def execute(self):
c12axis = self.calculate_c12axis()
if self.interp_from_htc:
cni = self.c12axis_init.shape[0]
if self.c12axis_init[-1, 2] > 1.:
self.c12axis_init /= self.blade_length
# interpolate blade_ae distribution onto c12 distribution
self.blade_ae.c12axis = np.zeros((cni, 4))
for i in range(4):
tck = pchip(c12axis[:, 2], c12axis[:, i])
self.blade_ae.c12axis[:, i] = tck(self.c12axis_init[:, 2])
else:
ds_root = 1. / self.blade_ni_span
ds_tip = 1. / self.blade_ni_span / 3.
dist = np.array([[0., ds_root, 1],
[1., ds_tip, self.blade_ni_span]])
x = distfunc(dist)
self.blade_ae.c12axis = np.zeros((x.shape[0], 4))
for i in range(4):
tck = pchip(c12axis[:, 2], c12axis[:, i])
self.blade_ae.c12axis[:, i] = tck(x)
# scale main axis according to radius
self.blade_ae.c12axis[:, :3] *= self.blade_length
self.blade_ae.radius = self.blade_length + self.hub_radius
self.blade_ae.s = self.bladegeom.smax * self.bladegeom.s * self.blade_length
self.blade_ae.rthick = self.bladegeom.rthick * 100.
self.blade_ae.chord = self.bladegeom.chord * self.blade_length
self.blade_ae.aeset = np.ones(len(self.blade_ae.s))
def execute(self):
c12axis = self.calculate_c12axis()
if self.interp_from_htc:
cni = self.c12axis_init.shape[0]
if self.c12axis_init[-1, 2] > 1.:
self.c12axis_init /= self.blade_length
# interpolate blade_ae distribution onto c12 distribution
self.blade_ae.c12axis = np.zeros((cni, 4))
for i in range(4):
tck = pchip(c12axis[:, 2], c12axis[:,i])
self.blade_ae.c12axis[:, i] = tck(self.c12axis_init[:, 2])
else:
ds_root = 1. / self.blade_ni_span
ds_tip = 1. / self.blade_ni_span / 3.
dist = np.array([[0., ds_root, 1],
[1., ds_tip, self.blade_ni_span]])
x = distfunc(dist)
self.blade_ae.c12axis = np.zeros((x.shape[0], 4))
for i in range(4):
tck = pchip(c12axis[:, 2], c12axis[:,i])
self.blade_ae.c12axis[:, i] = tck(x)
# scale main axis according to radius
self.blade_ae.c12axis[:,:3] *= self.blade_length
self.blade_ae.radius = self.blade_length + self.hub_radius
self.blade_ae.s = self.bladegeom.smax * self.bladegeom.s * self.blade_length
self.blade_ae.rthick = self.bladegeom.rthick * 100.
self.blade_ae.chord = self.bladegeom.chord * self.blade_length
self.blade_ae.aeset = np.ones(len(self.blade_ae.s))
def execute(self):
if self.x_dist.shape[0] > 0:
self.x = self.x_dist
else:
self.x = distfunc([[0.0, -1, 1], [1.0, 0.2 * 1.0 / self.span_ni, self.span_ni]])
print self.x
def redistribute(self, dist=None, s=None):
if dist is not None:
self.s = distfunc(dist)
else:
self.s = s
self.ni = self.s.shape[0]
points = np.zeros((self.ni, self.points.shape[1]))
for i in range(points.shape[1]):
points[:, i] = self._splines[i](self.s)
self.initialize(points)
def redistribute(self, ni, even=False, dist=None, dLE=False, dTE=-1.):
"""
redistribute the points on the airfoil using fusedwind.lib.distfunc
Parameters
----------
ni : int
total number of points on airfoil
even : bool
flag for getting an even distribution of points
dist : list
optional list of control points with the form
[[s0, ds0, n0], [s1, ds1, n1], ... [s<n>, ds<n>, n<n>]]
where\n
s<n> is the normalized curve fraction at each control point,\n
ds<n> is the normalized cell size at each control point,\n
n<n> is the cell count at each control point.
dLE : bool
optional flag for automatically calculating a suitable leading edge cell
size based on the local curvature
dTE : float
optional trailing edge cell size. If set to -1 the cell size will increase
from the LE to TE according to the tanh distribution function used
in distfunc
"""
if even:
dist = [[0, 1. / np.float(ni - 1), 1],
[self.sLE, 1. / np.float(ni - 1),
int(ni * self.sLE)], [1, 1. / np.float(ni - 1), ni]]
elif dLE:
dist = [[0., dTE, 1],
[self.sLE, self.leading_edge_dist(ni), ni / 2],
[1., dTE, ni]]
s = distfunc(dist)
points = np.zeros((ni, 2))
for i in range(2):
points[:, i] = self.spline[i](s)
return AirfoilShape(points)
def redistribute(self, ni, even=False, dist=None, dLE=False, dTE=-1.):
"""
redistribute the points on the airfoil using fusedwind.lib.distfunc
Parameters
----------
ni : int
total number of points on airfoil
even : bool
flag for getting an even distribution of points
dist : list
optional list of control points with the form
[[s0, ds0, n0], [s1, ds1, n1], ... [s<n>, ds<n>, n<n>]]
where\n
s<n> is the normalized curve fraction at each control point,\n
ds<n> is the normalized cell size at each control point,\n
n<n> is the cell count at each control point.
dLE : bool
optional flag for automatically calculating a suitable leading edge cell
size based on the local curvature
dTE : float
optional trailing edge cell size. If set to -1 the cell size will increase
from the LE to TE according to the tanh distribution function used
in distfunc
"""
if even:
dist = [[0, 1./np.float(ni-1), 1], [self.sLE, 1./np.float(ni-1), int(ni*self.sLE)], [1, 1./np.float(ni-1), ni]]
elif dLE:
dist = [[0., dTE, 1], [self.sLE, self.leading_edge_dist(ni), ni / 2], [1., dTE, ni]]
s = distfunc(dist)
points = np.zeros((ni, 2))
for i in range(2):
points[:, i] = self.spline[i](s)
return AirfoilShape(points)
def execute(self):
self.x = distfunc([[0., -1, 1],
[1., 0.2 * 1. / self.span_ni, self.span_ni]])
def compute_x(self):
# simple distfunc for now
self.x_dist = distfunc([[0., -1, 1],
[1., 0.2 * 1. / self.span_ni, self.span_ni]])
def execute(self):
self.x = distfunc([[0., -1, 1], [1., 0.2 * 1./self.span_ni, self.span_ni]])
