5. Technologies and Methodologies
5.5 Encounter Mechanisms and Simulation
Main contributing authors:
Robert Luckner, David Bieniek − TU Berlin
Tobias Bauer, Dietrich Fischenberg, Carsten Schwarz, Dennis Vechtel - Institut für Flugsystemtechnik, DLR Braunschweig
5.5.1 Overview
Figure 84 shows how the complex physical process of the wake vortex encounter (WVE) mechanism - from vortex generation to encounter hazard - is modelled by linking several sub-models. The overall process model is suited to determine the conditions for which modified separation minima are safe and can be applied to risk assessments that are needed to revise wake turbulence separation standards as well as for evaluation of new operational ATC concepts. Parts of this overall model can be used for other applications, such as real- time flight simulations to investigate WVE physics or for the development of vortex detection, warning and avoidance (DWA) systems for onboard and on-ground applications.
Figure 84: Modelling wake vortex encounter physics from vortex generation to encounter risk.
For the sub-process “aircraft reaction”, well-established and validated models exist. They are used in aircraft development by aircraft manufacturers and for pilot training by airlines. For all other sub-processes models exist as well. However, they still need to be validated and accepted before they can be used in safety cases for new air traffic regulations.
The sub-processes that describe the dynamic process from wake evolution, via wake encounter and aircraft reaction to encounter hazard have been addressed in a WN3E Specific Workshop, see (Luckner and Amelsberg 2010). They comprise:
• Wake vortex models that describe the vortex-induced velocity field • Aerodynamic interaction models,
• Models of pilot control behaviour, and • Models for severity assessment.
Figure 85 illustrates the application of WVE simulations in real-time and in fast-time (Monte Carlo) flight simulations. Real-time simulations are used, for example, to record pilot control inputs during an encounter and their severity assessment. The recorded data are used to develop models that can replace the pilot in fast-time simulations.
Pilot-in-the-loop Simulator tests
PM Test evaluation (Time histories & Questionnaires) Model optimization Pilot task Pilot model Normal / special procedure FCOM
Flight Crew Operational Manual
Wake vortex
WVE Risk analysis / Hazard rating
PM
Control inputs
Monte Carlo Simulation
Normal / special procedure Wake Vortex 0 10 20 30 40 50 60 70 80 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 time [s] n o rm . S S R O [- ]
test file: 777-0.tub330.8 simulation
Level D A330/340 Full Flight Simulator at TU Berlin
Figure 85: Application of WVE simulations in real-time and in fast-time flight simulations.
5.5.2 State-of-the-art
5.5.2.1 WVE Flight Simulations and Flight Simulators
A wake vortex encounter (WVE) package has been developed in the European S-WAKE project. It consists of two main modules: the Wake Vortex Model (WVM) and the Aerodynamic Interaction Model (AIM). The WVM computes the vortex-induced velocities and the AIM computes the aerodynamic delta forces and moments resulting from the interaction between flow field and aircraft; see Figure 86. Details can be found in (de Bruin 2003). Since then, the implementation has been improved to adequately represent the distributed 3- D flow field in the kinematical equations of the basic simulation, see Luckner et al. 2004.
Simulation Model plus incremental Wake Vortex Model Host Simulation (Basic Aircraft) Incremental Wake Vortex Encounter
Model
(WVE dd )
Aerodynamic Interaction Model
Air Data Sensor Model Wake Vortex
Model
Figure 86: Incremental WVE Model as add-on to any basic aircraft simulation
Various flight simulators (see Figure 87) have been equipped in 2001 with the WVE software package to investigate aircraft and pilot reactions in WVEs and to answer the question: Which level of vortex-induced disturbances is unacceptable?
Aircraft / ICAO Category Simulator type Location Airbus 330-300
HEAVY
Certified Training Simulator (FAA level D) Motion system, visual system, side sticks, Fly-by-Wire flight control, Autopilot, additional research facility
TU Berlin
Fokker 100
MEDIUM
Research Flight Simulator of NLR
Motion system, visual system, control column, mech. Flight Control System
NLR, Amsterdam
VFW614-ATD
MEDIUM
Develoment Flight Simulator THOR of Airbus Visual system, generic cockpit, side sticks, programmable displays, Fly-by-Wire flight control, no motion
Airbus, Hamburg
Cessna Citation
SMALL
Research Flight Simulator of NLR
Motion system, visual system, control column, mech. flight control
NLR, Amsterdam
Dornier Do 228-200
SMALL
Certified Training Simulator (JAR level B) Motion system, visual system, control column, mech. flight control
Simtec GmbH, Braunschweig
Figure 87: Flight simulators used in the S-WAKE project, from (Luckner et al. 2004).
Recent Developments
Since then, TU Berlin developed an enhanced WVE software package for its VFW614-ATD research flight simulator SEPHIR that is capable to simulate straight, wavy vortices and ring vortices as well as vortices that move with wind and in ground effect. In cooperation with UCL and DLR, complex WVMs have been generated. UCL’s LES models are being integrated directly into the VFW614-ATD flight simulator. Vortex- induced forces and moments that DLR calculated offline with their LES models have been integrated into an Airbus A330 flight simulator.
Airbus has developed an A320 wake vortex encounter simulation on its THOR development simulator that was used in the EC projects I-WAKE (see Rouwhorst et al. 2008) and FlySafe for development of vortex detection, warning and avoidance systems and in CREDOS to investigate WVEs during departures. TU Berlin investigated the impact of the flight simulator’s motion system on pilot response during a WVE in a certified Airbus A330-300 flight simulator; see (Fucke and Luckner 2007).
DLR conducted a simulator campaign in an A330 flight simulator to analyse the impact of vortex curvature on the encounter hazard (Vechtel 2010, Vechtel 2012). One major issue of this campaign was the interaction between pilot and aircraft for controller augmented aircraft and the capability of modern flight control systems to cope with the wake vortex disturbances. The flow-fields of the wavy vortices were generated by Large- Eddy-Simulations prior to the very simulation campaign. The vortex-induced forces and moments were pre- computed for different predefined tracks through the vortex flow-field using an aerodynamic interaction model (strip method). The force and moment time histories were then applied to the simulation depending on a specific playback height. More details are given below in the section on “Vortex models for fast-time simulations”.
In-flight simulations have been conducted by DLR with the research aircraft VFW614-ATTAS. Recorded vortex-induced forces and moments were mimicked by the experimental system using flight control deflections. In this way it was possible to investigate the influence of vortex deformation on the encounter hazard under real flight conditions (Vechtel 2012).
In the US, NASA Ames and Langley Research Centers investigated WVEs in flight simulators in the 1970s and 1980s; see (Hastings and Keyser 1988, Sammonds and Stinnett 1976, Stewart 1998). In the 1990s, the FAA equipped a Boeing 737 flight training simulator with WVE simulation; see (Dillard et al. 1999). Although this package is available on some training flight simulators, it is not used for routine pilot training. Currently FAA AFS is conducting WVE simulations on a certified 737-800 (new generation) and a certified Airbus A330- 200 full flight training simulator to develop a wake hazard severity matrix as well as standards for determining acceptability of wake vortex encounters from a pilot’s perspective. The tests are embedded in simulator training or in experiments in order to avoid learning effects; see (Greenhaw 2010).
5.5.2.2 Wake Vortex Models
This section describes the state-of-the-art in simplified wake vortex models that are needed for wake vortex encounter (WVE) risk assessment. The more sophisticated vortex behaviour models that are discussed in §5.1 are not suited for such applications.
Simplified wake vortex models are used in fast-time simulations and in real-time flight simulations to model aspects of the vortex physics that are relevant for WVE risk. Such models exist and they have been applied to compute:
• Encounter probability: To determine the encounter probability, the minimum distance between the trajectory of a follower and the vortex pair of the leading aircraft has to be computed. This is achieved by simulating the flight trajectories of two aircraft (vortex generator and follower) as well as the evolution of the vortex pair that the leading aircraft generates. If the distance between follower and one of the vortices falls below a certain limit, this is counted as an encounter and transferred to an encounter severity assessment for further analysis.
• Encounter severity: If an encounter is discovered, the influence of the vortex-induced flow field on the encountering aircraft has to be simulated, taking the encounter geometry and initial conditions into account. The evaluation of the resulting aircraft reaction leads to the encounter severity.
The encounter probability computation requires a wake vortex evolution model that computes vortex transport and vortex strength taking the decay into account, whereas the encounter severity computation requires the 3D vortex-induced velocity field around the encountering aircraft.
Various wake vortex evolution models (WVM) exist that compute this temporal-spatial distribution with different levels of fidelity. The highest fidelity is achieved with Large Eddy Simulation (LES) models (see §5.1) that cover the vortex and its complex flow structure nearly over the whole life-time. LES models are not suited for fast-time and real-time simulations as they require extremely high computational performance. However, pre-computed LES flow field data can be used for both simulations. In any case, LES data can be used to validate simplified models that are better suited for real-time and fast-time simulations.
The encounter severity computation requires a flow field model that computes vortex-induced velocities at the position of the encountering aircraft (three velocity components as function of space and time). Its temporal evolution depends on characteristics of the generating aircraft and on atmospheric conditions. Flow field models that are currently applied assume a stationary 2D flow field and define it either by a parameterized analytical model (circulation, vortex separation, core radius, straight or disturbed (curved or wavy) vortex axes and an analytical velocity distribution model) or by a numerical model.
Existing tools that compute encounter probabilities are WakeScene (DLR/Airbus), WAKE-4D (UCL), WIDAT™/ASAT (ATSI/FAA), WAVIR (NLR). Refer to §6.2 for more details.
Recent Developments
Improved WVMs for real-time and for fast-time flight simulations have been developed in Europe by UCL and DLR recently. They have been integrated into simulation tools to simulate the vortex evolution behind an airplane in space and time:
• The Deterministic and Probabilistic wake Vortex Model (DVM/PVM), see §5.1, was integrated into UCL’s tool WAKE4D; see §6.1.
• The Deterministic and Probabilistic 2-Phase Model (D2P/P2P), see §5.1, was integrated into DLR’s tool WakeScene; see §6.1.
Vortex modelling for real-time simulations
University of Sheffield has developed an approach to use high quality vortex data in real-time flight simulation, see (Allerton and Spence 2010). The vortex data is generated by LES methods, compressed and reorganised for real-time access. The aim is to capture evolving short and long wave instabilities (e.g. Crow instabilities). The LES methods require high resolution meshes to reproduce vortex decay (spatial and temporal). One wavy wake vortex segment was calculated in 3-D boxes and linked to a vortex trajectory. Each segment contains a vortex state dependent on the time behind the wake generating aircraft.
Vortex models for fast-time simulations
Boeing has conducted a 737-300 autopilot offline simulator campaign to determine the role of vortex strength versus distortion on encountering aircraft upsets (Loucel and Crouch 2005). Encounters in three different vortex states were investigated: straight vortices (analytical solution for velocities), wavy vortices (numerical integration for velocities) and ring vortices (numerical integration for velocities). All vortex states were modelled by vortex filaments frozen in space and time to be fast-time capable. Results show a significant reduction in maximum bank angle upset experienced by an encountering airplane, due to vortex distortion and break-up. Findings are that a large number of fast-time simulations are required to assess potential upset severity and the potential upset may be linked to vortex characteristics using simple measures.
Figure 89: Reduction of maximum bank angles in WVEs with different, distorted vortices (Loucel and Crouch 2005).
Airbus has implemented three vortex models into the Airbus VESA (see §6.2) tool:
• simple straight vortices of infinite length are used to investigate the influence of vortex parameters (strength, position, intercept angles, etc.) on encounter severity;
• simulated parameterized wavy and ring vortices, useful for investigating influence of vortex waviness and break-up into vortex rings on encounter severity;
Figure 88: Wake vortex visualisation in the visual system of a flight simulator; from (Spence et al. 2005).
• curved, segmented vortices, increasing realism of vortex representation along a flight path without undue computational effort (developed by UCL within CREDOS).
The approaches are in general applicable to all flight phases and have been successfully applied for specific investigations (e.g. S-WAKE, CREDOS). The maturity of the models is assumed to be good and adequate for the application, especially for the out-of-ground (OGE) case. A combination of the different models into a single one, i.e. curved vortices plus waviness, is not envisaged (Kauertz 2009, Kauertz 2010, Kauertz et al. 2012).
Figure 90: Vortex encounter simulation during departure with curved vortices; source: Airbus.
DLR has developed the WakeScene (Wake Vortex Scenarios Simulation) Package (including Deterministic and Probabilistic 2-Phase Model, D2P/P2P) that allows assessing the encounter probabilities and the related vortex strengths behind different wake vortex generating aircraft for arrivals and departures. WakeScene is described in §6.2.
Currently DLR develops a wake model for real-time and fast-time simulations as a compromise between the simplicity of analytical vortex models and the flow field accuracy of LES. The simulation model applies the BIOT-SAVART law to arbitrarily shaped vortices. The vortex geometry and decay is adapted to LES results for different atmospheric conditions. With this model encounter simulations can be performed with realistically shaped vortices covering the whole decay process (from initially straight vortices via wavy vortices to vortex rings) without the necessity of extensive data management like with LES flow fields.
5.5.2.3 Aerodynamic Interaction Models
Several aerodynamic interaction models (AIM), which model the aerodynamic forces and moments that are induced by the wake vortex flow field, exist for use in offline simulations for e.g. hazard assessment as well as in real-time flight simulations for upset training. Well established and easy to use are D-AIM-models, which can be simply added to the basic aerodynamic model without any need to change the basic aero model structure.
Two established D-AIM-models are the LSM, a lifting surface method, and the SM, a strip method. Both methods were validated in former EU-projects, in a quasi static manner using wind tunnel tests (WAVENC, 1999), and also with flight test data in (S-WAKE, 2000- 2002); see (Fischenberg 2002, Reinke and Fischenberg 2002). In S-WAKE, a validation procedure using wake encounter flight test data was derived (Reinke and Fischenberg 2002) including sensor calibration, wake identification and basic model identification. The simulation quality of both the LSM and the SM were compared to the test data and the model quality could be assessed. The data were gathered by the Dornier Do 128 aircraft flying into the wake of the medium size VFW-614 ATTAS.
For the Do128 aircraft with its rectangular wing plan form, it was shown that both the SM and the LSM are capable to reproduce the most important vertical and roll degrees-of-freedom in high quality. The pitching motion is represented also with sufficient quality. However, the simulated yawing motion showed discrepancies to the flight test data. Overall, there were no significant quality differences between the LSM and the SM method. A conclusion was that the much simpler SM can be recommended to be used in WVE simulations. However, validation for aircraft with swept wing, for flight at higher Mach numbers (compressible aerodynamics) and for flow with separation is still needed.
Recent Developments
Some effort to further improve the SM model quality was undertaken in the DLR project “Wetter & Fliegen” 2008-2011 using the Do128 flight test data of the S-WAKE project. A simple model extension for drag effects was derived, keeping the idea that the strip model should have an incremental structure independent from the basic aerodynamic model. Additionally, the model was extended for fuselage effects. A further model improvement in the yaw and in the longitudinal axis was achieved, and the model quality in other axes (roll, lateral and vertical) could be enhanced. Despite those improvements, the fidelity of the AIM, especially regarding the induced side force and yawing moment, should be enhanced further.
In order to validate the strip method as an aerodynamic interaction model for swept wings, in the project “Wetter & Fliegen” DLR recorded more than 60 wake encounters with the swept wing aircraft Falcon 20 behind its VFW-614 ATTAS in 2010, both aircraft in approach configuration and about 200 wake encounters in cruise with the Falcon 20 in 2011. Final analysis of this data is still pending, and so is the AIM validation work.
Figure 91: State-of-the-art simulation quality with SM-AIM for lateral wake encounter with a rectangular wing aircraft (Do128); model output (_____); flight test data (_____), from (Fischenberg
Also in an effort to validate the aerodynamic interaction models, Airbus has compared AIM model results with wake encounter flight tests in approach configuration for two leader and two generator aircraft. Results of this effort have not been published.
5.5.2.4 Pilot Behaviour Models
Systematic research in modelling the control behaviour of a human pilot controlling an aircraft began in the 1950s. The objective of the models was “to summarize behavioural data, to provide a basis for rationalization
and understanding of pilot control actions, and, most important of all, to be used in conjunction with vehicle dynamics in forming predictions or in explaining behaviour of pilot-vehicle systems” (McRuer and Jex 1967).
The models of pilot control behaviour can be classified into two main categories:
• Behavioural models that describe the intentional pilot action during (compensatory) tracking tasks. • Biomechanical models that simulate involuntary control inputs that are caused by the pilot acting as a
passive biodynamic element within the pilot-aircraft system.
Biomechanical models have been successfully applied to describe phenomena such as “roll ratcheting”. However, behavioural pilot models are better suited to describe pilot control behaviour when they compensate vortex induced disturbances or recover from vortex encounters. A brief overview on existing behavioural pilot models is given below.
The analytical theory of manual control of vehicles that has been developed in the 1950ties and 60ties has emerged as a useful engineering tool for the explanation of test results and prediction of new phenomena. It became part of handling quality criteria and was used for aircraft and flight control law design. An essential feature of this theory is the use of quasi-linear analytical models for the human pilot wherein the models' form and parameters are adapted to the task variables involved in the particular pilot-vehicle situation (McRuer and Krendel 1959).
An important class of situations for which pilot vehicle models are useful are closed-loop compensatory
tracking tasks, in which the pilot acts on the displayed error between a desired command input and the
comparable vehicle output motion to produce a control action. A fundamental finding of McRuer and Krendel was that human pilots adapt their behaviour in such a way that dynamic deficiencies of the controlled aircraft are compensated, essentially leading to similar open-loop behaviour of the pilot-aircraft system near the crossover frequency2, see (McRuer and Jex 1967). Based on this finding, they proposed the simple four parameter Crossover model. It is the most commonly used pilot model. Further research expanded the model from a single axis to the multi-axis control case and resulted in more complex models such as:
• Precision model: An extension to the Crossover model considering a representation of the human neuromuscular system.
• Structural pilot model: An explicit description of the human signal processing including sub-models for