Technology Screening, Selection and Modelling for ...

Technology Screening, Selection and Modelling for an Environmentally Sustainable Aviation Peter Nolte,∗ Thomas Zill∗ Arno Apffelstaedt∗ and Eike Stumpf† German Aerospace Center (DLR), Hamburg, 21079, Germany

Stephane Dufresne,‡ Simon Briceno‡ and Christopher Raczynksi‡ Aerospace Systems Design Laboratory (ASDL), Georgia Institute of Technology, Atlanta, Georgia, 30332-0150

Thomas R¨otger,§ International Air Transport Assoziation (IATA), Geneva, 1215, Switzerland

In the effort to achieve carbon neutral growth on the path to a zero emission future in aviation IATA outlined its “technology roadmap for environmentally sustainable aviation” (TERESA) initiative bringing together manufacturers, scientists, government agencies, infrastructure providers and airlines. Under this initiative DLR and ASDL conducted the screening, assessment and selection of appropriate technologies. Furthermore the application of the technologies into as well a short range as also a long range baseline vehicle on their particular mission was modelled. Recently the model is expanded to also account for the effects of technology introduction on world fleet level.

Nomenclature ACARE ACM ATS ASDL DLR DoE IATA PrADO

Advisory Council for Aeronautic Research in Europe Aviation Carbon Model Air Transportation System Aircraft System Design Laboratory Deutsches Zentrum f¨ ur Luft- und Raumfahrt Design of Experiments International Air Transport Association Preliminary Aircraft Design Optimisation Program

I.

RSE TERESA TIES UTE

Response Surface Equation Technology Roadmap for Environmentally Sustainable Aviation Technology, Identification, Evaluation and Screening Unified Trade-Off Environment

Introduction

In the summer of 2009 IATA announced the airline industry’s commitment for a global approach to reducing avia-tion emissions, with three high-level goals: • An average improvement in fuel efficiency of 1.5% per year from 2009 to 2020 • A cap on aviation CO2 emissions from 2020 (carbon-neutral growth • A reduction in CO2 emissions of 50% by 2050 relative to 2005 levels These collective goals were endorsed by the whole aviation industry (airlines, manufacturers, airports and air navi-gation service providers) in the joint industry submission to ICAO in September 2009. ICAO in its Climate Change Resolution 17/2 at the 37th General Assembly in October 2010 then increased the ∗ Research

Engineer, Institute of Air Transportation Systems, Blohmstrasse 18, AIAA Member. of Department, Institute of Air Transportation Systems, Blohmstrasse 18, AIAA Member. ‡ Research Engineer II, School of Aerospace Engineering, 270 Ferst Dr., AIAA Member. § Assistant Director Environment Technology, AIAA Member. † Head

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fuel efficiency goal to 2% p.a. and took over the carbon neutral growth goal from 2020. Note that the 2% p.a. is a goal for States and includes govern-ment-controlled measures such as air traffic management infrastructure (SES and NextGen), whereas the 1.5% in-dustry commitment only includes measures under industry control. This announcement succeeds IATA’s 2007 vision of a carbon-emission-free aviation and the availability of zero carbon-emission aircraft, which is also in line with the world-wide demand for a more environmentally friendly aviation industry. The aviation industry is united behind IATA’s Four Pillar Strategy of technology investment, efficient infrastructure, effective operations and positive economic measures. The first pillar of technology investment has been indentified as the one most promising in achieving the desired objectives in emission reduction. These high-level carbon aviation emissions goals are the cornerstone of IATA’s environmental initiatives. Their achievement is strongly correlated with the development and implementation of new technologies by aircraft and systems manufacturers. The ultimate benefits of these technology impacts to the environment (through a better fuel efficiency and thus lower carbon emissions) will come from the airlines and their fleet operations. There is an underlying challenge to select the appropriate technologies as they are driven by sometimes uncertain factors such as their current development status, risk, benefits and their R&D costs. To assist the airlines in this endeavor, IATA has created the Technology Roadmap for Environmentally Sustainable Aviation (TERESA) project that aims at providing an overview of green technologies and their impacts at the aircraft level. The work on the Roadmap started in 2008 by collecting an extensive amount of data on technologies from the four identified technology fields airframe, engine, atm and alternative fuels. The technologies were assessed qualitatively by industry and research experts of the fields under a workshop in Atlanta (September 2008). A further identifica-tion and down selection of the most promising technologies happened under a second workshop in Hamburg in October 2009. The found technologies and their implications were described more thoroughly and their introduction into the reference short- and long-range aircraft was modeled. The current phase focuses on the evaluation of the technology impact on world fleet level. Furthermore the technology and implication database is currently updated as the technology development progresses and new information becomes available. The impacts on world fleet level are mainly affected by the airframe manufacturers replacement programs and the airline fleet composition being determined by this.

II.

Technical Approach - The TERESA Process

The TERESA program includes 3 phases, as illustrated in Figure 1. The first phase, conducted in 2008, consisted of two main activities: [1.] survey of a large set of aerospace technologies that could positively impact the environment, and [2.] the creation of a high level trade-off environment. The environment is based on qualitative assessments, from representative subject matter experts, that relate the surveyed technologies to the IATA’s goals. The outcomes of this phase were used to create a strategic roadmap which was published as the IATA Technology roadmap report.2 The second phase of the program focused on a subset of technologies defined and selected in Phase 1 and expanded the qualitative impacts of the technology into quantitative values that could be modeled in a physics-based environment (e.g. technology X will reduce the baseline aircraft wing weight by 10%). Therefore the comprehensive list of over 80 technologies from phase one was reduced with the help of scenarios to 22 robust technologies over the four different technology fields airframe, engine, atm and alternative fuels. Based on the results from Phase 2, Phase 3 consists of modeling the technology impacts on two baseline aircraft (i.e. short and long range). Up to now only technologies from the airframe and engine sector are modelled. The results of these analyses are used to estimate the world fleet impacts of the green technologies on the reduction of CO2 emissions. This section describes in more detail the approach taken for each phase of the program. A.

Technology assessment using the subject matter experts process

Prior to the first qualitative workshop in Atlanta (30.09.-01.10.2008) an extensive technology screening and data mining was conducted via several telephone conferences. Thus the workshop already started with a prefilled assessment framework of technologies from the four identified technology fields airframe, engine, atm and alternative fuels. This qualitative layout was supported with a down selection of the most promising technologies and a collection of more quantitative technology details during a second workshop in Hamburg (06.10.-07.10.2009).

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TERESA Phase 1

Phase 2

Phase 3

Subject Matter Expert Environment

Physics – Based Environment

Model Impact on Worldfleet

(2008)

(2009 - 2010)

(2011)

Figure 1. The TERESA-Phases

1.

Qualitative assessment

The subject matter experts qualitative technology assessment is based on the ASDL’s Strategic Prioritization and Planning (SP2) process.1 The SP2 process provides a structured, traceable, and transparent approach using a hierarchical decomposition from the top level IATA goals, to aircraft attributes (e.g. airframe weight), and these attributes are then mapped against technology alternatives. This process can be tailored to any desired level of detail to enhance the decision-making process for investment strategies, risk mitigation, and system integration. The resulting decision making tool enables the analysis of “what if” scenarios to be played through a dynamic and interactive environment. The hierarchical decomposition enables two types of scenario analysis: [1.] top-down and [2.] bottom-up. The top-down scenarios consists of varying the relative importance of the IATA goals and analyzing which aircraft attributes are becoming more important. This will result in a prioritized list of technology alternatives. The bottom-up scenarios imply selecting specific technology alternatives which would emphasize the importance of a sub-set of aircraft attributes and consequently highlight how well the technologies could meet the IATA goals. The results of the SP2 process were used to create the Technology Roadmap published by IATA in June 2009,2 and it was used as the foundation for the quantitative technology assessment process. 2.

Quantitative technology assessment process

A technology assessment based on experts’ knowledge was conducted at the TERESA workshop in Hamburg, Germany, in October 2009, gathering about 50 participants from aircraft, engine and system manufacturers, airlines, airports, fuel suppliers and research. The workshop objectives were as follows: 1. Down-select the number of technologies from the roadmap document based on their robustness with respect to different future scenarios to a manageable set that can be modeled within a limited timeframe, 2. Identify technology factors (e.g. weight reduction) and 3. Estimate ranges of variability for the technology factors. The quantitative technology assessment is based on ASDL’s Technology Identification, Evaluation and Selection (TIES) process.5 The technology assessment started with a scenario setting: Four scenarios representing potential future socio-political reference points were selected, namely the ACARE scenarios3 (see Figure 2). The scenarios are called Basic Scenario 2020, Segmented Business Models, Block Building and Constrained Air Traffic Growth. Each scenario is described with specific characteristics for indicators out of the seven fields economy, politics, society, ecology&energy, general air traffic, infrastructure and airlines. For each of these four scenarios, the workshop participants were asked to select a set of technologies that appear most likely to be implemented (technology mapping). Typically, a subset of technologies will emerge as being “robust” to multiple scenarios and can thus be identified as attractive to airlines and manufacturers for their 3 of 24 American Institute of Aeronautics and Astronautics

cost/benefit attributes. Indeed, the workshop outcome showed that all major promising technologies were named under all four scenarios.

Figure 2. The four ACARE Air Traffic Scenarios3

For each robust technology an operational context is identified as well as a quantitative factor, referred to as a technology factor, in order to model the technology within a design environment. Examples of technology factors include weight reduction of specific aircraft components (e.g. wing weight), and performance characteristics such as induced, and friction drag coefficients. For this study the technology impacts were modeled using the Preliminary Aircraft Design and Optimization (PrADO)6 program, which facilitates the identification of primary and secondary impacts of the technology implementation. Technology factors are numeric physical quantities describing the change of relevant performance indicators (weight, drag etc.) by the introduction of a technology to a reference aircraft. They are used to model the impact of technologies within the physics based environment. A list of technology factors is summarized in Table 1 for the airframe technologies and Table 2 for the engine technologies. The given values represent the global min/max values for the selected robust technologies. Table 1. Technology Factors Airframe

Airframe ∆ wing weight ∆ fuselage weight ∆ empennage weight ∆ hydraulics weight ∆ electrical weight ∆ cabin weight ∆ APU ∆ water weight ∆ L/D ∆ induced drag ∆ friction drag

B.

min % -30 -30 -10 -100 0 -40 -100 -10 0 -20 -25

Table 2. Technology Factors Engine

max % +20 +10 +10 0 +20 0 0 0 +25 0 0

Engine ∆ SFC ∆ engine weight ∆ size

min % -20 -20 0

max % +10 +10 +10

Physics-Based Trade-Off Environment

A physics-based trade-off environment for mission level assessment is set up based on aircraft designs done with the aircraft design tool PrADO of the University of Technology in Braunschweig. The series of designs

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covers the design space defined by the selected technologies resp. the corresponding technology factors, see Table 1. Coupled to the physics-based trade-off environment is a world fleet forecast model that allows for assessment of technology impact on world fleet level using the results of the trade-off environment as input. The set-up and usage of the trade-off environment and world fleet impact assessment include six steps, illustrated in Figure 3. 1. Define the baseline vehicles and representative baseline mission; this provides environmental references using mature technologies integrated into state-of-the-art-aircraft as of 2008. 2. Identify specific technologies to be modeled; considering the amount of resources required to model the technologies (“Technology Factors and Ranges”). 3. Model the reference vehicles and integrated technologies within PrADO (“Design of Experiments, Response Surface Equation”). 4. Explore the technology space to detect the greatest environmental impact (“Interactive Prediction Profiler”). 5. Synthesize the modeling and visualization within a unique trade - off environment. 6. Model the impact of the technology introduction on a world fleet level by synthesizing public fleet fore-casts and using emission deltas on mission level from step 5 as input.

Figure 3. Physics-Based Trade-Off Environment

C.

Model impact on world fleet

In phase 3 of TERESA a comprehensive assessment of technology sets on world fleet level is envisaged in order to enable the derivation of a technology roadmap that sustains carbon neutral aviation growth in the medium term. Within this paper the methodology set-up is described and a limited number of use cases are presented as proof of concept. See section IV.

III.

Technology modeling

The technologies are modeled within two reference configurations: • A short range configuration similar to an Airbus A320-200 • A long range configuration similar to an Airbus A330-300 5 of 24 American Institute of Aeronautics and Astronautics

A.

Reference Vehicles and Missions

Technologies required to minimize emissions might differ for varying commercial missions. Thus, a short and a long-range aircraft will be used as reference vehicles to assess the technologies. The short-range aircraft is based on an Airbus A320-like configuration carrying 150 passengers with a maximum range of 5000 km (2700 nm), a cruise speed of Ma 0.78 and a take-off field length of 2000 m. The long-range aircraft configuration is based on a Airbus A330-like configuration carrying 300 passengers; maximum range of 15000 km (8100 nm), cruising speed of Ma 0.84 and take-off field length of 3500 m. Figure 4 shows the two reference aircraft as well as their respective cabin layout.

Figure 4. The short range (A320 / left side) and long range (A330 / right side) reference configuration

A trip from Frankfurt to Istanbul was selected as reference mission for the short range aircraft (illustrated in Figure 5); this corresponds to a range of approximately 1000 nm, which is an average flight length in this category - although relatively long at a European scale, it is shorter than many “short range” routes in North America or Asia. The long-range reference mission, corresponding to a range of 3500 nm, represents a trip from Frankfurt to New York (illustrated in Figure 6).

Figure 5. Reference short range mission (FrankfurtIstanbul)

B.

Figure 6. Reference long range mission (FrankfurtNewYork)

Technology Space Exploration

The technology space exploration using a physics-based environment enables the simultaneous assessment of requirements (e.g. range), design variables (e.g. wing span) and technology variables (e.g. weight reduction). The motivation behind the space exploration is to capture complex interactions that are difficult to assess qualitatively or even quantitatively if the interaction are more than linear. An important feature of the technology space exploration is the visualization of these interactions. Figure 7 illustrates three different groups of interactions: top level requirements, design variables, and technology impact factors referred in

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this figure as k-factors. This type of visualization is referred in this report as prediction profiler, which can be viewed as mathematical equations where the variables are staked horizontally and the responses are staked vertically. The mathematical model used to create the environment is known as Response Surface Methodology (RSM),7 however other types of surrogate modelling techniques could be used to obtain a similar framework. The grouping of prediction profilers including the requirements, design variables and technology factor is referred as the Unified Tradeoff Environment (UTE). This section will introduce the process of creating the technology space exploration. More specifically it will introduce the concept of Design of Experiments (DoE) and Response Surface Equation (RSE).

Figure 7. Unified Trade-Off Environment (new figure needed)

1.

Design of Experiments

An efficient UTE needs to be interactive for the user and at the same time relatively fast. Since PrADO and other physics based environments can take relatively long run time (i.e. in the order of 5-60 min per case), it becomes impractical to explore the space if each time a variable is changed the user has to wait 5-60 min to visualize the results. An efficient way to speed up the process is to regress equations from the physics-based environment. In this report, these equations are referred to as Response Surface Equation (RSE) and the cases needed to obtain these equations (i.e. the settings of the variables) are obtained through a Design of Experiments or (DoE). A DoE is a structured way to set up and conduct numerical experiments. It is a powerful approach since it maximizes the information gained from the physics-based environment, while minimizing the resources, in this case run time. The selection of the appropriate type of DoE is an important step in the process.8 The user needs to take into account specific aspects of the physics-based environment such as assumptions (e.g. linearization), run time, accuracy, etc.. The approach used for this study is the Latin Hypercube DoE. The DoE includes 275 cases which are run in PrADO. The design variables are shown in Table 3 and the responses used in this DoE are listed in Table 4. 2.

Response Surface Equation

A Response Surface Equation is a linnear regression that approximates the more accurate physics-based models quickly and efficiently. In general an RSE is created from a curve fit of the results of a DoE. Universally a second order RSE is used to approximate the model which follows the form below: y = b0 +

k X j=1

bj x j +

k X

bjj x2j +

j=1

k k X X i

bij xi xj

(1)

j=1,i 2020 15

New Design < 2020 Current Production

10

5

0 T1

T2

T3

T4

T5

T6

T7

T8

T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T21 T22 T23 T24

Figure 22. Frequency of Technologies appearing in the top 10 (short range)

Long Range Configuration 30

Frequency Technology appears in Top 10

25

20

New Design > 2020 15

New Design < 2020 Current Production

10

5

0 T1

T2

T3

T4

T5

T6

T7

T8

T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T21 T22 T23 T24

Figure 23. Frequency of Technologies appearing in the top 10 (long range)

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CO2 Reduction Potential per technology combination (Long Range) Long Range  45%

40%

35%

(%) CO2 Reduction 

30%

25%

20%

15%

10%

Retro Max CO2 Retro ‐ Min CO2  CP: Max CO2 CP:  Min CO2 ND  2020: Min CO2

5%

0% 0

5

10

15

20

25

TRL  

Figure 24. Min - Max long range CO2 reduction potential per technology combination

Long Range 45%

40%

35%

(%) CO2 Reduction 

30%

25%

20% Retro Max CO2 Retro ‐ Min CO2  CP: Max CO2 CP:  Min CO2 ND  2020: Min CO2

15%

10%

5%

0% 0

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

R & D Investment Required (Millions )

Figure 25. Min - Max long range CO2 reduction potential per R& D Investment Required

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CO2 Reduction Potential per technology combination (Short Range) Short Range

40%

35%

30%

(%) CO2 Reduction 

25%

20%

15% Retro Max CO2 Retro ‐ Min CO2  CP: Max CO2 CP:  Min CO2 ND  2020: Min CO2

10%

5%

0% 0

5

10

15

20

25

TRL  

Figure 26. Min - Max short range CO2 reduction potential per technology combination

Short Range 40%

35%

30%

(%) CO2 Reduction 

25%

20%

15% Retro Max CO2 Retro ‐ Min CO2  CP: Max CO2 CP:  Min CO2 ND  2020: Min CO2

10%

5%

0% 0

500

1.000

1.500

2.000

2.500

3.000

3.500

4.000

R & D Investment Required (Millions )

Figure 27. Min - Max short range CO2 reduction potential per R&D Investment Required

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References 1 Kirby, M.R., Raczynski, C.M, Mavris, D.N.: An Approach for Strategic Planning of Future Technology Portfolios, Paper presented at the 6 th AIAA Aviation Technology, Integration and Operations Conference (ATIO), Wichita, KS. 2 IATA: The IATA Technology Roadmap Report, 3rd edition, Geneva, June 2009. 3 ACARE: Strategic Research Agenda, Advisory Council for Aeronautics Research in Europe, 2nd edition, 2004. 4 DLR and ASDL: Technology Assessment Workshop Report, IATA internal, December 2009. 5 Kirby, M.R.: A Methodology for Technology Identification, Evaluation, and Selection in Conceptual and preliminary Aircraft Design Ph.D. Thesis, Georgia Institute of Technology, March 2001. 6 Heinze W.: Ein Beitrag zur quantitativen Analyse der technischen und wirtschaftlichen Auslegungsgrenzen verschiedener Flugzeugkonzepte f¨ ur den Transport grosser Nutzlasten, PhD-thesis TU Braunschweig, ZLR Forschungsbericht 94-01. 7 Myers,

R. and Montgomery, D.:

Response Surface

Methodology: Process and Product Optimization Using Design Experiments John Wiley & Sons, Inc, 1995. 8 Barros, P.A., Kirby, M. R., and Mavris, D.N.: Impact of Sampling Technique Selection on the Creation of Response Surface Model, SAE transactions, vol. 113, no 1 (2073 p.), 2004. 9 Zill, T.: HYdRA-Verwendung von Mehrkernprozessoren zur Parallelisierung von Simulationsrechnungen im Flugzeugvorentwurf, DLR Internal Report, IB-328-2010-02, Hamburg, 2010, (unpublished). 10 Horstmann, K. H.: Ein Mehrfach-Traglinienverfahren und seine Verwendung f¨ ur Entwurf und Nachrechnung nichtplanarer Fl¨ ugelanordnungen, PhD Thesis DFVLR FB 87-51, 1987. 11 Stumpf, E. et.al.: A Methodology for Holistic Air Transport System Analysis, paper submitted to 11th ATIO conference, Virginia Beach, 2011. 12 Apffelstaedt, A.; Langhans, S. and Gollnick, V.: Identifying carbon dioxide reducing aircraft technologies and estimating their impact on global CO2 emissions, Deutscher Luft- und Raumfahrtkongress (DLRK), 8.-10. September 2009, Aachen, Germany.

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