for y1, y2, cl1, cl2, c1, c2 in zip(y[:-1], y[1:], cl[:-1], cl[1:], c[:-1],
c[1:]):
sec_cl = 0.5 * (cl1 * c1 + cl2 * c2) * (y2 - y1)
if y2 <= 0.0:
continue
if y2 < switch[i]:
inboard_wing_cl_hodson += sec_cl
else:
outboard_wing_cl_hodson += sec_cl
inboard_wing_cl_panair = 0.0
outboard_wing_cl_panair = 0.0
y = sec_cl_panair[i][0]
cl = sec_cl_panair[i][1]
w = wing.Elliptic(ra, ra, 20)
for y1, y2, cl1, c1 in zip(w.y[:-1] / ra, w.y[1:] / ra, cl, w.cc):
sec_cl = cl1 * c1 * (y2 - y1)
if y2 <= 0.0:
continue
if y2 < switch[i]:
inboard_wing_cl_panair += sec_cl
else:
outboard_wing_cl_panair += sec_cl
diff = inboard_wing_cl_panair - inboard_wing_cl_hodson
total = panair_wing_cla.cla(10.0, 9.0) * np.radians(1.0)
pct_diff = diff / total * 100
print("RA={:7.4f}, shift = {:7.4f}%".format(ra, pct_diff))
plt.show()
if pr_modified_slender.setup(overwrite=False):
pr_modified_slender.execute()
a_modified_slender.append(pr_modified_slender.WingLiftSlope)
## Get numerical lifting surface results
#krienes = wing_cla.a_krienes()
#kinner = wing_cla.a_kinner()
#jordan = wing_cla.a_jordan()
#medan = wing_cla.a_medan()
# Get Panair results
#A_panair = np.concatenate((np.linspace(0.25, 2.0, 8), np.linspace(2.5, 3.0, 2),
# np.linspace(4.0, 5.0, 2), np.linspace(6.0, 10.0, 3)))
A_panair = np.linspace(1.0, 8.0, 8)
c = 10.0
cla_panair = [panair_wing_cla.cla(c, x, rt, False) for x in A_panair]
#cla_machup = [machup_wing_cla_tapered.a_machup(x, rt, False) for x in A_panair]
# Define cycles for line patterns and markers
lines = cycle([(0, ()), (0, (1, 1)), (0, (5, 5)), (0, (3, 5, 1, 5))])
markers = cycle(['o', 's', '^', 'D', 'v'])
lw = 0.5 # Line width
# Set up a new plot
plt.rc('font', **{'family': 'serif', 'serif': ['Times New Roman']})
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams["font.size"] = 10
plt.rcParams["lines.markersize"] = 4
plt.figure(figsize=(4.0, 2.5))
# Plot the analytical relations
pr_hodson = pralines.Pralines(w, a0, 'Hodson')
if pr_hodson.setup(overwrite=False):
pr_hodson.execute()
a_hodson.append(pr_hodson.WingLiftSlope)
pr_modified_slender = pralines.Pralines(w, a0, 'ModifiedSlender')
if pr_modified_slender.setup(overwrite=False):
pr_modified_slender.execute()
a_modified_slender.append(pr_modified_slender.WingLiftSlope)
# Get Panair results
A_panair = np.linspace(1.0, 8.0, 8)
c = 10.0
cla_panair = [
panair_wing_cla.cla(c, x, rt, False, True, False) for x in A_panair
]
# Set up a new plot
plt.rc('font', **{'family': 'serif', 'serif': ['Times New Roman']})
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams["font.size"] = 10
plt.rcParams["lines.markersize"] = 4
plt.figure(figsize=(4.0, 2.5))
lw = 0.5 # Line width
# Plot the analytical relations
lines = cycle([(0, ()), (0, (1, 1)), (0, (5, 5)), (0, (3, 5, 1, 5))])
plt.plot(A,
a_classical,
label="Classical lifting line theory",
from phd_scripts.utility_scripts import wing_cla
from phd_scripts.utility_scripts import pralines_wing_cla
from phd_scripts.utility_scripts import panair_wing_cla
from phd_scripts.utility_scripts import machup_wing_cla
# Use lift slope from thin airfoil theory
a0 = 2.0 * np.pi
# Average chord length
c_panair = 10.0
# Calculate the lift slope using Pralines
A_analytical = np.linspace(0.01, 8, 800)
A_numerical = np.linspace(0.1, 8, 80)
A_vortexpanel = np.linspace(1, 8, 8)
a_panair = [panair_wing_cla.cla(c_panair, x) for x in A_vortexpanel]
a_classical = wing_cla.a_classical(A_analytical, a0)
a_machup_classical = [
machup_wing_cla.cla(x, lowra_method='Classical') for x in A_numerical
]
# Set up a new plot
plt.rc('font', **{'family': 'serif', 'serif': ['Times New Roman']})
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams["font.size"] = 10
plt.rcParams["lines.markersize"] = 4
plt.figure(figsize=(4.0, 2.5))
lw = 0.5 # Line width
# Plot the analytical relations
A_vandyke_1 = A[:len(a_vandyke_1)]
a_vandyke_2 = a_vandyke[len(a_vandyke_1):]
A_vandyke_2 = A[len(a_vandyke_1):]
# Get numerical lifting surface results
krienes = wing_cla.a_krienes()
kinner = wing_cla.a_kinner()
jordan = wing_cla.a_jordan()
medan = wing_cla.a_medan()
# Get Panair results
#A_panair = np.concatenate((np.linspace(0.25, 2.0, 8), np.linspace(2.5, 3.0, 2),
# np.linspace(4.0, 5.0, 2), np.linspace(6.0, 10.0, 3)))
A_panair = np.linspace(1.0, 8.0, 8)
c_panair = 10.0
cla_panair = [panair_wing_cla.cla(c_panair, x) for x in A_panair]
# Define cycles for line patterns and markers
#lines = cycle([(0, ()), (0, (1,1)), (0, (10,10)), (0, (3,10,1,10)), (0, (10,10,5,10)), (0, (3,10,1,10,1,10)), (0, (5,10)), (0, (15,5,1,5,5,5,1,5)), (0, (1,5))])
lines = cycle([(0, ()), (0, (1, 1)), (0, (5, 5)), (0, (3, 5, 1, 5))])
markers = cycle(['o', 's', '^', 'D', 'v'])
lw = 0.5 # Line width
# Set up a new plot
plt.rc('font', **{'family': 'serif', 'serif': ['Times New Roman']})
plt.rcParams["font.family"] = "Times New Roman"
plt.rcParams["font.size"] = 10
plt.rcParams["lines.markersize"] = 4
plt.figure(figsize=(6.0, 4.0))
# Plot the analytical relations
