71 lines
3.3 KiB
Julia
71 lines
3.3 KiB
Julia
@testset "NLP Solver" begin
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using PlotlyJS: savefig
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println("Testing NLP solver")
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# Test the optimizer for a one-phase mission
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# The lambert's solver said this should be pretty valid
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launch_window = [DateTime(1992,11,1), DateTime(1992,12,1)]
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latest_arrival = DateTime(1993,6,1)
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leave, arrive = DateTime(1992,11,19), DateTime(1993,4,1)
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test_leave = DateTime(1992,11,12)
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earth_state = state(Earth, leave)
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venus_state = state(Venus, arrive)
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v∞_out, v∞_in, tof = Thesis.lamberts(Earth, Venus, leave, arrive)
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# We can get the thrust profile and tof pretty wrong and still be ok
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phase = Phase(Venus, 1.1v∞_in, v∞_in, 0.9*tof, 0.1*ones(20,3))
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guess = Mission_Guess(bepi, 12_000., test_leave, 0.9*v∞_out, [phase])
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m = solve_mission(guess, launch_window, latest_arrival, verbose=true)
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@test typeof(m) == Mission
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@test m.converged == true
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# Now we can plot the results to check visually
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p = plot(m, title="NLP Test Solution")
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savefig(p,"../plots/nlp_test_1_phase.html")
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# Now we can look at a slightly more complicated trajectory
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flybys = [Earth, Venus, Mars]
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launch_window = [DateTime(2021,10,1), DateTime(2021,12,1)]
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latest_arrival = DateTime(2023,1,1)
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dates = [DateTime(2021,11,1), DateTime(2022,3,27), DateTime(2022,8,28)]
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phases = Vector{Phase}()
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launch_v∞, _, tof1 = Thesis.lamberts(flybys[1], flybys[2], dates[1], dates[2])
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for i in 1:length(dates)-2
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v∞_out1, v∞_in1, tof1 = Thesis.lamberts(flybys[i], flybys[i+1], dates[i], dates[i+1])
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v∞_out2, v∞_in2, tof2 = Thesis.lamberts(flybys[i+1], flybys[i+2], dates[i+1], dates[i+2])
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push!(phases, Phase(flybys[i+1], v∞_in1, v∞_out2, tof1, 0.01*ones(20,3)))
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end
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v∞_out, v∞_in, tof = Thesis.lamberts(flybys[end-1], flybys[end], dates[end-1], dates[end])
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push!(phases, Phase(flybys[end], v∞_in, v∞_in, tof, 0.01*ones(20,3)))
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guess = Mission_Guess(bepi, 12_000., dates[1], launch_v∞, phases)
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m = solve_mission(guess, launch_window, latest_arrival, verbose=true)
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p = plot(m, title="NLP Test Solution (2 Phases)")
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savefig(p,"../plots/nlp_test_2_phase.html")
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# Here is the final, most complicated, trajectory to test
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flybys = [Earth, Venus, Earth, Mars, Earth, Jupiter]
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launch_window = [DateTime(2023,1,1), DateTime(2024,1,1)]
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latest_arrival = DateTime(2031,1,1)
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dates = [DateTime(2023,6,28),
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DateTime(2023,10,30),
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DateTime(2024,9,2),
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DateTime(2025,2,10),
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DateTime(2026,11,26),
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DateTime(2029,10,16)]
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phases = Vector{Phase}()
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launch_v∞, _, tof1 = Thesis.lamberts(flybys[1], flybys[2], dates[1], dates[2])
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for i in 1:length(dates)-2
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v∞_out1, v∞_in1, tof1 = Thesis.lamberts(flybys[i], flybys[i+1], dates[i], dates[i+1])
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v∞_out2, v∞_in2, tof2 = Thesis.lamberts(flybys[i+1], flybys[i+2], dates[i+1], dates[i+2])
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push!(phases, Phase(flybys[i+1], v∞_in1, v∞_out2, tof1, 0.01*ones(20,3)))
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end
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v∞_out, v∞_in, tof = Thesis.lamberts(flybys[end-1], flybys[end], dates[end-1], dates[end])
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push!(phases, Phase(flybys[end], v∞_in, v∞_in, tof, 0.01*ones(20,3)))
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guess = Mission_Guess(bepi, 12_000., dates[1], launch_v∞, phases)
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m = solve_mission(guess, launch_window, latest_arrival, verbose=true)
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p = plot(m, title="NLP Test Solution (5 Phases)")
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savefig(p,"../plots/nlp_test_5_phase.html")
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end
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