Comparing the Environmental Effectiveness of Candidate Metrics and Test Points


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This presentation and accompanying methodological summary compares the environmental effectiveness of candidate metrics and test points for an ICAO CO2 standard. Two primary comparisons are made: between metrics with simple payload components (e.g. ton-km and seat-km) and those which include proxies of aircraft productivity (e.g. take-off weight and useful load), and between a standard measured at maximum aircraft potentials (here, maximum range at maximum payload) vs. at a test point more representative of actual operations.

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Comparing the Environmental Effectiveness of Candidate Metrics and Test Points

  1. 1. Comparing the environmental effectiveness of candidate metrics and test points Dan Rutherford, Ph.D. Mazyar Zeinali, Ph.D. June 2010
  2. 2. Outline   Objectives   Method   Results   Discussion   Conclusions   Recommendations
  3. 3. Objective   Compare metrics in terms of “environmental effectiveness” How much a given metric credits reductions in fuel burn   Intuition suggests that metrics proposed to date (5/27) likely credit fuel burn improvements differently Mostly a function of the weight parameter in a metric   Consider both tech improvement (SFC, aero, structural eff) and optimization of aircraft performance requirements to operational mission (range, payload, speed)   Also look at metric/test point interaction –  R1 vs. a simple weighted range/payload approach   Aim for a better understanding of candidate metrics before considering possible correlation factors
  4. 4. Methodology   Three representative aircraft: TA, SA, and RJ (ongoing)   Test metric sensitivity of six proposed metrics to: –  10% tech improvements (SFC, aero, structural) –  Increased optimization to mission through 10% reductions in max performance characteristics (R1 and cargo capacity) –  Speed not yet modeled   Tech improvements as user factor on PIANO parameters with all other parameters at PIANO default   Resize aircraft for all cases to keep thrust/weight, weight/ span constant   FL 310 and 350 and step up   99% SAR cruise for SAR metrics   Results summarized here on TA only -> SA results similar
  5. 5. Increased optimization by decreasing maximum performance requirements Cargo Range
  6. 6. Mission/test point sensitivity   Tested metric sensitivity to improvement at both R1 and three “operational” test points (BTS 12/06 data)   TA test points: −  Weighted range •  25% mission: 5500 km, 27.8 tonne payload, 183 pax •  50% mission: 6900 km, 30.4 tonne payload, 227 pax •  75% mission: 8700 km, 31.8 tonne payload, 213 pax −  R1 mission 10,400 km, 55 tonne payload, 301 pax
  7. 7. Caveat emptor  Very simplistic modeling   Initial work (fall out from fuel burn tech review)   No interdependencies modeled –  Increase SFC through bypass ratio  increase engine weight and drag –  Reduces overall benefit, makes benefit more dependent on mission?   Some issues raised here may be correctable through correlation parameters   Broad trends instructive, but should not overinterpret and requires technical follow-up
  8. 8. Metrics considered Mass Type Metric Units Mass term parameter g CO2/ seat-range Pax mass Pax * 100 kg seat-km g CO2/ payload-range Pax mass + cargo TOW-fuel-OEW tonne-km Block fuel useful load- g CO2/ Pax mass + cargo TOW-OEW range tonne-km + fuel g CO2/ Pax mass + cargo TOW-range TOW tonne-km + fuel + airframe tonne-m/ SAR-payload Pax mass + cargo TOW-fuel-OEW Point kg fuel performance tonne-m/ Pax mass + cargo SAR-TOW TOW kg fuel + fuel + airframe
  9. 9. Metric sensitivity: technology at weighted range “Snowball effects” Punishment of technology Undercrediting of technology
  10. 10. Metric sensitivity: technology at R1 SAR metrics overcredited at R1?
  11. 11. Metric sensitivity: optimization at WR Largest optimization benefits
  12. 12. Metric sensitivity: optimization at R1 Optimization penalized
  13. 13. Comparison of R1 to WR sensitivities: SFC R1 and WR behavior very similar
  14. 14. Comparison of R1 to WR sensitivities: cargo R1 and WR behavior different for UL and payload metrics
  15. 15. Comparison of R1 to WR sensitivities: range R1 and WR behavior different for UL and TOW metrics
  16. 16. Divergence of metrics at R1 and WR creates potential for unintended consequences Volumetric Increasing Constraint range (illustrative) Where aircraft are volumetrically constrained, Increasing maximum structural payload increasing MSP improves R1 efficiency decreases efficiency at WR without providing real-world utility. due to heavier airframe.
  17. 17. Discussion: Block fuel metrics   Technology improvements –  Seat-range and payload-range provide largest credit –  Useful load-range discounts tech benefits by about 2/3rds –  TOW-range discounts engines, aero benefit by 1/3rd and penalizes lightweight materials   Optimization to mission –  Seat-range provide largest credit for optimization –  UL-km and TOW-km behave differently at test points •  Close to indifferent at WR •  Strongly negative at R1 –  Key finding: for UL or TOW metrics, reducing margin between max performance capability and and operational mission reduces fuel burn in-operation but measures as an decrease in efficiency at R1
  18. 18. Discussion: SAR metrics   SAR is a “neutral” parameter like 1/[block fuel burn/ km] –  Metric behavior highly influenced by weight parameter included –  SAR-payload  tonne-km/kg fuel –  SAR-TOW  TOW-km/kg fuel   Tech improvement –  SAR-payload: “excess” credit for tech improvements? –  SAR-MTOW: credits engines and aero, punishes lightweight materials   Optimization to mission –  SAR-payload: credits –  SAR-MTOW: punishes
  19. 19. Discussion: R1 vs. weighted-range   For technology improvements, R1 and weighted- range test points similar Caveat: simplistic modeling   For optimization to mission, test points matter! –  Cargo: cargo metrics and UL diverge –  Range: UL and TOW diverge –  Speed: ???   R1 to WR divergence a cause for concern –  Reduce fuel burn in operation  fall in efficiency shown at R1 –  Improve metric at R1  increase fuel burn in operation
  20. 20. Previous ICSA work suggests technology interdependencies may be large R1 WR CAEP9_WG3_CO2-1_WP10
  21. 21. As a general rule, metrics with lower environmental benefit correlate better ICCT analysis, PIANO model, 37 aircraft types included in Appendix A of CAEP9-WG3-CO2-2-WP23
  22. 22. Conclusions   Payload-range metrics provide consistent credit to technology and optimization of aircraft   UL-range and TOW-range metrics undercredit technology and punish optimization at R1   SAR metric behavior highly dependent on weight parameter used   SAR metrics may “overcredit” technology gains relative to block fuel metrics   Test point clearly matters for optimization   Divergence of R1 and WR performance for UL and TOW metrics suggest potential for unintended consequences