Periodic Reporting for period 2 - TeDiMo (Technology Diffusion Model)
Reporting period: 2020-06-03 to 2020-12-02
Europe follows a compelling research agenda within the field of aeronautics since year 2001. At that time, ambitious goals to be reached in the year 2020 have been defined for the European aviation sector and published in the “Vision 2020”. In 2011, the follow-up strategic document “Flightpath 2050” (FP2050) was released with even more challenging goals to be reached in year 2050.
One key element of the research strategy of the European Commission is the concentration of research power in Joint Technology Initiatives (JTI). For aeronautic research, the JTI Clean Sky was initiated, followed by JTI Clean Sky 2 (CS2). Building upon the FP2050-objectives but with a runtime until 2024 CS2 has defined individual goals:
1) Reduction of CO2 emissions and fuel burn by:
o 20% in 2025 compared to reference year 2014
o 30% in 2035 compared to reference year 2014
2) Reduction of NOx emissions by:
o 20% in 2025 compared to reference year 2014
o 40% in 2035 compared to reference year 2014
3) Reduction of population exposed to noise by 75% in 2035 compared to reference year 2014
Most objectives listed above apply to progress obtained on single vehicle level. An estimation of the achieved overall progress, i.e. reduction of CO2, NOx and noise emissions of aviation, requires an assessment on fleet level. The composition of a future fleet is, among others, dependent on new available technologies contributing to improved aircraft eco-efficiency. Thus, it is essential to know whether and to which extent new technologies find their way on the aircraft (either as new configuration or as retrofit). This question exactly forms the background to the project TeDiMo (Technology Diffusion Model).
The main objective of TeDiMo was to establish a technology diffusion model in the context of the Technology Evaluator (TE) that facilitates the investigation of the propagation of new technologies developed in Clean Sky 2. For that goal, stakeholders and drivers of the air transport system were identified, the impact of technologies developed within Clean Sky on the established diffusion drivers was analysed and a diffusion model was developed to simulate the diffusion of new technologies into the aviation market. The concept of the diffusion model has been proved and due to the agend-based nature of the model its functionality is easily extendable and it offers a wide range of application.
One key element of the research strategy of the European Commission is the concentration of research power in Joint Technology Initiatives (JTI). For aeronautic research, the JTI Clean Sky was initiated, followed by JTI Clean Sky 2 (CS2). Building upon the FP2050-objectives but with a runtime until 2024 CS2 has defined individual goals:
1) Reduction of CO2 emissions and fuel burn by:
o 20% in 2025 compared to reference year 2014
o 30% in 2035 compared to reference year 2014
2) Reduction of NOx emissions by:
o 20% in 2025 compared to reference year 2014
o 40% in 2035 compared to reference year 2014
3) Reduction of population exposed to noise by 75% in 2035 compared to reference year 2014
Most objectives listed above apply to progress obtained on single vehicle level. An estimation of the achieved overall progress, i.e. reduction of CO2, NOx and noise emissions of aviation, requires an assessment on fleet level. The composition of a future fleet is, among others, dependent on new available technologies contributing to improved aircraft eco-efficiency. Thus, it is essential to know whether and to which extent new technologies find their way on the aircraft (either as new configuration or as retrofit). This question exactly forms the background to the project TeDiMo (Technology Diffusion Model).
The main objective of TeDiMo was to establish a technology diffusion model in the context of the Technology Evaluator (TE) that facilitates the investigation of the propagation of new technologies developed in Clean Sky 2. For that goal, stakeholders and drivers of the air transport system were identified, the impact of technologies developed within Clean Sky on the established diffusion drivers was analysed and a diffusion model was developed to simulate the diffusion of new technologies into the aviation market. The concept of the diffusion model has been proved and due to the agend-based nature of the model its functionality is easily extendable and it offers a wide range of application.
Works performed during the current reporting period includes firstly the identification of the motivational background for the introduction and application of new technologies into present and future air vehicles. Stakeholders of the air transport system were identified and their role and respective interaction were described. Airlines and lessors were found to be the only stakeholders that directly influence the composition of the world fleet and are therefore the most important players regarding technology diffusion into the market. An analysis of drivers for fleet composition of airlines and in a third step the transformation of those drivers into discrete parameters characterizing technology diffusion followed.
Different kinds of modelling approaches have been followed to model technology diffusion based on the identified diffusion drivers: An epidemic model, a simple agent-based model (ABM) and an advanced agent-based model.
The epidemic model describes the diffusion process at the macro level and requires historical sales data and technical parameters. Both agent-based models consider the decision making process of airlines. A simple version of ABM without airline characteristics and their respective historical fleet was developed that requires technical and cost parameters of the aircraft series to be adopted. The advanced ABM includes airline characteristics and requires the largest amount of input data, for example the fleet of each airline included in the model, cost data and technical parameters of aircraft series to be adopted. Moreover, the adoption behaviour of each airlines is needed to set up the model. Both ABMs and the epidemic model show varying results and can be modified to include more parameters.
When applying the developed diffusion model to Clean Sky 2 technologies, as planned within TeDiMo, it is essential to know the influence of these new technologies on the elaborated diffusion drivers. For that reason, an impact assessment has been conducted based on a comprehensive literature review and calculations with the in-house aircraft design tool MICADO. This impact assessment set the basis for following simulations of different groups of Clean Sky 2 technologies with the developed diffusion model.
Lastly, the diffusion model was applied to Clean Sky 2 technologies. For the application, the advanced agent-based model (ABM) is chosen due to its flexibility and inclusion of more parameters compared to the two other approaches. Different kinds of sensitivity studies were performed and it was shown that the model reacts sensible to changes of fleet scenarios, requirements or aircraft performance. Cumulative diffusion patterns of single aircraft and fleet developments over time were calculated.
Results of the project TeDiMo were disseminated via two scientific papers and contributions to several Clean Sky meetings. They will be expoited by the Clean Sky 2 Technology Evaluator, with whom a workshop was conducted to exchange data, code and explain the utilization of the model.
Different kinds of modelling approaches have been followed to model technology diffusion based on the identified diffusion drivers: An epidemic model, a simple agent-based model (ABM) and an advanced agent-based model.
The epidemic model describes the diffusion process at the macro level and requires historical sales data and technical parameters. Both agent-based models consider the decision making process of airlines. A simple version of ABM without airline characteristics and their respective historical fleet was developed that requires technical and cost parameters of the aircraft series to be adopted. The advanced ABM includes airline characteristics and requires the largest amount of input data, for example the fleet of each airline included in the model, cost data and technical parameters of aircraft series to be adopted. Moreover, the adoption behaviour of each airlines is needed to set up the model. Both ABMs and the epidemic model show varying results and can be modified to include more parameters.
When applying the developed diffusion model to Clean Sky 2 technologies, as planned within TeDiMo, it is essential to know the influence of these new technologies on the elaborated diffusion drivers. For that reason, an impact assessment has been conducted based on a comprehensive literature review and calculations with the in-house aircraft design tool MICADO. This impact assessment set the basis for following simulations of different groups of Clean Sky 2 technologies with the developed diffusion model.
Lastly, the diffusion model was applied to Clean Sky 2 technologies. For the application, the advanced agent-based model (ABM) is chosen due to its flexibility and inclusion of more parameters compared to the two other approaches. Different kinds of sensitivity studies were performed and it was shown that the model reacts sensible to changes of fleet scenarios, requirements or aircraft performance. Cumulative diffusion patterns of single aircraft and fleet developments over time were calculated.
Results of the project TeDiMo were disseminated via two scientific papers and contributions to several Clean Sky meetings. They will be expoited by the Clean Sky 2 Technology Evaluator, with whom a workshop was conducted to exchange data, code and explain the utilization of the model.
In the aviation sector, there have been few prognostic possibilities to quantify and estimate the success and benefits of individual technologies and their distribution to other aircraft models. The effect of new technologies could only be recognized, analysed and evaluated after having them implemented and used for a certain period of time. The project TeDiMo will therefore support the Clean Sky 2 Technology Evaluator by developing a prediction capability for the diffusion of Clean Sky 2 technology bricks within different vehicle classes. Results at the end of the project are enhanced knowledge and understanding of the general mechanisms driving technology diffusion and first predictions of the propagation of Clean Sky 2 technology bricks.