def get_max_cl(Re, r):
"""
Analyze airfoil at a fixed Re,
changing aoa from 10 to 15 by 0.1
and returns cl, cd, aoa that makes maximum cl
"""
xf = XFoil()
if r <= 0.175:
xf.airfoil = naca6409
else:
xf.airfoil = naca2412
xf.Re = Re
xf.Re = Re
xf.max_iter = 200
xf.n_crit = 9.00
xf.xtr = [1.00, 1.00]
xf.M = 0
a_seq, cl_seq, cd_seq, cm_seq, cp_seq = xf.aseq(10, 15, 0.1)
# ignore nan by making it 0
cl_seq = np.nan_to_num(cl_seq)
# find the maximum cl
cl_maxi = np.max(cl_seq)
# index of the maximum cl
idx = np.argmax(cl_seq)
return round(cl_maxi, 2), round(a_seq[idx], 2), round(cd_seq[idx], 2)
def get_torque(angular_velocity):
torque_sum_small = 0
torque_sum_large = 0
w = angular_velocity
for key, value in dfdict.items():
value["blade_velocity"] = value['r_position'] * w
value["relative_velocity"] = round(
math.sqrt(value["blade_velocity"]**2 + value["wind_velocity"]**2),
2)
value["arctan"] = math.degrees(
math.atan2(value["wind_velocity"], value["blade_velocity"]))
aoa = round(value["arctan"] - value["pitch_angle"], 2)
value["angle_of_attack"] = aoa
re_n = round(
value["relative_velocity"] * value["chord_length"] / 0.00001511, 3)
value["Reynolds_number"] = re_n
xf = XFoil()
if key < 13:
xf.airfoil = naca6409
else:
xf.airfoil = naca2412
xf.Re = re_n
xf.max_iter = 100
xf.n_crit = 9.00
xf.xtr = [1.00, 1.00]
xf.M = 0
value["Cl"], value["Cd"], value["Cm"], value["Cp"] = xf.a(aoa)
force_reference = 0.5 * density * value["relative_velocity"]**2
if math.isnan(value["Cl"]):
value["torque"] = 0
else:
lift = value["Cl"] * force_reference * 0.0125 * value[
'chord_length']
drag = value["Cd"] * force_reference * 0.0125 * value[
'chord_length']
value["torque"] = value["r_position"] * (
lift * math.sin(math.radians(value["pitch_angle"])) -
drag * math.cos(math.radians(value["pitch_angle"])))
if key < 13:
torque_sum_small += value["torque"]
else:
pass
if key > 0:
torque_sum_large += value["torque"]
else:
pass
df2 = pd.DataFrame.from_dict(dfdict, orient="index")
df_collection.append(df2)
torque_sum_avg = 0.5 * (torque_sum_small + torque_sum_large)
return torque_sum_avg
def total_dict(angular_velocity):
torque_sum = 0
w = angular_velocity
for key, value in dfdict.items():
value["blade_velocity"] = value['r_position'] * w
value["relative_velocity"] = round(
math.sqrt(value["blade_velocity"]**2 + value["wind_velocity"]**2),
2)
value["arctan"] = math.degrees(
math.atan2(value["wind_velocity"], value["blade_velocity"]))
aoa = round(value["arctan"] - value["pitch_angle"], 1)
value["angle_of_attack"] = aoa
re_n = round(value["relative_velocity"] * value["chord_length"] /
0.00001511)
value["Reynolds_number"] = re_n
xf = XFoil()
if key < 13:
xf.airfoil = naca6409
else:
xf.airfoil = naca2412
xf.Re = round(re_n / 100) * 100
xf.max_iter = 200
xf.n_crit = 9.00
xf.xtr = [1.00, 1.00]
xf.M = 0
c_l, c_d, c_m, c_p = xf.a(aoa)
force_reference = 0.5 * density * value["relative_velocity"]**2
if math.isnan(c_l):
pass
else:
value["Cl"] = c_l
value["Cd"] = c_d
value["Cm"] = c_m
value["Cp"] = c_p
lift = c_l * force_reference * 0.0125 * value['chord_length']
drag = c_d * force_reference * 0.0125 * value['chord_length']
value["lift"] = lift
value["drag"] = drag
# value["torque"] = value["r_position"] * lift * math.sin(math.radians(value["pitch_angle"]))
torque = value["r_position"] * (
lift * math.sin(math.radians(value["pitch_angle"])) -
drag * math.cos(math.radians(value["pitch_angle"])))
value["torque"] = torque
torque_sum += torque
xf.reset_bls()
# detailed_df = pd.DataFrame.from_dict(dfdict, orient="index")
# print(detailed_df)
print(torque_sum, angular_velocity)
return dfdict, torque_sum
def analyze_airfoil(x,
y_u,
y_l,
cl,
rey,
mach=0,
xf=None,
pool=None,
show_output=False):
"""
Analyze an airfoil at a given lift coefficient for given Reynolds and Mach numbers using XFoil.
Parameters
----------
x : array_like
Airfoil x-coordinates
y_u, y_l : array_like
Airfoil upper and lower curve y-coordinates
cl : float
Target lift coefficient
rey, mach : float
Reynolds and Mach numbers
xf : XFoil, optional
An instance of the XFoil class to use to perform the analysis. Will be created if not given
pool : multiprocessing.ThreadPool, optional
An instance of the multiprocessing.Threadpool class used to run the xfoil_worker. Will be created if not given
show_output : bool, optional
If True, a debug string will be printed after analyses. False by default.
Returns
-------
cd, cm : float or np.nan
Drag and moment coefficients of the airfoil at specified conditions, or nan if XFoil did not run successfully
"""
# If the lower and upper curves swap, this is a bad, self-intersecting airfoil. Return 1e27 immediately.
if np.any(y_l > y_u):
return np.nan
else:
clean_xf = False
if xf is None:
xf = XFoil()
xf.print = show_output
clean_xf = True
clean_pool = False
if pool is None:
pool = ThreadPool(processes=1)
clean_pool = True
xf.airfoil = Airfoil(x=np.concatenate((x[-1:0:-1], x)),
y=np.concatenate((y_u[-1:0:-1], y_l)))
xf.Re = rey
xf.M = mach
xf.max_iter = 100
xf.n_crit = 0.1
cd, cm = pool.apply(xfoil_worker, args=(xf, cl))
if clean_xf:
del xf
if clean_pool:
del pool
return cd, cm, None if clean_xf else xf
def feature_xfoil(cst_u,
cst_l,
t,
Minf: float,
Re,
AoA,
n_crit=0.1,
fname='feature-xfoil.txt'):
'''
Evaluate by xfoil and extract features.
Inputs:
---
cst-u, cst-l: list of upper/lower CST coefficients of the airfoil. \n
t: airfoil thickness or None \n
Minf: free stream Mach number for wall Mach number calculation \n
Re, AoA (deg): flight condition (s), float or list, for Xfoil \n
n_crit: critical amplification ratio for transition in xfoil \n
fname: output file name. If None, then no output \n
### Dependencies: cst-modeling3d, xfoil
'''
from cst_modeling.foil import cst_foil
from xfoil import XFoil
from xfoil.model import Airfoil
#TODO: Build foil
#! 201 is the maximum amount of points that xfoil can handle
#! tail = 0.001 is to avoid point overlap
xx, yu, yl, t0, R0 = cst_foil(201, cst_u, cst_l, x=None, t=t, tail=0.001)
#! xfoil do not support leading edge of (0,0) on both upper and lower surface
x = np.array(list(reversed(xx[1:])) + xx[1:])
y = np.array(list(reversed(yu[1:])) + yl[1:])
foil = Airfoil(x, y)
#TODO: Xfoil
xf = XFoil()
xf.print = False
xf.airfoil = foil
xf.max_iter = 40
#* Transition by power law
xf.n_crit = n_crit
#TODO: Xfoil calculation
if not isinstance(Re, list):
Re = [Re]
AoA = [AoA]
n = len(Re)
for i in range(n):
xf.reset_bls()
if Re[i] is not None:
xf.Re = Re[i]
cl, cd, cm, cp = xf.a(AoA[i])
x, cp = xf.get_cp_distribution()
print(xf.Re, AoA[i], cl)
#* Extract features
fF = PhysicalXfoil(Minf, AoA[i], Re[i])
fF.setdata(x, y, cp)
fF.extract_features()
#* Output
if fname is None:
continue
if i == 0:
f = open(fname, 'w')
else:
f = open(fname, 'a')
f.write('\n')
f.write('%10s %15.6f \n' % ('Minf', Minf))
f.write('%10s %15.6f \n' % ('AoA', AoA[i]))
f.write('%10s %15.6f \n' % ('Re', Re[i] / 1e6))
f.write('%10s %15.6f \n' % ('CL', cl))
f.write('%10s %15.6f \n' % ('Cd', cd))
f.write('%10s %15.6f \n' % ('Cm', cm))
f.close()
fF.output_features(fname=fname, append=True)