Collaboration of wake vortex models and sensors in modern avionic systems

Abstract

Today's airport capacity is severely limited by separation of approaching and to a lesser extent departing aircraft to ensure that following aircraft do not encounter the wake vortex generated by the preceding one. The encounter of wake vortices, especially during take-off and landing, can cause critical or even catastrophic flight situations for the succeeding plane. Historically, the wake vortex separation standards are based on a 'worst-casescenario' assuming calm and still air conditions responsible for a relatively long wake vortex lifetime. They have proven sufficiently safe but are unnecessarily limiting capacity in favourable, even in average weather conditions. Thus a capacity increase brought about by any change in separation rules has at least to preserve (or, given the expected traffic growth, even improve) the current level of safety. Accordingly, wake vortex warning systems have been devised to increase airside capacity and are nearing experimental implementation. The current systems contain a forecasting component based on meteorological conditions and on propagating the vortex evolution. Secondly a sensing system is ensuring the required level of safety. Both the model predictions and the sensing systems each have their advantages but also individual drawbacks. This paper presents a novel approach on collaboration of the wake vortex prediction and the sensing part. A general overview on the wake vortex phenomenon is given and an approach of fusing the wake vortex prediction with wake vortex measurement is shown. By means of examples the major advantages of the collaboration approach are presented. A discussion on implementation constraints of the proposed system closes the paper.

In-Flight Testing of Airborne LIDAR for Wake Vortex Detection, Characterization and Tracking

“…During the measurement update in Eq. (7), the pendant to the LIDAR measurements need to be calculated from the system state. While the wake vortex strength is directly estimated in the system state, the position needs to be transferred into the measurement coordinate system of the LIDAR.…”

New Concept for Wake-Vortex Hazard Mitigation Using Onboard Measurement Equipment

“…An additional goal of the simulation environment is to find the minimum operational performance for the lidar sensor that is needed to achieve the target of monitoring the own-ship wake-vortex decay. To reach this goal, the system itself is composed of a wake-vortex prediction algorithm and an integrated fusion filter that combines the wakevortex prediction and the lidar measurement in a similar way as described in [6]. The simulation framework will allow an investigation on how parameters of wing geometry can describe, for example, the influence of flap settings within the wake-vortex transport and decay prediction.…”

“…Recently conducted studies (e.g., Boeing, see [5]) have shown that the use of active flaps could be beneficial for a faster decay of wake vortices, presuming that meteorological influences on wake decay are weak. Combined with the knowledge of the atmosphere behind the aircraft (which the aircraft has just passed), a prediction about the decay behind the aircraft based on numeric models (see for example [6]) will be possible. The Institute of Flight Guidance of the Technische Universität Braunschweig (TUBS-IFF) is working on a simulation environment where the own-ship vortices will be modeled, combined with a backward-looking onboard lidar installation.…”

Towards wake vortex safety and capacity increase: the integrated fusion approach and its demands on prediction models and detection sensors

“…The measurement matrix H is used to relate the vector of measured error z and the filter state vector to each other according to equation (16), where the superscript þ means a posteriori state, i.e. after correction by measurement updatê…”

“…The approach described above aims at providing more accurate information on the wake vortex state than the sole prediction or only sensor measurements. In order to investigate this ability, simulations were used 16 where intentionally erroneous meteorological information was provided to the model. The crosswind that is an essential mechanism for lateral wake transport was charged with a constant offset up to the normalized time t à ¼ 4.…”