-
Notifications
You must be signed in to change notification settings - Fork 0
/
spm_Airfoil.py
246 lines (215 loc) · 10.5 KB
/
spm_Airfoil.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.ticker as ticker
import matplotlib.patches as patches
from src.util import PanelGenerator
from src.util import compute_ellipse_and_circulation
from src.panel_methods.spm import run_panel_method
from src.code_collections import data_collections as dc
from src.util import generate_four_digit_NACA
from aeropy import xfoil_module as xf
from pathlib import Path
import numpy as np
import pandas as pd
airfoil = '2412'
AoA = 6
res = None
x_foil_cl = 0
xfoil_airfoil_dir = Path('../xfoil_usable')
loc = str(xfoil_airfoil_dir) + '/' + 'naca' + airfoil + '.txt'
try:
"""
This module calls xfoil to compute the pressure coefficients for the airfoil
Xfoil needs to be in this directory
If you set NACA=True, then the airfoil is generated using the NACA four digit series so only pass in the 'naca' + four digit series
If you set NACA=False, then the airfoil is loaded from a file so pass in the location of the file
"""
res = xf.find_pressure_coefficients(airfoil=loc, alpha=AoA, NACA=True, delete=True)
x_foil_cl = xf.find_coefficients(airfoil=loc, alpha=AoA, NACA=True, delete=True)['CL']
xfoil_cp = pd.DataFrame(res)
xfoil_cp_upp = xfoil_cp[xfoil_cp['y'] >= 0]
xfoil_cp_low = xfoil_cp[xfoil_cp['y'] < 0]
except:
print('XFOIL Error')
res = xf.find_pressure_coefficients(airfoil='naca' + airfoil, alpha=0, NACA=True, delete=True)
xfoil_cp = pd.DataFrame(res)
xfoil_cp_upp = xfoil_cp[xfoil_cp['y'] >= 0]
xfoil_cp_low = xfoil_cp[xfoil_cp['y'] < 0]
numB = len(xfoil_cp // 2) * 2 + 1
num_grid = 101 # Change this if it is too slow or you run out of memory
X_NEG_LIMIT = -0.5
X_POS_LIMIT = 1.25
Y_NEG_LIMIT = -0.5
Y_POS_LIMIT = 0.5
V = 1
x, y = np.linspace(X_NEG_LIMIT, X_POS_LIMIT, num_grid), np.linspace(Y_NEG_LIMIT, Y_POS_LIMIT,
num_grid)
# %% THE ABOVE CODE DOES NOT PROVIDE THE RIGHT BOUNDARY POINTS FOR THE AIRFOIL. LOAD THE AIRFOIL DATA FROM A FILE
# Actually it does but there a problem with generating the naca airfoil. it always returns an odd number of points even
# when the number of points is even. This causes the panel method to fail. Simple fixes are to either use an and odd
# number of points or to update `numB` after generating the airfoil.
# TODO: FIX THIS
# %% PANEL METHOD GEOMETRY SETUP
XB, YB = generate_four_digit_NACA(num_NACA=airfoil, num_points=numB, chord_length=1, b=2)
geometry = dc.Geometry(x=XB, y=YB, AoA=AoA)
panelized_geometry = PanelGenerator.compute_geometric_quantities(geometry=geometry)
X = geometry.x
Y = geometry.y
panel_normal_vector_X = panelized_geometry.xC + panelized_geometry.S / 2 * np.cos(panelized_geometry.delta)
panel_normal_vector_Y = panelized_geometry.yC + panelized_geometry.S / 2 * np.sin(panelized_geometry.delta)
# %% SOURCE PANEL METHOD
V_normal, V_tangential, lam, u, v = run_panel_method(panelized_geometry=panelized_geometry, V=V, AoA=AoA,
x=x, y=y)
sumLambda = np.sum(lam * panelized_geometry.S)
print(f'Sum of Source Strengths: {sumLambda}')
# %% Pressure Coefficient for the grid
local_v = np.sqrt(u ** 2 + v ** 2)
cp = 1 - (local_v / V) ** 2
# %% Pressure Coefficient for the airfoil
panel_velocities = V_tangential ** 2
Cp = 1 - panel_velocities / V ** 2
spm_CP = pd.DataFrame({
'x': panelized_geometry.xC,
'y': panelized_geometry.yC,
'Cp': Cp
})
spm_CP_upp = spm_CP[spm_CP['y'] >= 0]
spm_CP_low = spm_CP[spm_CP['y'] <= 0]
CN = -Cp * np.sin(panelized_geometry.beta) * panelized_geometry.S # Normal coefficient
CA = -Cp * np.cos(panelized_geometry.beta) * panelized_geometry.S # Axial coefficient
CL = np.sum(CN * np.cos(AoA * np.pi / 180)) - np.sum(CA * np.sin(AoA * np.pi / 180)) # Lift coefficient
CD = np.sum(CN * np.sin(AoA * np.pi / 180)) + np.sum(CA * np.cos(AoA * np.pi / 180)) # Drag coefficient
# %% Compute circulation
airfoil_ellipse = dc.Ellipse(0.5, 0, 0.6, 0.2)
flowfieldproperties = dc.FlowFieldProperties(x=x, y=y, u=u, v=v)
circulation = compute_ellipse_and_circulation(flowfieldproperties, airfoil_ellipse, 1000)
# %% PLOTTING
fig, axs = plt.subplots(2, 3, figsize=(25, 12), dpi=100, layout="constrained")
fig.suptitle(f'Source Panel Method Results for NACA {airfoil} at {AoA} Degrees Angle of Attack', fontsize=16)
# Panel Geometry
axs[0, 0].set_title('Panel Geometry')
for i in range(len(panelized_geometry.S)):
axs[0, 0].plot([X[i], X[i + 1]], [Y[i], Y[i + 1]], 'k')
axs[0, 0].plot([X[i]], [Y[i]], 'ro', markersize=0.5)
axs[0, 0].plot([panelized_geometry.xC[i]], [panelized_geometry.yC[i]], 'bo', markersize=0.5)
if i == 0:
axs[0, 0].plot([panelized_geometry.xC[i], panel_normal_vector_X[i]],
[panelized_geometry.yC[i], panel_normal_vector_Y[i]],
'k', label='First Panel')
if i == 1:
axs[0, 0].plot([panelized_geometry.xC[i], panel_normal_vector_X[i]],
[panelized_geometry.yC[i], panel_normal_vector_Y[i]],
'k', label='Second Panel')
else:
axs[0, 0].plot([panelized_geometry.xC[i], panel_normal_vector_X[i]],
[panelized_geometry.yC[i], panel_normal_vector_Y[i]],
'k')
axs[0, 0].plot([panel_normal_vector_X[i]], [panel_normal_vector_Y[i]], 'go', markersize=0.5)
axs[0, 0].set_xlabel('X')
axs[0, 0].set_ylabel('Y')
axs[0, 0].set_aspect('equal', adjustable='box')
# Streamlines
axs[0, 1].set_title('Streamlines')
axs[0, 1].streamplot(x, y, u, v, density=2, color='b')
axs[0, 1].set_xlim(X_NEG_LIMIT, X_POS_LIMIT)
axs[0, 1].set_ylim(Y_NEG_LIMIT, Y_POS_LIMIT)
axs[0, 1].fill(geometry.x, geometry.y, 'k') # Plot polygon (circle or airfoil)
# draw_circle at the trailing edge
axs[0, 1].add_patch(
patches.Circle(
(geometry.x[0], geometry.y[0]),
0.1,
fill=False,
color='r',
linewidth=3
))
axs[0, 1].annotate('Kutta Condition not Enforced', xy=(geometry.x[0], geometry.y[0]), xycoords='data',
xytext=(.99, -.1), textcoords='axes fraction',
va='top', ha='left',
arrowprops=dict(facecolor='black', shrink=0.05))
axs[0, 1].set_xlabel('X')
axs[0, 1].set_ylabel('Y')
axs[0, 1].set_aspect('equal', adjustable='box')
# Pressure Contours
axs[0, 2].set_title('Pressure Contours')
x_m, y_m = np.meshgrid(x, y)
cp_plot = axs[0, 2].pcolor(x_m, y_m, cp, cmap='jet', norm=colors.SymLogNorm(linthresh=0.03, linscale=0.03,
vmin=cp.min(), vmax=cp.max()))
ratio = np.abs(cp.max() / cp.min())
num_ticks = 10
ticks = -np.flip(np.geomspace(0.05, np.abs(cp.min()), 7))[:-1]
ticks = np.append(ticks, np.geomspace(0.01, cp.max(), 3))
cbar = fig.colorbar(cp_plot, ax=axs[0, 2], label="Pressure Coefficient", ticks=ticks)
cbar.ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%0.2f'))
axs[0, 2].fill(geometry.x, geometry.y, 'k')
axs[0, 2].set_ylim(Y_NEG_LIMIT, Y_POS_LIMIT)
axs[0, 2].set_xlim(X_NEG_LIMIT, X_POS_LIMIT)
axs[0, 2].set_aspect('equal', adjustable='box')
axs[0, 2].set_xlabel('X')
axs[0, 2].set_ylabel('Y')
axs[0, 2].set_aspect('equal', adjustable='box')
# Cp Distribution
axs[1, 0].set_title('Cp Distribution')
axs[1, 0].invert_yaxis()
axs[1, 0].plot(xfoil_cp_upp['x'], xfoil_cp_upp['Cp'], 'r', label='Upper Surface Xfoil')
axs[1, 0].plot(xfoil_cp_low['x'], xfoil_cp_low['Cp'], 'b', label='Lower Surface Xfoil')
axs[1, 0].plot(spm_CP_upp['x'], spm_CP_upp['Cp'], 'bo', label='Upper Surface SPM')
axs[1, 0].plot(spm_CP_low['x'], spm_CP_low['Cp'], 'ro', label='Lower Surface SPM')
axs[1, 0].legend()
axs[1, 0].set_xlabel('x/c')
axs[1, 0].set_ylabel('Cp')
axs[1, 0].legend()
# Pressure Vectors
axs[1, 1].set_title('Pressure Vectors')
dCPx = np.cos(panelized_geometry.beta) * Cp
dCPy = np.sin(panelized_geometry.beta) * Cp
positive_top = np.where(Cp >= 0, True, False) & np.where(panelized_geometry.yC >= 0, True, False)
positive_bottom = np.where(Cp >= 0, True, False) & np.where(panelized_geometry.yC < 0, True, False)
negative_top = np.where(Cp < 0, True, False) & np.where(panelized_geometry.yC >= 0, True, False)
negative_bottom = np.where(Cp < 0, True, False) & np.where(panelized_geometry.yC < 0, True, False)
axs[1, 1].fill(geometry.x, geometry.y, 'k')
axs[1, 1].quiver(panelized_geometry.xC[positive_top], panelized_geometry.yC[positive_top], -dCPx[positive_top],
-dCPy[positive_top], pivot='tip', scale=10, color='r', width=0.0025, headwidth=2, headlength=4)
axs[1, 1].quiver(panelized_geometry.xC[positive_bottom], panelized_geometry.yC[positive_bottom],
dCPx[positive_bottom], dCPy[positive_bottom], pivot='tail', scale=10, color='r', width=0.0025,
headwidth=2, headlength=4)
axs[1, 1].quiver(panelized_geometry.xC[negative_top], panelized_geometry.yC[negative_top], -dCPx[negative_top],
-dCPy[negative_top], pivot='tail', scale=10, color='b', width=0.0025, headwidth=2, headlength=4)
axs[1, 1].quiver(panelized_geometry.xC[negative_bottom], panelized_geometry.yC[negative_bottom],
dCPx[negative_bottom], dCPy[negative_bottom], pivot='tip', scale=10, color='b', width=0.0025,
headwidth=2, headlength=4)
axs[1, 1].set_xlim(X_NEG_LIMIT, X_POS_LIMIT)
axs[1, 1].set_ylim(Y_NEG_LIMIT, Y_POS_LIMIT)
axs[1, 1].set_xlabel('x/c')
axs[1, 1].set_ylabel('Cp')
axs[1, 1].set_aspect('equal', adjustable='box')
# Results Table
axs[1, 2].axis('off')
table_data = pd.DataFrame({
"Sum of Source Strengths": [round(sumLambda, 6)],
"cl (Calculated from CP)": [round(CL, 6)],
'Circulation (Evaluated from grid velocities)': [round(circulation.circulation, 6)],
'cl (Kutta-Joukowski)': [round(2 * circulation.circulation, 6)],
'cl (XFOIL)': [round(x_foil_cl, 6)],
})
table_data = table_data.round(6)
table_data = table_data.T
row_labels = table_data.index.values
# Customize the table appearance
table = axs[1, 2].table(cellText=table_data.values,
rowLabels=row_labels,
loc='center',
cellLoc='center',
bbox=[0.5, 0.6, 0.18, 0.2])
# Adjust font size and style
table.auto_set_font_size(False)
table.set_fontsize(10)
table.auto_set_column_width([0]) # Adjust column width
# Add a title to the table
axs[1, 2].set_title('Results', fontsize=12, pad=20) # Use 'pad' to adjust the distance from the top
# Optionally add grid lines
axs[1, 2].grid(False)
# Optionally add a border around the table
for key, cell in table.get_celld().items():
cell.set_linewidth(0.5)
plt.show()