_v,rat_v,tns_v = paramfit(xd[i],men,spr,scl,rat,tns)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,tns_v),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.text(0.3,0.8,tex,fontsize=fs)
if i <= 23:
plt.setp(ax1.get_xticklabels(), visible=False)
else:
plt.xlabel('$y/D$',fontsize=fs)
plt.xticks(fontsize=fs)
if i == 0 or i == 6 or i == 12 or i == 18 or i ==24:
plt.ylabel(r'$u/U_\infty$',fontsize=fs)
plt.yticks(fontsize=fs)
else:
plt.setp(ax1.get_yticklabels(), visible=False)
elif paper == False:
for i in range(30):
name = str(i+1)
plt.figure(1)
plt.subplot(5,6,i+1)
color = 'bo'
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
men_v,spr_v,scl_v,rat_v,tns_v = paramfit(xd[i],men,spr,scl,rat,tns)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,tns_v),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
# plt.legend(loc=1)
plt.xlabel('Normalized Velocity')
plt.ylabel('$y/D$')
return men,spr,scl,rat,tns
# mlab.start()
#
# res = mlab.lsqcurvefit(sheet,param,posdntr,velodtr)
# print res
#
# mlab.stop
if fit_opt == 'snopt':
optProb = Optimization('VAWTWake_Velo', obj_func)
optProb.addObj('obj')
optProb.addVarGroup('param', 90, 'c', lower=None, upper=None, value=param0)
opt = SNOPT()
opt.setOption('Scale option',2)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptSheet/SNOPT_print'+str(argv[1])+str(argv[2])+str(argv[3])+'ORD'+str(argv[4])+str(argv[5])+str(argv[6])+str(argv[7])+str(argv[8])+str(argv[9])+str(argv[10])+str(argv[11])+str(argv[12])+'.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptSheet/SNOPT_summary'+str(argv[1])+str(argv[2])+str(argv[3])+'ORD'+str(argv[4])+str(argv[5])+str(argv[6])+str(argv[7])+str(argv[8])+str(argv[9])+str(argv[10])+str(argv[11])+str(argv[12])+'.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/CrossVal/SNOPT_Files/SNOPT_print'+str(argv[1])+str(argv[2])+str(argv[3])+'ORD'+str(argv[4])+str(argv[5])+str(argv[6])+str(argv[7])+str(argv[8])+str(argv[9])+str(argv[10])+str(argv[11])+str(argv[12])+'.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/CrossVal/SNOPT_Files/SNOPT_summary'+str(argv[1])+str(argv[2])+str(argv[3])+'ORD'+str(argv[4])+str(argv[5])+str(argv[6])+str(argv[7])+str(argv[8])+str(argv[9])+str(argv[10])+str(argv[11])+str(argv[12])+'.out')
result = opt(optProb, sens=None)
res = result.xStar['param']
elif fit_opt == 'scipy':
res,_ = curve_fit(sheet,posdntr,velodtr,p0=param0,maxfev=2820000)
coef0 = np.array([])
optProb.addVarGroup('sdv2s', ords, 'c', lower=None, upper=None, value=sdv2s0)
optProb.addVarGroup('sdv3s', ords, 'c', lower=None, upper=None, value=sdv3s0)
optProb.addVarGroup('sdv4s', ords, 'c', lower=None, upper=None, value=sdv4s0)
optProb.addVarGroup('rats', ords, 'c', lower=None, upper=None, value=rats0)
optProb.addVarGroup('tnss', ords, 'c', lower=None, upper=None, value=tnss0)
optProb.addVarGroup('spr1s', ords, 'c', lower=None, upper=None, value=spr1s0)
optProb.addVarGroup('spr2s', ords, 'c', lower=None, upper=None, value=spr2s0)
optProb.addVarGroup('spr3s', ords, 'c', lower=None, upper=None, value=spr3s0)
optProb.addVarGroup('spr4s', ords, 'c', lower=None, upper=None, value=spr4s0)
optProb.addVarGroup('scl1s', ords, 'c', lower=None, upper=None, value=scl1s0)
optProb.addVarGroup('scl2s', ords, 'c', lower=None, upper=None, value=scl2s0)
optProb.addVarGroup('scl3s', ords, 'c', lower=None, upper=None, value=scl3s0)
opt = SNOPT()
opt.setOption('Scale option',0)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptSheet/cv_cvx_SNOPT_print.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptSheet/cv_cvx_SNOPT_summary.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/OptSheet/cv_cvx_'+argv[1]+argv[2]+argv[3]+argv[4]+'_SNOPT_OptSheet_print.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/OptSheet/cv_cvx_'+argv[1]+argv[2]+argv[3]+argv[4]+'_SNOPT_OptSheet_summary.out')
res = opt(optProb, sens=None)
if opt_print == True:
print res
error = res.fStar
mentf = res.xStar['ment']
sdv1tf = res.xStar['sdv1t']
sdv2tf = res.xStar['sdv2t']
def fit(s,t,length,plot,comp,read_data,opt_print):
global xd
global pos1tr
global pos2tr
global pos3tr
global pos4tr
global pos5tr
global pos6tr
global pos7tr
global pos8tr
global pos9tr
global pos10tr
global pos11tr
global pos12tr
global pos13tr
global pos14tr
global pos15tr
global pos16tr
global pos17tr
global pos18tr
global pos19tr
global pos20tr
global pos21tr
global pos22tr
global pos23tr
global pos24tr
global pos25tr
global pos26tr
global pos27tr
global pos28tr
global pos29tr
global pos30tr
global velo1tr
global velo2tr
global velo3tr
global velo4tr
global velo5tr
global velo6tr
global velo7tr
global velo8tr
global velo9tr
global velo10tr
global velo11tr
global velo12tr
global velo13tr
global velo14tr
global velo15tr
global velo16tr
global velo17tr
global velo18tr
global velo19tr
global velo20tr
global velo21tr
global velo22tr
global velo23tr
global velo24tr
global velo25tr
global velo26tr
global velo27tr
global velo28tr
global velo29tr
global velo30tr
t2 = t+'.0'
wfit = s+'_'+t2
wfit2 = s+'_'+t2
wfit3 = s+'_'+t2
wfit4 = s+'_'+t2
wfit5 = s+'_'+t2
wfit6 = s+'_'+t2
length2 = length
length3 = length
length4 = length
length5 = length
length6 = length
wind = 15.
wind2 = 14.
wind3 = 12.
wind4 = 16.
rad = 3.
dia = rad*2.
tsr = float(wfit[3]+'.'+wfit[4]+wfit[5])
rot = tsr*wind/rad
rot2 = tsr*wind2/rad
rot3 = tsr*wind3/rad
rot4 = tsr*wind4/rad
rot5 = 17.
rot6 = 18.
wind5 = rot5*rad/tsr
wind6 = rot6*rad/tsr
if comp == 'mac':
# fdata = '/Users/ning1/Documents/Flow Lab/STAR-CCM+/NACA0021/MoveForward/test.csv'
fdata = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/Velocity Sections/'+wfit+'.csv'
fdata2 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'fsl':
fdata = '/fslhome/ebtingey/compute/moveForward/Velocity/'+wfit+'.csv'
fdata2 = '/fslhome/ebtingey/compute/moveForward/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/fslhome/ebtingey/compute/moveForward/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/fslhome/ebtingey/compute/moveForward/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/fslhome/ebtingey/compute/moveForward/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/fslhome/ebtingey/compute/moveForward/rot18/Velocity/'+wfit6+'.csv'
if read_data ==1:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata]),dia,np.array([wind]),opt_print)
if read_data ==2:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),opt_print)
if read_data ==3:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),opt_print)
if read_data ==4:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),opt_print)
if read_data ==5:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),opt_print)
if read_data ==6:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),opt_print)
start = length/30.
xd = np.linspace(start,length,30)/dia
cvtest = 0.3
pos1tr,pos1ts,velo1tr,velo1ts = train_test_split(pos1d,velo1d,test_size=cvtest)
pos2tr,pos2ts,velo2tr,velo2ts = train_test_split(pos2d,velo2d,test_size=cvtest)
pos3tr,pos3ts,velo3tr,velo3ts = train_test_split(pos3d,velo3d,test_size=cvtest)
pos4tr,pos4ts,velo4tr,velo4ts = train_test_split(pos4d,velo4d,test_size=cvtest)
pos5tr,pos5ts,velo5tr,velo5ts = train_test_split(pos5d,velo5d,test_size=cvtest)
pos6tr,pos6ts,velo6tr,velo6ts = train_test_split(pos6d,velo6d,test_size=cvtest)
pos7tr,pos7ts,velo7tr,velo7ts = train_test_split(pos7d,velo7d,test_size=cvtest)
pos8tr,pos8ts,velo8tr,velo8ts = train_test_split(pos8d,velo8d,test_size=cvtest)
pos9tr,pos9ts,velo9tr,velo9ts = train_test_split(pos9d,velo9d,test_size=cvtest)
pos10tr,pos10ts,velo10tr,velo10ts = train_test_split(pos10d,velo10d,test_size=cvtest)
pos11tr,pos11ts,velo11tr,velo11ts = train_test_split(pos11d,velo11d,test_size=cvtest)
pos12tr,pos12ts,velo12tr,velo12ts = train_test_split(pos12d,velo12d,test_size=cvtest)
pos13tr,pos13ts,velo13tr,velo13ts = train_test_split(pos13d,velo13d,test_size=cvtest)
pos14tr,pos14ts,velo14tr,velo14ts = train_test_split(pos14d,velo14d,test_size=cvtest)
pos15tr,pos15ts,velo15tr,velo15ts = train_test_split(pos15d,velo15d,test_size=cvtest)
pos16tr,pos16ts,velo16tr,velo16ts = train_test_split(pos16d,velo16d,test_size=cvtest)
pos17tr,pos17ts,velo17tr,velo17ts = train_test_split(pos17d,velo17d,test_size=cvtest)
pos18tr,pos18ts,velo18tr,velo18ts = train_test_split(pos18d,velo18d,test_size=cvtest)
pos19tr,pos19ts,velo19tr,velo19ts = train_test_split(pos19d,velo19d,test_size=cvtest)
pos20tr,pos20ts,velo20tr,velo20ts = train_test_split(pos20d,velo20d,test_size=cvtest)
pos21tr,pos21ts,velo21tr,velo21ts = train_test_split(pos21d,velo21d,test_size=cvtest)
pos22tr,pos22ts,velo22tr,velo22ts = train_test_split(pos22d,velo22d,test_size=cvtest)
pos23tr,pos23ts,velo23tr,velo23ts = train_test_split(pos23d,velo23d,test_size=cvtest)
pos24tr,pos24ts,velo24tr,velo24ts = train_test_split(pos24d,velo24d,test_size=cvtest)
pos25tr,pos25ts,velo25tr,velo25ts = train_test_split(pos25d,velo25d,test_size=cvtest)
pos26tr,pos26ts,velo26tr,velo26ts = train_test_split(pos26d,velo26d,test_size=cvtest)
pos27tr,pos27ts,velo27tr,velo27ts = train_test_split(pos27d,velo27d,test_size=cvtest)
pos28tr,pos28ts,velo28tr,velo28ts = train_test_split(pos28d,velo28d,test_size=cvtest)
pos29tr,pos29ts,velo29tr,velo29ts = train_test_split(pos29d,velo29d,test_size=cvtest)
pos30tr,pos30ts,velo30tr,velo30ts = train_test_split(pos30d,velo30d,test_size=cvtest)
## Optimization
optProb = Optimization('VAWTWake_Velo', obj_func)
optProb.addObj('obj')
param0 = np.array([2.91638655e-04, -1.70286993e-03 , 2.38051673e-02 , -7.65610623e-01,6.40509205e-02 , 6.99046413e-01, 7.83484187e-01 , 4.55408268e-01, 1.18716383e-01 , 2.05484572e+01 , -2.67741935e+00 , 4.43022575e+01,-2.10925147e+00 , 3.30400554e+01])
param_l = np.array([-1.,-1,-1.,-1.,-1,-1.,0.,0.,0.,None,0.,None,0.])
param_u = np.array([1.,1.,1.,1.,1.,1.,None,None,None,0.,None,0.,None])
nparam = np.size(param0)
optProb.addVarGroup('param', nparam, 'c', lower=None, upper=None, value=param0)
opt = SNOPT()
opt.setOption('Scale option',2)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_summary'+s+'_'+t+'.out')
res = opt(optProb, sens=None)
if opt_print == True:
print res
pow = res.fStar
paramf = res.xStar['param']
if opt_print == True:
print paramf
men = np.array([paramf[0],paramf[1],paramf[2]])
spr = np.array([paramf[3],paramf[4],paramf[5],paramf[6]])
scl = np.array([paramf[7],paramf[8],paramf[9]])
rat = np.array([paramf[10],paramf[11]])
tns = np.array([paramf[12],paramf[13]])
cv_error = 0.
for i in range(30):
name = str(i+1)
ind = str(i)
exec('if xd['+ind+'] > 0.58:\n\tmen_v,spr_v,scl_v,rat_v,tns_v = paramfit(xd['+ind+'],men,spr,scl,rat,tns)\n\tfor j in range(np.size(pos'+name+'ts)):\n\t\tvel = (-scl_v/(spr_v*sqrt(2.*pi))*exp(-(pos'+name+'ts[j]+men_v)**2/(2.*spr_v**2)))*(1./(1. + exp(rat_v*fabs(pos'+name+'ts[j])-tns_v))) + 1.\n\t\tcv_error = cv_error + (vel-velo'+name+'ts[j])**2')
if plot == True:
for i in range(30):
name = str(i+1)
plt.figure(1)
plt.subplot(5,6,i+1)
color = 'bo'
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
men_v,spr_v,scl_v,rat_v,tns_v = paramfit(xd[i],men,spr,scl,rat,tns)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,tns_v),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
# plt.legend(loc=1)
plt.xlabel('Normalized Velocity')
plt.ylabel('$y/D$')
return men,spr,scl,rat,tns,cv_error
"""Optimization"""
optProb = Optimization('Wind_Farm_AEP', obj_func_no_damage)
optProb.addObj('AEP')
# optProb.addObj('damage')
optProb.addVarGroup('turbineX', nTurbs, type='c', lower=min(xBounds), upper=max(xBounds), value=turbineX)
optProb.addVarGroup('turbineY', nTurbs, type='c', lower=min(yBounds), upper=max(yBounds), value=turbineY)
num_cons_sep = (nTurbs-1)*nTurbs/2
optProb.addConGroup('sep', num_cons_sep, lower=0., upper=None)
optProb.addConGroup('bound', nTurbs, lower=0., upper=None)
# optProb.addConGroup('damage', nTurbs, lower=None, upper=damage_lim)
# optProb.addCon('damage', lower=aep_lim, upper=None)
opt = SNOPT()
opt.setOption('Scale option',0)
opt.setOption('Iterations limit',1000000)
opt.setOption('Summary file','%s/summary.out'%folder)
opt.setOption('Major optimality tolerance',1.e-4)
opt.setOption('Major feasibility tolerance',1.e-6)
res = opt(optProb)
x1 = res.xStar['turbineX']
y1 = res.xStar['turbineY']
input = {'turbineX':x1,'turbineY':y1}
funcs,_ = obj_func_damage(input)
separation = min(funcs['sep'])
xupp = points[-1]+spacing*area
ylow = points[0]-spacing*area
yupp = points[-1]+spacing*area
optProb = Optimization('VAWT_Power', obj_func)
optProb.addObj('obj')
n = np.size(x0)
optProb.addVarGroup('xvars', n, 'c', lower=xlow, upper=xupp, value=x0)
optProb.addVarGroup('yvars', n, 'c', lower=ylow, upper=yupp, value=y0)
num_cons_sep = (n-1)*n/2
optProb.addConGroup('sep', num_cons_sep, lower=0, upper=None)
opt = SNOPT()
opt.setOption('Scale option',0)
res = opt(optProb, sens=None)
print res
pow = np.array(-1*res.fStar)
xf = res.xStar['xvars']
yf = res.xStar['yvars']
power_turb = funcs['power_turb']
veleff = funcs['veleff']
print 'The power is:',pow,'kJ'
print 'The isolated power is:',power_iso*np.size(xt),'kJ'
print 'The x-locations:',xf
print 'The y-locations:',yf
print 'The effective wind speeds:',veleff
print 'The power of each turbine (kJ):',power_turb
def fit(s, t, length, plot, comp, read_data, opt_print):
global posdn
global poslt
global velod
t2 = t + ".0"
wfit = s + "_" + t2
wfit2 = s + "_" + t2
wfit3 = s + "_" + t2
wfit4 = s + "_" + t2
wfit5 = s + "_" + t2
wfit6 = s + "_" + t2
length2 = length
length3 = length
length4 = length
length5 = length
length6 = length
wind = 15.0
wind2 = 14.0
wind3 = 12.0
wind4 = 16.0
rad = 3.0
dia = rad * 2.0
tsr = float(wfit[3] + "." + wfit[4] + wfit[5])
rot = tsr * wind / rad
rot2 = tsr * wind2 / rad
rot3 = tsr * wind3 / rad
rot4 = tsr * wind4 / rad
rot5 = 17.0
rot6 = 18.0
wind5 = rot5 * rad / tsr
wind6 = rot6 * rad / tsr
if comp == "mac":
# fdata = '/Users/ning1/Documents/Flow Lab/STAR-CCM+/NACA0021/MoveForward/test.csv'
fdata = "/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/Velocity Sections/" + wfit + ".csv"
fdata2 = (
"/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel14/Velocity/"
+ wfit2
+ ".csv"
)
fdata3 = (
"/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel12/Velocity/"
+ wfit3
+ ".csv"
)
fdata4 = (
"/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel16/Velocity/"
+ wfit4
+ ".csv"
)
fdata5 = (
"/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot17/Velocity/"
+ wfit5
+ ".csv"
)
fdata6 = (
"/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot18/Velocity/"
+ wfit6
+ ".csv"
)
elif comp == "fsl":
fdata = "/fslhome/ebtingey/compute/moveForward/Velocity/" + wfit + ".csv"
fdata2 = "/fslhome/ebtingey/compute/moveForward/vel14/Velocity/" + wfit2 + ".csv"
fdata3 = "/fslhome/ebtingey/compute/moveForward/vel12/Velocity/" + wfit3 + ".csv"
fdata4 = "/fslhome/ebtingey/compute/moveForward/vel16/Velocity/" + wfit4 + ".csv"
fdata5 = "/fslhome/ebtingey/compute/moveForward/rot17/Velocity/" + wfit5 + ".csv"
fdata6 = "/fslhome/ebtingey/compute/moveForward/rot18/Velocity/" + wfit6 + ".csv"
elif comp == "win":
fdata = (
"C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//Velocity Sections//"
+ wfit
+ ".csv"
)
fdata2 = (
"C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel14//Velocity//"
+ wfit2
+ ".csv"
)
fdata3 = (
"C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel12//Velocity//"
+ wfit3
+ ".csv"
)
fdata4 = (
"C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//vel16//Velocity//"
+ wfit4
+ ".csv"
)
fdata5 = (
"C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot17//Velocity//"
+ wfit5
+ ".csv"
)
fdata6 = (
"C://Users//TingeyPC//Documents//zStar-CCM//STAR-CCM//NACA0021//MoveForward//CrossValidate//rot18//Velocity//"
+ wfit6
+ ".csv"
)
if read_data == 1:
posdn, poslt, velod = starccm_read(np.array([fdata]), dia, np.array([wind]), length, opt_print)
if read_data == 2:
posdn, poslt, velod = starccm_read(np.array([fdata, fdata2]), dia, np.array([wind, wind2]), length, opt_print)
if read_data == 3:
posdn, poslt, velod = starccm_read(
np.array([fdata, fdata2, fdata3]), dia, np.array([wind, wind2, wind3]), length, opt_print
)
if read_data == 4:
posdn, poslt, velod = starccm_read(
np.array([fdata, fdata2, fdata3, fdata4]), dia, np.array([wind, wind2, wind3, wind4]), length, opt_print
)
if read_data == 5:
posdn, poslt, velod = starccm_read(
np.array([fdata, fdata2, fdata3, fdata4, fdata5]),
dia,
np.array([wind, wind2, wind3, wind4, wind5]),
length,
opt_print,
)
if read_data == 6:
posdn, poslt, velod = starccm_read(
np.array([fdata, fdata2, fdata3, fdata4, fdata5, fdata6]),
dia,
np.array([wind, wind2, wind3, wind4, wind5, wind6]),
length,
opt_print,
)
if plot == True:
if read_data == 1:
pos1d, pos2d, pos3d, pos4d, pos5d, pos6d, pos7d, pos8d, pos9d, pos10d, pos11d, pos12d, pos13d, pos14d, pos15d, pos16d, pos17d, pos18d, pos19d, pos20d, pos21d, pos22d, pos23d, pos24d, pos25d, pos26d, pos27d, pos28d, pos29d, pos30d, velo1d, velo2d, velo3d, velo4d, velo5d, velo6d, velo7d, velo8d, velo9d, velo10d, velo11d, velo12d, velo13d, velo14d, velo15d, velo16d, velo17d, velo18d, velo19d, velo20d, velo21d, velo22d, velo23d, velo24d, velo25d, velo26d, velo27d, velo28d, velo29d, velo30d = starccm_read2(
np.array([fdata]), dia, np.array([wind]), opt_print
)
if read_data == 2:
pos1d, pos2d, pos3d, pos4d, pos5d, pos6d, pos7d, pos8d, pos9d, pos10d, pos11d, pos12d, pos13d, pos14d, pos15d, pos16d, pos17d, pos18d, pos19d, pos20d, pos21d, pos22d, pos23d, pos24d, pos25d, pos26d, pos27d, pos28d, pos29d, pos30d, velo1d, velo2d, velo3d, velo4d, velo5d, velo6d, velo7d, velo8d, velo9d, velo10d, velo11d, velo12d, velo13d, velo14d, velo15d, velo16d, velo17d, velo18d, velo19d, velo20d, velo21d, velo22d, velo23d, velo24d, velo25d, velo26d, velo27d, velo28d, velo29d, velo30d = starccm_read2(
np.array([fdata, fdata2]), dia, np.array([wind, wind2]), opt_print
)
if read_data == 3:
pos1d, pos2d, pos3d, pos4d, pos5d, pos6d, pos7d, pos8d, pos9d, pos10d, pos11d, pos12d, pos13d, pos14d, pos15d, pos16d, pos17d, pos18d, pos19d, pos20d, pos21d, pos22d, pos23d, pos24d, pos25d, pos26d, pos27d, pos28d, pos29d, pos30d, velo1d, velo2d, velo3d, velo4d, velo5d, velo6d, velo7d, velo8d, velo9d, velo10d, velo11d, velo12d, velo13d, velo14d, velo15d, velo16d, velo17d, velo18d, velo19d, velo20d, velo21d, velo22d, velo23d, velo24d, velo25d, velo26d, velo27d, velo28d, velo29d, velo30d = starccm_read2(
np.array([fdata, fdata2, fdata3]), dia, np.array([wind, wind2, wind3]), opt_print
)
if read_data == 4:
pos1d, pos2d, pos3d, pos4d, pos5d, pos6d, pos7d, pos8d, pos9d, pos10d, pos11d, pos12d, pos13d, pos14d, pos15d, pos16d, pos17d, pos18d, pos19d, pos20d, pos21d, pos22d, pos23d, pos24d, pos25d, pos26d, pos27d, pos28d, pos29d, pos30d, velo1d, velo2d, velo3d, velo4d, velo5d, velo6d, velo7d, velo8d, velo9d, velo10d, velo11d, velo12d, velo13d, velo14d, velo15d, velo16d, velo17d, velo18d, velo19d, velo20d, velo21d, velo22d, velo23d, velo24d, velo25d, velo26d, velo27d, velo28d, velo29d, velo30d = starccm_read2(
np.array([fdata, fdata2, fdata3, fdata4]), dia, np.array([wind, wind2, wind3, wind4]), opt_print
)
if read_data == 5:
pos1d, pos2d, pos3d, pos4d, pos5d, pos6d, pos7d, pos8d, pos9d, pos10d, pos11d, pos12d, pos13d, pos14d, pos15d, pos16d, pos17d, pos18d, pos19d, pos20d, pos21d, pos22d, pos23d, pos24d, pos25d, pos26d, pos27d, pos28d, pos29d, pos30d, velo1d, velo2d, velo3d, velo4d, velo5d, velo6d, velo7d, velo8d, velo9d, velo10d, velo11d, velo12d, velo13d, velo14d, velo15d, velo16d, velo17d, velo18d, velo19d, velo20d, velo21d, velo22d, velo23d, velo24d, velo25d, velo26d, velo27d, velo28d, velo29d, velo30d = starccm_read2(
np.array([fdata, fdata2, fdata3, fdata4, fdata5]),
dia,
np.array([wind, wind2, wind3, wind4, wind5]),
opt_print,
)
if read_data == 6:
pos1d, pos2d, pos3d, pos4d, pos5d, pos6d, pos7d, pos8d, pos9d, pos10d, pos11d, pos12d, pos13d, pos14d, pos15d, pos16d, pos17d, pos18d, pos19d, pos20d, pos21d, pos22d, pos23d, pos24d, pos25d, pos26d, pos27d, pos28d, pos29d, pos30d, velo1d, velo2d, velo3d, velo4d, velo5d, velo6d, velo7d, velo8d, velo9d, velo10d, velo11d, velo12d, velo13d, velo14d, velo15d, velo16d, velo17d, velo18d, velo19d, velo20d, velo21d, velo22d, velo23d, velo24d, velo25d, velo26d, velo27d, velo28d, velo29d, velo30d = starccm_read2(
np.array([fdata, fdata2, fdata3, fdata4, fdata5, fdata6]),
dia,
np.array([wind, wind2, wind3, wind4, wind5, wind6]),
opt_print,
)
start = length / 30.0
xd = np.linspace(start, length, 30) / dia
## Optimization
optProb = Optimization("VAWTWake_Velo", obj_func)
optProb.addObj("obj")
spr10 = 10.0
pow10 = 10.0
pow20 = 0.5
pow30 = 1.0
spr20 = 2.0
skw0 = 0.0
scl10 = 0.5
scl20 = 0.1
scl30 = 10.0
spr10 = 10.0
pow10 = 5.0
pow20 = 0.5
pow30 = 1.0
spr20 = 2.0
skw0 = 0.0
scl10 = 0.5
scl20 = 0.1
scl30 = 20.0
spr10 = 213.8593169
pow10 = 10.39210953
pow20 = 2.086951239
pow30 = 0.035659319
spr20 = 0.007589688
skw0 = 10.63462155
scl10 = 0.537566448
scl20 = 0.041077603
scl30 = 56.74689143
spr10 = 100.0
pow10 = 10.0
pow20 = 0.5
pow30 = 0.0 # 1.0
spr20 = 20.0
skw0 = 0.0
scl10 = 0.5
scl20 = 0.1
scl30 = 10.0
param0 = np.array([spr10, pow10, pow20, pow30, spr20, skw0, scl10, scl20, scl30])
param_l = np.array([0.0, 0.0, 0.0, 0.0, 0.0, None, 0.0, 0.0, 0.0])
param_u = np.array([None, None, None, None, None, None, 1.0, 1.0, None])
nparam = np.size(param0)
optProb.addVarGroup("param", nparam, "c", lower=param_l, upper=param_u, value=param0)
opt = SNOPT()
opt.setOption("Scale option", 2)
if comp == "mac":
opt.setOption(
"Print file",
"/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_print" + s + "_" + t + ".out",
)
opt.setOption(
"Summary file",
"/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_summary" + s + "_" + t + ".out",
)
elif comp == "fsl":
opt.setOption("Print file", "/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_print" + s + "_" + t + ".out")
opt.setOption(
"Summary file", "/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_summary" + s + "_" + t + ".out"
)
elif comp == "win":
opt.setOption(
"Print file",
"C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//optVel//SNOPT_print"
+ s
+ "_"
+ t
+ ".out",
)
opt.setOption(
"Summary file",
"C://Users//TingeyPC//Documents//FLOW Lab//VAWTWakeModel//wake_model//data//OptVel//SNOPT_summary"
+ s
+ "_"
+ t
+ ".out",
)
res = opt(optProb, sens=None)
if opt_print == True:
print res
pow = res.fStar
paramf = res.xStar["param"]
if opt_print == True:
print paramf[0]
print paramf[1]
print paramf[2]
print paramf[3]
print paramf[4]
print paramf[5]
print paramf[6]
print paramf[7]
print paramf[8]
spr1 = paramf[0]
pow1 = paramf[1]
pow2 = paramf[2]
pow3 = paramf[3]
spr2 = paramf[4]
skw = paramf[5]
scl1 = paramf[6]
scl2 = paramf[7]
scl3 = paramf[8]
paper = False
if plot == True:
if paper == True:
for i in range(30):
name = str(i + 1)
ind = str(i)
plt.figure(1)
ax1 = plt.subplot(5, 6, i + 1)
color = "bo"
color2 = "r-"
fs = 15
lab = "CFD"
lab2 = "Trend"
tex = "$x/D$ = " + str("{0:.2f}".format(x[i] / dia))
exec ("xfit = np.linspace(min(pos" + name + "/dia)-1.,max(pos" + name + "/dia)+1.,500)")
if i == 5:
exec ("xfit = np.linspace(min(pos" + name + "d)-1.,max(pos" + name + "d)+1.,500)")
exec ("plt.plot(velo" + name + "d,pos" + name + "d,color,label=lab)")
skw_v, spr_v, scl_v, rat_v, spr_v = paramfit(xd[i], skw, spr, scl, rat, spr)
plt.plot(veldist(xfit, skw_v, spr_v, scl_v, rat_v, spr_v), xfit, "r-", linewidth=2, label=lab2)
plt.xlim(0.0, 1.5)
# plt.ylim(-4.,4.)
plt.legend(loc="upper left", bbox_to_anchor=(1, 1), fontsize=fs)
else:
exec ("xfit = np.linspace(min(pos" + name + "d)-1.,max(pos" + name + "d)+1.,500)")
exec ("plt.plot(velo" + name + "d,pos" + name + "d,color)")
skw_v, spr_v, scl_v, rat_v, spr_v = paramfit(xd[i], skw, spr, scl, rat, spr)
plt.plot(veldist(xfit, skw_v, spr_v, scl_v, rat_v, spr_v), xfit, "r-", linewidth=2)
plt.xlim(0.0, 1.5)
# plt.ylim(-4.,4.)
plt.text(0.3, 0.8, tex, fontsize=fs)
if i <= 23:
plt.setp(ax1.get_xticklabels(), visible=False)
else:
plt.xlabel("$y/D$", fontsize=fs)
plt.xticks(fontsize=fs)
if i == 0 or i == 6 or i == 12 or i == 18 or i == 24:
plt.ylabel(r"$u/U_\infty$", fontsize=fs)
plt.yticks(fontsize=fs)
else:
plt.setp(ax1.get_yticklabels(), visible=False)
elif paper == False:
for i in range(30):
name = str(i + 1)
plt.figure(1)
plt.subplot(5, 6, i + 1)
color = "bo"
exec ("xfit = np.linspace(min(pos" + name + "d)-1.,max(pos" + name + "d)+1.,500)")
exec ("plt.plot(velo" + name + "d,pos" + name + "d,color)")
plt.plot(
veldist(xd[i], xfit, spr1, pow1, pow2, pow3, spr2, skw, scl1, scl2, scl3), xfit, "r-", linewidth=2
)
plt.xlim(0.0, 1.5)
# plt.ylim(-4.,4.)
# plt.legend(loc=1)
plt.xlabel("Normalized Velocity")
plt.ylabel("$y/D$")
return spr1, pow1, pow2, pow3, spr2, skw, scl1, scl2, scl3
def fit(s,t,length,plot,comp,read_data,opt_print):
global posdntr
global poslttr
global velodtr
t2 = t+'.0'
wfit = s+'_'+t2
wfit2 = s+'_'+t2
wfit3 = s+'_'+t2
wfit4 = s+'_'+t2
wfit5 = s+'_'+t2
wfit6 = s+'_'+t2
length2 = length
length3 = length
length4 = length
length5 = length
length6 = length
wind = 15.
wind2 = 14.
wind3 = 12.
wind4 = 16.
rad = 3.
dia = rad*2.
tsr = float(wfit[3]+'.'+wfit[4]+wfit[5])
rot = tsr*wind/rad
rot2 = tsr*wind2/rad
rot3 = tsr*wind3/rad
rot4 = tsr*wind4/rad
rot5 = 17.
rot6 = 18.
wind5 = rot5*rad/tsr
wind6 = rot6*rad/tsr
if comp == 'mac':
# fdata = '/Users/ning1/Documents/Flow Lab/STAR-CCM+/NACA0021/MoveForward/test.csv'
fdata = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/Velocity Sections/'+wfit+'.csv'
fdata2 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/Users/ning1/Documents/FLOW Lab/STAR-CCM+/NACA0021/MoveForward/CrossValidate/rot18/Velocity/'+wfit6+'.csv'
elif comp == 'fsl':
fdata = '/fslhome/ebtingey/compute/moveForward/Velocity/'+wfit+'.csv'
fdata2 = '/fslhome/ebtingey/compute/moveForward/vel14/Velocity/'+wfit2+'.csv'
fdata3 = '/fslhome/ebtingey/compute/moveForward/vel12/Velocity/'+wfit3+'.csv'
fdata4 = '/fslhome/ebtingey/compute/moveForward/vel16/Velocity/'+wfit4+'.csv'
fdata5 = '/fslhome/ebtingey/compute/moveForward/rot17/Velocity/'+wfit5+'.csv'
fdata6 = '/fslhome/ebtingey/compute/moveForward/rot18/Velocity/'+wfit6+'.csv'
if read_data ==1:
posdn,poslt,velod = starccm_read(np.array([fdata]),dia,np.array([wind]),length,opt_print)
if read_data ==2:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),length,opt_print)
if read_data ==3:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),length,opt_print)
if read_data ==4:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),length,opt_print)
if read_data ==5:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),length,opt_print)
if read_data ==6:
posdn,poslt,velod = starccm_read(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),length,opt_print)
if plot == True:
if read_data ==1:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata]),dia,np.array([wind]),opt_print)
if read_data ==2:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2]),dia,np.array([wind,wind2]),opt_print)
if read_data ==3:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3]),dia,np.array([wind,wind2,wind3]),opt_print)
if read_data ==4:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4]),dia,np.array([wind,wind2,wind3,wind4]),opt_print)
if read_data ==5:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4,fdata5]),dia,np.array([wind,wind2,wind3,wind4,wind5]),opt_print)
if read_data ==6:
pos1d,pos2d,pos3d,pos4d,pos5d,pos6d,pos7d,pos8d,pos9d,pos10d,pos11d,pos12d,pos13d,pos14d,pos15d,pos16d,pos17d,pos18d,pos19d,pos20d,pos21d,pos22d,pos23d,pos24d,pos25d,pos26d,pos27d,pos28d,pos29d,pos30d,velo1d,velo2d,velo3d,velo4d,velo5d,velo6d,velo7d,velo8d,velo9d,velo10d,velo11d,velo12d,velo13d,velo14d,velo15d,velo16d,velo17d,velo18d,velo19d,velo20d,velo21d,velo22d,velo23d,velo24d,velo25d,velo26d,velo27d,velo28d,velo29d,velo30d = starccm_read2(np.array([fdata,fdata2,fdata3,fdata4,fdata5,fdata6]),dia,np.array([wind,wind2,wind3,wind4,wind5,wind6]),opt_print)
start = length/30.
xd = np.linspace(start,length,30)/dia
cvtest = 0.3
posdntr,posdnts,poslttr,posltts,velodtr,velodts = train_test_split(posdn,poslt,velod,test_size=cvtest)
## Optimization
optProb = Optimization('VAWTWake_Velo', obj_func)
optProb.addObj('obj')
men0 = 0.
sdv10 = 0.5
sdv20 = 0.1
sdv30 = 10.
sdv40 = 0.5
rat0 = 10.
spr0 = 10.
bow1 = 0.5
bow2 = 0.1
bow3 = 20.
bow4 = 1.
# bow1 = -1.
# bow2 = 1.
# bow3 = 1.
# bow4 = 1.
param0 = np.array([men0,sdv10,sdv20,sdv30,sdv40,rat0,bow4,spr0,bow1,bow2,bow3,0.5,0.1,20.])
param_l = np.array([None,0.,0.,0.,0.,None,0.,None,0.,0.,0.,0.])
param_u = np.array([None,None,None,None,None,0.,None,0.,None,1.,1.,50.])
param_l = np.array([None,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.])
param_u = np.array([None,10.,1.,50.,None,None,None,None,1.,1.,50.,1.,1.,None])
# param_l = np.array([None,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.,0.])
# param_u = np.array([None,None,None,None,None,None,None,None,None,None,None,1.,1.,None])
# param_l = np.array([None,0.,0.,0.,0.,0.,None,0.,None,None,None,0.,0.,0.])
# param_u = np.array([None,1.,1.,50.,None,None,None,None,None,None,None,1.,1.,50.])
nparam = np.size(param0)
optProb.addVarGroup('param', nparam, 'c', lower=param_l, upper=param_u, value=param0)
opt = SNOPT()
opt.setOption('Scale option',2)
if comp == 'mac':
opt.setOption('Print file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/Users/ning1/Documents/FLOW Lab/VAWTWakeModel/wake_model/data/OptVel/SNOPT_summary'+s+'_'+t+'.out')
elif comp == 'fsl':
opt.setOption('Print file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_print'+s+'_'+t+'.out')
opt.setOption('Summary file','/fslhome/ebtingey/compute/VAWTWakeModel/OptVel/SNOPT_summary'+s+'_'+t+'.out')
res = opt(optProb, sens=None)
if opt_print == True:
print res
pow = res.fStar
paramf = res.xStar['param']
if opt_print == True:
print paramf[0]
print paramf[1]
print paramf[2]
print paramf[3]
print paramf[4]
print paramf[5]
print paramf[6]
print paramf[7]
print paramf[8]
print paramf[9]
print paramf[10]
print paramf[11]
print paramf[12]
print paramf[13]
men = paramf[0]
sdv1 = paramf[1]
sdv2 = paramf[2]
sdv3 = paramf[3]
sdv4 = paramf[4]
rat1 = paramf[5]
rat2 = paramf[6]
spr1 = paramf[7]
spr2 = paramf[8]
spr3 = paramf[9]
spr4 = paramf[10]
scl1 = paramf[11]
scl2 = paramf[12]
scl3 = paramf[13]
cv_error = 0.
for i in range(np.size(posdnts)):
if posdnts[i] > 0.58:
vel = veldist(posdnts[i],posltts[i],men,sdv1,sdv2,sdv3,sdv4,rat1,rat2,spr1,spr2,spr3,spr4,scl1,scl2,scl3)
cv_error = cv_error + (vel-velodts[i])**2
# men = np.array([paramf[0],paramf[1],paramf[2]])
# spr = np.array([paramf[3],paramf[4],paramf[5],paramf[6]])
# scl = np.array([paramf[7],paramf[8],paramf[9]])
# rat = np.array([paramf[10],paramf[11]])
# spr = np.array([paramf[12],paramf[13]])
#
paper = False
if plot == True:
if paper == True:
for i in range(30):
name = str(i+1)
ind = str(i)
plt.figure(1)
ax1 = plt.subplot(5,6,i+1)
color = 'bo'
color2 = 'r-'
fs = 15
lab = 'CFD'
lab2 = 'Trend'
tex = '$x/D$ = '+str("{0:.2f}".format(x[i]/dia))
exec('xfit = np.linspace(min(pos'+name+'/dia)-1.,max(pos'+name+'/dia)+1.,500)')
if i == 5:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color,label=lab)')
men_v,spr_v,scl_v,rat_v,spr_v = paramfit(xd[i],men,spr,scl,rat,spr)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,spr_v),xfit,'r-',linewidth=2,label=lab2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.legend(loc="upper left", bbox_to_anchor=(1,1),fontsize=fs)
else:
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
men_v,spr_v,scl_v,rat_v,spr_v = paramfit(xd[i],men,spr,scl,rat,spr)
plt.plot(veldist(xfit,men_v,spr_v,scl_v,rat_v,spr_v),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
plt.text(0.3,0.8,tex,fontsize=fs)
if i <= 23:
plt.setp(ax1.get_xticklabels(), visible=False)
else:
plt.xlabel('$y/D$',fontsize=fs)
plt.xticks(fontsize=fs)
if i == 0 or i == 6 or i == 12 or i == 18 or i ==24:
plt.ylabel(r'$u/U_\infty$',fontsize=fs)
plt.yticks(fontsize=fs)
else:
plt.setp(ax1.get_yticklabels(), visible=False)
elif paper == False:
for i in range(30):
name = str(i+1)
plt.figure(1)
plt.subplot(5,6,i+1)
color = 'bo'
exec('xfit = np.linspace(min(pos'+name+'d)-1.,max(pos'+name+'d)+1.,500)')
exec('plt.plot(velo'+name+'d,pos'+name+'d,color)')
plt.plot(veldist(xd[i],xfit,men,sdv1,sdv2,sdv3,sdv4,rat1,rat2,spr1,spr2,spr3,spr4,scl1,scl2,scl3),xfit,'r-',linewidth=2)
plt.xlim(0.,1.5)
# plt.ylim(-4.,4.)
# plt.legend(loc=1)
plt.xlabel('Normalized Velocity')
plt.ylabel('$y/D$')
return men,sdv1,sdv2,sdv3,sdv4,rat1,rat2,spr1,spr2,spr3,spr4,scl1,scl2,scl3,cv_error
optProb.addObj('obj')
# Design Variables (scaled to 1)
nrpm = nturb*nwind
optProb.addVarGroup('xvars', nturb, 'c', lower=xlow/100., upper=xupp/100., value=turbineX/100.) # x positions
optProb.addVarGroup('yvars', nturb, 'c', lower=ylow/100., upper=yupp/100., value=turbineY/100.) # y positions
optProb.addVarGroup('rpm', nrpm, 'c', lower=rpmlow/10., upper=rpmupp/10., value=rpm/10.) # rpm values
# Constraints (scaled to 1)
num_cons_sep = (nturb-1)*nturb/2
optProb.addConGroup('sep', num_cons_sep, lower=0., upper=None) # separation between turbines
num_cons_spl = nwind*nobs
optProb.addConGroup('SPL', num_cons_spl, lower=0., upper=SPLlim/10.) # SPL limit
opt = SNOPT()
opt.setOption('Scale option',0)
opt.setOption('Iterations limit',1000000)
res = opt(optProb)
# Printing optimization results (SNOPT format)
print res
# Final results (scaled back to original values)
pow = np.array(-1*res.fStar)*1e4
xf = res.xStar['xvars']*100.
yf = res.xStar['yvars']*100.
rpmf = res.xStar['rpm']*10.
rpmfw = np.zeros((nwind,nturb))
k = 0
for i in range(nwind):
for j in range(nturb):
rpmfw[i,j] = rpmf[k]
plt.plot(n, function_calls_array, 'ro-', label = "Genetic Algorithm Gradient Free")
elif part[i] == "b":
plt.plot(n, function_calls_array, 'go-', label = "Finite Difference Gradient")
elif part[i] == "c":
plt.plot(n, function_calls_array, 'ko-', label = "Analytic Gradient")
x = [0., 0.]
function_calls_array = np.array([])
x = np.zeros(2)
enn = len(x)
ub = 5.*np.ones(enn)
lb = -5.*np.ones(enn)
fin = mul_dim_rosen(x, "a")
optimizer = NSGA2()
optimizer.setOption('maxGen', 1500)
optimizer.setOption('PopSize', enn*20)
xopt, fopt, info = optimize(mul_dim_rosen, x, lb, ub, optimizer, args = [part[0]])
print "----------------- Part: ", part[0], " -------------------"
print "xin: ", x
print "fin: ", fin
print "xopt: ", xopt
print "fopt: ", fopt
print "function calls: ", function_calls
print info, "\n\n"
# I hard coded these in cuz they were taking forever each time running it
function_calls_b = np.array([94, 48774, 86692, 159464, 294067, 512269])
function_calls_c = np.array([7151, 19242, 39978, 80003, 160003, 320003]) #pop size = 10*n
function_calls_c2 = np.array([15247, 39768, 79980, 160003, 320003, 640003]) #pop size = 20*n
