1. MIT Space Systems Laboratory Chart: 1 April 18, 2003 Quantitative Assessment of Technology Infusion in Communications Satellite Constellations Olivier de Weck and Darren Chang, MIT, U.S.A. Ryutaro Suzuki, CRL, Tokyo, Japan Eihisa Morikawa, NeLS, Kanagawa, Japan Unit 3 21st International Communications Satellite Systems Conference, 15-19 April 2003, Yokohama, Japan

2. MIT Space Systems Laboratory Chart: 2 April 18, 2003 Outline Introduction and Motivation Previous Work in Technology Assessment Quantitative Technology Infusion Assessment Methodology Application to Satellite Communications Constellations Conclusions Future Work

3. MIT Space Systems Laboratory Chart: 3 April 18, 2003 Motivation The architecture of satellite communications systems (concept) must be carefully selected Selection of architectures can be done quantitatively based on performance, cost and capacity predictions – see AIAA-2002-1866 A number of new technologies are currently under development for GEO and LEO Systems, e.g. –Large Deployable Reflector (LDR) Antennas –Optical Inter-satellite Links (OISL) System Designers/Architects must often choose between competing technologies – need a quantitative method Generally, better understand the relationship between architectures and technologies

4. MIT Space Systems Laboratory Chart: 4 April 18, 2003 Conceptual Design Space Can we quantify the conceptual system design problem using simulation and optimization? Simulator Design (Input) Vector Performance Capacity Cost

5. MIT Space Systems Laboratory Chart: 5 April 18, 2003 Key Idea: Pareto Impact

6. MIT Space Systems Laboratory Chart: 6 April 18, 2003 Previous Work Metrics for comsat architecture evaluation: “cost per function” – CPF – [$/min] at a fixed data rate R, BER p b and link margin (Hastings, Shaw….) Architecture Evaluation and Selection using MDO (multi- disciplinary design optimization) – Miller, Jilla, de Weck Research in new generations of satellite constellations (e.g. NeLs, R. Suzuki) and new technologies Technology assessment proposed by Management of Technology (MOT): Utterback, van Wyk, Henderson and Clark Technology Selection: Mavris, DeLaurentis > Perceive a missing link between architecture evaluation and technology selection

7. MIT Space Systems Laboratory Chart: 7 April 18, 2003 Assumptions Possible to create “high-fidelity” simulations of satellite communications systems during conceptual design Performance per channel is fixed: –Data Rate –Bit-Error-Rate –Link Fading Margin Tradeoff between system capacity and lifecycle cost Architectures are realizable with existing, mature technologies

8. MIT Space Systems Laboratory Chart: 8 April 18, 2003 Proposed Methodology

9. MIT Space Systems Laboratory Chart: 9 April 18, 2003 Steps 1-3 Step 1: Baseline Trade Space Exploration - Obtain Baseline Pareto Frontier  o Step 2: Technology Identification, Classification, Modeling - Understand technology dependencies: T c, T d - Technology modeling : physics based, prototype data, empirical relationships (expert interviews) Step 3: Technology Infusion Interface Development

10. MIT Space Systems Laboratory Chart: 10 April 18, 2003 Steps 4-6 Step 4: Individual Technology Assessment Step 5: Assessment of allowable combinations of technologies Step 6: Comparison and Interpretation - based on Pareto Impact Metrics (4)

11. MIT Space Systems Laboratory Chart: 11 April 18, 2003 Application: LEO Com Sat LEO Constellation 50 satellites 5 planes h=800 km

12. MIT Space Systems Laboratory Chart: 12 April 18, 2003 Benchmarking Benchmarking : validating a simulation by comparing the predicted response against reality.

13. MIT Space Systems Laboratory Chart: 13 April 18, 2003 Design Trade Space 1728 Full Factorial Combinatorial Design Space Design Vector

14. MIT Space Systems Laboratory Chart: 14 April 18, 2003 Baseline Case Baseline Design Space – Uses only Existing, Mature Technologies R=4.8 [kbps] p b =0.001 LM=16 [dB] Channel Perf:

15. MIT Space Systems Laboratory Chart: 15 April 18, 2003 Technology Portfolio T1: Optical Inter-Satellite Links (OISL) T2: Asynchronous Transfer Mode (ATM) T3: Large Deployable Reflectors (LDR) T4: Digital/Analog Beamforming (DBF) Technology Dependency Matrix T1T2T3T4 T10100 T20000 T30000 T40010 TechnoDescriptionSatellite(+)(-) OISLReplace RF ISLSpot-4R>10GbpsPointing requirement ATMPacket/circuit switchingACTSEfficiencyMass penalty LDRD A up to 20mETS-VIIIGain ~ 38- 45 dBi Large Stowage Volume DBFGround fixed cellsTBDHandoverComplexity

16. MIT Space Systems Laboratory Chart: 16 April 18, 2003 Example: Impact of LDR Large Deployable Reflectors (LDR) ETS-VIII E.g. for D A =6[m] -> G T ~39 [dBi], =0.19[m], TFU=2.45 [M$]

17. MIT Space Systems Laboratory Chart: 17 April 18, 2003 Pareto Impact – Example LDR  o is the normalized Pareto front with baseline technologies alone.  3 is the normalized Pareto front with LDR. Decreased utopia point distance

18. MIT Space Systems Laboratory Chart: 18 April 18, 2003 Overview Results - M  LDR has a large effect on  min and CPF, but caution… benefits only come in for high capacity/throughput. OISL in isolation shows less benefit, however the system here is narrowband (4.8 kbps), expect benefit for broadband. All technologies increase throughput, good for NeLS !

19. MIT Space Systems Laboratory Chart: 19 April 18, 2003 Conclusions Presented a methodology for quantitatively assessing technology impact on Communication Satellites –Choose between mature, existing technologies versus newly emerging, competing technologies –Technology portfolio & technology investment decisions Use simulation to predict performance, cost and capacity for a set of candidate architectures –Careful benchmarking required –Modular simulation architecture eases investigation of a large set of technologies Current technologies under development for NeLS make sense for broadband, multimedia system Engineering Systems Industry Study will be available

20. MIT Space Systems Laboratory Chart: 20 April 18, 2003 Future Work “Harden” and verify this methodology by deploying in an industrial/satellite manufacturer setting, apply to GEO Uncertainty in effect on Pareto front  due to technology maturity – e.g. measured via NASA’s Technology Readiness Levels (TRL) – probabilistic Expand work to more than two (2) objectives How to deal with “disruptive” technologies that enable new architectures – in that case don’t have a baseline Pareto front to compare to, e.g. introduction of ISL when only “bent-pipe” was known. Understand relationship between Pareto Impact metrics and technology obsolescence