Probabilistic Wake Vortex Decay Model Predictions Compared with Observations of Four Field Measurement Campaigns

Transcription

1 Probabilistic Wake Vortex Decay Model Predictions Compared with Observations of Four Field Measurement Campaigns Frank Holzäpfel Institut für Physik der Atmosphäre,, DLR Oberpfaffenhofen,, Germany P2P 1 : idea, method, and capabilities application to Memphis, DFW 97, WakeOP 2, and WakeToul data model performance campaign specifics (strategy, sensors, locations, resolution, aircraft, meteo) aircraft spacing reduction potential 1 F. Holzäpfel: : Probabilistic Two-Phase Wake Vortex Decay and Transport Model, J. Aircraft 40 (2) F. Holzäpfel,, R.E. Robins: Probabilistic Two-Phase Aircraft Wake Vortex Model: Application and Assessment, accepted for publ.. in J. Aircraft,, also AIAA Paper P2P idea, method, and capabilities well defined circulation 1 : sound hydrodynamic basis: Γ 5 15, ( Γ 3 8) 2 Γ( r, t) r = 1 exp Γ 4υt 0 adaptation to and calibration with LES 2,3 and exp.. data probabilistic formulation e.g. inclusion of 98% behavior ongoing adaptation to all available data 1 Holzäpfel F., Gerz T., Köpp F., Stumpf E., Harris M., Young R.I., Dolfi-Bouteyre A.: Strategies for circulation evaluation of aircraft wake vortices measured by lidar, J. Atmos. Ocean. Tech. 20 (8) F.H. Proctor, G.F. Switzer, G.F.: Numerical Simulation of Aircraft Trailing Vortices, 9th Conf. on Aviation, Range and Aerospace Meteorology F. Holzäpfel, T. Gerz, R. Baumann: The turbulent decay of trailing vortex pairs in stably stratified environments, Aerospace Science and Technology

2 P2P idea, method, and capabilities P2P accounts for effects of wind, (shear), turbulence, stable thermal stratification, and ground proximity input data: a/c: met: y, z α Γ b 0 0,, 0, 0 u( z), v( z), w( z), q( z), ε ( z), N( z) Model Equations Two-Phase Decay Γ R R ( t) = A exp exp ν ( t T ) ν ( t T ) diffusion rapid decay onset of rapid decay respective decay rate diffusion phase = f (ε, N) 2 rapid decay 2

3 WakeToul 2 µm pulsed Lidar measurement data 1 corroborate heavily disputed two-phase decay 1 Köpp F., Rahm S., Smalikho I., Characterisation of aircraft wake vortices by 2 µm pulsed Doppler lidar, accepted for publ. in J. Atmos. Ocean. Tech. fit of decay parameters example onset of rapid decay: T2 = T2,0 exp( 0.185T2,0 N ) 1 1 T. Sarpkaya: New Model for Vortex Decay in the Atmosphere, Journal of Aircraft

4 descent speed non-linear relation between circulation and descent speed stagnating or even rebounding vortices in strongly stably stratified quiescent environment Deterministic Behaviour? 4

5 probabilistic formulation accounts for uncertainty of flight path and a/c data uncertainty, variability, and limited predictability of environmental parameters stochastic nature of turbulence complex vortex instabilities and deformations by variation of decay parameters uncertainty allowances ( υ2, u,0.8t2 );( υ2, l,1.2t 2 ) ± 0.2Γ y u( l ), z 0 u( l ) = y, z + ( ) ( 1+ C q ( z ) dt ) y, z application P2P D2P Memphis, case

6 Statistics (211 Memphis cases) D(eterministic) 2P versus Sarpkaya s model RMS Γ3 10 / Γ0 z RMS0 / b RMS y / b 0 Sarp. (APA): median 90 th perc no tuning D2P: median 90 th perc meteo site ~ 2 km from lidar site non-conservative predictions (4 of 211 cases) life time underestimated in nocturnal low level jets Memphis case 1150 dv/dz = 0.6 enhanced life time in constant shear also found in simulations: T. Hofbauer, F. Holzäpfel: Behavior of Aircraft Wake Vortices Subject to Wind Shear, AIAA

7 tilt angle 53 deg b = 2.2 b 0 slightly non-conservative prediction (WakeOP 7-08) transport modified in turbulent shear layer dv/dz 0.4 WakeToul transport modified in shear layer dv/dz = (2.3) 7

8 WakeToul transport modified in shear layer dv/dz 1.06 dv/dz 0.85 WakeToul transport modified in shear layer dv/dz = (3.7) tentative conclusion: prolongated lifetimes for dv/dz>0.5 in extended layers stalling vortices for dv/dz>1 in narrow jets 8

9 preliminary parameterization: for v > 1: y = y + ( ) u( l ) u( l ) ( 1+ ( q + C v ) dt ) z = z + u u ( 1+ C( q + v ) dt ) + temp. relaxation of v preliminary parameterization: for v > 1: y = y + ( ) u( l ) u( l ) ( 1+ ( q + C v ) dt ) z = z + u u ( 1+ C( q + v ) dt ) + temp. relaxation of v 9

10 preliminary parameterization: for v > 1: y = y + ( ) u( l ) u( l ) ( 1+ ( q + C v ) dt ) z = z + u u ( 1+ C( q + v ) dt ) + temp. relaxation of v Extrapolation of meteorological data... can be quite tricky 10

11 P2P circulation based reduced spacing durations exceed ICAO standards transport causes current safety level main potential for reduced separations provided by transport Lateral Transport WakeOP time to leave the safety corridor effect of crosswind data sources potential for appreciable capacity benefits forecast error ~ spatial variability of measured crosswind 11

12 Statistics for Lateral Transport WakeOP le a ve corridor [%] ACOT [s ] NoCoPs [-] observation 76 (76) 17 (20) - P2P fed with crosswind from: SODAR 56 (56) 37 (46) 0 (0) Lidar 61 (61) 32 (39) 1 (1) aircraft 68 (66) 35 (38) 3 (2) NOWVIV 54 (46) 44 (39) 1 (0) ( ): with additional allowance for static hazard area ACOT: Average Corridor Occupation Time NoCoPs: Non-Conservative Predictions for lateral transport Lateral Transport Variability ATTAS wind data P2P Lidar need of: probabilistic predictions optimal wind prediction 12

13 Conclusions decay too slow to reduce separations spacing reduction must focus on transport strong significance of spatiotemporal wind variability quality of meteo measurements / predictions determines efficiency shear may prolongate life time and modify transport P2P high level of skill two-phase decay confirmed appropriate uncertainty allowances tentative shear parameterization NOWVIV-P2P during WakeOP: spacing potentially reducible to < 50 s in 39% (only advection) WakeOP campaign 2001 the site 13

14 WakeOP : measurement tools T hree continuous-wave 10µm lidars sodar/rass (F a. Metek) L aunch of a radios onde (Uni München) poldirad 04 short-term weather forecast model system - NOWVIV (A. Tafferner) L M forecas t domain (DWD) MM5 forecast domain MM5 Z, T, vertical H, U, V grid MM LM m/s 14