Integration of Reusable Launch Vehicles (RLV) into the Air
Traffic Management System
J.K. Kuchar, K.A. Khan, J. Falkner
Department of Aeronautics
and Astronautics
A.A. Trani, H.D. Sherali
J.C. Smith, S. Sale, Q. Chuanwen
Department of Civil Engineering
Department of Industrial Engineering
Massachusetts Institute of Technology
Virginia Tech
Sponsor: FAA Office of Commercial Space
Kelvin Coleman
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Motivation
•
Current mode of airspace utilization for space operations
• Activate Special Use Airspace (SUA), reroute air traffic
• Large spatial and temporal safety buffers
• Limited flexibility
• Disparity between air and space user costs
•
Improvements in sensors & datalink capabilities
• Potential to reduce uncertainty of some vehicle trajectories
• More efficient information flow between ATC - space operator
• Example: launch delay of STS-87 (with John Glenn) due to
intruding GA aircraft
•
Advanced ATM models could streamline the integration of RLVs
with ATC
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Opportunities and K
•
ey Issues
Some vehicle types / missions might be integrated with air traffic
• Reusable Launch Vehicles with conventional phases of flight
•
Key Research Issues
1) What is required for integrated operations to occur?
Equipage, communications, surveillance requirements
2) How should those operations be carried out?
Flow management procedures
3) What are the user & service provider cost / benefit trade-offs?
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Scope of Work
•
Investigate current and future methods of RLV-aircraft separation
in use at the Special Use Airspace (SUA) areas around the US
Launch Ranges (Cape Canaveral, Vandenberg AFB, and Wallops
AFB)
•
Identify mission profiles of the proposed RLVs and characteristics
of the respective phases of flight
•
Develop a generalized model of airspace / air traffic / RLV
operations to provide a consistent framework to describe and
evaluate options
•
Define potential modes of operation / airspace utilization for RLV
operations by understanding current requirements and procedures,
and explore possible alternatives
•
Develop a methodology to estimate RLV operation impacts
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Summary of Pr
•
•
•
evious Acti vities
Collected data on proposed RLVs
Surveyed typical phases of flight / mission profiles
Identified 8 potential modes of operation
• Continue use of SUA (strategic segregation)
• Activate new SUA
• Mission-specific SUA
• Controlled Space Activity Zone (c.f. Class B airspace)
• RLV corridor
• RLV as high-priority vehicle (vectors)
• RLV as nominal-priority vehicle
• Self-separation
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Phase II Acti vities
•
Investigate trade-offs in tactical modes of operation
• Appropriate safety buffer size and duration
• Equipage and procedural requirements
• Explore limits of tactical ATC vectors (heading / altitude /
speed)
• Ability to manage high speeds / vertical rates
• Display / procedure / control issues
•
Preliminary model development
•
•
•
•
Describe when SUA vs. Tactical operation can/should be used
Impact of state uncertainties, vehicle profile & performance
Airspace conflict and sector analysis models
Airspace planning model development and validation
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RLV Airspace Usage Implications
VPI MIT
Safety
VPI
Vehicle equipment
requirements
Air traffic flow
MIT
Airspace
Usage
RLV operations
Ground equipment
requirements
MIT
Operator workload
VPI
VPI MIT
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RLV Operation Modes
Current Operational Mode
a) Strategic
b) Large SUA
c) Indirect integration
Airline Operations
Center (AOC)
Air Traffic
Control
Future Operational Modes
a) Tactical
b) Smaller SUA
c) Direct integration
RLV Operations
Center (ROC)
Airline Operations
Center (AOC)
Air Traffic
Control
RLV Operations
Center (ROC)
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Framew ork to Model RL
Modes of Operation
Analysis
ATC Scenario
Restriction Database
Airspace Scenario
Generation
V Impacts
Flight Plans/Tracks
Trajectory Descriptor
Simulation Model
RAMS and SIMMOD
Cost Benefit
Analysis
1 AOM = Airspace Occupancy Model
2 AEM = Aircraft Encounter Model
3Airspace
AOM1, AEM2
Models
Optimization
Model3
Non-airspace
User Cost
Planning Model
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NEXT OR/MITRE Relationship
MITRE (Looking at current operational practices)
•
Quantified operational cost for two launches from CCAS using
actual traffic data and perceived delays
•
Same approach to evaluate Kodiak Island operations
NEXTOR (Studying future operational practices)
•
Identified possible tactical separation envelopes and SUA regions
•
Modeling generic size spaceports (Phase III) using simulation
tools
•
Quantifying costs for future Free Flight conditions
•
Minimizing detour impacts (optimization model)
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Confl ict Detection and Resolution
•
Protected Zone: safety buffer around each vehicle
• Aircraft: 5 nmi, ± 1000 or 2000 ft.
•
Alert Zone: Space in which action must be taken to prevent PZ
violation
Alert Zone
Protected Zone
Protected Zone
•
As Alert Zone size increases, SUA becomes more attractive
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Tactical/Strategic Confl
ict Ev aluation
Velocity
Traffic density
Geometry
Conflict
Model
Alert Zone size
Airspace
Model
Projected Trajectory
Uncertainties
Alert rates
Spatial extent
of alerts
Maneuverability
Delays
Tactical
Strategic
SUA
Mode of Operation
Comparison aids in
Cost / Benefit studies
Current State
Uncertainties
Reroute rates
Spatial extent
of rerouting
Traffic density
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Simplifi ed Tactical Alert Zone Model
Intruder
A
Intruder
B
Left turn
swept area
Right turn
swept area
Alert Zone
Ownship
Enables first-order tradeoff analysis (uncertainty vs. AZ)
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Example Pr eliminary
Effect of Maximum Turning Angle
45
Trade Studies
Effect of Turn Rate
35
0.4°/s
40
30
0.8°/s
15°
35
1.4°/s
25
Instantaneous
30
30°
20
25
45°
20
15
60°
15
10
10
5
5
0
0
-10
-5
0
5
10
NM
-10
-5
0
5
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Alert Zone Gr
owth Due to
Uncertainty
Trajectory
Apex Distance, NM
200
150
5
2
1
100
VI / VO = 0.5
50
0
0
30
60
90
Uncertainty, ±°
Tactical conflict resolution untenable at large trajectory uncertainties & velocities
Tactical conflict resolution untenable at large trajectory uncertainties & velocities
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Netw ork Flo w Modeling and Optimization
•
Traffic flow model analysis tools
• SIMMOD - predicts delays and changes in travel times for
current scenario conditions)
• RAMS - predicts delays, travel times and sector workload for
current and anticipated 2005 conditions (i.e., Free Flight)
•
Development of an optimization model to reduce the impacts of
RLV operations around sites
• Dynamically schedules flights affected by RLV operations to
minimize a performance index (cost and workload)
• Model development tools: Matlab1 and CPLEX2
1.Matlab is a trademark of the Mathworks Inc.
2.CPLEX is a trademark of ILOG International
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Sample Sector Occupancies
Latitude (deg)
30
29
28
27
26
25
-88
-86
-84
-82
-80
-78
Longitude (deg)
-76
-74
-72
15
Traffic (acft)
STS SUA Activation
10
5
0
400
600
800
1000
Zulu Time (min)
1200
1400
1600
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Optimization Model to Integrate RL
V with
Minimum Cost to F AA and Airspace Users
•
Attempts to mimic and advanced ATM system of the future (i.e.,
2005, 2010)
•
A mature form of Collaborative Decision Making is in place (i.e.,
airlines and FAA share information about flight schedules and
possible delays associated with each flight)
•
Free Flight operations will be routine across NAS for all enroute
sectors and flight levels
•
Considers FAA resources (i.e., a function of traffic density), sector
and airline equity constraints
•
Serves as a policy tool to evaluate operations around spaceports
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Optimization Model Rationale
Several flight plan
detour strategies
can be studied. The
best strategy should
be a system optimal
alternative with
KSC-CCAS
War ning
Ar eas
minimum effects
on users and service
providers
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Airspace Planning Model Optimization
Model
Objective function
Min
∑∑
i ∈ M p ∈ Pi
ns
C ip x ip +
∑∑
µ sn y sn + µ e ( x eu – x el ) + µ u x eu
s∈S n = 1
Cost of adopting Penalty associated Penalty to maintain
flight plan (i,p) with sector load equity among airlines
Assignment Constraint (a single flight plan is selected)
Sector Load Constraint (restricts the number of flights to sector n s )
Airline Equity Constraint (penalizes equally all airlines flying)
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Application of the Optimization Model
The optimization model selects among surrogate flight plans to
minimize the cost to users and service providers
34
33
113
Jacksonville
Enroute Center
91
131
126
129
32
31
93
87
61
86
62
29
51
119
30
23
24
115
89
77
40
121
49
143
55
75
84
28
SUA
27
Surrogate FP
Original FP
26
-88
-86
-84
-82
-80
-78
-76
Longitude (deg.)
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Integration with Existing N
•
ARIM Tools
Currently generation of surrogate flight plans is done using a
simple globe circle flight planning module
•
Future will use proven tools such as OPGEN1
•
OPGEN output data structure is already integrated in AOM/AEM
OPGEN
AOM
ATM Strategy
Demand Func.
AEM
APM
1.A single flight optimization program developed by CSSI for FAA
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Implementation of Optimization Model
The current optimization model is implemented in CPLEX a
standard mixed-integer
From, AOMprogramming solver
AEM Model
Extract Flight
Proximity Information
a) Time adjacency
b) Spatial adjacency
c) Sector adjacency
Translate Conflict
and Workload
Constraints
Proximal Flight Conflict
Generation
Based on time, space
and proximal sector
adjacency
Conflict Analysis
Statistics
Flight Path
Conflict Analysis
Compiles C.A. statistics
and estimates conflict
metrics
Analytical model to find
conflict times and acft.
conflict geometries
CPLEX Solver
Routines
Optimal Cost
ATM
Flow Strategy
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Possible ATM Extensions of the
Optimization Model
•
Analysis of catastrophic RLV failures (i.e., estimation of the
number of aircraft affected)
•
Special Use Airspace is only one of many possible airspace
restrictions in NAS
•
Bad weather phenomena can be treated as a special case of SUA
(i.e., dynamic SUA)
•
Dynamic allocation of flight plans in the future will continue be a
mutual agreement between airlines and FAA and without any
doubt will consider the ATC resources available (i.e., decentralized
control)
•
Dynamic airspace sectorization problems (time varying airspace
sectors to balance sector loads)
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