def plot_regions(self, axes, best_regions, plane):
"""
Function to plot the best efficiency regions, once they've been calculated.
"""
axes.autoscale(True)
# colors = itertools.cycle(["r", "b", "g", "y", "c","m"])
idx = np.linspace(0, 1, len(best_regions))
cmap = cm.get_cmap('Accent')
colors = itertools.cycle(cmap(idx))
legend_handles = []
axes.clear()
axes.axis([
self.optimisation['Umin'], self.optimisation['Umax'],
self.optimisation['Tmin'], self.optimisation['Tmax']
])
battery = self.battery
atmosphere = self.atmosphere
# fig, ax = plt.subplots()
# axes.set_color_cycle(jet(idx))
for patch in best_regions:
zone = patch['region']
combo_colour = next(colors)
area = 0
for i, p in enumerate(zone):
axes.add_collection(
collections.PathCollection(zone, color=combo_colour))
bbx = p.get_extents()
length = bbx.x1 - bbx.x0
height = bbx.y1 - bbx.y0
area = area + length * height
if area > 0.1:
combo_patch = mpatches.Patch(color=combo_colour,
label=patch['combo_name'])
legend_handles.append(combo_patch)
# plt.scatter(points[:,0], points[:,1])
axes.set_title(r"Most efficient combination")
axes.set_ylabel(r"Thrust produced [N]")
axes.set_xlabel(r"Air speed (in steady flight) [m/s]")
axes.autoscale(False)
Uplot = np.linspace(self.optimisation['Umin'] + 0.1,
self.optimisation['Umax'], 100)
dragP = optimised_consumption.dragFunc(Uplot, plane, atmosphere)
self.plane_curve = [Uplot, dragP]
axes.plot(Uplot, dragP, color='k', label='Plane')
plane_line = mlines.Line2D([], [], color='k', label='Plane drag')
legend_handles.append(plane_line)
axes.legend(handles=legend_handles)
self.canvas.draw()
def top_choices(plane, motors, propellers, atmosphere, folder, conditions,
number):
"""
Function that returns the best motor-prop combinations for a specified plane
subject to a number of conditions. The conditions can be turned on or off
and the number of top results the function returns is given by the
'number' parameter.
"""
best_combos = []
combo_name_list = []
result_list = []
no_low_thrust = 0
no_over_current = 0
no_over_mass = 0
no_too_weak = 0
no_analysed = 0
no_suitable = 0
#To be safe we use the urange and trange from the generated power surface
#properties.
gen_props = pickle.load(open(folder + 'generation_properties.pk', "rb"))
Urange = gen_props['Urange']
Trange = gen_props['Trange']
#Make sure the velocity and thrust ranges match, if they don't... Well it's probably not too bad but it's better to play safe.
Umesh, Tmesh = np.meshgrid(Urange, Trange)
Psurf_col = np.empty(np.shape(Umesh))
Capacity = gen_props['battery']['capacity'] * 3.6 * gen_props['battery'][
'V']
Tlimit = conditions['static_thrust']
masslimit = conditions['motor_mass']
if conditions['static_thrust'] == False:
Tlimit = 0
if conditions['allowable_overload'] == False:
overload = float('inf')
else:
overload = conditions['allowable_overload']
if conditions['motor_mass'] == False:
masslimit = float('inf')
#Check to make sure all requested power surfaces have been calculated and filter them by conditions.
for motor in motors:
for propeller in propellers:
no_analysed += 1
combo_name = motor['name'] + ' - ' + propeller['name']
save_name = folder + motor['name'] + propeller['name'] + '.pk'
if os.path.isfile(save_name):
result = pickle.load(open(save_name, "rb"))
Ilimit = overload * result['motor']['Imax']
if result['Tmax'] < Tlimit:
no_low_thrust += 1
continue
if result['Imax'] > Ilimit:
no_over_current += 1
continue
if result['motor']['mass'] > masslimit:
no_over_mass += 1
continue
#ADDD MAX SPEED CONDITION
combo_name_list.append(combo_name)
Pfunc = result['Pinterp']
drag_min_vel = sp.optimize.minimize_scalar(
lambda x: optimised_consumption.dragFunc(
x, plane, atmosphere),
bounds=(0.5, max(Urange)),
method='bounded')
d_max_vel = drag_min_vel.x
drag_min = optimised_consumption.dragFunc(
d_max_vel, plane, atmosphere)
P_d_max = Pfunc(d_max_vel, drag_min)
if P_d_max != P_d_max:
no_too_weak += 1
continue
# P_min_vel=sp.optimize.minimize_scalar(lambda x : Pfunc(x, optimised_consumption.dragFunc(x, plane, atmosphere)), bounds=(0.5, max(Urange)), method='bounded')
# P_min_vel=P_min_vel.x
# P_min=Pfunc(P_min_vel, optimised_consumption.dragFunc(P_min_vel, plane, atmosphere))
no_suitable += 1
if conditions['fixed_speed'] == False:
try:
P_min_vel = sp.optimize.minimize_scalar(
lambda x: Pfunc(
x,
optimised_consumption.dragFunc(
x, plane, atmosphere)),
bounds=(0.5, max(Urange)),
method='bounded')
P_min_vel = P_min_vel.x
P_min_vel_drag = optimised_consumption.dragFunc(
P_min_vel, plane, atmosphere)
P_min = Pfunc(P_min_vel, P_min_vel_drag)
except Exception as e:
print(e)
pass
result['max_time'] = int(Capacity / P_min)
result['max_time_speed'] = P_min_vel
result['max_time_drag'] = P_min_vel_drag
result['max_time_distance'] = result['max_time'] * result[
'max_time_speed']
result['max_distance_time'] = int(Capacity / P_d_max)
result['max_distance_speed'] = d_max_vel
result['max_distance_drag'] = drag_min
result['max_distance'] = result[
'max_distance_time'] * result['max_distance_speed']
else:
P_min_vel = conditions['fixed_speed']
P_min = Pfunc(
P_min_vel,
optimised_consumption.dragFunc(P_min_vel, plane,
atmosphere))
P_min_vel_drag = optimised_consumption.dragFunc(
P_min_vel, plane, atmosphere)
result['max_time'] = int(Capacity / P_min)
result['max_time_speed'] = P_min_vel
result['max_time_drag'] = P_min_vel_drag
result['max_time_distance'] = result['max_time'] * result[
'max_time_speed']
d_max_vel = conditions['fixed_speed']
P_d_max = Pfunc(
d_max_vel,
optimised_consumption.dragFunc(d_max_vel, plane,
atmosphere))
result['max_distance_time'] = int(Capacity / P_d_max)
result['max_distance_speed'] = d_max_vel
result['max_distance_drag'] = P_min_vel_drag
result['max_distance'] = result[
'max_distance_time'] * result['max_distance_speed']
result_list.append(result)
Psurf_col = np.dstack(
(Psurf_col, result['Psurf'])) #Collect power surface
else:
print('Combination missing')
return
#Now we sort the results by whatever we're interested in
if conditions['maximise_distance'] == True:
best_combos = sorted(result_list,
key=itemgetter('max_distance'),
reverse=True)[:number]
else:
best_combos = sorted(result_list,
key=itemgetter('max_time'),
reverse=True)[:number]
#Summarise the analysis
report = {
'analysed': no_analysed,
'low_thrust': no_low_thrust,
'over_current': no_over_current,
'over_mass': no_over_mass,
'too_weak': no_too_weak,
'suitable': no_suitable,
}
return best_combos, report
def plot_eff(self, axes):
# try:
# plt.delaxes(axes.axes[1])
# except Exception as e:
# print(e)
selected_combos = []
axes.clear()
item_model = self.listView_results.model()
for row in self.listView_results.selectedIndexes():
item = item_model.itemFromIndex(row)
selected_combos.append(item.combo)
combo = selected_combos[0]
plane = self.plane
atmosphere = self.atmosphere
# plt.suptitle(image_name, fontsize=14, fontweight='bold')
axes.set_title(r"Total efficiency of combination")
axes.set_ylabel(r"Thrust produced [N]")
axes.set_xlabel(r"Air speed (in steady flight) [m/s]")
levels = [0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.70, 0.75]
colormap = cm.get_cmap('RdYlGn')
CS_filled = axes.contourf(combo['Umesh'],
combo['Tmesh'],
combo['eta_system'],
levels,
cmap=colormap,
extend='both')
if hasattr(self, 'cbar'):
self.cbar = plt.colorbar(CS_filled, cax=self.cbar.ax)
self.cbar.ax.set_ylabel('Total efficiency')
else:
self.cbar = plt.colorbar(CS_filled, ax=axes)
self.cbar.ax.set_ylabel('Total efficiency')
CS = plt.contour(combo['Umesh'],
combo['Tmesh'],
combo['eta_system'],
levels,
colors=('k', ),
linewidths=(1, ))
axes.clabel(CS, fmt='%2.1f', colors='k', fontsize=14)
id_valid = (np.array(combo['Pmesh']) == np.array(combo['Pmesh']))
T = combo['Tmesh']
T[~id_valid] = float('nan')
axes.axis([0, np.nanmax(combo['Umesh']), 0, np.nanmax(T)])
# axes.plot(self.plane_curve[0],self.plane_curve[1], color='k', label='PLane')
if self.radioButton_fixed_speed.isChecked():
axes.scatter(combo['max_time_speed'],
combo['max_time_drag'],
c='b',
label='Cruise speed')
else:
axes.scatter(combo['max_distance_speed'],
combo['max_distance_drag'],
c='b',
label='Max range conditions')
axes.scatter(combo['max_time_speed'],
combo['max_time_drag'],
c='r',
label='Max flight time conditions')
try:
axes.plot(self.plane_curve[0],
self.plane_curve[1],
color='k',
label='Plane drag')
except AttributeError:
Uplot = np.linspace(self.optimisation['Umin'] + 0.1,
self.optimisation['Umax'], 100)
dragP = optimised_consumption.dragFunc(Uplot, plane, atmosphere)
self.plane_curve = [Uplot, dragP]
axes.plot(Uplot, dragP, color='k', label='Plane drag')
plane_line = mlines.Line2D([], [], color='k', label='Plane drag')
axes.legend(loc=1)
self.canvas.draw()
def plot_perf(self, axes):
"""
Function to plot performance of currently selected result.
"""
selected_combos = []
axes.clear()
item_model = self.listView_results.model()
for row in self.listView_results.selectedIndexes():
item = item_model.itemFromIndex(row)
selected_combos.append(item.combo)
combo = selected_combos[0]
plane = self.plane
atmosphere = self.atmosphere
# plt.suptitle(image_name, fontsize=14, fontweight='bold')
axes.set_title(r"Power consumption of system")
axes.set_ylabel(r"Thrust produced [N]")
axes.set_xlabel(r"Air speed (in steady flight) [m/s]")
Pmax = combo['motor']['Imax'] * self.battery['V']
levels = np.linspace(
20, Pmax,
10) #[20,40,60,80,100,120,140,160,180,200,220,240,260,280]
colormap = cm.get_cmap('coolwarm')
CS_filled = axes.contourf(combo['Umesh'],
combo['Tmesh'],
combo['Pmesh'],
levels,
cmap=colormap,
extend='both')
if hasattr(self, 'cbar'):
# image.set_data(CS_filled) #use this if you use new array
self.cbar = plt.colorbar(CS_filled, cax=self.cbar.ax)
self.cbar.ax.set_ylabel('Power [W]')
else:
self.cbar = plt.colorbar(CS_filled, ax=axes)
self.cbar.ax.set_ylabel('Power [W]')
CS = axes.contour(combo['Umesh'],
combo['Tmesh'],
combo['Pmesh'],
levels,
colors=('k', ),
linewidths=(1, ))
axes.clabel(CS, fmt='%2.1f', colors='k', fontsize=14)
axes.contourf(combo['Umesh'],
combo['Tmesh'],
combo['Pmesh'], [Pmax, Pmax * 1.2],
hatches=['//'],
colors='none',
extend='max',
alpha=0.0)
#Find the max because autoscale can't handle the abundunce of nana in these arrays.
id_valid = (np.array(combo['Pmesh']) == np.array(combo['Pmesh']))
T = combo['Tmesh']
T[~id_valid] = float('nan')
axes.axis([0, np.nanmax(combo['Umesh']), 0, np.nanmax(T)])
# axes.autoscale(True)
if self.radioButton_fixed_speed.isChecked():
axes.scatter(combo['max_time_speed'],
combo['max_time_drag'],
c='b',
label='Cruise speed')
else:
axes.scatter(combo['max_distance_speed'],
combo['max_distance_drag'],
c='b',
label='Max range conditions')
axes.scatter(combo['max_time_speed'],
combo['max_time_drag'],
c='r',
label='Max flight time conditions')
try:
axes.plot(self.plane_curve[0],
self.plane_curve[1],
color='k',
label='Plane drag')
except AttributeError:
Uplot = np.linspace(self.optimisation['Umin'] + 0.1,
self.optimisation['Umax'], 100)
dragP = optimised_consumption.dragFunc(Uplot, plane, atmosphere)
self.plane_curve = [Uplot, dragP]
axes.plot(Uplot, dragP, color='k', label='Plane drag')
plane_line = mlines.Line2D([], [], color='k', label='Plane drag')
axes.legend(loc=1)
self.canvas.draw()
'mass': 1.15, #mass in kilos
'C_1': 0.1156,
'C_2': -0.0069,
'C_3': 0.0174,
'loss_factor': 1.0, #Assumed to scale with reynolds
'loss_ref_length': 1,
'theta_sweep': 30,
'body_length': 0.4,
'aero_factor':0.5
}
atmosphere={
'rho':1.10, #
'mu':1.8e-5, #dynamic viscosity
'Ta': 25, #Ambient temperature
}
plane_mass=payload/payload_frac
U_range=np.linspace(5,20,100)
drag=dragFunc(U_range,plane1,atmosphere)
drag_real=consumption_functions.dragFunc(U_range,plane1,atmosphere)
plt.plot(U_range, drag, label='Estimate')
plt.plot(U_range, drag_real, label='real')
plt.legend(loc=4)
plt.show()