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Dynamical system theory and numerical methodsapplied to Astrodynamics
Roberto Castelli
Institute for Industrial Mathematics
University of Paderborn
BCAM, Bilbao, 20th December, 2010
AstroNet Dynamical system theory for mission design Roberto Castelli 1 / 70
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Introduction
Introduction
In space mission design
I Consider the Force Field acting on the Spacecraft
I Consider Physical and Technical constraints
I Satisfy some mission requirements
I Take care of the fuel consumption and the travelling time
I ....
Genesis Mission
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Introduction
Introduction
N-BODY PROBLEM⇓
First guess trajectoriesdesigned in simplified model
I Two-body model
I Restricted Three-bodyproblem
I Bicircular model
I . . .
Numerical Optimisation in Fullsystem
I Direct/Indirect methods
I Multiple shootingtechnique
I Multiobjectiveoptimisation
Different type of Propulsion (Electric - Chemical)
⇓
Low thrust propulsion – Impulsive manoeuvre
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Introduction
Introduction
N-BODY PROBLEM⇓
First guess trajectoriesdesigned in simplified model
I Two-body model
I Restricted Three-bodyproblem
I Bicircular model
I . . .
Numerical Optimisation in Fullsystem
I Direct/Indirect methods
I Multiple shootingtechnique
I Multiobjectiveoptimisation
Different type of Propulsion (Electric - Chemical)
⇓
Low thrust propulsion – Impulsive manoeuvre
AstroNet Dynamical system theory for mission design Roberto Castelli 3 / 70
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Introduction
Introduction
N-BODY PROBLEM⇓
First guess trajectoriesdesigned in simplified model
I Two-body model
I Restricted Three-bodyproblem
I Bicircular model
I . . .
Numerical Optimisation in Fullsystem
I Direct/Indirect methods
I Multiple shootingtechnique
I Multiobjectiveoptimisation
Different type of Propulsion (Electric - Chemical)
⇓
Low thrust propulsion – Impulsive manoeuvre
AstroNet Dynamical system theory for mission design Roberto Castelli 3 / 70
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Introduction
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
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Dynamical model CRTBP
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
AstroNet Dynamical system theory for mission design Roberto Castelli 5 / 70
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Dynamical model CRTBP
Circular Restricted Three-Body problem
I Two Primaries move in circular orbits under the mutual gravitationalattraction
I Massless particle moves under the gravitational influence of twoprimaries
In a rotating, adimensional reference frame, µ = m2/(m1 + m2),
(CR3BP)
x − 2y = Ωx
y + 2x = Ωy
z = Ωz
Ω(x , y , z) =12(x2+y2)+ 1−µ
r1+ µ
r2+ 1
2µ(1−µ)
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Dynamical model CRTBP
Properties of CRTBP
I Non integrable Autonomous Hamiltonian SystemI Symmetry (x , y , z , x , y , z ; t)→ (x ,−y , z ,−x , y ,−z ;−t)I Jacobi Integral: C = 2Ω(x , y , z)− (x2 + y2 + z2) = −2EI Equilibrium points: Lagrangian Points Lj , j = 1, ..., 5.I Hill’s Region: H(C ) = (x , y , z) : 2Ω(x , y , z)− C ≥ 0
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Dynamical model CRTBP Periodic orbits
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
AstroNet Dynamical system theory for mission design Roberto Castelli 8 / 70
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Dynamical model CRTBP Periodic orbits
Families of Periodic Orbits.
Hamiltonian system⇓
continuous families of periodicorbits.
PO bifurcating from L1 [E. J. Doedel et al.]
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Dynamical model CRTBP Periodic orbits
Families of Periodic Orbits.
Hamiltonian system⇓
continuous families of periodicorbits.
PO bifurcating from L1 [E. J. Doedel et al.]
Simple Symmetric periodic orbits
Differential correction scheme,based on the variational eq.Find δVy such that the firstx-axis crossing of
φt(X0, 0, 0,Vy + δVy)
is perpendicularAstroNet Dynamical system theory for mission design Roberto Castelli 10 / 70
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Dynamical model CRTBP Periodic orbits
Periodic Orbits Diagram
Diagram of Simple Symmetric PO in SE system.
(X0,Vy) → (X0, 0, 0,Vy ),Earth < X0 < L2, Vy > 0
Lyapunov and Halo orbits
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Dynamical model CRTBP Periodic orbits
Periodic orbits: DPO
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Dynamical model CRTBP Periodic orbits
Resonant Orbits
For which resonances there exist families of periodic orbits?(Collaboration with Prof. P. Zgliczynski)
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Dynamical model CRTBP Tube Dynamics
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
AstroNet Dynamical system theory for mission design Roberto Castelli 14 / 70
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Dynamical model CRTBP Tube Dynamics
Dynamics near periodic orbits
I The periodic orbits separates two necks in the Hill’s region
I Linear Dynamics: saddle × center
I 3 types of orbits: asymptotic, transit, non-transit
[W.S. Koon et al.]
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Dynamical model CRTBP Tube Dynamics
Invariant manifolds
The Stable/Unstable Invariant manifoldsSet of orbits asymptotic to the periodic orbit for t → ±∞
[G. Gomez at al.]
I are topologically equivalent toN − 2 dimensional cylinders inthe N − 1 dim. energy manifold
I act as separatrices in the phasespace between transit andnon-transit orbit
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Dynamical model CRTBP Tube Dynamics
Invariant manifolds
The Stable/Unstable Invariant manifoldsSet of orbits asymptotic to the periodic orbit for t → ±∞
I are topologically equivalent toN − 2 dimensional cylinders inthe N − 1 dim. energy manifold
I act as separatrices in the phasespace between transit andnon-transit orbit
I approach the smaller primary
I tangent to the eigenspace of thelinearized system (monodromymatrix)
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Dynamical model CRTBP Patched CRTBP approximation
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
AstroNet Dynamical system theory for mission design Roberto Castelli 18 / 70
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Dynamical model CRTBP Patched CRTBP approximation
Mission Design: dynamical system theory
Dynamical system theory in low energy trajectory designPatched 3-body problem
• The 4-Body system isapproximated with thesuperpositions of two RestrictedThree-Body problems
• The invariant manifold structuresare exploited to design legs oftrajectory
• The design restricts to theselection of a connection point ona suitable Poincare section
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Dynamical model CRTBP Patched CRTBP approximation
Some examples
Low energy transfer to the Moon (Fig. from [W.S. Koon et al.])
Petit Grand Tour of the moons of Jupiter, (Fig. from [G. Gomez at al.])
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Computational methods: Set Oriented Numerics
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
AstroNet Dynamical system theory for mission design Roberto Castelli 21 / 70
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Computational methods: Set Oriented Numerics
Set Oriented Numerics
Main Goal: Study the long termbehavior of complex and chaoticDynamical Systems
2010
010
20 4020
020
400
10
20
30
40
50
Set Oriented Numerics
I Computation of several short term trajectories instead of single longterm trajectory
I Approximation of Global structureI Invariant Sets : global attractors, Invariant manifoldsI Invariant measures, almost invariant setsI Transport operatorsI Multiobjective optimization (Pareto set)
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Computational methods: Set Oriented Numerics
Methodology
Consider a discrete dynamical system
xk+1 = f (xk), k = 0, 1, 2, . . . , f : Rn → Rn
Aim: Approximation of a structure within a bounded set Q.Method: Generate a sequence of collections B1,B2 . . . of subsets of Q s.tB0 = Q, and iteratively Bk from Bk−1
I Subdivision: define a new collection Bk such that⋃B∈Bk
B =⋃
B∈Bk−1
B
diam(Bk) ≤ θkdiam(Bk−1), θk ∈ (0, 1)
I Selection: define Bk as
Bk = B ∈ Bk : such that g(B) ∩ B 6= ∅ for some B ∈ Bk
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Computational methods: Set Oriented Numerics Covering of Invariant Sets
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
AstroNet Dynamical system theory for mission design Roberto Castelli 24 / 70
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Computational methods: Set Oriented Numerics Covering of Invariant Sets
Relative Global Attractor
Definition Relative Global attractor
Let Q ⊂ Rn be a compact set. Define global attractor relative to Q by
AQ =⋂j≥0
f j(Q)
PropertiesAQ ⊂ Qf −1(AQ) ⊂ AQ but not necessarily f (AQ) ⊂ AQ .
Selection step: define Bk as
Bk = B ∈ Bk : such that f −1(B) ∩ B 6= ∅ for some B ∈ Bk
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Computational methods: Set Oriented Numerics Covering of Invariant Sets
Henon map xk+1 = 1− ax2
k + byk a = 1.4, b = 0.3yk+1 = xk
Covering of the attractor relative to [−2, 2]2, k = 8, 12, 16, 20
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Computational methods: Set Oriented Numerics Covering of Invariant Sets
Computing the unstable manifold
Initialization - Continuation Algorithm
Aim: Compute the unstable manifold of a point p into a (large ) compactset Q, ( p ∈ Q)
1 Given Q, compute P0,P1, . . .Pl nested sequence of fine partitions ofQ. Select the element C ∈ Pl such that p ∈ C and AC = W u
loc(p)∩C
2 Initialization Starting from B0 = C, refine the approximation of
W uloc(p) ∩ C by subdivision, yielding B(0)
k ⊂ Pl+k
3 Continuation From B(j−1)k compute
B(j)k = B ∈ Pl+k : f (B ′) ∩ B 6= ∅, for some B ′ ∈ B(j−1)
k
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Computational methods: Set Oriented Numerics Covering of Invariant Sets
Computing the unstable manifold
Initialization - Continuation Algorithm
Aim: Compute the unstable manifold of a point p into a (large ) compactset Q, ( p ∈ Q)
1 Given Q, compute P0,P1, . . .Pl nested sequence of fine partitions ofQ. Select the element C ∈ Pl such that p ∈ C and AC = W u
loc(p)∩C
2 Initialization Starting from B0 = C, refine the approximation of
W uloc(p) ∩ C by subdivision, yielding B(0)
k ⊂ Pl+k
3 Continuation From B(j−1)k compute
B(j)k = B ∈ Pl+k : f (B ′) ∩ B 6= ∅, for some B ′ ∈ B(j−1)
k
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Computational methods: Set Oriented Numerics Covering of Invariant Sets
Lorenz systemx = σ(y − x)y = ρx − y − xzz = −βz + xy
(0, 0, 0) is fixed pointσ = 10, ρ = 28, β = 8/3
Covering of the two-dimensional stable manifold of the originLeft: l = 9, k = 6, j = 4 initial box Q = [−70, 70]× [−70, 70]× [−80, 80],
Right:l = 21, k = 0, j = 10, initial box Q = [−120, 120]× [−120, 120]× [−160, 160]
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Computational methods: Set Oriented Numerics Covering of Invariant Sets
Box covering of the part of unstable manifold of an Halo orbit in theSun-Earth CRTBP.
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Computational methods: Set Oriented Numerics GAIO implementation
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
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Computational methods: Set Oriented Numerics GAIO implementation
Implementation – GAIO package
GAIO: Global Analysis Invariant Object ([M. Dellnitz et al.])
Boxes : Generalized rectangle B(c , r) ⊂ Rn
B(c , r) = y ∈ Rn : |yi − ci | ≤ ri , i : 1 . . . n
identified by a centre and vector of radii, c , r ∈ Rn.
Subdivision : by bisection along one of the coordinate direction.
Storage of boxes: Binary tree
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Computational methods: Set Oriented Numerics GAIO implementation
Image of a Box: Choice of test-points
The image of a box B in the collection B is defined
FB(B) = B ′ ∈ B : f (B) ∩ B ′ 6= ∅
In low dimensional phase space (d ≤ 3)I N points on the edges of the boxes + the centerI on uniform grid within the box
In higher dimensionI randomly distributed
Remark: Rigorous choice of test point in such a way that no boxes are lost due to
the discretization could be done if the Lipschitz constant of the map f is known.AstroNet Dynamical system theory for mission design Roberto Castelli 32 / 70
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Computational methods: Set Oriented Numerics GAIO implementation
Image of a Box: Choice of test-points
The image of a box B in the collection B is defined
FB(B) = B ′ ∈ B : f (B) ∩ B ′ 6= ∅
In low dimensional phase space (d ≤ 3)I N points on the edges of the boxes + the centerI on uniform grid within the box
In higher dimensionI randomly distributed
Remark: Rigorous choice of test point in such a way that no boxes are lost due to
the discretization could be done if the Lipschitz constant of the map f is known.AstroNet Dynamical system theory for mission design Roberto Castelli 32 / 70
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Examples of mission design
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
AstroNet Dynamical system theory for mission design Roberto Castelli 33 / 70
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Examples of mission design Earth to Halo
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
AstroNet Dynamical system theory for mission design Roberto Castelli 34 / 70
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Examples of mission design Earth to Halo
Leo to Halo mission design
I Scientific purposes: Solar observer [ISEE, SOHO, Genesis], Lunarfar-side data relay
I Low energy ballistic transfers made up of impulsive manoeuvres.
I two coupled Restricted Three-Body Problem Planar + Spatial
I Statement of the problem: Optimisation theory, with dynamics
described by the Restricted Four-Body model - bicircular, spatial -
with the Sun gravitational influence (Sun perturbed CRTBP).
[R. Castelli et al.]
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Examples of mission design Earth to Halo
Mission Design
Earth escape stage:
Planar Sun-Earth modelLaunch point on LEO (167 km)Tangential manoeuvre (∆V )
Halo orbit arrival
Spatial Earth-Moon modelStable manifoldBallistic capture to the Halo
Poincare section along a line in configuration space
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Examples of mission design Earth to Halo
Transfer Points
I Properties of transfer points:
I Necessary condition for a feasible transfer:
the pair of points on the section must have
the same location in configuration space.
I The discontinuity in terms of ∆v has to be small.
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Examples of mission design Earth to Halo
Poincare maps →Transfer Points
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Examples of mission design Earth to Halo
Technique: Box approach
Box Covering of the EMPoincare section
Intersection with the SEPoincare section
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Examples of mission design Earth to Halo
Sample first guess trajectory
I First guess trajectories with JEM = 3.159738 (Az = 8000 km)
and JEM = 3.161327 (Az = 10000 km) are later optimized
in the bicircular Sun-perturbed EM model.
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Examples of mission design Earth to Halo
Designed trajectories
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Examples of mission design Earth to Halo
SOLUTION PERFORMANCES
Name Type ∆vi [m/s] ∆vf [m/s] ∆vt [m/s] ∆t [days]
sol.1.1 Two-Imp. 3110 214 3324 106
sol.1.2 Sing-Imp. 3161 0 3161 105
sol.2.1 Two-Imp. 3150 228 3378 128
sol.2.2 Sing-Imp. 3201 0 3201 134
Mingotti Two-Imp. – – 3676 65
Parker Two-Imp. 3132 618 3750 –
Parker Sing-Imp. 3235 – 3235 –
Mingtao Three-Imp. 3120 360 3480 17
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Examples of mission design Regions of prevalence
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
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Examples of mission design Regions of prevalence
Choice of the Poincare section
How to chose the Poincare section in the Patched CRTBP approximation?
I Usually: on a straight line
I Sometimes: on the boundary of the sphere of influence
I Here: The PS is set according with the prevalence of each CRTBP[R. Castelli]
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Examples of mission design Regions of prevalence
The regions of prevalence
How to chose the Poincare section in the Patched CRTBP approximation?
I Usually: on a straight line
I Sometimes: on the boundary of the sphere of influence
I Here: The PS is set according with the prevalence of each CRTBP
Comparison: Bicircular model ⇔ two CRTBP∆SE (z) =‖ BCP − CR3BPSE ‖, ∆EM(z) =‖ BCP − CR3BPEM ‖
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Examples of mission design Regions of prevalence
The regions of prevalence
The Regions of Prevalence of each CR3BP is defined according with bythe sign of
∆E (z) = (∆SE −∆EM)(z)
For a choice of the relative phase of the primaries θ
RPEM(θ) = z ∈ C : ∆E (z) > 0 EM Region of Prevalence
RPSE (θ) = z ∈ C : ∆E (z) < 0 SE Region of Prevalence
The curve Γ(θ) = z ∈ C : ∆E (z) = 0• is a closed, simple curve
• is defined implicitly as a function of (x , y)
• depends on θ → changes in time.
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Examples of mission design Regions of prevalence
Plot of the curve ∆E = 0
Figure: Γ(θ) for θ = 0, 2/3π, 4/3π in SE (left) and EM (right) coordinatesframe.
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Examples of mission design Regions of prevalence
* [J.S. Parker]
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Examples of mission design Regions of prevalence
Coupled CR3BP Approximation
Choice of the Poincare section as the boundary of the Regions of Prevalence
For every value of θ
• The points LEM1,2 are in
the EM Region ofInfluence
• The points LSE1,2 are in
the SE Region ofInfluence
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Examples of mission design Regions of prevalence
Trajectory design
Plot of W sEM,2(γ1) and W u
SE ,2(γ2) until the curve Γ(π/3) is reached
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Examples of mission design Regions of prevalence
Detection of the connection points
Box Covering Approach, GAIOI Compute the Poincare map W s
EM,2(γ1) ∩ Γ(θ) and cover it with BoxStructures
I Cover the transfer region
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Examples of mission design Regions of prevalence
Detection of the connection points
Box Covering Approach, GAIOI Compute the Poincare map W s
EM,2(γ1) ∩ Γ(θ) and cover it with BoxStructures
I Cover the transfer regionI Intersect the Box Covering with W u
SE ,1,2(γ2)
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Examples of mission design Regions of prevalence
The box covering approach
I Allows to close the Poincare section
I Allows to control the accuracy of the intersection
I Find systematically all the possible intersections within a certaindistance
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Examples of mission design Regions of prevalence
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Examples of mission design Sun-Earth DPO to Earth-Moon DPO
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
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Examples of mission design Sun-Earth DPO to Earth-Moon DPO
First stage: Leo to SE-DPO
Look for impulsive manoeuvre transfer from Leo to DPO in SE-CRTBP
I Integrate backwards thestable manifold
I Intersect the manifoldwith Leo
I Select those intersectionsthat are tangent to Leow.r.t geocentriccoordinates
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Examples of mission design Sun-Earth DPO to Earth-Moon DPO
∆V at Leo
Remark A manoeuvre |v | appliedin the same direction of the motionproduces the maximal change ofJacobi constant. It holds
∆J = |v |2 + 2|C ||v ||z |
where C = Vt
|z| − 1 depends on the
Leo altitude and |z | is the
geocentric distance, Vt orbital
velocity
The Jac.const on a Leo ( 167 Km alt.) is about 3.070352The Jac.const. of family g is in the range [3.00014;3.00092]
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Examples of mission design Sun-Earth DPO to Earth-Moon DPO
Jacobi- DPO h-LEO (Km) ∆V (m/s)
3.000464798057 220 3190
3.000464798057 160 3212
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Examples of mission design Sun-Earth DPO to Earth-Moon DPO
Second stage: SE-DPO to EM-DPO
Procedure for design the transfer:
1) Select two DPOs
2) Compute the Poincare map of (un)-stable manifold on a section( line through the Earth with slope θSE and θEM).
Left: Stable manifold in the interior region for a DPO in the EM-CRTBP.
Right: Unstable manifold of a DPO in SE-CRTBP
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Examples of mission design Sun-Earth DPO to Earth-Moon DPO
Design
Procedure for design the transfer:
I Select two DPOsI Compute the Poincare map of (un)-stable manifold on a section
3) Write the two maps in the same system of coordinates, beingθ = θSE − θEM the relative phase of the primaries at the transfer time
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Examples of mission design Sun-Earth DPO to Earth-Moon DPO
Design
Procedure for design the transfer:
I Select two DPOsI Compute the Poincare map of (un)-stable manifold on a sectionI Write the two maps in the same system of coordinates, beingθ = θSE − θEM the relative phase of the primaries
4) Look for possible connections on the Poicare section
Projection of the Poincare maps onto the (x , vx) plane and (x , vy ) plane, in EM-rf
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Examples of mission design Sun-Earth DPO to Earth-Moon DPO
Results: Interior connection
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Examples of mission design Sun-Earth DPO to Earth-Moon DPO
Results: Exterior connection
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Examples of mission design Sun-Earth DPO to Earth-Moon DPO
SE-Jacobi EM-Jacobi Time, (days) ∆V (m/s)?
Interior 3.0004647980 3.02599 115 339
Exterior 3.00043012418 3.026764 116 8? Connection between two DPOs
Type Start Target Time ∆V (m/s)
Mingotti Ext- Optim. LEO DPO Jac=? 90 3160
Ming Exterior LEO Retr. DPO 101 3207
Ming Interior LEO Retr. DPO 33 3802
[G. Mingotti et al.] : Earth to EM-DPO with low thrust propulsion[X. Ming at el.]: Earth to retrograde stable orbit around the Moon
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Conclusion
OUTLINE
Dynamical model CRTBPPeriodic orbitsTube DynamicsPatched CRTBP approximation
Computational methods: Set Oriented NumericsCovering of Invariant SetsGAIO implementation
Examples of mission designEarth to HaloRegions of prevalenceSun-Earth DPO to Earth-Moon DPO
Conclusion
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Conclusion
Conclusion
I Dynamical model
I The CRTBP is introduced to model the dynamics
I The families of periodic orbits have been investigated
I The invariant manifolds provide low energy transfers in the phase space
I Design technique:
I The patched CRTBP approximation has been formalized.
I Immediate definition of the transfer points in the phase space throughthe box covering approach.
I A technique to design impulsive transfers has been developed.
I Designed trajectory:
I Efficient trajectories in terms of ∆v have been designed in the planarand spatial case
I A non-classical Poincare section has been presented in terms of Regionsof prevalence.
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Conclusion
REFERENCES
E. J. Doedel, R. C. Paffenroth, H. B. Keller, D. J. Dichmann, J. Galan, A.Vanderbauwhede, Computation of periodic solutions in conservative systems withapplication to the 3-Body problem, Int. J. Bifurcation and Chaos, Vol. 13(6), 2003,1-29
W. S. Koon, M. W. Lo, J. E. Marsden, E. Jerrold, and S. D. Ross. Heteroclinicconnections between periodic orbits and resonance transitions in celestial mechanicsChaos, 10(2):427–469, 2000.
G. Gomez, W.S. Koon, M.W. Lo, J.E. Marsden, J. Masdemont and S.D. Ross –Invariant Manifolds, the Spatial Three-Body Problem and Space Mission Design –Advances in the Astronautical Sciences, 2002
W. S. Koon, M. W. Lo, J. E. Marsden and S. D. Roos, – Low energy transfer tothe Moon – Celestial Mech. Dynam. Astronom, Vol 81, pp 63-73, 2001
R. Castelli, G. Mingotti, A. Zanzottera and M. Dellnitz, Intersecting InvariantManifolds in Spatial Restricted Three-Body Problems: Design and Optimization ofEarth-to-Halo Transfers in the Sun–Earth–Moon Scenario, submitted to Commun.Nonlinear Sci. Numer. Simulat.
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Conclusion
REFERENCES
R. Castelli Regions of Prevalence in the Coupled Restricted Three-Body ProblemsApproximation Submitted to Commun. Nonlinear Sci. Numer. Simulat.
M. Dellnitz; G. Froyland; O. Junge The algorithms behind GAIO – Set orientednumerical methods for dynamical systems B. Fiedler (ed.): Ergodic Theory,Analysis, and Efficient Simulation of Dynamical Systems, pp. 145-174, Springer,2001
M. Dellnitz and O. Junge – Set Oriented Numerical Methods for DynamicalSystems – Handbook of dynamical systems, 2002
J.S. Parker– Families of low-energy lunar halo trasfer – Proceedings of theAAS/AIAA Space Flight Mechanics Meeting, pp 483–502, 2006.
G.Mingotti, F. Topputo, and F. Bernelli-Zazzera, Exploiting Distant Periodic Orbitsand their Invariant Manifolds to Design Novel Space Trajectories to the Moon,Proceedings of the 20th AAS/AIAA Space Flight Mechanics Meeting, San Diego,California, 14-17 February, 2010
Ming X. and Shijie X. Exploration of distant retrograde orbits around Moon, ActaAstronautica, Vol.65, pp. 853–850, 2009
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Conclusion
Thank you
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Conclusion
⇒ Dynamical model
⇒ Set oriented Numerics
⇒ Mission Design
⇒ References
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