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Naoki Matayoshi 19 th Coherent Laser Radar Conference CLRC 2018, June 18 – 21 1 Wake Vortex Measurement Using Pulsed Doppler Lidar Naoki Matayoshi(a), Eiichi Yoshikawa(a) and Masayuki Yamamoto(b) (a) Japan Aerospace Exploration Agency 6-13-1 Osawa, Mitaka, Tokyo, Japan (b) Mitsubishi Space Software Co., LTD. 1-6-1 Takezono, Tsukuba, Ibaraki, Japan Lead Author e-mail address: [email protected] Abstract: The Japan Aerospace Exploration Agency (JAXA) developed the original algorithm to detect wake vortex shed from aircraft using spectral data of pulsed Doppler lidar. The JAXA has been conducting the wake vortex measurement at two major airports in Japan using a pulsed Doppler lidar and demonstrated the capability of extracting wake characteristics including wake position and circulation strength from lidar spectral data. The acquired wake characteristics will be used to support safe and efficient aircraft operations. Keywords: Pulsed Doppler Lidar, Wake Vortex, Aircraft Separation 1. Introduction There is a strong demand to increase airport capacity by reducing aircraft separations to cope with increasing air traffic demand, which has been steadily expanding two-fold every 15 years [1]. The wake vortex separation minima are a major impediment to this since they require 4–8 NM separations in trail, which are greater than the radar separation minima of 2.5–3 NM that apply under radar surveillance (table 1). To reduce wake vortex separations, the International Civil Aviation Organization (ICAO) is discussing the re-categorization of wake vortex separation, a process named ‘RECAT’ [2]. The final goal of the RECAT is to introduce dynamic wake vortex separation according to pairs of aircraft types and weather conditions. The dynamic separation allows to reduce separations in favorable weather conditions when wake durations on flight paths become shorter. To realize this dynamic separation, the relationship between wake vortex behavior and surrounding weather condition must be clarified by developing wake vortex database measured in various weather conditions. There are several wake measurement activities using pulsed Doppler lidars to support the RECAT standardization [3][4]. In Japan, air traffic demand is also expected to grow and this demand might exceed airport capacities particularly around the Tokyo metropolitan area [5]. To increase airport capacity, the Japan Civil Aviation Bureau has been discussing the introduction of RECAT including dynamic wake vortex separation. The Japan Aerospace Exploration Agency (JAXA) has been conducting wake vortex measurements using a pulsed Doppler lidar at two major airports in Tokyo metropolitan area to support the RECAT introduction in Japan [6] [7]. The Mitsubishi Space Software supports the JAXA to analyze measured lidar data. This paper describes the JAXA’s wake measurement activities. First, the JAXA’s wake vortex detection algorithm using lidar spectral data is described. The lidar setup for wake measurement and example of measured wake vortex data are then presented. Table 1. ICAO wake vortex separation minima. Leading aircraft Following aircraft A380 Heavy Medium Light A380 6 NM 7 NM 8 NM Heavy (≥ 136ton) 4 NM 5 NM 6 NM Medium (≥ 7ton) 5 NM Light Th6

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Page 1: Th6 Naoki Matayoshi Coherent Laser Radar Conference · Th6. Naoki Matayoshi 19th Coherent Laser Radar Conference CLRC 2018, June 18 – 21 2 2. Wake Vortex Detection Algorithm The

Naoki Matayoshi 19th Coherent Laser Radar Conference

CLRC 2018, June 18 – 21 1

Wake Vortex Measurement Using Pulsed Doppler Lidar

Naoki Matayoshi(a), Eiichi Yoshikawa(a) and Masayuki Yamamoto(b) (a) Japan Aerospace Exploration Agency

6-13-1 Osawa, Mitaka, Tokyo, Japan (b) Mitsubishi Space Software Co., LTD. 1-6-1 Takezono, Tsukuba, Ibaraki, Japan

Lead Author e-mail address: [email protected]

Abstract: The Japan Aerospace Exploration Agency (JAXA) developed the original algorithm to detect wake vortex shed from aircraft using spectral data of pulsed Doppler lidar. The JAXA has been conducting the wake vortex measurement at two major airports in Japan using a pulsed Doppler lidar and demonstrated the capability of extracting wake characteristics including wake position and circulation strength from lidar spectral data. The acquired wake characteristics will be used to support safe and efficient aircraft operations.

Keywords: Pulsed Doppler Lidar, Wake Vortex, Aircraft Separation

1. Introduction

There is a strong demand to increase airport capacity by reducing aircraft separations to cope with increasing air traffic demand, which has been steadily expanding two-fold every 15 years [1]. The wake vortex separation minima are a major impediment to this since they require 4–8 NM separations in trail, which are greater than the radar separation minima of 2.5–3 NM that apply under radar surveillance (table 1). To reduce wake vortex separations, the International Civil Aviation Organization (ICAO) is discussing the re-categorization of wake vortex separation, a process named ‘RECAT’ [2]. The final goal of the RECAT is to introduce dynamic wake vortex separation according to pairs of aircraft types and weather conditions. The dynamic separation allows to reduce separations in favorable weather conditions when wake durations on flight paths become shorter. To realize this dynamic separation, the relationship between wake vortex behavior and surrounding weather condition must be clarified by developing wake vortex database measured in various weather conditions. There are several wake measurement activities using pulsed Doppler lidars to support the RECAT standardization [3][4].

In Japan, air traffic demand is also expected to grow and this demand might exceed airport capacities particularly around the Tokyo metropolitan area [5]. To increase airport capacity, the Japan Civil Aviation Bureau has been discussing the introduction of RECAT including dynamic wake vortex separation. The Japan Aerospace Exploration Agency (JAXA) has been conducting wake vortex measurements using a pulsed Doppler lidar at two major airports in Tokyo metropolitan area to support the RECAT introduction in Japan [6] [7]. The Mitsubishi Space Software supports the JAXA to analyze measured lidar data. This paper describes the JAXA’s wake measurement activities. First, the JAXA’s wake vortex detection algorithm using lidar spectral data is described. The lidar setup for wake measurement and example of measured wake vortex data are then presented.

Table 1. ICAO wake vortex separation minima.

Leading aircraft Following aircraft

A380 Heavy Medium Light A380 – 6 NM 7 NM 8 NM

Heavy (≥ 136ton) – 4 NM 5 NM 6 NM Medium (≥ 7ton) – – – 5 NM

Light – – – –

Th6

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Naoki Matayoshi 19th Coherent Laser Radar Conference

CLRC 2018, June 18 – 21 2

2. Wake Vortex Detection Algorithm

The JAXA’s wake vortex detection algorithm estimates wake vortex parameters including vortex core positions and circulation strength using pulsed Doppler lidar spectral data [7]. Although the pulsed Doppler lidar has an advantage that can scan wide area in short time, its spatial resolution, especially range resolution of several tens of meters, is too coarse to measure wake vortex whose core size is several meters. The JAXA’s algorithm therefore uses lidar spectral data, which express probabilistic density distribution of radial winds in each lidar range bin, to estimate wake vortex parameter accurately.

The JAXA’s algorithm is an iterative procedure to optimally fit wake vortex parameters to measured Doppler spectra acquired in RHI (range-height-indication) scan mode whose observation plane is perpendicular to aircraft flight path (fig. 1). We use a 2-D vortex model (eqn. 1) to model wake induced wind velocity field and assumes constant background wind.

(1)

where : tangential wind speed, Γ: circulation strength, : vortex core size, : distance from core.

This assumption leads to a vector of sate variables that expresses wind velocity field in lidar observation plane (eqn. 2).

Γ ̅ (2)

where : lateral position of vortex core, : vertical position of vortex core, ̅ : averaged lateral background wind, : averaged vertical background wind.

An optimum solution of the state vector x is calculated in a Bayesian scheme by minimizing the following cost function (eqn. 3).

∑ (3)

where : Doppler spectral component estimated from the state vector x, : measured Doppler spectral component, σ: the standard deviation of the known probabilistic property of Doppler spectral component.

Figure 1. Flow of the wake vortex detection algorithm

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Naoki Matayoshi 19th Coherent Laser Radar Conference

CLRC 2018, June 18 – 21 3

3. Wake Vortex Measurement

The JAXA conducted wake vortex measurement at Narita international airport in 2013-2014 [6] and is conducting the measurement at Tokyo international airport from 2017. The JAXA uses the Leosphere WINDCUBE200S lidar for wake measurement (table 2). Figure 2 and table 3 show the lidar installation at Narita international airport. The high data output rate of 30 Hz enables rapid RHI scan of less than 10 sec while keeping enough elevation angle sampling of 0.2 deg for wake vortex detection. The lidar measured radial wind distribution in the vertical plane perpendicular to the flight path and wake vortex parameters were estimated for over 3000 landing aircraft. The collected data were mainly ‘out of ground effect’ wake vortex data since typical aircraft altitudes in the lidar measurement plane were about 400 ft. Whereas, the on-going measurement campaign at Tokyo international airport mainly collects ‘in-ground effect’ wake vortex data by locating lidar more closely to runway threshold. Typical aircraft altitudes in the lidar measurement plane are about 200 ft. As of March 2018, wake vortex data for nearly 4000 landing aircraft are collected. Figure 3 shows the example of measured ‘in-ground effect’ wake vortex data in calm wind condition. The wakes rebound near ground and linger long around landing path. The JAXA will develop the wake vortex database covering both ‘out of ground effect’ and ‘in-ground effect’ regions.

Table 2. Major specifications of Leosphere WINDCUBE200S

Wavelength 1543 nm Range gate interval 1 m (minimum) with range overlapping

Number of range gates 240 (max) Scan 0 – 360 deg (Azimuth), -10 – 190 deg (Elevation)

Pointing accuracy 0.1 deg Radial wind speed -30 – 30 m/sec

Measurements CNR, radial velocity, velocity dispersion,

Doppler spectrum, range, gazing angle, and time.

Table 3. Rapid RHI scan for wake vortex measurement

Range sampling Every 5 m from 100 to 885 m Physical range resolution About 50 m Elevation angle sampling Every 0.2 deg from 0 to 40 deg

Velocity sampling Every 3 m/sec from -30 to 30 m/s Pulse repetition 18,000 Hz

LOS data output rate 30 Hz Scan duration 6.7 sec (+2 sec to reset scanner head)

Figure 2. Lidar installation and example of measured data at Narita international airport

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Naoki Matayoshi 19th Coherent Laser Radar Conference

CLRC 2018, June 18 – 21 4

Figure 3. Example of measured wake vortex at Tokyo international airport

(lingered around landing path (indicated by red lines) for over 90 seconds)

4. Conclusions

This paper described the JAXA’s wake measurement activities using pulsed Doppler lidar. The collected wake vortex data will be used to support the RECAT introduction in Japan and the standardization of dynamic wake vortex separation in ICAO.

5. References

[1] The International Civil Aviation Organization, “Global Air Navigation Plan 2016-2030”, Doc 9750-AN/963 5th edition, (2016).

[2] J. Cheng, J. Tittsworth, W. Gallo and A. Awwad, “The Development of Wake Turbulence Recategorization in the United States”, 8th AIAA Atmospheric and Space Environments Conference, AIAA AVIATION Forum, (AIAA2016-3434).

[3] M. J. Pruis, D. P. Delisi, D. Jacob, and D. Lai. “Summary of NASA Wake and Weather Data Collection at Memphis International Airport: 2013-2015 (Invited)”, 8th AIAA Atmospheric and Space Environments Conference, AIAA AVIATION Forum, (AIAA 2016-3274).

[4] I. D. Visscher, V. Treve, and G. Winckelmans. “Characterization of Aircraft Wake Vortex Circulation Decay in Reasonable Worst Case Conditions”, 54th AIAA Aerospace Sciences Meeting, AIAA SciTech Forum, (AIAA 2016-1603)

[5] The Japan Civil Aviation Bureau, “Collaborative Actions for Renovation of Air Traffic Systems (CARATS)”, http://www.mlit.go.jp/en/koku/koku_fr13_000000.html (as of May 2018).

[6] N. Matayoshi and E. Yoshikawa. “Dynamic Wake Vortex Separation Combining with AMAN/DMAN Concept”, 15th AIAA Aviation Technology, Integration, and Operations Conference, AIAA AVIATION Forum, (AIAA 2015-3397).

[7] E. Yoshikawa and N. Matayoshi, “Aircraft Wake Vortex Retrieval Method on Lidar Lateral Range–Height Indicator Observation”, (AIAA Journal, Vol. 55, No. 7, 2017), pp. 2269-2278.