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.

1. Comparing the
environmental effectiveness
of candidate metrics
and test points
Dan Rutherford, Ph.D.
Mazyar Zeinali, Ph.D.
June 2010

2. Outline
 Objectives
 Method
 Results
 Discussion
 Conclusions
 Recommendations

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. 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. Increased optimization by decreasing
maximum performance requirements
Cargo
Range

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. 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. 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. Metric sensitivity: technology at weighted range
“Snowball effects”
Punishment of technology
Undercrediting of technology

10. Metric sensitivity: technology at R1
SAR metrics
overcredited
at R1?

11. Metric sensitivity: optimization at WR
Largest
optimization
benefits

12. Metric sensitivity: optimization at R1
Optimization
penalized

13. Comparison of R1 to WR sensitivities: SFC
R1 and WR behavior very similar

14. Comparison of R1 to WR sensitivities: cargo
R1 and WR behavior different for UL and payload metrics

15. Comparison of R1 to WR sensitivities: range
R1 and WR behavior different for UL and TOW metrics

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. 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. 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. 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. Previous ICSA work suggests technology
interdependencies may be large
R1
WR
CAEP9_WG3_CO2-1_WP10

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. 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