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thesis/julia/test/inner_loop/find_closest.jl
2021-09-02 21:40:19 -06:00

51 lines
1.7 KiB
Julia

@testset "Find Closest" begin
println("Testing NLP solver")
using NLsolve, PlotlyJS
# Initial Setup
sc = Sc("test")
a = rand(25000:1.:40000)
e = rand(0.01:0.01:0.05)
i = rand(0.01:0.01:π/6)
T = 2π*(a^3/μs["Earth"])
prop_time = T
n = 10
# A simple orbit raising
start = oe_to_xyz([ a, e, i, 0., 0., 0. ], μs["Earth"])
Tx, Ty, Tz = conv_T(repeat([0.6], n), repeat([0.], n), repeat([0.], n),
start,
sc.mass,
sc,
prop_time,
μs["Earth"])
final = prop(hcat(Tx, Ty, Tz), start, sc, μs["Earth"], prop_time)[3]
new_T = 2π*(xyz_to_oe(final, μs["Earth"])[1]^3/μs["Earth"])
# This should be close enough to 0.6 for convergence
Tx, Ty, Tz = conv_T(repeat([0.59], n), repeat([0.01], n), repeat([0.], n),
start,
sc.mass,
sc,
prop_time,
μs["Earth"])
result = nlp_solve(start, final, sc, μs["Earth"], 0.0, prop_time, hcat(Tx, Ty, Tz))
# Test and plot
@test converged(result)
path1 = prop(zeros((100,3)), start, sc, μs["Earth"], T)[1]
path2, mass, calc_final = prop(tanh.(result.zero), start, sc, μs["Earth"], prop_time)
path3 = prop(zeros((100,3)), calc_final, sc, μs["Earth"], new_T)[1]
path4 = prop(zeros((100,3)), final, sc, μs["Earth"], new_T)[1]
savefig(plot_orbits([path1, path2, path3, path4],
labels=["initial", "transit", "after transit", "final"],
colors=["#FFFFFF","#FF4444","#44FF44","#4444FF"]),
"../plots/find_closest_test.html")
if converged(result)
@test norm(calc_final - final) < 1e-4
end
end