Helicopter Wake Encounters in the Context of RECAT-EU

This work presents a first attempt to apply the RECAT-EU (European Wake Turbulence Categorisation and Separation Minima) methodology of fixed-wing aircraft separation to helicopters. The approach is based on a classification of helicopters in categories using their rotor diameter and weight combined with wake comparisons between different classes of fixed-wing aircraft and helicopters. Where necessary the upset caused by a wake encounter to a simple helicopter model is used to establish safe separation distances. The work is based on a very limited amount of data for wake strengths but shows that the principles of the RECAT-EU methodology are directly applicable to helicopters at least for landing and take-off. This research calls for further measurements of helicopter wakes with modern methods so that the suggested separation distances can be further ascertained and ultimately refined allowing for better and safer integration of fixed and rotary-wing traffic at airports

Hamiltonian formulation and analysis of a collisionless fluid reconnection model

The Hamiltonian formulation of a plasma four-field fluid model that describes collisionless reconnection is presented. The formulation is noncanonical with a corresponding Lie-Poisson bracket. The bracket is used to obtain new independent families of invariants, so-called Casimir invariants, three of which are directly related to Lagrangian invariants of the system. The Casimirs are used to obtain a variational principle for equilibrium equations that generalize the Grad-Shafranov equation to include flow. Dipole and homogeneous equilibria are constructed. The linear dynamics of the latter is treated in detail in a Hamiltonian context: canonically conjugate variables are obtained; the dispersion relation is analyzed and exact thresholds for spectral stability are obtained; the canonical transformation to normal form is described; an unambiguous definition of negative energy modes is given; and thresholds sufficient for energy-Casimir stability are obtained. The Hamiltonian formulation also is used to obtain an expression for the collisionless conductivity and it is further used to describe the linear growth and nonlinear saturation of the collisionless tearing mode.Comment: 4 figure

A novel machine learning model to predict abnormal Runway Occupancy Times and observe 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 percursors 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 aircraft Radar and operational Taxi data, the identification and prediction of abnormal Runway Occupancy Times and the observation of related percursors are not well developed. In our previous papers, three feasible methods were introduced: Lasso, Multi-Layer Perceptiona 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 Learing method, where we merge these feasible Machine Learning techniques for prediction the abnormal Runway Occupancy times of unique radar data patterns. Additionally we use in this study the Regression Tree method to observe 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 conductioned using runway and final approach aircraft radar data consisting of 78,321 Charles de Gaulle flights and were benchmarked against 500,000 Vienna flights

Evaluation of feasible machine learning techniques for predicting the time to fly and aircraft speed profile on final approach: Predictive dynamic support tool on final approach

currently, at many airports, the runway throughput is the limiting factor for the overall capacity. Among the most important constraining parameters is the separation minima expressed in distance. On the top of these minima, the difference of the leader and follower aircraft speed profiles imposes to consider buffer to cope with compression effect. Currently, Air Traffic Control Officers (ATCO’s) take these buffers on the basis of their training and experience. However, this experience will not be sufficient to safety deploy advanced concepts, like pair-wise separations, that increase variability in the separations to be delivered and therefore in the compression buffer to be considered. Systematic analysis of years of radar tracks has allowed to better predict the buffers to apply by characterising the time to fly (T2F) given a separation distance and True Airspeed (TAS) profile as a function of meteorological parameters.This paper presents how Machine Learning (ML) techniques may be used for predicting the T2F and TAS profile on final approach. Different ML techniques will be assessed on their forecast performance, computational time and amount of data needed for delivering a reliable prediction. The techniques will be applied on 2 different major European airports traffic and will be benchmarked against Optimized Runway Delivery (ORD) study using a Model Based Approach (MBA) for deriving the T2F and TAS. As a result the most efficient ML techniques will be applied on two case studies for predicting the T2F and TAS.Aerospace Transport & Operation

A novel machine learning model to predict abnormal Runway Occupancy Times and observe 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 percursors 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 aircraft Radar and operational Taxi data, the identification and prediction of abnormal Runway Occupancy Times and the observation of related percursors are not well developed. In our previous papers, three feasible methods were introduced: Lasso, Multi-Layer Perceptiona 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 Learing method, where we merge these feasible Machine Learning techniques for prediction the abnormal Runway Occupancy times of unique radar data patterns. Additionally we use in this study the Regression Tree method to observe 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 conductioned using runway and final approach aircraft radar data consisting of 78,321 Charles de Gaulle flights and were benchmarked against 500,000 Vienna flights.Aerospace Transport & Operation

Impact of Wind and Obstacles on Wake Vortex Evolution in Ground Proximity

Data from different numerical simulations and field measurement campaigns are used to investigate aircraft wake vortex evolution in ground proximity. A new hybrid simulation method is employed to capture wake vortex evolution from early roll-up to final decay in ground proximity. The method is applied to investigate effects of very low flight altitudes and the sudden loss of lift during touchdown. The investigations also comprise a parameter study of vortex behavior at different wind directions and strengths (headwind, crosswind, and combinations thereof) where the vortices are generated at a height of one initial vortex separation above ground. Furthermore, it is shown that vortex decay in ground proximity can be accelerated by the installation of plate lines at distances of a few hundred meters from the runway threshold. Field measurement data gathered during the campaigns WakeMUC at Munich airport and WakeOP at special airport Oberpfaffenhofen corroborate and complement the findings of the simulations. Experiments and simulations demonstrate that plate lines appreciably accelerate wake vortex decay and interfere favorably with end effects. This way safety can be further increased during the flight phase with most reported encounters