def get_oblique_shock_grid(delta, I):
# Grid as per ASM2010 AIAA code verification paper
# Ramp at I/2
J = int(I / 2)
x_min = -1
x_corner = 0
y = (x_corner - x_min)
x_end_dist = (x_corner - x_min)
# eta_min
start_point = Point(x_min, 0)
corner_point = Point(x_corner, 0)
end_point = Point(x_end_dist * np.cos(delta), x_end_dist * np.sin(delta))
r1 = BoundaryGrids.fit_line(start_point, corner_point, int(I / 2))
r2 = BoundaryGrids.fit_line(corner_point, end_point, int(I / 2))
r = np.append(r1[:-1], r2)
bd1 = Boundary(I, BdType.eta_min)
bd1.populate_boundary_ndar_points(r)
I = len(bd1.Points) - 1
# eta_max
start_point = Point(x_min, y)
corner_point = Point(x_corner, y)
# end_point = Point(x_end_dist*np.cos(delta), x_end_dist*np.sin(delta) + y)
end_point = Point(x_end_dist * np.cos(delta), y)
# r1 = BoundaryGrids.fit_line(start_point, corner_point, int(I/2))
# r2 = BoundaryGrids.fit_line(corner_point, end_point, int(I/2))
# r = np.append(r1[:-1], r2)
r = BoundaryGrids.fit_line(start_point, end_point, I)
bd2 = Boundary(I, BdType.eta_max)
bd2.populate_boundary_ndar_points(r)
# xi_min
start_point = Point(x_min, 0)
end_point = Point(x_min, y)
r = BoundaryGrids.fit_line(start_point, end_point, J)
bd3 = Boundary(J, BdType.xi_min)
bd3.populate_boundary_ndar_points(r)
# xi_max
start_point = Point(x_end_dist * np.cos(delta), x_end_dist * np.sin(delta))
end_point = Point(x_end_dist * np.cos(delta), y)
r = BoundaryGrids.fit_line(start_point, end_point, J)
bd4 = Boundary(J, BdType.xi_max)
bd4.populate_boundary_ndar_points(r)
Grid_obj = StructuredGridGeneration.get_univariate_grid(bd1, bd2, bd3, bd4)
Grid_obj.save_to_vtk('oblique_shock.vtk')
Grid_obj.save_to_txt('oblique_shock.txt')
def get_trap_grid(I, J):
# Grid as per ASM2010 AIAA code verification paper
# eta_min
start_point = Point(0., 0.)
end_point = Point(0.3, 0.0)
r = BoundaryGrids.fit_line(start_point, end_point, I)
bd1 = Boundary(I, BdType.eta_min)
bd1.populate_boundary_ndar_points(r)
# eta_max
start_point = Point(0., 0.1)
end_point = Point(0.3, 0.005)
r = BoundaryGrids.fit_line(start_point, end_point, I)
bd2 = Boundary(I, BdType.eta_max)
bd2.populate_boundary_ndar_points(r)
# xi_min
start_point = Point(0., 0.)
end_point = Point(0., 0.1)
r = BoundaryGrids.fit_line(start_point, end_point, J)
bd3 = Boundary(J, BdType.xi_min)
bd3.populate_boundary_ndar_points(r)
# xi_max
start_point = Point(0.3, 0.)
end_point = Point(0.3, 0.005)
r = BoundaryGrids.fit_line(start_point, end_point, J)
bd4 = Boundary(J, BdType.xi_max)
bd4.populate_boundary_ndar_points(r)
Grid_obj = StructuredGridGeneration.get_univariate_grid(bd1, bd2, bd3, bd4)
Grid_obj.save_to_vtk('trap.vtk')
Grid_obj.save_to_txt('trap.txt')
def get_circle_boundary(radius, centre, bd_eta_max):
# centre is an object of point data type
I = bd_eta_max.n
r = np.empty(I+1, dtype=object)
# Find point on circles such that line from circle
# to eta_max boundary is normal to circle
# theta = arctan((y - yc) / (x - xc))
points = bd_eta_max.Points
for xi in range(I+1):
point = points[xi]
xp, yp = point.x, point.y
dist = np.sqrt((xp - centre.x)**2 + (yp - centre.y)**2)
costheta = (xp - centre.x) / dist
sintheta = (yp - centre.y) / dist
x = centre.x + radius*costheta
y = centre.y + radius*sintheta
r[xi] = Point(x, y)
# x = np.empty(I+1)
# y = np.empty(I+1)
# for i in range(I+1):
# x[i] = r[i].x
# y[i] = r[i].y
# plt.plot(x, y, 'k.')
# plt.show()
bd = Boundary(I, BdType.eta_min)
bd.populate_boundary_ndar_points(r)
return bd
def get_shock_BL_grid(delta, I):
# Grid as per ASM2010 AIAA code verification paper
# Ramp at I/2
J = int(I / 2)
x_min = 0
x_corner = 0.1
x_mid = 0.2 * x_corner
y = 0.0368
x_end_dist = x_corner - x_min
# eta_max
start_point = Point(x_min, y)
turning_point = Point(x_mid, (x_mid - x_min) * np.tan(delta) + y)
end_point = Point(x_corner, -(x_corner-x_mid)*np.tan(delta) + \
turning_point.y)
r1 = BoundaryGrids.fit_line(start_point, turning_point, int(I * 0.2))
r2 = BoundaryGrids.fit_line(turning_point, end_point, int(I * 0.8))
r = np.append(r1[:-1], r2)
bd2 = Boundary(I, BdType.eta_max)
bd2.populate_boundary_ndar_points(r)
I = len(bd2.Points) - 1
# eta_min
start_point = Point(x_min, 0.)
end_point = Point(x_corner, .0)
r = BoundaryGrids.fit_line(start_point, end_point, I)
bd1 = Boundary(I, BdType.eta_min)
bd1.populate_boundary_ndar_points(r)
dt = 3.0
# xi_min
start_point = Point(x_min, 0)
end_point = Point(x_min, y)
stretching_f = lambda xi: 1 + (np.tanh(dt * (xi - 1)) / np.tanh(dt))
r = BoundaryGrids.fit_line(start_point, end_point, J, stretching_f)
bd3 = Boundary(J, BdType.xi_min)
bd3.populate_boundary_ndar_points(r)
# xi_max
start_point = Point(x_corner, 0.)
end_point = Point(x_corner, -(x_corner-x_mid)*np.tan(delta) + \
turning_point.y)
r = BoundaryGrids.fit_line(start_point, end_point, J, stretching_f)
bd4 = Boundary(J, BdType.xi_max)
bd4.populate_boundary_ndar_points(r)
Grid_obj = StructuredGridGeneration.get_univariate_grid(
bd1, bd2, bd3, bd4, stretching_f)
Grid_obj.save_to_vtk('shock_BL.vtk')
Grid_obj.save_to_txt('shock_BL.txt')
def get_airfoil_dat_gridgen():
xt, yt = zip(*np.loadtxt('top'))
xb, yb = zip(*np.loadtxt('bottom'))
CPt = []
CPb = []
for i in range(len(xt)):
CPt.append(Point(xt[i], yt[i]))
for i in range(len(xb)):
CPb.append(Point(xb[i], yb[i]))
I = 200
# xi_spacing_f = lambda xi, I: 0.5 * I * (1 - np.cos(xi * np.pi / I))
xi_spacing_f = lambda xi, I: xi
s_CP_f = lambda j, n, I: j * I / n
s_CP_inverse_f = lambda s, n, I: s * n / I
rt = fit_cubic_spline(CPt, I, xi_spacing_f, s_CP_f, s_CP_inverse_f)
rb = fit_cubic_spline(CPb, I, xi_spacing_f, s_CP_f, s_CP_inverse_f)
bd1 = Boundary(I, BdType.eta_min)
bd1.populate_boundary_ndar_points(rt)
bd2 = Boundary(I, BdType.eta_min)
bd2.populate_boundary_ndar_points(rb)
xtnew = []
ytnew = []
xbnew = []
ybnew = []
for i in range(len(rt)):
xtnew.append(rt[i].x)
ytnew.append(rt[i].y)
for i in range(len(rb)):
xbnew.append(rb[i].x)
ybnew.append(rb[i].y)
np.savetxt('top-new', zip(xtnew, ytnew))
np.savetxt('bottom-new', zip(xbnew, ybnew))
plt.plot(xtnew, ytnew, '*-')
plt.plot(xbnew, ybnew, '.-')
plt.axis('equal')
plt.show()
def fit_semicircle(Radius, t1, t2, centre, I, stretching_f=lambda xi: xi):
# Assuming 2D. centre is a dictionary with 'x' and 'y' as keys
# t1 and t2 - theta ranges - in radians
r = np.empty(I + 1, dtype=object)
for xi in range(I + 1):
s = stretching_f(xi)
t = (s * (t2 - t1) / I) + t1
x = centre['x'] + Radius * np.cos(t)
y = centre['y'] + Radius * np.sin(t)
r[xi] = Point(x, y)
return r
def fit_line_old(start, end, I, stretching_f=lambda xi: xi):
# I + 1 is the number of grid points
# start and stop are 2 dictionaries have x and y as keys
r = np.empty(I + 1, dtype=object)
for xi in range(I + 1):
s = stretching_f(xi / I)
print start['x']
print xi
x = ((1 - s) * start['x']) + (s * end['x'])
y = ((1 - s) * start['y']) + (s * end['y'])
r[xi] = Point(x, y)
return r
def get_exp_plane_grid(I, J):
delta = -10 * np.pi / 180
x_min = -1
x_corner = 0
y_max = (x_corner - x_min) * 1.5
x_end_dist = (x_corner - x_min) * 2
x_max = x_end_dist * np.cos(delta)
y_min = x_end_dist * np.sin(delta)
x_min = 0
x_max = 1
y_min = 0
y_max = 0.2
start_point = Point(x_min, y_min)
end_point = Point(x_max, y_min)
r = BoundaryGrids.fit_line(start_point, end_point, int(I))
bd1 = Boundary(len(r) - 1, BdType.eta_min)
bd1.populate_boundary_ndar_points(r)
start_point = Point(x_min, y_max)
end_point = Point(x_max, y_max)
r = BoundaryGrids.fit_line(start_point, end_point, int(I))
bd2 = Boundary(len(r) - 1, BdType.eta_max)
bd2.populate_boundary_ndar_points(r)
start_point = Point(x_min, y_min)
end_point = Point(x_min, y_max)
r = BoundaryGrids.fit_line(start_point, end_point, int(J))
bd3 = Boundary(len(r) - 1, BdType.xi_min)
bd3.populate_boundary_ndar_points(r)
start_point = Point(x_max, y_min)
end_point = Point(x_max, y_max)
r = BoundaryGrids.fit_line(start_point, end_point, int(J))
bd4 = Boundary(len(r) - 1, BdType.xi_max)
bd4.populate_boundary_ndar_points(r)
Grid_obj = StructuredGridGeneration.get_univariate_grid(bd1, bd2, \
bd3, bd4)
# Grid_obj.save_to_vtk('ExpFan-IB.vtk', 'ExpFan-IB')
# Grid_obj.save_to_txt('ExpFan-IB.txt')
# Grid_obj.save_to_vtk('RampChannel-IB.vtk', 'RampChannel-IB')
Grid_obj.save_to_txt('Rchannel-IB-2.txt')
def extract_control_points(filename):
f = open(filename, 'r')
f.readline() # First line - name of airfoil
n = int(float(f.readline().split()[0]))
# n is the number of control points or airfoil coordinates given
f.readline() # Blank line
# Start reading coordinates
p_list = []
for i in range(n):
x, y = f.readline().split()
x, y = float(x), float(y)
p_list.append(Point(x, y))
return p_list
def get_naca_grid_gridgen():
# Use points from naca equation as control points so that the trailing
# edge is sharp
x, y = get_IB_naca_4_dig('0012', chord_len=1.0, num_points=66)
CP = []
for i in range(len(x)):
CP.append(Point(x[i], y[i]))
################ Generating around airfoil
I = 100
# xi_spacing_f = lambda xi, I: 0.5 * I * (1 - np.cos(xi * np.pi / I))
xi_spacing_f = lambda xi, I: xi
s_CP_f = lambda j, n, I: j * I / n
s_CP_inverse_f = lambda s, n, I: s * n / I
r = fit_cubic_spline(CP, I, xi_spacing_f, s_CP_f, s_CP_inverse_f)
bd1 = Boundary(I, BdType.eta_min)
bd1.populate_boundary_ndar_points(r)
x = []
y = []
for i in range(len(r)):
x.append(r[i].x)
y.append(r[i].y)
return (x, y)
def get_circle(radius, centre, I):
r = np.empty(I + 1, dtype=object)
for xi in range(I + 1):
t = 2 * np.pi * (1 - xi / I)
x = centre.x + radius * np.cos(t)
y = centre.y + radius * np.sin(t)
r[xi] = Point(x, y)
start = []
end = []
normals = []
for i in range(len(r) - 1):
start.append([r[i].x, r[i].y])
end.append([r[i + 1].x, r[i + 1].y])
# Given a line segment, find dx = x2 - x1 and dy = y2 - y1
# The normal vector to the line segment is given by (dy, -dx)
# We also need to normalise it
dx = r[i + 1].x - r[i].x
dy = r[i + 1].y - r[i].y
ds = np.sqrt(dx**2 + dy**2)
dx = dx / ds
dy = dy / ds
normals.append([-dy, dx])
return (start, end, normals)
def get_boundary_grids():
# Eta_max boundary
I = 300
radius = 0.001
p1 = Point(21*radius, 5*radius)
p2 = Point(-6*radius, 5*radius)
p3 = Point(-6*radius, -5*radius)
p4 = Point(21*radius, -5*radius)
bd_eta_max, i_list = get_eta_max_boundary(p1, p2, p3, p4, I)
I = bd_eta_max.n
# Eta_min boundary
centre = Point(0, 0)
bd_eta_min = get_circle_boundary(radius, centre, bd_eta_max)
# Xi min and max boundaries
I = 200
start = Point(centre.x + radius, centre.y)
end = 0.5 * (p1 + p4)
bd_xi_min, bd_xi_max = get_xi_boundaries(start, end, I)
print 'Index list: ', i_list
return (bd_xi_min, bd_xi_max, bd_eta_min, bd_eta_max, i_list)
def get_ramp_grid(I, J):
# Grid as per ASM2010 AIAA code verification paper
# Ramp at I/3
x_bl = 0.
y_bl = 0.
x_rs = 0.25
y_rs = 0.0
x_re = 0.5
y_re = 0.067
x_br = 1.0
y_br = 0.067
x_tl = 0.0
y_tl = 0.2
x_tr = 1.0
y_tr = 0.2
# eta_min
start_point = Point(x_bl, y_bl)
end_point = Point(x_rs, y_rs)
r1 = BoundaryGrids.fit_line(start_point, end_point, int(I / 4))
start_point = Point(x_rs, y_rs)
end_point = Point(x_re, y_re)
r2 = BoundaryGrids.fit_line(start_point, end_point, int(I / 4))
start_point = Point(x_re, y_re)
end_point = Point(x_br, y_br)
r3 = BoundaryGrids.fit_line(start_point, end_point, int(I / 2))
r = np.append(r1[:-1], r2)
r = np.append(r[:-1], r3)
bd1 = Boundary(I, BdType.eta_min)
bd1.populate_boundary_ndar_points(r)
I = len(bd1.Points) - 1
# eta_max
start_point = Point(x_tl, y_tl)
end_point = Point(x_tr, y_tr)
r = BoundaryGrids.fit_line(start_point, end_point, I)
bd2 = Boundary(I, BdType.eta_max)
bd2.populate_boundary_ndar_points(r)
# xi_min
start_point = Point(x_bl, y_bl)
end_point = Point(x_tl, y_tl)
r = BoundaryGrids.fit_line(start_point, end_point, J)
bd3 = Boundary(J, BdType.xi_min)
bd3.populate_boundary_ndar_points(r)
# xi_max
start_point = Point(x_br, y_br)
end_point = Point(x_tr, y_tr)
r = BoundaryGrids.fit_line(start_point, end_point, J)
bd4 = Boundary(J, BdType.xi_max)
bd4.populate_boundary_ndar_points(r)
print len(bd1.Points), len(bd2.Points), len(bd3.Points), len(bd4.Points)
Grid_obj = StructuredGridGeneration.get_univariate_grid(bd1, bd2, bd3, bd4)
Grid_obj.save_to_vtk('ramp_channel_uniform.vtk')
Grid_obj.save_to_txt('ramp_channel_uniform.txt')
def get_plane_IB(delta, I, J):
global plt
x_min = -1.0
x_max = 5.0
y_min = -5.0
y_max = 5.0
airfoil_start = x_min + 43.5 / 100 * (x_max - x_min)
airfoil_end = x_min + 57.5 / 100 * (x_max - x_min)
airfoil_y_s = y_min + 48 / 100 * (y_max - y_min)
airfoil_y_e = y_min + 52 / 100 * (y_max - y_min)
dx = 0.01
dy = 0.01
# x_min = -6.0 * 0.01
# x_max = 21.0 * 0.01
# y_min = -5.0 * 0.01
# y_max = 5.0 * 0.01
# airfoil_start = x_min + 15.38/100*(x_max - x_min)
# airfoil_end = x_min + 26.92/100*(x_max - x_min)
# airfoil_start = -1 * 0.01
# airfoil_end = 1 * 0.01
# airfoil_y_s = y_min + 40./100*(y_max - y_min)
# airfoil_y_e = y_min + 60./100*(y_max - y_min)
# airfoil_y_s = -2.5 * 0.01
# airfoil_y_e = 2.5 * 0.01
# dx = 0.02 * 0.01
# dy = 0.02 * 0.01
length1 = abs(airfoil_start - y_min)
length2 = abs(x_max - airfoil_end)
delta1 = BoundaryGrids.find_hyp_tan_stretching_factor(
dx, int(I / 4), length1)
delta2 = BoundaryGrids.find_hyp_tan_stretching_factor(
dx, int(3 * I / 4), length2)
stretching_f1 = lambda xi: 1 + (np.tanh(delta1 *
(xi - 1)) / np.tanh(delta1))
stretching_f2 = lambda xi: 1 + (np.tanh(delta2 *
(xi - 1)) / np.tanh(delta2))
end_point = Point(x_min, y_min)
start_point = Point(airfoil_start, y_min)
r1_t = BoundaryGrids.fit_line(start_point, end_point, int(I / 4),
stretching_f1)
r1 = np.empty(len(r1_t), dtype=object)
for i in range(len(r1)):
r1[i] = r1_t[len(r1) - 1 - i]
num = int((airfoil_end - airfoil_start) / dx) + 1
start_point = Point(airfoil_start, y_min)
end_point = Point(airfoil_end, y_min)
r2 = BoundaryGrids.fit_line(start_point, end_point, num)
start_point = Point(airfoil_end, y_min)
end_point = Point(x_max, y_min)
r3 = BoundaryGrids.fit_line(start_point, end_point, int(3 * I / 4),
stretching_f2)
r = np.append(r1[:-1], r2)
r = np.append(r[:-1], r3)
bd1 = Boundary(len(r) - 1, BdType.eta_min)
bd1.populate_boundary_ndar_points(r)
f = plt.figure()
plt = bd1.plot_boundary(plt, 'eta_min')
end_point = Point(x_min, y_max)
start_point = Point(airfoil_start, y_max)
r1_t = BoundaryGrids.fit_line(start_point, end_point, int(I / 4),
stretching_f1)
r1 = np.empty(len(r1_t), dtype=object)
for i in range(len(r1)):
r1[i] = r1_t[len(r1) - 1 - i]
num = int((airfoil_end - airfoil_start) / dx) + 1
start_point = Point(airfoil_start, y_max)
end_point = Point(airfoil_end, y_max)
r2 = BoundaryGrids.fit_line(start_point, end_point, num)
start_point = Point(airfoil_end, y_max)
end_point = Point(x_max, y_max)
r3 = BoundaryGrids.fit_line(start_point, end_point, int(3 * I / 4),
stretching_f2)
r = np.append(r1[:-1], r2)
r = np.append(r[:-1], r3)
bd2 = Boundary(len(r) - 1, BdType.eta_max)
bd2.populate_boundary_ndar_points(r)
plt = bd2.plot_boundary(plt, 'eta_max')
I = len(r) - 1
# start_point = Point(x_min, y_min)
# end_point = Point(x_min, y_max)
# A = 3
# L = 1
# yc = 0.5
# stretching_f = lambda xi: (L*xi) + A*(yc - L*xi)*(1 - xi)*xi
# r = BoundaryGrids.fit_line(start_point, end_point, int(J), stretching_f)
# bd3 = Boundary(len(r)-1, BdType.xi_min)
# bd3.populate_boundary_ndar_points(r)
# plt = bd3.plot_boundary(plt, 'xi_min')
# start_point = Point(x_max, y_min)
# end_point = Point(x_max, y_max)
# A = 3
# L = 1
# yc = 0.5
# stretching_f = lambda xi: (L*xi) + A*(yc - L*xi)*(1 - xi)*xi
# r = BoundaryGrids.fit_line(start_point, end_point, int(J), stretching_f)
# bd4 = Boundary(len(r)-1, BdType.xi_max)
# bd4.populate_boundary_ndar_points(r)
# plt = bd4.plot_boundary(plt, 'xi_max')
# plt.show()
num = int((airfoil_y_e - airfoil_y_s) / dy) + 1
K = int(J / 2) + int(J / 2) + num
Grid_obj = TwoDGrid(I + 1, K + 1)
Grid_obj.set_boundary(bd1)
Grid_obj.set_boundary(bd2)
Grid = Grid_obj.Grid
length1 = abs(airfoil_y_s - y_min)
length2 = abs(y_max - airfoil_y_e)
delta1 = BoundaryGrids.find_hyp_tan_stretching_factor(
dy, int(J / 2), length1)
delta2 = BoundaryGrids.find_hyp_tan_stretching_factor(
dy, int(J / 2), length2)
stretching_f1 = lambda xi: 1 + (np.tanh(delta1 *
(xi - 1)) / np.tanh(delta1))
stretching_f2 = lambda xi: 1 + (np.tanh(delta2 *
(xi - 1)) / np.tanh(delta2))
for xi in range(0, I + 1):
P_eta_min = Grid[xi, 0]
P_eta_max = Grid[xi, K]
end_point = P_eta_min
start_point = Point(P_eta_min.x, airfoil_y_s)
r1_t = BoundaryGrids.fit_line(start_point, end_point, int(J / 2),
stretching_f1)
r1 = np.empty(len(r1_t), dtype=object)
for i in range(len(r1)):
r1[i] = r1_t[len(r1) - 1 - i]
# num = int((airfoil_y_e - airfoil_y_s)/ 0.005) + 1
start_point = Point(P_eta_min.x, airfoil_y_s)
end_point = Point(P_eta_max.x, airfoil_y_e)
r2 = BoundaryGrids.fit_line(start_point, end_point, num)
start_point = Point(P_eta_max.x, airfoil_y_e)
end_point = P_eta_max
r3 = BoundaryGrids.fit_line(start_point, end_point, int(J / 2),
stretching_f2)
r = np.append(r1[:-1], r2)
r = np.append(r[:-1], r3)
# K = int(J/2) + int(J/2) + num
# print len(r1), len(r2), len(r3), len(r), J, K
Grid[xi, :] = r
# Grid_obj = StructuredGridGeneration.get_univariate_grid(bd1, bd2, \
# bd3, bd4, stretching_f)
Grid_obj.save_to_vtk('Circle-IB.vtk', 'Circle-IB')
Grid_obj.save_to_txt('Circle-IB.txt')
def fit_cubic_spline(CP, I, xi_spacing_f=lambda xi: xi, s_CP_f=lambda j,n,I: \
j*I/n, s_CP_inverse_f=lambda s,n,I: s*n/I):
# CP is the list of control points
# It should be a list of dictionaries with keys 'x' and 'y'
# I + 1 is the number of grid points
# Note: xi ranges from 0 to I and is an integer only
# xi_spacing_f is a function handle that transforms the spacing between
# consecutive xi. This should return values in the same range as that of xi.
# The default argument is equal spacing.
# Note that now, s = xi_spacing(xi). Hence the grid generation parameter
# is 's' and not xi. However since xi is an integer only, it can be used
# as array index.
# s_CP_f is a function handle which gives the value of s given the index of
# control point j, i.e., it return s_j. The values of s at the intermediate
# control points s_j can be got from equal spacing between s_0 and s_I.
# s_CP_inverse_f is the function that returns the value of 'j' given a value
# s. The s_CP_f function can be though of a map between the 'j' space and
# the 's' space (0<=j<=n and 0<=s<=I). This function is the reverse map.
# Non integer values of 'j' dont have any physical meaning. But the floor
# and ceil of j correspond to s_j and s_j+1 which give the location of the
# j and j+1 th control point respectively. This can be used to give the
# equation of the jth cubic spline that was generated
n = len(CP)
# Solving for second derivative of r, implicitly
r_dd_CP_x = np.zeros((n))
r_dd_CP_y = np.zeros((n))
r_dd_CP_x[0] = 0
r_dd_CP_x[n - 1] = 0
r_dd_CP_y[0] = 0
r_dd_CP_y[n - 1] = 0
# M is the same for x or y coordinate
M = np.zeros((n - 2, n - 2)) # No need to solve for first and last r_dd
b_x = np.zeros((n - 2)) # RHS of the implicit equation
b_y = np.zeros((n - 2)) # RHS of the implicit equation
delta = lambda j: s_CP_f(j + 1, n - 1, I) - s_CP_f(j, n - 1, I)
for j in range(n - 2):
# The delta function is written taking into account that the value of j
# ranges from 0 to n-1. The inner points hence take value of j such that
# 1 <= j <= n-2. But the computational domain is from 0 <= j <= n-3
# (array index).
# Hence all delta function calls should be made with j -> j+1
# Also, all calls to CP should also be made with j -> j+1
if j > 0:
M[j, j - 1] = delta(j - 1 + 1) / 6.0 # c_i
M[j, j] = (delta(j - 1 + 1) + delta(j + 1)) / 3.0 # d_i
if j < n - 3:
M[j, j + 1] = delta(j + 1) / 6.0 # a_i
b_x[j] = (-1 * (CP[j + 1].x - CP[j-1 + 1].x) / delta(j-1 + 1)) + \
(+1 * (CP[j+1 + 1].x - CP[j + 1].x) / delta(j + 1))
b_y[j] = (-1 * (CP[j + 1].y - CP[j-1 + 1].y) / delta(j-1 + 1)) + \
(+1 * (CP[j+1 + 1].y - CP[j + 1].y) / delta(j + 1))
# Now use TDMA solver
r_dd_CP_x[1:n - 1] = Thomas_algorithm(M, b_x)
r_dd_CP_y[1:n - 1] = Thomas_algorithm(M, b_y)
r_dd_CP = np.empty(n, dtype=object)
for i in range(n):
r_dd_CP[i] = Point(r_dd_CP_x[i], r_dd_CP_y[i])
# Now we can construct the cubic spline
# Note, we will construct the cubic spline at integer values of xi.
# The equivalent 's' is found as xi_spacing_f(xi).
# The values of s at the control points are found using the function
# s_CP_f.
# Cubic polynomials have been constructed between the control points
# But, given a value of s, it is necessary to know which interval that s
# belongs to, i.e., which 'j'
# That shall be the inverse of the function s_CP_f which is s_CP_inverse_f.
# But the inverse will return a fractional value. Hence, the floor and ceil
# of that number will give the required interval
r = np.empty(I + 1, dtype=object)
r[0] = Point(CP[0].x, CP[0].y)
r[I] = Point(CP[n - 1].x, CP[n - 1].y)
for xi in range(1, I):
s = xi_spacing_f(xi, I)
j = s_CP_inverse_f(s, n - 1, I)
if int(round(j * 10)) % 10 == 0:
j_l = int(j)
if j_l == n:
j_l = n - 1
j_u = j_l + 1
else:
j_l = int(np.floor(j))
j_u = int(np.ceil(j))
s_l = s_CP_f(j_l, n - 1, I)
s_u = s_CP_f(j_u, n - 1, I)
# xi is used as the array index since it is always an integer
r[xi] = (s_u - s)**3 * r_dd_CP[j_l] / (6.0 * (s_u - s_l)) + \
(s - s_l)**3 * r_dd_CP[j_u] / (6.0 * (s_u - s_l)) + \
( (s_u - s) * \
((CP[j_l] / (s_u - s_l)) - ((s_u - s_l) * \
r_dd_CP[j_l]/6.0)) ) + \
( (s - s_l) * \
((CP[j_u] / (s_u - s_l)) - ((s_u - s_l) * \
r_dd_CP[j_u]/6.0)) )
return r
def read_from_file(filename):
start = []
end = []
normals = []
f = open(filename, 'r')
# First line contains number of iblines
num_iblines = int(f.readline())
for i in range(num_iblines):
line = f.readline()
data = line.split()
data = [float(d) for d in data]
start.append([data[0], data[1]])
end.append([data[2], data[3]])
normals.append([data[4], data[5]])
return (start, end, normals)
if __name__ == '__main__':
# x, y = get_naca_grid_gridgen()
# start, end, normals = get_lines_from_upper_airfoil(x, y)
radius = 0.005
centre = Point(0., 0.)
I = 50
# start, end, normals = get_circle(radius, centre, I)
# start, end, normals = get_exp_fan_IB()
start, end, normals = get_concave_polygon()
plot_start_end_normals(start, end, normals)
filename = 'concave.dat'
put_to_file(start, end, normals, filename)
