Paris Charles De Gaulle Airport Research Articles

Comparison of Cabin Noise of Airport Express Rail Systems

In this paper, the cabin noise of four airport express rail systems, namely the Taiwan Taoyuan International Airport MRT, the Hong Kong Airport Express, RER B service from Paris Gare du Nord to Paris Charles de Gaulle Airport, and the Shanghai Maglev, have been measured. These four airport express rail systems have different specifications and maximum speeds, ranging from 100 to 300 km/h. The results show a significant low-frequency noise content below 100 Hz, which would not be captured if the measurements were conducted in dB(A). The difference between Leq in terms of dB(C) and dB(A) ranges from 11.3 to 17.0 dB. The maximum speed of the Taoyuan Airport MRT was found to be the lowest at 100 km/h and with the lowest Leq in terms of 66.4 dB(A) and 81.4 dB(C). The Shanghai Maglev has a maximum speed of 300 km/h but a relatively low Leq of 69.7 dB(A), although its top speed is almost three times the maximum of the other airport rail systems. It also has the lowest Lmax of 73.1 dB(A) among the four rail systems. Moreover, the Paris RER B railway system, with its top speed of 120 km/h, was measured to have the highest Leq and Lmax values of 72.8 dB(A) and 83.8 dB(A), respectively.

Analysis of Small-Scale Spatial Variability of Fog at Paris Charles de Gaulle Airport

The small-scale variability of fog over Paris-CdG airport was explored by using the twelve visibility measurements installed on runways. The variability inside this sub-kilometre area is significant for fog, whatever the studied fog characteristics. These results confirm that about 50% of fog is thin and heterogeneous at the airport scale. This study indicated that one point of measurement may be far from the representativeness of sub-kilometre scale area and can create problems when used for verifying numerical models. The fog characteristics cannot be captured by a local measurement of visibility, and it is not possible to deduce fog extension at the local scale from a local measurement, even for low visibility such as for LVP cases (Low Visibility Procedures). This work demonstrated with a perfect forecast framework that a large dispersion of forecast scores could be obtained when using one observation at the local-scale due to representativeness errors of visibility measurements. This dispersion has the same order of magnitude as the current NWP forecast quality of fog. An attempt to quantify the scale heterogeneity of fog was made using the Gini index. This index has allowed highlighting the appearance of waves during the dissipation phase of fog.

Air Traffic Management during Rare Events Such as a Pandemic: Paris Charles de Gaulle Case Study

Paris Charles de Gaulle Airport was the second European airport in terms of traffic in 2019, having transported 76.2 million passengers. Its large infrastructures include four runways, a large taxiway network, and 298 aircraft parking stands (131 contact) among three terminals. With the current pandemic in place, the European air traffic network has declined by −65% flights when compared with 2019 traffic (pre-COVID-19), having a severe negative impact on the aviation industry. More and more often taxiways and runways are used as parking spaces for aircraft as consequence of the drastic decrease in air traffic. Furthermore, due to safety reasons, passenger terminals at many airports have been partially closed. In this work we want to study the effect of the reduction in the physical facilities at airports on airspace and airport capacity, especially in the Terminal Manoeuvring Area (TMA) airspace, and in the airport ground side. We have developed a methodology that considers rare events such as the current pandemic, and evaluates reduced access to airport facilities, considers air traffic management restrictions and evaluates the capacity of airport ground side and airspace. We built scenarios based on real public information on the current use of the airport facilities of Paris Charles de Gaulle Airport and conducted different experiments based on current and hypothetical traffic recovery scenarios. An already known optimization metaheuristic was implemented for optimizing the traffic with the aim of avoiding airspace conflicts and avoiding capacity overloads on the ground side. The results show that the main bottleneck of the system is the terminal capacity, as it starts to become congested even at low traffic (35% of 2019 traffic). When the traffic starts to increase, a ground delay strategy is effective for mitigating airspace conflicts; however, it reveals the need for additional runways.

An optimization–simulation closed-loop feedback framework for modeling the airport capacity management problem under uncertainty

This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for driving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.

Comparative Analysis of Departure Metering at United States and European Airports

Departure metering has the potential to mitigate airport surface congestion and decrease flight delays. This paper considers several candidate departure metering techniques, including a trajectory-based optimization approach using a node-link model and three aggregate queue-based approaches (a scheduler meant to represent NASA’s Airspace Technology Demonstration-2 logic, an optimal control approach, and a robust control approach). The outcomes of these different approaches are compared for two major airports: Paris Charles De Gaulle Airport (CDG) in Europe and Charlotte Douglas International Airport (CLT) in the United States. These two airports have similar aggregate demand and capacity, but the banking of demand at CLT leads to congestion, resulting in higher taxi-out delays and more potential for departure metering at CLT. Stochastic simulations are used to show that departure metering with the robust control approach best accommodates operational uncertainties, and that departure metering yields higher taxi-out time savings at CLT compared to CDG, irrespective of the approach.

Calculation of Airplane Wake Turbulence Re-Categorisation Effects

All aircraft generate wake turbulence that significantly affects flight operations and safety of following airplanes that might fly into it. In order to reduce the risk of wake turbulence related incidents and accidents, airplanes are divided into categories regarding the strength of the vortices they can generate and are separated depending on the category they belong to. Safety is a priority in aviation, but there are other factors that are being taken into consideration both in planning and execution of flight operations. For instance, efficiency in flight operations, airspace and airport capacity and cost-effectiveness are also factors that are to be considered. In order to optimise those factors without jeopardising the level of safety, a new aircraft categorisation has been proposed that divides them into more groups and reduces mandatory standard separation in some cases. This paper analyses possible impacts of re-categorisation. The data being used is randomly generated figures for a simulated airport as well as traffic information from Paris Charles de Gaulle airport where re-categorisation has been introduced. A comparison was made between these two groups of data and conclusions were drawn on that basis.

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.

Tackling Uncertainty for the Development of Efficient Decision Support System in Air Traffic Management

Airport capacity has become a constraint in the air transportation networks due to the growth of air traffic demand and the lack of resources able to accommodate this demand. This paper presents the algorithmic implementations of a decision support system for making a more efficient use of the airspace and ground capacity. The system would be able to provide support for air traffic controllers in handling large amount of flights while reducing to a minimum potential conflicts. In this framework, airspace together with ground airport operations is considered. The conflicts are defined as separation minima violation between aircraft for what concerns airspace and runways and as capacity overloads for taxiway network and terminals. The methodology proposed in this paper consists of an iterative approach that couples optimization and simulation to find solutions that are resilient to perturbations due to the uncertainty present in different phases of the arrival and departure process. An optimization model was employed to find a (sub)optimal solution, while a discrete event-based simulation model evaluated the objective function. By coupling simulation with optimization, we generate more robust solutions resilient to variability in the operations, and this is supported by a case study of Paris Charles de Gaulle Airport.

Optimization of departure runway scheduling incorporating arrival crossings

AbstractThe runway is a key airport resource and an efficient runway operation is critical to enhance the airport efficiency and to reduce delays. This paper addresses the problem of scheduling aircraft departures incorporating arrival crossings. Constraints for wake turbulence separations, flight time window restrictions, and holding queue capacity at runway threshold are explicitly considered. We present two integer linear programming models and a simulated annealing (SA) algorithm. Comparison tests are conducted for the south side of Paris Charles De Gaulle Airport and show that the three proposed methods can significantly improve the solutions based on the simple first‐come‐first‐served rule. Moreover, SA has sufficiently small computation time for real‐time deployment.

Integrated optimization of terminal maneuvering area and airport at the macroscopic level

Airports and surrounding airspaces are limited in terms of capacity and represent the major bottlenecks of the air traffic management system. This paper addresses the problems of terminal airspace management and airport congestion management at the macroscopic level through the integrated control of arrivals and departures. Conflict detection and resolution methods are applied to a predefined terminal route structure. Different airside components are modeled using network abstraction. Speed, arrival and departure times, and runway assignment are managed by using an optimization method. An adapted simulated annealing heuristic combined with a time decomposition approach is proposed to solve the corresponding problem. Computational experiments performed on case studies of Paris Charles De-Gaulle airport show some potential improvements: First, when the airport capacity is decreased, until a certain threshold, the overload can be mitigated properly by adjusting the aircraft entry time in the Terminal Maneuvering Area and the pushback time. Second, landing and take-off runway assignments in peak hours with imbalanced runway throughputs can significantly reduce flight delays. A decrease of 37% arrival delays and 36% departure delays was reached compared to baseline case.

Exclusion Through Imperio

This article explores and unpacks the entanglements between law and geography that enable and advance the exclusion of non-citizens from entry into sovereign territory. We suggest that states manipulate jurisdiction within and beyond sovereign territory to extend enforcement. This jurisdiction applies primarily to the bodies of migrants themselves as opposed to fixed spaces. Like Elden’s (2009, 2013) imperio, or imperial power, this extension is spatially boundless, limitless in internal checks and administrative. Imperio places migrant bodies into new legal regimes with subjectivities that overlap and override existing protections, such as international refugee law. We develop our argument by considering enforcement practices in three areas: the waiting zone at Paris Charles de Gaulle airport, search and rescue areas on the Central Mediterranean Sea and Australian excision zones.

Predicting Abnormal Runway Occupancy Times and Observing Related Precursors

Accidents on the runway triggered the development and implementation of mitigation strategies. Therefore, the airline industry is moving toward proactive risk management, which aims to identify and predict risk precursors and to mitigate risks before accidents occur. For certain predictions machine learning techniques can be used. Although many studies have explored and applied novel machine learning techniques on different radar and A-SMGCS data, the identification and prediction of abnormal runway occupancy times and the observation of related precursors are not well developed. In our previous papers, three existing methods were introduced, lasso, multi-layer perception, and neural networks, to predict the taxi-out time on the taxiway and the time to fly and true airspeed profile on final approach. This paper presents a new machine learning method where the existing machine learning techniques are combined for predicting the abnormal runway occupancy times of unique radar data patterns. Additionally the regression tree method is used in this study to observe the key related precursors extracted from the top 10 features. Compared with existing methods, the new method no longer requires predefined criteria or domain knowledge. Tests were conducted using final approach radar data and A-SMGCS runway data consisting of 78,321 flights at Paris Charles de Gaulle airport and were benchmarked against 500,000 flights at Vienna airport.

Development and Evaluation of Enhanced Airfield Signage for Temporary Information

The aviation community has faced several accidents and incidents on infrastructures and procedures temporarily modified for the purpose of construction works. The analysis of these events shows that usual means of communication to the air crews are the weak link of the safety chain. To address the key challenge of situational awareness during construction works, the Airport Construction Advisory Council of FAA and Paris–Charles de Gaulle Airport (Paris-CDG) developed and evaluated with the airfield community, from 2011 to 2016, an innovative aviation signage. Parallel and complementary studies in human factors led to the definition of specifications for a temporary information signage, also called orange construction sign. Paris-CDG focused on the development of specific messages for each one of the major hazards that could require an increased situational awareness of the air crews during taxiing and takeoff. The results of the evaluation conducted by FAA were published in September 2015. The purpose of this paper is to present the parallel study performed by the author at Paris-CDG with the coalition of the airside operations stakeholders. Both research projects are convergent and confirm the relevance of the orange sign concept for increasing the situational awareness and preventing safety events during airfield construction.

The Impact of Vertical Resolution on Fog Forecasting in the Kilometric-Scale Model AROME: A Case Study and Statistics

Abstract The impact of vertical resolution on numerical fog forecasting is studied in detail for a specific case and evaluated statistically over a winter season. Three vertical resolutions are tested with the kilometric-scale Applications of Research to Operations at Mesoscale (AROME) numerical weather prediction model over Paris Charles de Gaulle Airport (Paris-CDG) in Paris, France. For the case studied, the vertical resolution has a strong impact on fog onset. The nocturnal jet and the turbulence created by wind shear at the top of the nocturnal boundary layer are more pronounced with a finer vertical resolution, and the turbulence close to the ground is also stronger with high vertical resolution. Local circulations created by the terrain induce different simulated processes during the fog onset. The fog is simulated as advection–radiation fog in the finer vertical resolution run and as radiation fog in the others. The vertical resolution has little impact on the mature and dissipation phases. A statistical study over a winter season confirms the results obtained in the fog case study. High vertical resolution simulates earlier onset, as well as longer-lasting and more spatially heterogeneous fogs. The high vertical resolution configuration simulates more fog events than are found at low resolution (LR); these fog events generally form north of Paris-CDG. No observations are available in this area, leading to many simulated but no observed fog events in the fine-resolution runs. The ceiling of low clouds is not well simulated by the numerical model no matter what vertical resolution is used.

Reliability study and simulation of the progressive collapse of Roissy Charles de Gaulle Airport

Paris Charles de Gaulle Airport also known as Roissy Airport is the world's eighth-busiest airport in passengers served. In May 2004, the news of collapse of a portion of Terminal 2E leaving four casualties shook the world. Luckily, no boarding had been taking place in the collapsed area which consisted of a boarding area and three footbridges. This part of the terminal had an innovative design consisting of a vaulted concrete tube. We chose to model a representative part of the terminal to observe the structure's behavior. The purpose of our research is to explain the structure's collapse and to see if there were deficiencies from the design phase. Also, our new fine-grained model using Ansys Software makes it possible to explain the progressive collapse of the structure, which was the main challenge of our study. Moreover, a sensitivity analysis was performed in order to study the importance of each of the variables taken into account in the model.

Nowcasting with the AROME Model: First Results from the High-Resolution AROME Airport

Abstract Applications of Research to Operations at Mesoscale (AROME) Airport is the high-resolution version of the French operational numerical weather prediction model AROME. The purpose of AROME Airport is to provide rapidly updated forecasts with a 500-m resolution for nowcasting over an airport and to help with the prediction of wake vortices in order to increase the efficiency of the airport. Here, the model is evaluated for the area around Paris Charles de Gaulle Airport (CDG), France. AROME Airport and other configurations of the AROME model are compared to observations during two 4-week periods, when additional observations are available for the area around CDG (May–June 2011 and September–October 2012). The root-mean-square error and the bias are calculated for the 2-m temperature, 10-m wind speed, and the vertical distribution of the wind speed using the available measurements. The performance of AROME Airport is compared to the operational configuration of the AROME model both for synoptic hours and in a more realistic setting using the forecasts from AROME Airport starting every hour. It is shown that the forecasts from AROME Airport are an improvement with respect to the operational when comparing runs from all forecast hours, particularly for the wind speed, but when comparing the synoptic hours, the results are less clear. The sensitivity of AROME Airport to its data assimilation and initial and lateral boundary conditions is also discussed.

Effect of small‐scale surface heterogeneities and buildings on radiation fog: Large‐eddy simulation study at Paris–Charles de Gaulle airport

Large‐eddy simulations (LES) of radiation fog were performed over an airport area to study the effect of urban canopy on fog. These LES were performed with the Meso‐NH research model at very high resolution: 1.5 m in the horizontal and 1 m in the vertical and over a domain 4.5 km × 1.5 km.The blocking effect of the airport buildings led to strong wind shear and consequently to the production of turbulent kinetic energy (TKE). The airport buildings also had a strong effect on vertical velocity, with a subsidence region behind the buildings. The increase of both turbulence and vertical velocity strongly modified the fog formation. The fog layer took more time to form in the airport area, but the increase in turbulence facilitated the vertical development of the fog layer.The fog took 1.5 h to form over the whole simulated airport area. The fog height was heterogeneous during the formation phase, with the formation of very low clouds locally. The effect of airport buildings on vertical velocity could explain these heterogeneities of the fog height. During the mature phase of the fog, the buildings had little impact on the fog layer characteristics. The fog dynamics were mainly controlled by processes at its top.These results suggest that the inclusion of high levels of detail in the building representation remains important for the local forecasting of fog formation. Particularly, small‐scale heterogeneities can explain the spatial variability of fog formation. It seems necessary to take small‐scale variability of the urban canopy into account for local and accurate forecasts of fog formation over airport areas.

Skill of a Ceiling and Visibility Local Ensemble Prediction System (LEPS) according to Fog-Type Prediction at Paris-Charles de Gaulle Airport

Abstract A specific event, called a low-visibility procedure (LVP), has been defined when visibility is under 600 m and/or the ceiling is under 60 m at Paris-Charles de Gaulle Airport, Paris, France, to ensure air traffic safety and to reduce the economic issues related to poor visibility conditions. The Local Ensemble Prediction System (LEPS) has been designed to estimate LVP likelihood in order to help forecasters in their tasks. This work evaluates the skill of LEPS for each type of LVP that takes place at the airport area during five winter seasons from 2002 to 2007. An event-based classification reveals that stratus base lowering, advection, and radiation fogs make up for 78% of the LVP cases that occurred near the airport during this period. This study also demonstrates that LEPS is skillful on these types of event for short-term forecasts. When the ensemble runs start with initialized LVP events, the prediction of advection fogs is as skillful as the prediction of radiation fog events and stratus base lowering. At 3 and 6 h before the runs where LVP events were initialized, LEPS still shows positive skill for radiation fog events and stratus base lowering cases.

Composants Electroniques 87

The 28th Salon des Composants Electroniques opened its doors on 14 November 1987 for its second time at the Parc des Expositions venue, Villepinte. This location, with its spacious adjoining halls all at ground level, has obvious advantages—especially for international visitors—in terms of proximity to Paris Charles de Gaulle Airport. Many exhibitors and visitors choose to stay in the hotels nearby for the benefit of speedy access to the show on the five mornings of its duration. However, the regular RER train service from Chatelet offers rapid transit to and from Paris North for the better advised who opt for the undoubted advantage of being able to savour Paris nightlife unhampered by thoughts of ‘last train’ times.