Arrival trade-offs considering total flight and passenger delays and fairness

This paper studies trade-offs between flight and passenger delays and fairness when assigning delay pre-tactically (on-ground at origin airport) due to reduced airport capacity. The paper also defines and analyses efficiency-fairness trade- offs. The optimisation model is based on the ground holding problem and uses various objective functions: total delay for flights (considering reactionary delay), total delay for passengers (considering outbound connections), and deviation from a Ration By Schedule solution (to get a measure of the fairness of the solution). The scenario considered takes place at Paris Charles de Gaulle airport, a busy European hub airport, and includes realistic values of traffic

Pilot3 D2.1 - Trade-off report on multi criteria decision making techniques

This deliverable describes the decision making approach that will be followed in Pilot3. It presents a domain-driven analysis of the characteristics of Pilot3 objective function and optimisation framework. This has been done considering inputs from deliverable D1.1 - Technical Resources and Problem definition, from interaction with the Topic Manager, but most importantly from a dedicated Advisory Board workshop and follow-up consultation. The Advisory Board is formed by relevant stakeholders including airlines, flight operation experts, pilots, and other relevant ATM experts. A review of the different multi-criteria decision making techniques available in the literature is presented. Considering the domain-driven characteristics of Pilot3 and inputs on how the tool could be used by airlines and crew. Then, the most suitable methods for multi-criteria optimisation are selected for each of the phases of the optimisation framework

Pilot3 D1.1 - Technical resources and problem definition

This deliverable starts with the proposal of Pilot3 but incorporates the development produced during the first four months of the project: activities on different workpackages, interaction with Topic Manager and Project Officer, and input received during the first Advisory Board meeting. This deliverable presents the definition of Pilot3 concept and methodology. It includes the high level the requirements of the prototype, preliminary data requirements, preliminary indicators that will be considered and a preliminary definition of case studies. The deliverable aims at defining the view of the consortium on the project at these early stages, while highlighting the feedback obtained from the Advisory Board and the further activities required to define some of the aspects of the project

Simulating debris impacts on hydrokinetic infrastructure

The discrete element method was applied to model the impact force of woody debris (trees) on hydrokinetic infrastructure in a river setting. The DEM model, termed the Hydrokinetic Debris Impact Simulator (HDIS) was used to investigate interactions of debris with UAF’s research debris diversion platform (RDDP). The RDDP was designed to protect river energy converters and other hydrokinetic energy infrastructure from floating, woody debris. The RDDP is typically tethered to a surface float that is connected by line and chain to an embedment anchor upstream and below the river bottom. Surface mounted turbines are then tethered behind the RDDP. In this way, the RDDP protects hydrokinetic infrastructure from the impact of trees. HDIS successfully models the interaction and dynamics of discrete bodies (in this case, debris and the RDDP) and determines the forces and torques acting on these bodies due to contact, water, and other, external forces. A key component of the discrete element method is the contact model describing particle-particle interactions. Overall, the discrete element method allows for a realistic simulation of debris-RDDP interactions. In order to improve the fidelity of the drag and buoyancy forces as well as the debris behavior, HDIS was coupled to a CFD package, OpenFOAM. The OpenFOAM package allows for realistic simulations of the complex hydrodynamics that contribute to the RDDP’s ability to effectively shed debris and at the same time broadens the utility of HDIS for simulating more complex debris-infrastructure interactions including evaluating failure modes of the RDDP. Example results from HDIS are shown in Figure 1 and output from an OpenFOAM simulation of the flow field around the RDDP are shown in Figure 2.Peer ReviewedPostprint (published version

Flight and passenger efficiency-fairness trade-off for ATFM delay assignment

This paper studies trade-offs between efficiency (performance) and fairness (equity), when assigning ATFM delay pre- tactically (on-ground at origin airport) due to reduced airport capacity at destination. Delay is assigned as the result of the optimisation of a deterministic multi-objective problem considering flight and passenger perspectives when defining objectives of performance and fairness. Two optimisation cases are presented: one where objectives are based on flight metrics, and another one where they are based on passenger metrics. The paper defines and analyses efficiency-fairness trade-offs: the concepts of price of fairness for flights and passengers are defined as the percentage of efficiency loss due to the consideration in the optimisation of fairness; whereas the price of efficiency is considered as the fairness loss relative to the maximum value of the fairness metric, when considering flight or passenger delay in the optimisation. The optimisation model is based on the ground holding problem and uses various objective functions. For performance, total delay for flights (considering reactionary delay), and total delay for passengers (considering outbound connections) are defined. For fairness, the deviation of flight arrivals from a Ration By Schedule solution, and the deviation of delay experienced by passengers with respect to the one obtained in an RBS situation are used. An illustrative application on traffic at Paris Charles de Gaulle airport, a busy European hub airport, and including realistic values of traffic is modelled. A comprehensive trade-off analysis is presented. Results show, how in some cases, gains on one stakeholder can be achieved without implying any detriment on the other one. Passengers are more sensitive to the optimisation and hence, their consideration when assigning delay is recommended. Further research should explore how to combine flight and passenger indicators in the optimisation and consider how the lack of data availability could be mitigated

Simulating debris impacts on hydrokinetic infrastructure

The discrete element method was applied to model the impact force of woody debris (trees) on hydrokinetic infrastructure in a river setting. The DEM model, termed the Hydrokinetic Debris Impact Simulator (HDIS) was used to investigate interactions of debris with UAF’s research debris diversion platform (RDDP). The RDDP was designed to protect river energy converters and other hydrokinetic energy infrastructure from floating, woody debris. The RDDP is typically tethered to a surface float that is connected by line and chain to an embedment anchor upstream and below the river bottom. Surface mounted turbines are then tethered behind the RDDP. In this way, the RDDP protects hydrokinetic infrastructure from the impact of trees. HDIS successfully models the interaction and dynamics of discrete bodies (in this case, debris and the RDDP) and determines the forces and torques acting on these bodies due to contact, water, and other, external forces. A key component of the discrete element method is the contact model describing particle-particle interactions. Overall, the discrete element method allows for a realistic simulation of debris-RDDP interactions. In order to improve the fidelity of the drag and buoyancy forces as well as the debris behavior, HDIS was coupled to a CFD package, OpenFOAM. The OpenFOAM package allows for realistic simulations of the complex hydrodynamics that contribute to the RDDP’s ability to effectively shed debris and at the same time broadens the utility of HDIS for simulating more complex debris-infrastructure interactions including evaluating failure modes of the RDDP. Example results from HDIS are shown in Figure 1 and output from an OpenFOAM simulation of the flow field around the RDDP are shown in Figure 2.Peer Reviewe

Simulating debris impacts on hydrokinetic infrastructure

The discrete element method was applied to model the impact force of woody debris (trees) on hydrokinetic infrastructure in a river setting. The DEM model, termed the Hydrokinetic Debris Impact Simulator (HDIS) was used to investigate interactions of debris with UAF’s research debris diversion platform (RDDP). The RDDP was designed to protect river energy converters and other hydrokinetic energy infrastructure from floating, woody debris. The RDDP is typically tethered to a surface float that is connected by line and chain to an embedment anchor upstream and below the river bottom. Surface mounted turbines are then tethered behind the RDDP. In this way, the RDDP protects hydrokinetic infrastructure from the impact of trees. HDIS successfully models the interaction and dynamics of discrete bodies (in this case, debris and the RDDP) and determines the forces and torques acting on these bodies due to contact, water, and other, external forces. A key component of the discrete element method is the contact model describing particle-particle interactions. Overall, the discrete element method allows for a realistic simulation of debris-RDDP interactions. In order to improve the fidelity of the drag and buoyancy forces as well as the debris behavior, HDIS was coupled to a CFD package, OpenFOAM. The OpenFOAM package allows for realistic simulations of the complex hydrodynamics that contribute to the RDDP’s ability to effectively shed debris and at the same time broadens the utility of HDIS for simulating more complex debris-infrastructure interactions including evaluating failure modes of the RDDP. Example results from HDIS are shown in Figure 1 and output from an OpenFOAM simulation of the flow field around the RDDP are shown in Figure 2.Peer Reviewe