Wake Vortex Measurement, - DLR Portal€¦ · • or dynamic pairwise separations • demonstration...
Transcript of Wake Vortex Measurement, - DLR Portal€¦ · • or dynamic pairwise separations • demonstration...
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 !