Conflict pattern analysis under the consideration of ... Methodology Case study Conclusions ATM Performances Research question What is the inﬂuence of continuous operations in ATM

Motivation Methodology Case study Conclusions

Conflict pattern analysis under the

consideration of optimal trajectories

in the European ATM

Sergio Ruiz1 and Manuel Soler2

1Universidad Autonoma de Barcelona2 Universidad Carlos III de Madrid

ATM Seminar’15Lisbon, June 25th, 2015

Sergio Ruiz and Manuel Soler ATM Seminar’15


Continuous Operations

Natural result to solving an aircraft trajectory optimisation problemwith no vertically structured airspace, i.e., no FLs.

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Continuous Operation

Baseline (actual flight plan)

Optimized, procedured profile

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Figure: Medium haul vertical profile [Soler et al., J. Aircraft, 2012].



Continuous Operations → Benefits

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Savings of 385 [kg] --> 5.9%

Baseline Savings of 298 [kg] --> 4.6%

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Other studies

Continuous Cruise [Dalmau and Prats, Trans. Res. D, 2015]: fuel savings1%-2% (reduction trip times 1%-5%)




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h [m]

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Other studies

Continuous Cruise [Dalmau and Prats, Trans. Res. D, 2015]: fuel savings1%-2% (reduction trip times 1%-5%)AIRE Trials: 400 kg of CO2 savings per flight [roughly 100-120 kg fuel].Continuous cruise: fuel savings 0.5%-1%.




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h [m]

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t


Other studies

Continuous Cruise [Dalmau and Prats, Trans. Res. D, 2015]: fuel savings1%-2% (reduction trip times 1%-5%)AIRE Trials: 400 kg of CO2 savings per flight [roughly 100-120 kg fuel].Continuous cruise: fuel savings 0.5%-1%.ATM Seminar’15 papers:




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Savings of 385 [kg] --> 5.9%


h [m]

t 0 2000 4000 6000 80000

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VCAS [m/s]

t


Other studies

Continuous Cruise [Dalmau and Prats, Trans. Res. D, 2015]: fuel savings1%-2% (reduction trip times 1%-5%)AIRE Trials: 400 kg of CO2 savings per flight [roughly 100-120 kg fuel].Continuous cruise: fuel savings 0.5%-1%.ATM Seminar’15 papers: [Jensen et al., ATM-Sem., 2015] (cont. cruise; fuelsavings of 1.87% and for Long Range Cruise Speed time savings of 1 min 42sec.);




0 2000 4000 6000 80000

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Savings of 385 [kg] --> 5.9%


h [m]

t 0 2000 4000 6000 80000

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100

150

VCAS [m/s]

t


Other studies

Continuous Cruise [Dalmau and Prats, Trans. Res. D, 2015]: fuel savings1%-2% (reduction trip times 1%-5%)AIRE Trials: 400 kg of CO2 savings per flight [roughly 100-120 kg fuel].Continuous cruise: fuel savings 0.5%-1%.ATM Seminar’15 papers: [Jensen et al., ATM-Sem., 2015] (cont. cruise; fuelsavings of 1.87% and for Long Range Cruise Speed time savings of 1 min 42sec.); [McConnachie et al., ATM-Sem., 2015] (cont. climb); [Fricke et al.,ATM-Sem., 2015] (cont. desc.).



ATM PerformancesResearch question




What is the influence of continuous operations in ATM Performances?





Fuel

Climate

Time





Fuel

Climate

Time

Fuel Efficiency → XX (rough figures:30000 flights a day in Europe; 5000 kgfuel on average consumed; 1% savings →1.5M C per day!)





Fuel

Climate

Time


Time Efficiency → XX (1 min-2 min perflight)





Fuel

Climate

Time



Environment → X (rough figures:savings of 4.5 million tons of CO2 perday!)





Fuel

Climate

Time



Environment → X (rough figures:savings of 4.5 million tons of CO2 perday!) Trade-offs CO2 and persistentcontrails impact? [Soler, Zou, andHansen, Trans. Res. C, 2014]





Fuel

Climate

Time




Safety, Capacity, ATM cost → ?





Fuel

Climate

Time




Safety, Capacity, ATM cost → ?Problem: Analysis of continuous operations influence of safety and capacity KPAs.





Fuel

Climate

Time




Safety, Capacity, ATM cost → ?Problem: Analysis of continuous operations influence of safety and capacity KPAs.

Hypothesis: number of conflicts and its patterns as indicator of Safety and Capacity



Outline

2 Methodological ApproachMicroscale: optimal trajectory planningMesoscale: conflict patterns analysis



Outline


3 Case studySimulation scenarioSimulation results



Outline



4 Conclusions and future workConclusionsFuture work



Methodology

MicroScale (Trajectory Planning) to Mesoscale (traffic parterns)

Figure: Block diagram of the simulation architecture.Sergio Ruiz and Manuel Soler ATM Seminar’15


Trajectory planning

Outline

1 Motivation






Trajectory planning

Optimal control problem

t ∈ [t I , tF ]

t I tF



Trajectory planning


t ∈ [t I , tF ]

x(t) = f [x(t), u(t), l ]

0 = g [x(t), u(t), l ]

t I tF



Trajectory planning


t ∈ [t I , tF ]

x(t) = f [x(t), u(t), l ]

0 = g [x(t), u(t), l ]

t I tF

x(t I ) = x I

ψ(x(tF )) = 0



Trajectory planning


t ∈ [t I , tF ]

x(t) = f [x(t), u(t), l ]

0 = g [x(t), u(t), l ]

t I tF

x(t I ) = x I

ψ(x(tF )) = 0

φ[x(t), u(t), l ] ≤ 0



Trajectory planning


t ∈ [t I , tF ]

x(t) = f [x(t), u(t), l ]

0 = g [x(t), u(t), l ]

t I tF

x(t I ) = x I

ψ(x(tF )) = 0

u∗(t)

φ[x(t), u(t), l ] ≤ 0



Trajectory planning


t ∈ [t I , tF ]

x(t) = f [x(t), u(t), l ]

0 = g [x(t), u(t), l ]

t I tF

x(t I ) = x I

ψ(x(tF )) = 0x(t)

u∗(t)

φ[x(t), u(t), l ] ≤ 0



Trajectory planning


J(t, x(t), u(t), l)

t ∈ [t I , tF ]

x(t) = f [x(t), u(t), l ]

0 = g [x(t), u(t), l ]

t I tF

x(t I ) = x I

ψ(x(tF )) = 0x(t)

u∗(t)

φ[x(t), u(t), l ] ≤ 0



Trajectory planning

Statement

Problem (Optimal Control Problem)



Trajectory planning

Statement


min J(t, x(t), u(t), l) = E(tF , x(tF )) +

∫ tF

t IL(x(t), u(t), l)dt;

subject to:

x(t) = f (x(t), u(t), l), dynamic equations;

0 = g(x(t), u(t), l), algebraic equations;

φl ≤ φ[x(t), u(t), l ] ≤ φu, path constraints.

x(t I ) = xI, initial boundary conditions;

ψ(x(tF )) = 0, terminal boundary conditions;

(OCP)



Trajectory planning

Statement


min J(t, x(t), u(t), l) = E(tF , x(tF )) +

∫ tF

t IL(x(t), u(t), l)dt;

subject to:

x(t) = f (x(t), u(t), l), dynamic equations;

0 = g(x(t), u(t), l), algebraic equations;

φl ≤ φ[x(t), u(t), l ] ≤ φu, path constraints.

x(t I ) = xI, initial boundary conditions;

ψ(x(tF )) = 0, terminal boundary conditions;

(OCP)

[Bryson and Ho, 1975], [Betts, 2010]



Trajectory planning

Solving methods



Trajectory planning

Solving methodsTrajectory optimization

Analytical optimal control Numerical optimal control

Indirect methods Direct methods Dynamic programming

Shooting methods Collocation methods

Pseudospectral collocation HLGL collocation

Gauss Gauss-Lobatto Gauss-Radau

Chebyshev LegendreHermite-Simpson 5th degree

Singular arc



Trajectory planning

Aircraft 3-DOF dynamic model



Trajectory planning



d

dt

V

χ

γ

λe

θehem

=

T (t)−D(he(t),V (t),CL(t))−m(t)·g·sinγ(t)m(t)

L(he (t),V (t),CL(t))·sinµ(t)m(t)·V (t)·cosγ(t)

L(he (t),V (t),CL(t))·cosµ(t)−m(t)·g·cosγ(t)m(t)·V (t)

V (t)·cosγ(t)·cosχ(t)R·cos θe (t)

+Wx (λe (t), θe(t), he(t))V (t)·cosγ(t)·sinχ(t)

R+Wy (λe (t), θe(t), he (t))

V (t) · sin γ(t) +Wz(λe (t), θe(t), he (t))−T (t) · η(V (t))

(1)

xe

xw

yeyw

χT

D

(d) Top view

ye

yw

Lhehw

µ

mg

(e) Front view

xe

xw

T

L

D

hehw

γ

mg

(f) Lateral viewSergio Ruiz and Manuel Soler ATM Seminar’15


Trajectory planning

Framework and models

Multiphase optimal control framework for flight planning problems[Bonami et, al., J. Guidance 2013, Soler et, al., J. Aircraft 2015]



Trajectory planning



BADA 3 and BADA 4.



Trajectory planning



BADA 3 and BADA 4.

Airspace Structure (waypoints, FLs, SIDs, STARs ).



Trajectory planning



BADA 3 and BADA 4.


Variate of constraints (operational procedures, RTAs, etc.)



Trajectory planning



BADA 3 and BADA 4.



Variate of cost functions (min fuel, min time, CI based, climate based-including contrails-, ANSP overfly fees, etc.)



Trajectory planning



BADA 3 and BADA 4.




Deterministic wind optimal profiles [moving now towards probabilistic]



Trajectory planning



BADA 3 and BADA 4.




Deterministic wind optimal profiles [moving now towards probabilistic]

Different numerical methods.



Conflict patterns

Outline

1 Motivation






Conflict patterns

Methodology for strategic CD& R



Conflict patterns

Methodology - Conflict Detection



Conflict patterns

Methodology - Conflict Resolution



Conflict patterns

Methodology - Conflict Resolution



Conflict patterns

Methodology - Clustering

GOAL → Reduce the solution space combinatorial exploration



Conflict patterns

Methodology - Video



Conflict patterns

Methodology - Video



Outline

1 Motivation






Scenario

Simulation ScenarioBased on STREAM SESAR WP-E Project



Scenario

Scenario

Same scenario as in STREAM

Microscale

4010 Trajectories

Time window 2 peak hours.

Each aircraft → min. fuel problem,

BADA 3.9,

clean config.,

airport-to-airport loxodromic,

free vertical profile.



Scenario

Scenario

Same scenario as in STREAM

Microscale

4010 Trajectories

Time window 2 peak hours.

Each aircraft → min. fuel problem,

BADA 3.9,

clean config.,

airport-to-airport loxodromic,

free vertical profile.

Mesoscale

Conflict detection

Conflict pattern analysis.



Results

Set of Optimal Trajectories

(g) Altitude Profiles (h) Speed Profiles

Figure: Set of optimal trajectories.



Results

Conflicts in Europe

Figure: European airspace with 4010 trajectories following Direct Routes andContinuous Operations; 1496 conflicts detected (1211 en-route).



Results

Distribution of conflicts per Flight Levels

Figure: Distribution of conflicts per Flight Levels



Results

Distribution of trajectories per number of conflicts

Figure: Distribution of trajectories per number of conflicts. 884 trajectories with 1conflict; 390 trajectories with 2 conflicts; 194 with 3 conflicts; etc.

56% of the conflicts were between two trajectories

4 trajectories had the maximum number of trajectories per flight



Results

Cluster size distribution

Figure: Cluster size distribution



Results

Comparison with STREAM Results



Results




Results




Results

ATM performance Assessment

CAPACITY



Results


CAPACITY SAFETY



Results


CAPACITY SAFETY COST EFFECTIVENESS



Results

Results corollay

Flight Level Scheme seems to work fairly well to strategically de-conflictthe traffic.

Relatively good levels of safety

Relatively good capacity performances

Relatively good ATM cost-effectiveness

The cost seems quite low.1-2% of fuel savings per flight



Results

Results corollay






. . . still 1.5 million of Euro per day in savings



Results

Results corollay






. . . still 1.5 million of Euro per day in savings

Could we apply strategic separation to control complexity?



Outline

1 Motivation






Conclusions

Conclusions

Main conclusion



Conclusions

Conclusions

Main conclusion

Results suggest that the introduction of continuous operations may notablyincrease both the number of conflicts during flight execution (by a factor of4 to 5 compared to flights under current ATM)



Conclusions

Conclusions

Main conclusion

Results suggest that the introduction of continuous operations may notablyincrease both the number of conflicts during flight execution (by a factor of4 to 5 compared to flights under current ATM) and the complexity of traffic.



Conclusions

Conclusions

Main conclusion


Thus, air traffic controllers workload would be potentially raised and, as aconsequence, a notable safety and capacity degradation shall be expected.



Conclusions

Conclusions

Main conclusion



Mitigation strategy



Conclusions

Conclusions

Main conclusion



Mitigation strategy

TBO context these conflicts could be predicted at strategic phase (2 hours inadvance) → strategic trajectory de-confliction:



Conclusions

Conclusions

Main conclusion



Mitigation strategy

TBO context these conflicts could be predicted at strategic phase (2 hours inadvance) → strategic trajectory de-confliction:

78% of the flights would be able to fly their optimal trajectories,

22% of the flights would be constraint.



Future work

Future work

A detailed comparison of flight efficiency gains (including CI basedtrajectories and climate based trajectories)



Future work

Future work


New metrics as indicator of safety and capacity (study of traffic withinsectors).



Future work

Future work



Deal with flow and complexity management complementing strategictrajectory separation (STAM measures and dynamic optimal sectoring)



Future work

Future work




New strategies for re-clustering the traffic, and thus finding globalde-conflicted solutions.



Future work

Future work





The addition of uncertainty to the algorithms,



Future work

Future work





The addition of uncertainty to the algorithms, e.g.., the consideration ofuncertainty at micro level (e.g., wind affecting individual trajectories) andits propagation effects from the microscale level to the mesoscale(de-confliction robustness), and vice-versa, i.e., with the consideration ofuncertainty at meso level (e.g., thunderstorms dropping the capacity ofsome airspace volumes) and its propagation effects from the mesoscalelevel to the microscale (flight planning robustness).



Future work

On-going - Uncertainty at the microscale

PhD funded by SESAR WP-e HALA! Network

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Figure: Wind EPS forecasts; optimal path ensemble; optimal state/control ensembleSergio Ruiz and Manuel Soler ATM Seminar’15

Documents

Conflict pattern analysis under the consideration of ... Methodology Case study Conclusions ATM Performances Research question What is the inﬂuence of continuous operations in ATM