def get_performance(o, c, t, v_climb):
chord_meters = c * radius
prop = propeller.Propeller(t,
chord_meters,
radius,
n_blades,
r,
y,
dr,
dy,
airfoils=airfoils,
Cl_tables=Cl_tables,
Cd_tables=Cd_tables)
perf = bemt.bemt_axial(prop,
pitch,
o,
allowable_Re=allowable_Re,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
tip_loss=tip_loss,
mach_corr=mach_corr,
output='long',
v_climb=v_climb)
return perf[-2], perf[-1]
def get_performance(o, c, t):
chord_meters = c * radius
prop = propeller.Propeller(t,
chord_meters,
radius,
n_blades,
r,
y,
dr,
dy,
airfoils=airfoils,
Cl_tables=Cl_tables,
Cd_tables=Cd_tables)
quad = quadrotor.Quadrotor(prop, vehicle_weight)
ff_kwargs = {
'propeller': prop,
'pitch': pitch,
'n_azi_elements': n_azi_elements,
'allowable_Re': allowable_Re,
'Cl_funs': Cl_funs,
'Cd_funs': Cd_funs,
'tip_loss': tip_loss,
'mach_corr': mach_corr,
'alt': alt,
'lift_curve_info_dict': lift_curve_info_dict
}
trim0 = np.array([alpha0, o])
alpha_trim, omega_trim, converged = trim.trim(quad, v_inf, trim0,
ff_kwargs)
T_ff, H_ff, P_ff = bemt.bemt_forward_flight(
quad,
pitch,
omega_trim,
alpha_trim,
v_inf,
n_azi_elements,
alt=alt,
tip_loss=tip_loss,
mach_corr=mach_corr,
allowable_Re=allowable_Re,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
lift_curve_info_dict=lift_curve_info_dict)
dT_h, P_h = bemt.bemt_axial(prop,
pitch,
o,
allowable_Re=allowable_Re,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
tip_loss=tip_loss,
mach_corr=mach_corr,
alt=alt)
return sum(dT_h), P_h, T_ff, P_ff, alpha_trim, omega_trim
def objfun(xn, **kwargs):
radius = kwargs['radius']
r = kwargs['r']
y = kwargs['y']
dr = kwargs['dr']
dy = kwargs['dy']
n_blades = kwargs['n_blades']
airfoils = kwargs['airfoils']
pitch = kwargs['pitch']
vehicle_weight = kwargs['vehicle_weight']
max_chord = kwargs['max_chord']
max_chord_tip = kwargs['max_chord_tip']
tip_loss = kwargs['tip_loss']
mach_corr = kwargs['mach_corr']
Cl_tables = kwargs['Cl_tables']
Cd_tables = kwargs['Cd_tables']
Cl_funs = kwargs['Cl_funs']
Cd_funs = kwargs['Cd_funs']
lift_curve_info_dict = kwargs['lift_curve_info_dict']
allowable_Re = kwargs['allowable_Re']
alt = kwargs['alt']
v_inf = kwargs['v_inf']
alpha0 = kwargs['alpha0']
n_azi_elements = kwargs['n_azi_elements']
mission_time = kwargs['mission_time']
omega_h = xn[0]
twist0 = xn[1]
chord0 = xn[
2] * radius # Convert to meters from c/R before we use in calculations
dtwist = np.array(xn[3:len(r) + 2])
dchord = np.array([x * radius for x in xn[len(r) + 2:2 * len(r) + 2]])
twist = calc_twist_dist(twist0, dtwist)
chord = calc_chord_dist(chord0, dchord)
f = 10000000.
fail = 0
g = [1.0] * (2 * len(r) + 2)
# Calculate geometric constraint values. If a genetic algorithm is used we can fail the case immediately if there
# are any violations. If a gradient-based algorithm is used this will cause the gradient calculation to fail so the
# constraints must be checked normally by the optimizer.
g[1] = chord[-1] / radius - max_chord_tip
g[2:] = get_geocons(chord, max_chord, radius)
prop = propeller.Propeller(twist,
chord,
radius,
n_blades,
r,
y,
dr,
dy,
airfoils=airfoils,
Cl_tables=Cl_tables,
Cd_tables=Cd_tables)
quad = quadrotor.Quadrotor(prop, vehicle_weight)
try:
dT_h, P_h = bemt.bemt_axial(prop,
pitch,
omega_h,
allowable_Re=allowable_Re,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
tip_loss=tip_loss,
mach_corr=mach_corr,
alt=alt)
except FloatingPointError:
print "Floating point error in axial BEMT"
fail = 1
return f, g, fail
except IndexError:
print "Index error in axial BEMT"
fail = 1
return f, g, fail
try:
trim0 = [
alpha0, omega_h
] # Use alpha0 (supplied by user) and the hover omega as initial guesses for trim
ff_kwargs = {
'propeller': prop,
'pitch': pitch,
'n_azi_elements': n_azi_elements,
'allowable_Re': allowable_Re,
'Cl_funs': Cl_funs,
'Cd_funs': Cd_funs,
'tip_loss': tip_loss,
'mach_corr': mach_corr,
'alt': alt,
'lift_curve_info_dict': lift_curve_info_dict
}
alpha_trim, omega_trim, converged = trim.trim(quad, v_inf, trim0,
ff_kwargs)
if not converged or not 0 < alpha_trim < np.pi / 2 or omega_trim < 0:
fail = 1
return f, g, fail
T_trim, H_trim, P_trim = bemt.bemt_forward_flight(
quad,
pitch,
omega_trim,
alpha_trim,
v_inf,
n_azi_elements,
alt=alt,
tip_loss=tip_loss,
mach_corr=mach_corr,
allowable_Re=allowable_Re,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
lift_curve_info_dict=lift_curve_info_dict)
except Exception as e:
print "{} in ff trim".format(type(e).__name__)
fail = 1
return f, g, fail
# Find total energy mission_times = [time_in_hover, time_in_ff] in seconds
energy = P_h * mission_time[0] + P_trim * mission_time[1]
f = energy
print "total energy = " + str(f)
print "Thrust hover = %s" % str(sum(dT_h))
print "(alpha, omega) = (%f, %f)" % (alpha_trim, omega_trim)
# Evaluate performance constraints.
g[0] = vehicle_weight / 4 - sum(dT_h)
if g[0] > 0:
print "hover thrust too low"
return f, g, fail
twist = np.array([6.97, 15.97, 21.77, 21.72, 19.91, 18.14, 16.55, 15.28, 14.01, 13., 12.18, 11.39, 10.76, 10.24, 9.85,
9.4, 9.07, 8.7, 8.46, 8.29, 8.19, 8.17]) * 2 * np.pi / 360
chord = in2m(np.array([0.77, 0.97, 1.18, 1.34, 1.44, 1.49, 1.50, 1.49, 1.46, 1.42, 1.38, 1.33, 1.27, 1.21, 1.14, 1.07, 1.,
0.93, 0.86, 0.77, 0.67, 0.44]))
prop = propeller.Propeller(twist, chord, radius, n_blades, r, y, dr, dy, airfoils=airfoils, Cl_tables=Cl_tables,
Cd_tables=Cd_tables)
quad = quadrotor.Quadrotor(prop, vehicle_weight)
ff_kwargs = {'propeller': prop, 'pitch': pitch, 'n_azi_elements': n_azi_elements, 'allowable_Re': allowable_Re,
'Cl_funs': Cl_funs, 'Cd_funs': Cd_funs, 'tip_loss': tip_loss, 'mach_corr': mach_corr, 'alt': alt,
'lift_curve_info_dict': lift_curve_info_dict}
omega = 2800 * 2*np.pi/60
Th, Ph = bemt.bemt_axial(prop, pitch, omega, allowable_Re=allowable_Re, Cd_funs=Cd_funs, Cl_funs=Cl_funs)
print "(T, P) for %d RPM = (%f, %f)" % (omega*60/2/np.pi, sum(Th)*0.2248*4, Ph)
omega = 4200 * 2*np.pi/60
Th, Ph = bemt.bemt_axial(prop, pitch, omega, allowable_Re=allowable_Re, Cd_funs=Cd_funs, Cl_funs=Cl_funs)
print "(T, P) for %d RPM = (%f, %f)" % (omega*60/2/np.pi, sum(Th)*0.2248*4, Ph)
## Hover performance of SUI Endurance for a range of rotor speeds
# hthrust = []
# hpower = []
# o_start = 2000.
# o_end = 4600.
# o_range = np.linspace(o_start, o_end, 10)
# for o in o_range*2*np.pi/60:
# dT_h, P_h = bemt.bemt_axial(prop, pitch, o, allowable_Re=allowable_Re, Cl_funs=Cl_funs, Cd_funs=Cd_funs,
# tip_loss=True, mach_corr=mach_corr, alt=alt)
def objfun_optimize_twist(xn, **kwargs):
radius = kwargs['radius']
r = kwargs['r']
y = kwargs['y']
dr = kwargs['dr']
dy = kwargs['dy']
n_blades = kwargs['n_blades']
airfoils = kwargs['airfoils']
pitch = kwargs['pitch']
target_thrust = kwargs['thrust']
tip_loss = kwargs['tip_loss']
mach_corr = kwargs['mach_corr']
allowable_Re = kwargs['allowable_Re']
Cl_tables = kwargs['Cl_tables']
Cd_tables = kwargs['Cd_tables']
Cl_funs = kwargs['Cl_funs']
Cd_funs = kwargs['Cd_funs']
chord = kwargs['chord'] * radius
alt = kwargs['alt']
# The algorithm works with radius-normalized chord lengths, but the BEMT takes real chord lengths, so multiply by R
omega = xn[0]
twist0 = xn[1]
dtwist = xn[2:]
twist = calc_twist_dist(twist0, dtwist)
f = 1000.
fail = 0
g = [1.0]
prop = propeller.Propeller(twist,
chord,
radius,
n_blades,
r,
y,
dr,
dy,
airfoils=airfoils,
Cl_tables=Cl_tables,
Cd_tables=Cd_tables)
try:
dT, P, FM = bemt.bemt_axial(prop,
pitch,
omega,
allowable_Re=allowable_Re,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
tip_loss=tip_loss,
mach_corr=mach_corr,
alt=alt)
except FloatingPointError:
fail = 1
return f, g, fail
except IndexError:
return 10000, g, fail
f = P
print "Power = " + str(f)
print "Thrust = %s" % str(sum(dT))
# Evaluate thrust constraint. Target thrust must be less than computed thrust
g[0] = target_thrust - sum(dT)
return f, g, fail
chord_inches = chord * radius
# Run BEMT on propeller
prop = propeller.Propeller(twist,
chord,
radius,
n_blades,
r,
y,
dr,
dy,
Clalpha,
airfoils=airfoils)
CT, CP, CQ, Cl, dT, pCT, pCP, Re, aoa = bemt.bemt_axial(prop, pitch, omega)
print "C_P = " + str(pCP)
print "Thrust = " + str(sum(dT))
plt.figure(1)
plt.plot(r, chord, '-')
plt.xlabel("radial station, r")
plt.ylabel("chord length, inches")
plt.figure(2)
plt.plot(r, twist, '-')
plt.xlabel("radial station, r")
plt.ylabel("twist, degrees")
plt.show()
def objfun(xn, **kwargs):
radius = kwargs['radius']
r = kwargs['r']
y = kwargs['y']
dr = kwargs['dr']
dy = kwargs['dy']
n_blades = kwargs['n_blades']
airfoils = kwargs['airfoils']
pitch = kwargs['pitch']
target_thrust = kwargs['vehicle_weight'] / 4
cval_max = kwargs['max_chord']
tip_loss = kwargs['tip_loss']
mach_corr = kwargs['mach_corr']
Cl_tables = kwargs['Cl_tables']
Cd_tables = kwargs['Cd_tables']
Cl_funs = kwargs['Cl_funs']
Cd_funs = kwargs['Cd_funs']
allowable_Re = kwargs['allowable_Re']
opt_method = kwargs['opt_method']
alt = kwargs['alt']
omega = xn[0]
twist0 = xn[1]
chord0 = xn[2] * radius
dtwist = np.array(xn[3:len(r) + 2])
dchord = np.array([x * radius for x in xn[len(r) + 2:2 * len(r) + 2]])
twist = calc_twist_dist(twist0, dtwist)
chord = calc_chord_dist(chord0, dchord)
f = 1000.
fail = 0
g = [1.0] * (2 * len(r) + 1)
# Calculate geometric constraint values. If a genetic algorithm is used we can fail the case immediately if there
# are any violations. If a gradient-based algorithm is used this will cause the gradient calculation to fail so the
# constraints must be checked normally by the optimizer.
g[1:] = get_geocons(chord, cval_max, radius)
if opt_method == 'nograd':
if any(geocon > 0.0 for geocon in g[1:]):
print "geocons violated"
return f, g, fail
prop = propeller.Propeller(twist,
chord,
radius,
n_blades,
r,
y,
dr,
dy,
airfoils=airfoils,
Cl_tables=Cl_tables,
Cd_tables=Cd_tables)
try:
dT, P = bemt.bemt_axial(prop,
pitch,
omega,
allowable_Re=allowable_Re,
Cl_funs=Cl_funs,
Cd_funs=Cd_funs,
tip_loss=tip_loss,
mach_corr=mach_corr,
alt=alt)
except (FloatingPointError, IndexError):
fail = 1
return f, g, fail
f = P
print f
print "Thrust = %s" % str(sum(dT))
# Evaluate thrust constraint. Target thrust must be less than computed thrust
g[0] = target_thrust - sum(dT)
return f, g, fail