def initialise(self): self.h[0] = self.h_init self.V[0] = self.V_init self.gamma[0] = self.gamma_init self.g[0] = grav_sphere(self.g_0, self.R, self.h_init) self.solver_rho[0], self.solver_a[0], self.solver_p[0], \ self.solver_T[0], self.solver_mu[0] = \ interpolate_atmosphere(self, self.h_init) self.p_dyn[0] = fcl.p_dyn(rho=self.solver_rho[0], V=self.V[0]) self.Ma[0] = self.V[0] / self.solver_a[0] self.mfp[0] = fcl.mean_free_path(self.solver_T[0], self.solver_p[0], self.atmosphere.d) self.Kn[0] = self.mfp[0] / self.spacecraft.L self.Re[0] = fcl.Reynolds(self.solver_rho[0], self.V[0], self.spacecraft.L, self.solver_mu[0]) if self.console_output == True: print 'MODEL INITIALISED. INITIAL STEP COUNT: %i' % self.steps_storage return None
def simulate_dopri(self, dt=1E-2):
"""
Run trajectory calculations using explicit Runge-Kutta method of order
4(5) from Dormand & Prince
"""
# Set timestep for ODE solver
self.dt = dt
self.time_steps = np.cumsum(self.dt * np.ones(self.steps_storage))
# Create ODE object from SciPy using Dormand-Prince RK solver
self.eq = integrate.ode(traj_3DOF_dt).set_integrator('dop853', nsteps=1E8,
rtol=1E-10)
# Set initial conditions
y_init = [self.V_init, self.gamma_init, self.h_init, self.r[0]]
self.eq.set_initial_value(y_init, t=self.time_steps[0])
# Generate counter
index = 1
self.index = index
# Initial conditions are: V, gamma, h, r. These are at index = 0
# Other parameters (like dynamic pressure and gravitational
# attraction) are calculated for this step (also index = 0)
# ODE solver then calculates V, gamma, h, and r at the next step (index = 1)
# Then parameters and updated as above, and the loop continues.
# So:
# INIT: Define V, gamma, h, r @ start
# Calculate parameters @ start
# SOLVE: Find V, gamma, h, r
#
# Solve ODE system using conditional statement based on altitude
while self.h[index-1] > 0:
# Update ODE solver parameters from spacecraft object and
# atmospheric model at each separate time step
if self.spacecraft.aero_coeffs_type == 'CONSTANT':
params = [self.R, self.g[index-1], self.spacecraft.ballistic_coeff,
self.solver_rho[index-1], self.spacecraft.Cl, self.spacecraft.Cd]
self.eq.set_f_params(params)
elif self.spacecraft.aero_coeffs_type == 'VARIABLE':
self.spacecraft.update_aero(self.index, self.Re[index-1],
self.Ma[index-1], self.Kn[index-1])
params = [self.R, self.g[index-1], self.spacecraft.ballistic_coeff[index-1],
self.solver_rho[index-1], self.spacecraft.Cl[index-1],
self.spacecraft.Cd[index-1]]
self.eq.set_f_params(params)
# Solve ODE system (sol[V, gamma, h, r])
self.sol[index, :] = self.eq.integrate(self.time_steps[index])
# Unpack ODE solver results into storage structures
self.V[index] = self.sol[index, 0]
self.gamma[index] = self.sol[index, 1]
self.h[index] = self.sol[index, 2]
self.r[index] = self.sol[index, 3]
# Interpolate for freestream density in atmosphere model
# (this avoids a direct call to an atmosphere model, allowing more
# flexibility when coding as different models have different interfaces)
self.solver_rho[index], self.solver_a[index], \
self.solver_p[index], self.solver_T[index], \
self.solver_mu[index] = interpolate_atmosphere(self, self.h[index])
# Calculate gravitational acceleration at current altitude
self.g[index] = grav_sphere(self.g_0, self.R, self.h[index])
# Calculate dynamic pressure iteration results
self.p_dyn[index] = fcl.p_dyn(rho=params[3], V=self.sol[index, 0])
# Calculate Mach, Knudsen, and Reynolds numbers
self.Ma[index] = self.V[index] / self.solver_a[index]
self.mfp[index] = fcl.mean_free_path(self.solver_T[index],
self.solver_p[index], self.atmosphere.d)
self.Kn[index] = self.mfp[index] / self.spacecraft.L
self.Re[index] = fcl.Reynolds(self.solver_rho[index],
self.V[index], self.spacecraft.L, self.solver_mu[index])
# Save inputs for inspection
self.solver_time[index] = self.eq.t
self.y_input[index, :] = self.eq.y
# Advance iteration counter
index += 1
self.index = index
# Check if solution storage array has reached maximum size
if index == len(self.sol)-10:
self.extend()
#print index
# Print solution progress to check for stability
if self.console_output == True:
if np.mod(index, self.steps_storage/100) == 0:
print 'ITERATION: %i; ALTITUDE: %f km' % (index, self.h[index-1]/1000)
# Subtract 1 from counter so that indexing is more convenient later on
self.index -= 1
# Truncate solution arrays to remove trailing zeros
self.truncate()
# Perform final step calculations for p_dyn, g, etc.
self.final_step_event()
#self.final_step_assign()
# Perform post solver calculations
#self.post_calc()
print 'TRAJECTORY COMPUTED (RK 4/5)'
print '%i ITERATIONS, TIMESTEP = %f s, TOTAL TIME = %f s' % \
(self.index, self.dt, self.solver_time[self.index-1])
return [self.sol, self.h, self.y_input, self.p_dyn, self.Ma]