plt.plot(ma, mCL)
plt.plot(xa, xCL, ".")
plt.xlabel("Angle of Attack [deg]")
plt.ylabel("Lift Coefficient $C_L$ [-]")
plt.sca(ax[0, 1])
plt.plot(ma, mCD)
plt.plot(xa, xCD, ".")
plt.ylim(0, 0.05)
plt.xlabel("Angle of Attack [deg]")
plt.ylabel("Drag Coefficient $C_D$ [-]")
plt.sca(ax[1, 0])
plt.plot(mCD, mCL)
plt.plot(xCD, xCL, ".")
plt.xlim(0, 0.05)
plt.xlabel("Drag Coefficient $C_D$ [-]")
plt.ylabel("Lift Coefficient $C_L$ [-]")
plt.sca(ax[1, 1])
plt.plot(ma, mCL / mCD)
plt.plot(xa, xCL / xCD, ".")
plt.xlabel("Angle of Attack [deg]")
plt.ylabel("Lift-to-Drag Ratio $C_L/C_D$ [-]")
show_plot()
##### Test optimization
opti = asb.Opti()
alpha = opti.variable(init_guess=0, lower_bound=-20, upper_bound=20)
LD = af.CL_function(alpha, 1e6) / af.CD_function(alpha, 1e6)
if __name__ == "__main__":
# Make AeroSandbox Atmosphere
altitude = np.linspace(-5e3, 100e3, 1000)
atmo_diff = Atmosphere(altitude=altitude)
atmo_isa = Atmosphere(altitude=altitude, method="isa")
from aerosandbox.tools.pretty_plots import plt, sns, mpl, show_plot, set_ticks
fig, ax = plt.subplots()
plt.plot(
((atmo_diff.pressure() - atmo_isa.pressure()) / atmo_isa.pressure()) *
100,
altitude / 1e3,
)
set_ticks(0.2, 0.1, 20, 10)
plt.xlim(-1, 1)
show_plot("AeroSandbox Atmosphere vs. ISA Atmosphere",
"Pressure, Relative Error [%]", "Altitude [km]")
fig, ax = plt.subplots()
plt.plot(
atmo_diff.temperature() - atmo_isa.temperature(),
altitude / 1e3,
)
set_ticks(1, 0.5, 20, 10)
plt.xlim(-5, 5)
show_plot("AeroSandbox Atmosphere vs. ISA Atmosphere",
"Temperature, Absolute Error [K]", "Altitude [km]")
def simulate(
self,
t_eval,
t_span=(0, 60),
initial_position=0,
initial_velocity=0,
):
res = integrate.solve_ivp(
fun=self.equations_of_motion,
t_span=t_span,
y0=np.array([initial_position, initial_velocity]),
t_eval=t_eval,
)
return res.y
if __name__ == '__main__':
bike = Bike()
from aerosandbox.tools.pretty_plots import plt, show_plot, set_ticks
fig, ax = plt.subplots()
t = np.linspace(0, 60, 500)
pos, vel = bike.simulate(t)
plt.plot(t, vel * 2.24)
set_ticks(2, 1, 4, 2)
plt.xlim(0, 20)
plt.ylim(0, 36)
show_plot("Electric Bike", xlabel="Time [s]", ylabel="Speed [mph]")
from scipy import optimize
fig, ax = plt.subplots()
speeds = np.linspace(0, 30, 500)
def get_mileage(speed):
bike = Bike()
try:
perf = bike.steady_state_performance(speed=speed, )
except ValueError:
return np.NaN
motor = perf['motor state']
power = motor['voltage'] * motor['current']
return power / speed
mileage = np.array([get_mileage(speed) for speed in speeds]) # J/m
mileage_Wh_per_mile = mileage / 3600 * 1000 * 1.609
plt.plot(speeds * 2.24, mileage_Wh_per_mile)
plt.xlim(left=0)
plt.ylim(bottom=0, top=50)
set_ticks(x_major=5, x_minor=1, y_major=5, y_minor=1)
show_plot(f"Electric Bike: Mileage vs. Speed",
xlabel="Speed [mph]",
ylabel=f"Energy Mileage [Wh per mile]")
from scipy import optimize
speed = 24 / 2.24
fig, ax = plt.subplots()
t = np.linspace(0, 10, 500)
gear_ratios = np.geomspace(0.020 / 0.700, 0.700 / 0.700, 300)
def get_efficiency(gear_ratio):
bike = Bike(gear_ratio=gear_ratio)
try:
perf = bike.steady_state_performance(speed=speed)
except ValueError:
return np.NaN
return perf['motor state']['efficiency']
eff = np.array([get_efficiency(gear_ratio) for gear_ratio in gear_ratios])
plt.plot(gear_ratios, eff * 100)
plt.xlim(gear_ratios[0], gear_ratios[-1])
plt.ylim(0, 100)
# plt.xscale('log')
set_ticks(x_major=0.1, x_minor=0.025, y_major=10, y_minor=2.5)
show_plot(f"Electric Bike: Gear Ratios at {speed * 2.24:.0f} mph",
xlabel="Gear Ratio",
ylabel=f"Efficiency [%]")