def sDWM(argv):
"""
Main wrapper to the core flow field DWM model
This function:
1) handles inputs arguments,
2) set up defaults values,
3) call the wind farm layout model,
4) create the wake tree by calling the expansion of the GCL model
5) main loop run for each turbine from most upstream to the most downstream in flow coordinate system
"""
# default run values
WD = 120.0;WS = 9.0;TI = 0.06;WTcoord=current_dir+'/data/Lill_full.dat'; WTG='NREL5MW'; HH=90.0; R=63.0; stab='N'; accum='dominant'
# Input arguments parser
try:
opts, args = getopt.getopt(argv,"hi:",["WD=","WS=","TI=","WTcoord=","WTG=","HH=","R=","stab=","accum="])
except getopt.GetoptError:
print('RUN_sDWM.py -i <WD> <WS> <TI> <WT coord.> <WTG> <HH> <R> <Stab.> <accum.> <optim>')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print('usage: RUN_sDWM.py -i <WD: deg, 0deg is the north> <WS: m/s> <TI: [-]> <WT coord. turbine coordinate file> <WTG name> <HH hub height: m> <R turbine radius: m> <stab: VS, S ... VU, U> <accum: linear, quadratic, dominant, ewma>')
sys.exit()
elif opt in ("-i"):
WD = float(sys.argv[2])
WS = float(sys.argv[3])
TI = float(sys.argv[4])
WTcoord = str(sys.argv[5])
WTG = str(sys.argv[6])
HH= float(sys.argv[7])
R= float(sys.argv[8])
stab= str(sys.argv[9])
accum= sys.argv[10]
ttt = time.time()
# Load wind turbine and wind farm
WT = wt.WindTurbine('Windturbine','../WT-data/'+WTG+'/'+WTG+'_PC.dat',HH,R)
WF = wf.WindFarm('Windfarm',WTcoord,WT)
print('***** sDWM v'+ __version__+' // WF: '+str(WF.nWT)+' WT(s) / WD '+str(WD)+'deg / WS '+str(WS)+' m/s / TI '+str(TI)+' / accumulation: '+accum+' ******************')
# Scaling wind farm to NREL's rotor size
if 'Lill' in WTcoord:
WF.vectWTtoWT=WF.vectWTtoWT*(WT.R/46.5) # 46.5 is the Lillgrund nominal radius of SWT turbine
# Compute distance WT to WT in mean flow coordinate system
distFlowCoord, nDownstream, id0= WF.turbineDistance(WD)
# Init dictionnaries
deficits, turb, inlets_ffor, inlets_ffor_deficits,inlets_ffor_turb,out, DWM, ID_waked, ID_wake_adj, Farm_p_out, WT_p_out, Vel_out, Pos_out=init(WF)
# Extreme wake to define WT's in each wake, including partial wakes. This is a call to GCL expansion model [12]
ID_wake = {id0[i]:(get_Rw(x=distFlowCoord[0,id0[i],:],\
R=2.*WF.WT.R,TI=TI,CT=WT.get_CT(WS),pars=[0.435449861,0.797853685,-0.124807893,0.136821858,15.6298,1.0])>\
np.abs(distFlowCoord[1,id0[i],:])).nonzero()[0] \
for i in range(WF.nWT)}
## Main DWM loop over turbines
for iT in range(WF.nWT):
# Define flow case geometry
cWT = id0[iT]
#Radial coordinates in cWT for wake affected WT's
x=distFlowCoord[0,cWT,ID_wake[cWT]]
C2C = distFlowCoord[1,cWT,ID_wake[cWT]]
print cWT
index_orig=np.argsort(x)
x=np.sort(x)
row= ID_wake[id0[iT]][index_orig]
C2C=C2C[index_orig]
# wind turbine positions
Pos_out[iT,0] = ((WF.pos[0,cWT]-min(WF.pos[0,:]))/(2.*WF.WT.R))*(WT.R/46.5)
Pos_out[iT,1] = ((WF.pos[1,cWT]-min(WF.pos[1,:]))/(2.*WF.WT.R))*(WT.R/46.5)
# Wrapping the DWM core model with inputs
par={
'WS' : WS, # wind speed [m/s]
'TI' : TI, # turbulence intensity
'atmo_stab' : stab, # atmospheric stability VU, U ,NU, N, NS, S, VS
'WTG' : WTG, # wind turbine name
'WTG_spec' : WT, # class holding wind turbine specifications
'wtg_ind' : row, # turbine index
'hub_z' : x/(2*WF.WT.R), # set up location of downstream turbines in flow direction
'hub_x' : np.ceil((2*(max(abs(C2C))+WF.WT.R))/WF.WT.R)*0.5+C2C/(WF.WT.R), # set up lateral displacements of downstream turbines
'C2C' : C2C, # parse the "Center to Center" distance between hubs
'lx' : np.ceil((2.*(max(abs(C2C))+WF.WT.R))/WF.WT.R), # set up length of the domain in lateral coordinates in rotor diameter [D]
'ly' : np.ceil((2.*(max(abs(C2C))+WF.WT.R))/WF.WT.R), # set up length of the domain in longitudinal in rotor diameter [D]
'accu_inlet' : False, # specify whether inlet propagation is enabled (more accurate but slows down simulations), if disable, it behaves like HAWC2-DWM
'accu' : accum, # type of wake accumulation (linear, dominant [4] or quadratic)
'full_output': False # Flag for generating the complete output with velocity deficit scalars (larger memory foot print if enabled)
}
ID_wake_adj[str(id0[iT])]=row
aero, meta, mfor, ffor, DWM, deficits,inlets_ffor,inlets_ffor_deficits, \
inlets_ffor_turb,turb, out,ID_waked = DWM_main_field_model(ID_waked,deficits,inlets_ffor,inlets_ffor_deficits,
inlets_ffor_turb,turb,DWM,out,**par)
# Total power
Farm_p_out= Farm_p_out+out[str(meta.wtg_ind[0])][0] # based on BEM,# Farm_p_out= Farm_p_out+out[str(meta.wtg_ind[0])][4] # based on power curve
# Power by each turbine
WT_p_out[iT]=out[str(meta.wtg_ind[0])][0]
Vel_out[iT]=out[str(meta.wtg_ind[0])][1]
elapsedttt = time.time() - ttt
print('***** sDWM v'+ __version__+' // Farm prod. is: %4.2f kW, %i WT(s) : %s -- in %4.2f sec *************' %(Farm_p_out,WF.nWT,np.array_str(WT_p_out),elapsedttt))
return Farm_p_out,WT_p_out,Vel_out,Pos_out,WF,WT,aero, meta, mfor, ffor, DWM, deficits,inlets_ffor,inlets_ffor_deficits, inlets_ffor_turb,turb, out,ID_waked
def run_sdwm(WD, WS, TI, WTcoord, WTG, HH, R, stab, accum):
"""
Main wrapper to the core flow field DWM model
This function:
1) handles inputs arguments,
2) set up defaults values,
3) call the wind farm layout model,
4) create the wake tree by calling the expansion of the GCL model
5) main loop run for each turbine from most upstream to the most downstream in flow coordinate system
Parameters
----------
WD: float
WS: float
TI: float
WTcoord: float
WTG: float
HH: float
R: float
stab: str
accum: str
Returns
-------
Farm_p_out:
WT_p_out:
Vel_out:
Pos_out:
WF:
WT:
aero:
meta:
mfor:
ffor:
DWM:
deficits:
inlets_ffor:
inlets_ffor_deficits:
inlets_ffor_turb:
turb:
out:
ID_waked:
"""
ttt = time.time()
# Load wind turbine and wind farm
WT = wt.WindTurbine('Windturbine',current_dir+'/WT-data/'+WTG+'/'+WTG+'_PC.dat',HH,R)
WF = wf.WindFarm('Windfarm', coordFile=WTcoord, WT=WT)
print '***** sDWM v'+ __version__+' // WF: '+str(WF.nWT)+' WT(s) / WD '+str(WD)+'deg / WS '+str(WS)+' m/s / TI '+str(TI)+' / accumulation: '+accum+' ******************'
# Scaling wind farm to NREL's rotor size
if 'Lill' in WTcoord:
WF.vectWTtoWT=WF.vectWTtoWT*(WT.R/46.5) # 46.5 is the Lillgrund nominal radius of SWT turbine
# Compute distance WT to WT in mean flow coordinate system
distFlowCoord, nDownstream, id0= WF.turbineDistance(WD)
# Init dictionnaries
deficits, turb, inlets_ffor, inlets_ffor_deficits,inlets_ffor_turb,out, DWM, ID_waked, ID_wake_adj, Farm_p_out, WT_p_out, Vel_out, Pos_out=init(WF)
# Extreme wake to define WT's in each wake, including partial wakes. This is a call to GCL expansion model [12]
ID_wake = {id0[i]:(get_Rw(x=distFlowCoord[0,id0[i],:],
R = 2. * WF.WT.R,
TI=TI,
CT=WT.get_CT(WS),
pars=[0.435449861,0.797853685,-0.124807893,0.136821858,15.6298,1.0])>\
np.abs(distFlowCoord[1,id0[i],:])).nonzero()[0] \
for i in range(WF.nWT)}
## Main DWM loop over turbines
for iT in range(WF.nWT):
print 'Simulating turbine '+ str(iT+1) +'/'+str(WF.nWT)
# Define flow case geometry
cWT = id0[iT]
#Radial coordinates in cWT for wake affected WT's
x=distFlowCoord[0,cWT,ID_wake[cWT]]
C2C = distFlowCoord[1,cWT,ID_wake[cWT]]
index_orig=np.argsort(x)
x=np.sort(x)
row= ID_wake[id0[iT]][index_orig]
C2C=C2C[index_orig]
# wind turbine positions
Pos_out[iT,0] = ((WF.pos[0,cWT]-min(WF.pos[0,:]))/(2.*WF.WT.R))*(WT.R/46.5)
Pos_out[iT,1] = ((WF.pos[1,cWT]-min(WF.pos[1,:]))/(2.*WF.WT.R))*(WT.R/46.5)
# Wrapping the DWM core model with inputs
par={
'WS' : WS, # wind speed [m/s]
'TI' : TI, # turbulence intensity
'atmo_stab' : stab, # atmospheric stability VU, U ,NU, N, NS, S, VS
'WTG' : WTG, # wind turbine name
'WTG_spec' : WT, # class holding wind turbine specifications
'wtg_ind' : row, # turbine index
'hub_z' : x/(2*WF.WT.R), # set up location of downstream turbines in flow direction
'hub_x' : np.ceil((3*(max(abs(C2C))+WF.WT.R))/WF.WT.R)*0.5+C2C/(WF.WT.R), # set up lateral displacements of downstream turbines
'C2C' : C2C, # parse the "Center to Center" distance between hubs
'lx' : np.ceil((3.*(max(abs(C2C))+WF.WT.R))/WF.WT.R), # set up length of the domain in lateral coordinates in rotor diameter [D]
'ly' : np.ceil((3.*(max(abs(C2C))+WF.WT.R))/WF.WT.R), # set up length of the domain in longitudinal in rotor diameter [D]
'accu_inlet' : True, # specify whether inlet propagation is enabled (more accurate but slows down simulations), if disable, it behaves like HAWC2-DWM
'accu' : accum, # type of wake accumulation (linear, dominant [4] or quadratic)
'full_output': True # Flag for generating the complete output with velocity deficit scalars (larger memory foot print if enabled)
}
ID_wake_adj[str(id0[iT])]=row
aero, meta, mfor, ffor, DWM, deficits,inlets_ffor,inlets_ffor_deficits, \
inlets_ffor_turb,turb, out,ID_waked = DWM_main_field_model(ID_waked,deficits,inlets_ffor,inlets_ffor_deficits,
inlets_ffor_turb,turb,DWM,out,**par)
# Total power
Farm_p_out= Farm_p_out+out[str(meta.wtg_ind[0])][0] # based on BEM,# Farm_p_out= Farm_p_out+out[str(meta.wtg_ind[0])][4] # based on power curve
# Power by each turbine
WT_p_out[iT]=out[str(meta.wtg_ind[0])][0]
Vel_out[iT]=out[str(meta.wtg_ind[0])][1]
elapsedttt = time.time() - ttt
print '***** sDWM v'+ __version__+' // Farm prod. is: %4.2f kW, %i WT(s) : %s -- in %4.2f sec *************' %(Farm_p_out,WF.nWT,np.array_str(WT_p_out),elapsedttt)
return Farm_p_out,WT_p_out,Vel_out,Pos_out,WF,WT,aero, meta, mfor, ffor, DWM, deficits,inlets_ffor,inlets_ffor_deficits, inlets_ffor_turb,turb, out,ID_waked
def sDWM(derating, kwargs, xind):
# ttt = time.time()
# WD,WS,TI,WTcoord,WTG,HH,R,stab,accum,optim,
WD = kwargs.get('WD')
WS = kwargs.get('WS')
TI = kwargs.get('TI')
WTcoord = kwargs.get('WTcoord')
WTG = kwargs.get('WTG')
HH = kwargs.get('HH')
R = kwargs.get('R')
stab = kwargs.get('stab')
accum = kwargs.get('accum')
optim = to_bool(kwargs.get('optim'))
dynamic = to_bool(kwargs.get('dynamic'))
Meandering_turb_box_name = kwargs.get('Meandering_turb_box_name')
WaT_turb_box_name = kwargs.get('WaT_turb_box_name')
# WT = wt.WindTurbine('Vestas v80 2MW offshore','V80_2MW_offshore.dat',70,40)
# WF = wf.WindFarm('Horns Rev 1','HR_coordinates.dat',WT)
WT = wt.WindTurbine('Windturbine',
'../WT-data/' + WTG + '/' + WTG + '_PC.dat', HH, R)
WF = wf.WindFarm('Windfarm', WTcoord, WT)
#########################################################################################################
if dynamic:
if Meandering_turb_box_name != None:
print '# ------------------------------------------------------------------------------------------------ #'
print '# -------------------------- Pre Init for Meandering --------------------------------------------- #'
if Meandering_turb_box_name[1] == 'Mann_Box':
filename = Meandering_turb_box_name[
0] # must come from sDWM Input
#R_wt = 46.5 # can come from sDWM
#WTG = 'NY2' # can come from sDWM
#HH = 90. # Hub height # can come from sDWM
Rw = 1. # try with no expansion
WF.U_mean = WS
WF.WT_R = WT.R
WF.WT_Rw = Rw
WF.TI = TI
WT = wt.WindTurbine('Windturbine', '../WT-data/' + WTG + '/' +
WTG + '_PC.dat', HH,
WT.R) # already present in sDWM
TurBox, WF = pre_init_turb(filename, WF, WT)
elif Meandering_turb_box_name[1] == 'LES_Box':
filename = Meandering_turb_box_name[
0] # must come from sDWM Input
# R_wt = 46.5 # can come from sDWM
# WTG = 'NY2' # can come from sDWM
# HH = 90. # Hub height # can come from sDWM
Rw = 1. # try with no expansion
WF.U_mean = WS
WF.WT_R = WT.R
WF.WT_Rw = Rw
WF.TI = TI
WT = wt.WindTurbine('Windturbine', '../WT-data/' + WTG + '/' +
WTG + '_PC.dat', HH,
WT.R) # already present in sDWM
TurBox, WF = pre_init_turb_LES(filename, WF, WT)
print '# -------------------------- End Pre Init for Meandering ----------------------------------------- #'
print '# ------------------------------------------------------------------------------------------------ #'
else:
print '# -------------------------- Used Saved DATA for MEANDERING / No Meandering ---------------------- #'
TurBox = ReadMannInput('1028')
if WaT_turb_box_name != None:
print '# ------------------------------------------------------------------------------------------------ #'
print '# -------------------------- Pre Init for WaT ---------------------------------------------------- #'
MannBox = pre_init_turb_WaT(WaT_turb_box_name)
MannBox.R_ref = R
print '# -------------------------- End Pre Init for WaT ------------------------------------------------ #'
print '# ------------------------------------------------------------------------------------------------ #'
else:
MannBox = None
print 'TI Input:', TI
#TI = TurBox.TI
print 'TI from TurbBox', TI
####################################################################################################################
if optim is True:
print 'Performing optimization'
WT.CP = np.load('../WT-data/' + WTG + '/' + WTG + '_CP.npy')
WT.CT = np.load('../WT-data/' + WTG + '/' + WTG + '_CT.npy')
#print 'Cp and then Ct are :'
#print WT.CP
#print WT.CT
WT.lambdaf3 = WT.CP[:, :, 0]
WT.PITCH3 = WT.CP[:, :, 1]
WT.CP3 = WT.CP[:, :, 2]
WT.CT3 = WT.CT[:, :, 2]
WT.CP3[WT.CP3 > (16. / 27.)] = 0
WT.CP3[np.isnan(WT.CP3)] = 0
WT.CT3[np.isnan(WT.CT3)] = 0
WT.CPc = cntr.Cntr(WT.PITCH3, WT.lambdaf3, WT.CP3)
WT.CTc = cntr.Cntr(WT.PITCH3, WT.lambdaf3, WT.CT3)
elif optim is False:
print 'Not Performing optimization'
derating = 1.0 * np.ones((WF.nWT))
WT.CP = None
WT.CT = None
# Scaling wind farm to NREL's rotor size
if 'Lill' in WTcoord:
WF.vectWTtoWT = WF.vectWTtoWT * (
WT.R / 46.5) # 46.5 is the Lillgrund nominal radius of SWT turbine
#print WT.R/46.5: 1.0
#print 'WF.vectWTtoWT: ', WF.vectWTtoWT
#raw_input('...')
# Compute distance WT to WT in mean flow coordinate system
distFlowCoord, nDownstream, id0 = WF.turbineDistance(WD)
"""
print 'distflowcoord', distFlowCoord
print 'ndownstream', nDownstream
print 'id0', id0
raw_input('...')
#"""
# Init dictionnaries
deficits, turb, inlets_ffor, inlets_ffor_deficits, inlets_ffor_turb, out, DWM, ID_waked, ID_wake_adj, Farm_p_out, WT_p_out, Vel_out, WT_pitch_out, WT_RPM_out = init(
WF)
#raw_input('entry')
# Extreme wake to define WT's in each wake, including partial wakes
# but it doesn't keep Rw, however Rw is an important quantity used to model Meandering Dynamic!
# I don't really understant what is following. (it comes from 2009 simple analytical wake model Glarsen)
# it seems to show wich wake we need for each iteration?
# To keep the thoughts of ewma I keep this part but I am not sure about this...
# For this reason we have to know TI from the turbulent box before this step.
ID_wake = {id0[i]:(get_Rw(x=distFlowCoord[0,id0[i],:],
R=2*WF.WT.R,TI=TI,CT=WT.get_CT(WS),pars=[0.435449861,0.797853685,-0.124807893,0.136821858,15.6298,1.0])>\
np.abs(distFlowCoord[1,id0[i],:])).nonzero()[0] \
for i in range(WF.nWT)}
#"""
print 'ID_wake {id: id with a wake}: '
print ID_wake
print ID_wake[1]
print distFlowCoord
# Power output list
Farm_p_out = 0.
WT_p_out = np.zeros((WF.nWT))
WT_pitch_out = np.zeros((WF.nWT, 2))
WT_RPM_out = np.zeros((WF.nWT, 2))
Vel_out = np.zeros((WF.nWT))
# COMPUTING TO PLOT
"""
POWER_TURBINE = []
VEL_plot=[]
RPM_plot=[]
PITCH_plot=[]
#"""
## Main DWM loop over turbines
FFOR_result = []
print '############################################################################################################'
print '# -------------------------- # MAIN LOOP OVER TURBINE PROCESSING # --------------------------------------- #'
for iT in range(WF.nWT):
print '# -------------------------- # PROCESSING for iteration ' + str(
iT) + ' # -------------------------------- #'
# Define flow case geometry
cWT = id0[iT]
#Radial coordinates in cWT for wake affected WT's
x = distFlowCoord[0, cWT, ID_wake[cWT]]
C2C = distFlowCoord[1, cWT, ID_wake[cWT]]
index_orig = np.argsort(x)
x = np.sort(x)
row = ID_wake[id0[iT]][index_orig]
C2C = C2C[index_orig]
# Wrapping the DWM core model with I/O
par = {
'WS':
WS,
'TI':
TI,
'atmo_stab':
stab,
'WTG':
WTG, #'WTG':'NREL5MW',
'WTG_spec':
WT,
'wtg_ind':
row, # turbine index
'hub_z':
x / (
2 * WF.WT.R
), # compute the flow field at downstream location of each downwind turbine !
'hub_x':
np.ceil((2 * (max(abs(C2C)) + WF.WT.R)) / WF.WT.R) * 0.5 + C2C / (
WF.WT.R
), # lateral offset of each downwind turbine with respect to the most upstream turbine in the tree
'C2C':
C2C, # center 2 center distances between hubs
'lx':
np.ceil((2. * (max(abs(C2C)) + WF.WT.R)) /
WF.WT.R), # length of the domain in lateral
'ly':
np.ceil((2. * (max(abs(C2C)) + WF.WT.R)) /
WF.WT.R), # length of the domain in longitudinal in D
'wake_ind_setting':
1,
'accu_inlet':
True,
'derating':
derating[row[0]],
'optim':
optim,
'accu':
accum, # type of wake accumulation
'full_output':
False, # Flag for generating the complete output with velocity deficit
'iT':
iT
}
ID_wake_adj[str(id0[iT])] = row
print row
#"""
if dynamic:
aero, meta, mfor, ffor, DWM, deficits, inlets_ffor, inlets_ffor_deficits, inlets_ffor_turb, turb, out, ID_waked = DWM_main_field_model_partly_dynamic(
ID_waked, deficits, inlets_ffor, inlets_ffor_deficits,
inlets_ffor_turb, turb, DWM, out, TurBox, WF, MannBox, **par)
"""
if meta.iT == 0:
FFOR_result = ffor.WS_axial_ffor
else:
FFOR_result[:,:,meta.iT:,:] = FFOR_result[:,:,meta.iT:,:] + ffor.WS_axial_ffor
#raw_input('....')
if meta.MEANDERING_Total_plot:
plt.ion()
plt.figure('Meandering draw in time for each turbine in the wake')
x = ffor.x_vec
y = ffor.y_vec
# X, Y = np.meshgrid(x, y)
X, Y = ffor.x_mat, ffor.y_mat
ref_rotor_x_emitting = (meta.hub_x[0] + np.cos(np.linspace(-pi, pi))) / 2.
ref_rotor_y_emitting = (meta.hub_y + np.sin(np.linspace(-pi, pi))) / 2.
for i_z in np.arange(0, 3):
# for i_z in np.arange(0, meta.nz):
ref_rotor_x_concerned = (meta.hub_x[i_z] + np.cos(np.linspace(-pi, pi))) / 2.
ref_rotor_y_concerned = (meta.hub_y + np.sin(np.linspace(-pi, pi))) / 2.
for i_t in np.arange(0, meta.nt, 2):
plt.cla()
plt.clf()
print 'i_t = ', i_t
if True:
plt.subplot(121)
CS1 = plt.contourf(X, Y, FFOR_result[:, :, i_z, i_t], np.linspace(0., 1., 20))
plt.xlabel('x'), plt.ylabel('y'), plt.title(
'Axial WS FFoR for Turbine ' + str(meta.wtg_ind[i_z])) # 7-iz
plt.plot(ref_rotor_x_emitting, ref_rotor_y_emitting, 'r', label='WT emitting')
plt.plot(ref_rotor_x_concerned, ref_rotor_y_concerned, 'k', label='WT concerned')
plt.legend()
plt.colorbar(CS1)
plt.draw()
plt.pause(0.0001)
#"""
else:
aero, meta, mfor, ffor, DWM, deficits, inlets_ffor, inlets_ffor_deficits, inlets_ffor_turb, turb, out, ID_waked = DWM_main_field_model(
ID_waked, deficits, inlets_ffor, inlets_ffor_deficits,
inlets_ffor_turb, turb, DWM, out, **par)
if meta.steadyBEM_AINSLIE:
if meta.iT == 0:
FFOR_result = ffor.WS_axial_ffor
else:
FFOR_result[:, :, meta.
iT:, :] = FFOR_result[:, :, meta.
iT:, :] + ffor.WS_axial_ffor
# raw_input('....')
if meta.MEANDERING_Total_plot:
plt.ion()
plt.figure(
'Meandering draw in time for each turbine in the wake')
x = ffor.x_vec
y = ffor.y_vec
# X, Y = np.meshgrid(x, y)
X, Y = ffor.x_mat, ffor.y_mat
ref_rotor_x_emitting = (meta.hub_x[0] +
np.cos(np.linspace(-pi, pi))) / 2.
ref_rotor_y_emitting = (meta.hub_y +
np.sin(np.linspace(-pi, pi))) / 2.
for i_z in np.arange(0, 3):
# for i_z in np.arange(0, meta.nz):
ref_rotor_x_concerned = (meta.hub_x[i_z] + np.cos(
np.linspace(-pi, pi))) / 2.
ref_rotor_y_concerned = (
meta.hub_y + np.sin(np.linspace(-pi, pi))) / 2.
for i_t in np.arange(0, meta.nt, 4):
plt.cla()
plt.clf()
print 'i_t = ', i_t
if True:
plt.subplot(121)
CS1 = plt.contourf(
X, Y, FFOR_result[:, :, i_z, i_t]
) #, np.linspace(0., 1., 20))
plt.xlabel('x'), plt.ylabel('y'), plt.title(
'Axial WS FFoR for Turbine ' +
str(meta.wtg_ind[i_z])) # 7-iz
plt.plot(ref_rotor_x_emitting,
ref_rotor_y_emitting,
'r',
label='WT emitting')
plt.plot(ref_rotor_x_concerned,
ref_rotor_y_concerned,
'k',
label='WT concerned')
plt.legend()
plt.colorbar(CS1)
plt.draw()
plt.pause(0.0001)
plt.ioff()
# Farm_p_out= Farm_p_out+out[str(meta.wtg_ind[0])][4] # based on power curve
# /!\/!\ not put in commentary this /!\/!\
#"""
# Total power
Farm_p_out = Farm_p_out + out[str(meta.wtg_ind[0])][0] # based on BEM
# Power by each turbine
WT_p_out[iT] = out[str(meta.wtg_ind[0])][0]
# Pitch and RPM
"""
WT_pitch_out[iT,0]=aero.PITCH
WT_pitch_out[iT,1]=aero.PITCH_opt
WT_RPM_out[iT,0]=aero.RPM
WT_RPM_out[iT,1]=aero.RPM_opt
#"""
Vel_out[iT] = out[str(meta.wtg_ind[0])][1]
#PLOTTING
"""
POWER_TURBINE=POWER_TURBINE+[out[str(meta.wtg_ind[0])][0]]
VEL_plot=VEL_plot+[meta.mean_WS_DWM]
RPM_plot=RPM_plot+[WT_RPM_out[iT,0]]
PITCH_plot=PITCH_plot+[WT_pitch_out[iT,0]]
#"""
print '# -------------------------- # PROCESSING for iteration ' + str(
iT) + ' ended # -------------------------- #'
print '########################################################################################################'
print '########################################################################################################'
print '# -------------------------- # MAIN LOOP OVER TURBINE PROCESS ENDED # ------------------------------------ #'
print '############################################################################################################'
#
#
# print id0
# print 'xind', xind
# print 'wtp', WT_p_out[id0]
# print 'pitch', WT_pitch_out[id0]
# print 'omega', WT_RPM_out[id0]
# print 'vel', Vel_out[id0]
# print 'xind', xind[id0]
""" Main Results Plot"""
"""
plt.plot(range(WF.nWT),POWER_TURBINE)
plt.title('Power Production for each Turbines'), plt.xlabel('Turbine Location'), plt.ylabel('Power (kW)')
plt.show()
plt.plot(range(WF.nWT), VEL_plot)
plt.title('Velocity for each Turbines'), plt.xlabel('Turbine Location'), plt.ylabel('Velocity (m/s)')
plt.show()
plt.plot(range(WF.nWT), RPM_plot)
plt.title('RPM for each Turbines'), plt.xlabel('Turbine Location'), plt.ylabel('RPM')
plt.show()
plt.plot(range(WF.nWT), PITCH_plot)
plt.title('Pitch for each Turbines'), plt.xlabel('Turbine Location'), plt.ylabel('Pitch ()')
plt.show()
"""
print 'The farm production is: %4.2f kW, where each turbine is: %s' % (
Farm_p_out, np.array_str(WT_p_out))
print 'Vel_out:', Vel_out
return Farm_p_out, WT_p_out[id0], WT_pitch_out[id0], WT_RPM_out[
id0], Vel_out[id0], id0
