Slow but steady progress on the mbh
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Connor
@@ -4,111 +4,42 @@
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println("Testing Monotonic Basin Hopper")
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# Initial Setup
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# sc = Sc("test")
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# a = rand(50_000:1.:100_000)
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# e = rand(0.01:0.01:0.5)
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# i = rand(0.01:0.01:π/6)
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# T = 2π*√(a^3/μs["Earth"])
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# prop_time = 0.5T
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# n = 20
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# start_mass = 10_000.
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# # A simple orbit raising
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# start = [oe_to_xyz([ a, e, i, 0., 0., 0. ], μs["Earth"]); start_mass]
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# Tx, Ty, Tz = conv_T(repeat([0.8], n), repeat([0.], n), repeat([0.], n),
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# start,
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# sc,
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# prop_time,
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# μs["Earth"])
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# nominal_path, final = prop(hcat(Tx, Ty, Tz), start, sc, μs["Earth"], prop_time)
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# new_T = 2π*√(xyz_to_oe(final, μs["Earth"])[1]^3/μs["Earth"])
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# # Find the best solution
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# best, archive = mbh(start,
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# final,
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# sc,
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# μs["Earth"],
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# 0.0,
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# prop_time,
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# n,
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# search_patience_lim=25,
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# drill_patience_lim=50,
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# verbose=true)
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# # Test and plot
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# @test best.converged
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# transit, calc_final = prop(best.zero, start, sc, μs["Earth"], prop_time)
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# initial_path = prop(zeros((100,3)), start, sc, μs["Earth"], T)[1]
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# after_transit = prop(zeros((100,3)), calc_final, sc, μs["Earth"], new_T)[1]
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# final_path = prop(zeros((100,3)), final, sc, μs["Earth"], new_T)[1]
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# savefig(plot_orbits([initial_path, nominal_path, final_path],
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# labels=["initial", "nominal transit", "final"],
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# colors=["#FF4444","#44FF44","#4444FF"]),
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# "../plots/mbh_nominal.html")
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# savefig(plot_orbits([initial_path, transit, after_transit, final_path],
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# labels=["initial", "transit", "after transit", "final"],
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# colors=["#FFFFFF", "#FF4444","#44FF44","#4444FF"]),
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# "../plots/mbh_best.html")
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# i = 0
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# best_mass = calc_final[end]
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# nominal_mass = final[end]
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# masses = []
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# for candidate in archive
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# @test candidate.converged
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# path2, calc_final = prop(candidate.zero, start, sc, μs["Earth"], prop_time)
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# push!(masses, calc_final[end])
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# @test norm(calc_final[1:6] - final[1:6]) < 1e-4
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# end
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# @test best_mass == maximum(masses)
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# # This won't always work since the test is reduced in fidelity,
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# # but hopefully will usually work:
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# @test (start_mass - best_mass) < 1.1 * (start_mass - nominal_mass)
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# Now let's test a sun case. This should be pretty close to begin with
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start_mass = 10_000.
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launch_date = DateTime(2016,3,28)
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launch_j2000 = utc2et(Dates.format(launch_date,"yyyy-mm-ddTHH:MM:SS"))
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earth_start = [spkssb(ids["Earth"], launch_j2000, "ECLIPJ2000"); start_mass]
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earth_speed = earth_start[4:6]
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v∞ = 3.0*earth_speed/norm(earth_speed)
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start = earth_start + [zeros(3); v∞; 0.0]
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tof = 3600*24*30*10.75
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mars_state = [spkssb(Thesis.ids["Mars"], launch_j2000+tof, "ECLIPJ2000"); start_mass]
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final = mars_state + [ zeros(3); [-1.1, -3., -2.6]; 0.0 ]
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a = xyz_to_oe(final, μs["Sun"])[1]
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T = 2π*√(a^3/μs["Sun"])
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n = 20
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# First we test the random mission guess generator
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flybys = [Earth, Venus, Jupiter]
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launch_window = ( DateTime(2021,12,25), DateTime(2025,12,25) )
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max_C3 = 10.
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max_v∞ = 8.
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latest_arrival = DateTime(2035,12,25)
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random_guess = Thesis.mission_guess(flybys, bepi, 12_000., launch_window, max_C3, max_v∞, latest_arrival)
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@test typeof(random_guess) == Mission_Guess
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# But we'll plot to see
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beginning_path = prop(zeros(100,3), start, Sc("test"), μs["Sun"], tof)[1]
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ending_path = prop(zeros(100,3), final, Sc("test"), μs["Sun"], T)[1]
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savefig(plot_orbits([beginning_path, ending_path],
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labels=["initial", "ending"],
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colors=["#F2F", "#2F2"]),
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"../plots/mbh_sun_initial.html")
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# Now we solve and plot the new case
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best, archive = mbh(start,
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final,
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Sc("test"),
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μs["Sun"],
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0.0,
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tof,
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n,
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search_patience_lim=25,
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drill_patience_lim=50,
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verbose=true)
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solved_path, solved_state = prop(best.zero, start, Sc("test"), μs["Sun"], tof)
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ending_path = prop(zeros(100,3), final, Sc("test"), μs["Sun"], T)[1]
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savefig(plot_orbits([solved_path, ending_path],
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labels=["best", "ending"],
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colors=["#C2F", "#2F2"]),
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"../plots/mbh_sun_solved.html")
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# We'll just make sure that this at least converged correctly
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@test norm(solved_state[1:6] - final[1:6]) < 1e-4
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# Then the perturb function
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mission_guess = Thesis.perturb(test_mission)
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@test mission_guess.launch_date != test_mission.launch_date
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@test mission_guess.launch_v∞ != test_mission.launch_v∞
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for i in 1:2
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@test mission_guess.phases[i].v∞_in != test_mission.phases[i].v∞_in
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@test mission_guess.phases[i].δ != test_mission.phases[i].δ
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@test mission_guess.phases[i].tof != test_mission.phases[i].tof
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end
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@test !mission_guess.converged
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# # Then the inner loop builder function
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mission = Thesis.inner_loop_solve(test_mission_guess)
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@test !mission.converged
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# For the valid case we need to use a lambert's solver
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# TODO: This is probably not acceptable for how close I have to be
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leave = DateTime(1992,11,19)
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arrive = DateTime(1993,4,1)
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time_leave = utc2et(Dates.format(leave,"yyyy-mm-ddTHH:MM:SS"))
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time_arrive = utc2et(Dates.format(arrive,"yyyy-mm-ddTHH:MM:SS"))
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earth_state = [spkssb(Earth.id, time_leave, "ECLIPJ2000"); 0.0]
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venus_state = [spkssb(Venus.id, time_arrive, "ECLIPJ2000"); 0.0]
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v∞_out, v∞_in, tof = Thesis.lamberts(Earth, Venus, leave, arrive)
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phase = Phase(Venus, 1.0001v∞_in, 0.2, tof, 0.0*ones(20,3))
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mission_guess = Mission_Guess(bepi, 12_000., leave, v∞_out, [phase])
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mission = Thesis.inner_loop_solve(mission_guess)
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@test mission.converged
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end
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