Wake Vortex Measurement, Simulation, and Prediction

Frank Holzäpfel et al. Institut für Physik der Atmosphäre, DLR, Oberpfaffenhofen

DLR project L-bows (2014 – 17) ground-based wind lidar & onboard lidar numerical simulation − hybrid LES mitigation − plate line advisory system − WSVBS prediction en route WV scenarios simulation package − WakeScene multi-model ensemble WV prediction

The DLR Project L-bows (Land-Based and Onboard Wake Systems)

ground-based prediction of optimized wake vortex separation distances & airborne avoidance of hazardous wake encounters in all flight phases on tactical and pre-tactical time scales April 2014 – March 2017 a cooperation of AS, FL, FT, FX, LY, PA Frank Holzäpfel Carsten Schwarz

objectives of L-bows

frequent encounters during final approach (PiReps, lidar, simulations)

answering the question: „Why approach and landing are safe despite the large number of encounters?“ is vital for

design and implementation of optimal WV advisory system development of innovative numerical simulation system −

from vortex roll-up to final decay even for transient flight conditions quantitative demonstration of accelerated WV decay during final

approach employing the patented plate lines

(i) final approach:

final approach cruise

objectives of L-bows

optimized WSVBS − dynamic pair-wise separations with plate lines provision of prediction data for simulation of air traffic from

take-off to touchdown weather- and traffic-dependent optimized routing (pre-)tactical measures (e.g. re-routing, ground delay)

(ii) ground-based approach:

objectives of L-bows

WV prediction in aircraft environment during all phases of flight issuing warnings for pilots & small-scale evasion manoeuvres system proof-of-concept in flight trials development, construction & demonstration of lidar prototype for

airborne WV & CAT detection

(iii) airborne approach:

Transceiver: wavelength 2.022 µm repetition rate 500 Hz pulse energy 1.5 mJ pulse length 0.5 µs

Off-axis telescope: aperture 10 cm Double Wedge Scanner: elevation sector +/- 30 ° scan speed variable

Data acquisition: early digitising 500 MHz with quick-look

Signal processing: four-stage algorithm since 1983 campaigns at airports in Frankfurt, Istres, Munich, Oberpfaffenhofen, Tarbes, and Toulouse

Wind Lidar

WV Lidar Measurements in formation flight

Simultaneous measurement of A380 and A340 wake vortices with airborne lidar on Falcon

velocity spectra

velo

city

Nadir angle

Ongoing development of Direct Detection Doppler Wind Lidar (working on air molecule backscatter)

- independent of aerosols all flight altitudes and regions

- possible short range gates (x10m) - synergy with far range system →

Airborne LIDAR Activities

Near range (x100 m): Wind vector field → feed-forward control Impact mitigation and load alleviation Wake vortices Gusts Turbulence

Doppler wind speed u (Line-of-Sight)

Far range (15-30 km): Clear Air Turbulence detection Warning Mitigation

Air density fluctuation (vertical wind speed w’)

ρρ∆[ ]iu

WV

CAT

Past EC project on airborne CAT-detection DELICAT (2009-2014)

- Lidar instrument verification - Tentative detection of CAT, method

verification

Planned CAT observation from ground - statistics of occurrence - sensitivity studies (N, …)

• Requirements from Wake Vortex impact alleviation function (cf. DLR-FT): Close range, short measurement bins, high update rate, all altitudes, …

• Doppler Wind Lidar:

• Direct detection – Fringe imaging of UV air backscatter for Doppler shift • Geometry: Field-widened design, Michelson interferometer • Airborne application: Highly stable w.r.t. temperature / vibration –

Compensated, monolithic design, backscatter scrambling • Design and performance estimation (see talk J. Herbst on upcoming ODAS):

Development of Direct Detection Doppler Wind Lidar at DLR – Institute of Atmospheric Physics - LIDAR

• σ ≤ 1-2 m/s • independent of alt.

collision

reorganization

bursting

t*=5.6 t*=5.9 t*=6.2

initiation of helical instability due to vortex linking

t*=6.5 t*=6.8 t*=7.3

t*=8.2 t*=10.0 t*=11.4

second vortex linking

collision

bursting

reorganization

photo Sven Lüke, 16 Nov. 2006, 8:53, http://www.4elements-earth.de

LES: pressure waves - helical instabilities - double rings - vortex bursting

Misaka et al : Vortex bursting and tracer transport of a counter rotating vortex pair Physics of Fluids 24 (2012)

t* = 4.6, ε* = 0.01, N* = 0.35, Lt* = 0.95

Vortex Bursting

vortex bursting: - visualized by passive tracer - caused by collisions of secondary vorticity structures propagating along vortex lines - not related to local vortex decay

t* = 2.3, ε* = 0.23, N* = 0.35, Lt* = 0.75

Radiative Transfer Simulation with libRadtran/MYSTIC T. Zinner, M. Schönegg, MIM

max. ice water content 0.2 g/m³, eff. radius 25 µm

Misaka et al.: Vortex bursting and tracer transport of a counter-rotating vortex pair, Physics of Fluids 24 (2012)

• Full Airbus A320 high-lift configuration with and without gears

• DLR TAU Code • steady and unsteady RANS • automatic mesh adaptation to refine

regions with wakes and vortices • Flow conditions derived from real A320

landing manoeuvres

• Steady RANS and unsteady RANS

Transient near-to-far-field coupling RANS simulations in ground effect (AS)

Hybrid RANS-LES simulations methodology

RANS solution is “flown” through LES domain

transition function

Misaka et al., AIAA J. 53, 2015, DOI:10.2514/1.J053671

RANS LES

f(y,α,β)

y

Hybrid RANS-LES simulations − vortex roll-up during approach and touchdown

Hybrid RANS-LES simulations vortex evolution during approach and touchdown with plate line

Hybrid RANS-LES simulations landing with crosswind and plate line

Reduction of Wake Turbulence Risk considering Wake Decay Enhancing Devices (Plate Lines)

t* = -0.15

t* = 0.5

t* = 0.1

t* = 1.0

Plate Lines − Vortex Dynamics

1. Ω shape causes self-induced fast approach to primary vortex (PV)

2. after SV has looped around PV it separates and travels along the PV (again driven by self induction)

3. decay of PV is accelerated by turbulent interaction of PV and SV

> Lecture > Author • Document > Date DLR.de • Chart 1

Lidarstrahl lidar beam

×

× Ultraschallanemometer ultrasonic anemometers

Plate Line

HALO Platzrunden

traffic pattern

WakeOP-GE Flugversuche mit DLR Forschungsflugzeug HALO

am Sonderflughafen Oberpfaffenhofen Flight experiment with DLR research aircraft HALO

at special airport Oberpfaffenhofen

Flight experiment with research aircraft HALO at special airport Oberpfaffenhofen on 29 - 30 April 2013

lifetime of the most long-lived and strongest vortices is reduced by one-third

on display at ILA Berlin Air Show 2016

Mögliche Plattenanordnung Possible Plate Line positioning

passiv, preiswert, robust und sicher passive, low-cost, robust, and safe

Wake Vortex Advisory System “WSVBS” • supports weather dependent dynamic separations

• on closely-spaced parallel runways • and single runways • for weight class combinations • or dynamic pairwise separations

• demonstration campaigns at • Frankfurt airport (winter 06/07) • Munich airport (summer 10, spring 11)

see also Air Traffic Control Quarterly, Vol. 17, No. 4, 2009

Wake Vortex Advisory System “WSVBS” supports weather dependent dynamic a/c separations

Air Traffic Control Quarterly, Vol. 17, No. 4, 2009

approach corridor

vortex area safety area large a/c

safety area small a/c

lidar

sodar/rass

wake vortex prediction planes

WSV

BS

meteo measurements SODAR/RASS USA

3 gates, 0.3 - 1 NM

numerical weather pred. COSMO-Airport 10 gates, 2 - 11 NM

wake-vortex prediction P2P

envelopes for y(t), z(t), Γ(t) in 13 gates

for (individual) heavy/medium pairings

safety area prediction SHAPe

ellipses for (individual) medium followers

temporal a/c separations for (individual) heavy/medium pairings

wake-vortex monitoring LIDAR

3 planes, 0.3 - 1 NM

conflict detection validation of vortex predictions

glide path adherence statistics FLIP

standard deviations in 13 gates

optionally a/c type comb. Flight Plan

a/c type, arrival time

procedures AMAN

STG, MSR, MSL, ICAO

Wake Vortex Prediction and Monitoring System

WSVBS

strong crosswind

consideration of head wind, end effects, and plate lines for single runway operations (RECAT 3)

Wake-Vortex Encounter Probability En Route (LY)

En-route encounter • encounter advisory en-route • route segment wise encounter possibilities • wake behaviour integration according to individual aircraft types

and prevailing meteo conditions • deduction of ATFM flow measures

Wake-Vortex Encounter Probability En Route (LY)

European ATM Network Model • Approximately 1700 network entities • Airports and sectors • Demand-capacity-balancing considering WV risks

Wake-Vortex Predictions • Network integration of wake-vortex predictions • Safety-related demand management • Cost formulation according to network KPI „Safety“

WakeScene − Wake Vortex Scenarios Simulation Package (D - Departure / A - Arrival)

purpose: Monte Carlo simulation of departures or landings and

estimation of frequency of encounters

components: traffic mix aircraft trajectories

meteorological conditions wake vortex transport and decay

identification of encounters statistical analysis

applications: A380, RECAT, WVAS,

sensitivity analysis, optimization, risk analysis

WakeScene − sensitivity analysis, risk assessment

Holzäpfel et al., Aircraft Wake Vortex Scenarios Simulation Package - WakeScene, Aerospace Science and Technology 13, 2009. Holzäpfel et al., Aircraft Wake Vortex Scenarios Simulation for TakeOff and Departure, Journal of Aircraft 46, 2009, 713-717. Holzäpfel & Kladetzke, Assessment of Wake Vortex Encounter Probabilities for Crosswind Departure Scenarios, J. Aircraft 48, 2011.

EDDF-2 WakeScene-D

full domain zoom on lidar domain

good agreement of global vortex properties in lidar measurement domain

⇒ WakeScene-D supports investigating realistic

wake vortex behaviour in domains and height ranges that are far out of reach of measurements

WakeScene-D ⇔ measurement data at Frankfurt airport lateral vortex transport in lidar plane (~10.000 departures)

Multi-Model Ensemble Wake Vortex Prediction Bayesian Model Averaging:

= weighted sum of probability density distributions

i = model number wi = model weight gi = probability density distribution of model i with standard deviation σ

- parameters wi and σ derived by maximum likelihood (vortex age dependent)

- deterministic improvement of D2P 3.2 %

vortex age dependency of ensemble parameters widening of the uncertainty envelopes

Thank you !