def pretty(self, x):
"""
prob.plot(x)
- x: encoded trajectory
Prints human readable information on the trajectory represented by the decision vector x
Example::
print(prob.pretty(x))
"""
super().pretty(x)
T, Vinfx, Vinfy, Vinfz = self._decode_times_and_vinf(x)
# We transform it (only the needed component) to an equatorial system rotating along x
# (this is an approximation, assuming vernal equinox is roughly x and the ecliptic plane is roughly xy)
earth_axis_inclination = 0.409072975
# This is different from the GTOP tanmEM problem, I think it was bugged there as the rotation was in the wrong direction.
Vinfz = -Vinfy * sin(earth_axis_inclination) + Vinfz * cos(
earth_axis_inclination)
# And we find the vinf declination (in degrees)
sindelta = Vinfz / x[3]
declination = asin(sindelta) / np.pi * 180.
m_initial = launchers.ariane5(x[3] / 1000., declination)
# And we can evaluate the final mass via Tsiolkowsky
Isp = 312.
g0 = 9.80665
DV = super().fitness(x)[0]
DV = DV + 275. # losses for 5 swgbys + insertion
m_final = m_initial * exp(-DV / Isp / g0)
print("\nInitial mass:", m_initial)
print("Final mass:", m_final)
print("Declination:", declination)
def fitness(self, x):
T, Vinfx, Vinfy, Vinfz = self._decode_times_and_vinf(x)
# We transform it (only the needed component) to an equatorial system rotating along x
# (this is an approximation, assuming vernal equinox is roughly x and the ecliptic plane is roughly xy)
earth_axis_inclination = 0.409072975
# This is different from the GTOP tanmEM problem, I think it was bugged there as the rotation was in the wrong direction.
Vinfz = -Vinfy * sin(earth_axis_inclination) + Vinfz * cos(
earth_axis_inclination)
# And we find the vinf declination (in degrees)
sindelta = Vinfz / x[3]
declination = asin(sindelta) / np.pi * 180.
# We now have the initial mass of the spacecraft
m_initial = launchers.ariane5(x[3] / 1000., declination)
# And we can evaluate the final mass via Tsiolkowsky
Isp = 312.
g0 = 9.80665
if self._multi_objective:
DV, T = super().fitness(x)
else:
DV, = super().fitness(x)
DV = DV + 275. # losses for 5 swingbys + insertion
m_final = m_initial * exp(-DV / Isp / g0)
# Numerical guard for the exponential
if m_final == 0:
m_final = 1e-320
encoded_m_final = -log(m_final)
if self._multi_objective:
return (encoded_m_final, T)
return (encoded_m_final, )