Open access peer-reviewed chapter

Modelling of Temporal‐Spatial Distribution of Airplane Wake Vortex for Scattering Analysis

By Jianbing Li, Zhongxun Liu and Xuesong Wang

Submitted: April 1st 2016Reviewed: October 26th 2016Published: March 1st 2017

DOI: 10.5772/66544

Downloaded: 745

Abstract

Aircraft wake vortex is a pair of intensive counter‐rotating airflow generated by a flying aircraft. Wake vortex is one of the most dangerous hazards in aviation because it may cause a following aircraft to roll out of control, particularly during the taking off and landing phases. The real‐time detection of wake vortex is a frontier scientific problem emerging from many fields like aviation safety and atmospheric physics, and the dynamics and scattering characteristics of it remain as key problems to develop corresponding detection technologies. This chapter aims at presenting a simulation scheme for the dynamics of wake vortex under different weather conditions. For wake vortex generated in clear air, changes of the atmospheric dielectric constant produced by the density variation and water vapour variation are analysed; for wake vortex generated in rainy condition, the raindrop distribution in the wake vortex is also analysed. Both of them are essential for further analysing the scattering characteristics and developing new detection algorithms.

Keywords

  • wake vortex
  • dynamics
  • clear air
  • wet weather

1. Introduction

Wake vortex is an inevitable physical phenomenon that exists in the rear zone of a flying aircraft, which rotates intensively and has a complex structure. The wake vortex generated by a large aircraft could be very hazardous to aviation safety since it might cause a following aircraft to roll out of control, particularly during the departure and landing phases.

In Air Traffic Management (ATM) field, International Civil Aviation Organization (ICAO) established a series of flight interval rules. These rules can ensure the flight safety in most time, but they are too conservative. In order to reach a good balance between avoiding the encountering hazard of wake vortex and increasing the transport capacity of airports, much attention has been paid on the real‐time monitoring and detection of wake vortex in the past decades. Some major ATM programmes like Single European Sky ATM Research (SESAR) and Next Generation Air Transportation System, USA (NextGen) have also launched many projects on this topic, and the representative research institutes include Thales, Office National d'Etudes et de Recherches Aerospatiales (ONERA), Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Université catholique de Louvain (UCL), National Aeronautics and Space Administration (NASA), Federal Aviation Administration (FAA), Lincoln Lab, Boeing, and so on. In all these studies, the characteristics, detection technology and parameter retrieval are the key issues, and the characteristics of wake vortex serve as the basis for the rest studies.

In aviation safety, we mainly concern the clear air condition and wet weather condition. According to the scattering theory, the scattering of wake vortex in clear air is mainly determined by the fluctuation of dielectric constant inside the wake; while under wet condition, the key scattering factor becomes the massive number of precipitation droplets carried by the velocity of wake vortex. In this chapter, we present simulation schemes for the dielectric constant distribution and droplet distribution of wake vortex. The distributions are caused by the dynamics of wake vortex and serve as the physical basis for scattering analysis.

First, we study the dielectric constant distribution of wake vortex generated in clear air.

2. Dielectric constant of wake vortex generated in clear air

2.1. Two key parameters for determining the dielectric constant of wake vortex

The relative dielectric constant of atmosphere (εr) can be well depicted by the following expression [1]:

εr=[1+0.776×106pT(1+7780qT)]2E1

where p,T,qare the pressure (pa), absolute temperature (K), and water vapour content (kg/kg), respectively. Generally, the second term in the square bracket is much smaller than 1, so the variation in dielectric constant between wake vortex and ambient air can be approximated as follows when the Taylor expansion is taken into account:

Δεr=εrεr,a1.552×106[pT(1+7780qT)paTa(1+7780qaTa)]E2

In the expression, the parameters with subscript “a” refer to the ambient parameters and those without “a” refer to the wake vortex's parameters.

As is known, an isentropic process is a process in which there is neither heat exchange nor any friction effect [2]. Typically, the wake vortex of a subsonic airplane can be assumed as an isentropic flow, and the thermodynamic parameters at different points along a streamline can be written as follows:

ppa=(ρρa)γ,TTa=(ρρa)γ1E3

where γ=1.4is the adiabatic coefficient for air. Consequently, the variation in dielectric constant is transformed to

Δεr1.552×106×[paTa(ξ1)+7780paTa2(ξ2γqqa)]E4

Here we have denoted ξ=ρ/ρa, and the effects due to density and water vapour are mixed in the term ξ2γq. In order to separate the two factors, this term is transformed to

ξ2γq=[1+(ξ1)]2γ(qa+Δq)E5

with Δq=qqabeing the water vapour variation between the local wake and the ambient air. Since the variations in density and water vapour for wake vortex are very small, say ξ1<<1and Δq<<qa, the dielectric constant variation can be approximated as follows when the Taylor expansion is adopted:

Δεr1.552×106paTa2×{[Ta+7780(2γ)qa](ξ1)+7780Δq}E6

In this expression there are two undetermined parameters, ξand Δq. They are separated into two different terms:

Δεr=Δεrd+ΔεrvE7

The first term is determined by the density variation ξ:

Δεrd1.552×106paTa2(Ta+4668qa)(ξ1)E8

and the second term is determined by the water vapour variation Δq:

Δεrv1.207×102paTa2ΔqE9

In this manner, the key of modelling the dielectric constant is to determine the two parameters, ξand Δq.

2.2. Effect of density variation on the dielectric constant

2.2.1. Velocity field of wake vortex

When the stationary phase of a subsonic wake vortex is taken into account, the dynamics can be well characterized by the steady Lamb momentum equation [2]:

Ω×V+12|V|2=1ρpE10

where Vis the total velocity of wake vortex, and Ω=×Vis the vorticity. In this expression, the velocity and density are separated, which makes it convenient to work out the thermodynamics parameters according to the velocity field.

Typically, when no cross‐wind is considered, a stable‐stage wake is composed of two contour rotating vortices of the same strength, and they descend at a velocity Vd. In this manner, in a coordinate system descending with the vortex cores, the wake vortex is steady, and the velocity for a given point Pcan be written as (see Figure 1):

V=Vd+VL+VRE11

Figure 1.

Velocity of counter‐rotating vortices.

where VLand VRare the velocities deduced by the left and right vortices, and Vdis the descending velocity. In Figure 1, the parameter Γis the circulation which defines the strength of the wake vortex.

  • In the expression, the deduced velocity of each vortex can be presented by existing velocity profile models, such as Rankine model, Lamb‐Oseen model, and so on. Among them, the Rankine model is a widely used one, and the corresponding tangential velocity (Vθ) follows [3]:

    Vθ(r)=Γ02πr{r2/rc2,r<rc1,rrcE12

where ris the distance of a given point to the vortex centre, rc0.052b0the vortex core radius, and b0the vortex spacing. As shown in Figure 2, the velocity outside the vortex core is irrotational, while the inside part is with a uniform vorticity:

ω0=Γ0πrc2E13
  • The descending velocity can be derived from the deduced velocity of a vortex core:

    Vd=Γ02πb0y^E14

Figure 2.

Rankine velocity profile.

2.2.2. Stream function of wake vortex

When the velocity components are determined, the vorticity is finally obtained as

Ω=×V=ω0H(r)E15

In this expression, we have considered ×Vd=0, and H(r)is an identification function for the left vortex core region (CL) and right one (CR):

H(r)={1,rCL1,rCR0,otherwiseE16

As a result, we have

Ω×V=ω0H(r)(vx^+uy^)=ω0H(r)ψE17

where uand vare the velocity components in xand ydirections, and ψis the stream function:

u=ψy,v=ψxE18

In this manner, the Lamb momentum equation for wake vortex is rewritten as

ω0H(r)ψ+12|V|2=1ρp.E19

Furthermore, integrating the above equation from a point rto infinity gives

I=ω0H(r)ψ+12Vd212|V|2=r1ρdpE20

where we have considered V=Vdy^, and the total stream function ψis

ψ=Vdx+ψL+ψR+CE21

with the four terms on the right-hand side being the up‐wash flow at infinity, the stream function due to the left vortex and right vortex, and a constant.

For a Rankine vortex, the stream function follows

ψRankine=Γ4π{r2r021+lnr02,r<rc;lnr2,rrc.E22

Then ψLand ψRcan be obtained by replacing the circulation Γwith Γ0and Γ0, respectively.

Also, the constant Cis chosen to meet ψ(r)=0when ris on the vortex core boundary:

C=ψ1(r)=VdxψLψRE23

Due to the impact between two vortices, the constant Chas a very slight variation along the vortex core boundary. In practice, the average of ψ1(r)along one vortex core boundary is chosen as the constant.

As a result, combination of the total stream function and velocity gives the distribution of integral Ias shown in Eq. (20), which is then used to calculate the density distribution of wake vortex.

2.2.3. Determination of dielectric constant due to density variation

Substituting the isentropic relationship (3) into the item on the right hand of Eq. (20) gives

I=r1ρdp=rγγ1paρaγdργ1=γγ1paρaγ(ργ1ρaγ1)E24

which can then be transformed as

ξ=ρρa=[1+(γ1)ρaγpaI]1γ1E25

For a wake vortex, the integral Ihas the same magnitude as Vθ2(generally not larger than 1000). On the other hand, ρaand paare, respectively, on the magnitude of 1 and 105. Therefore, the second term in Eq. (25) is much smaller than 1, and the Taylor expansion gives:

ξ1+ρaγpaIE26

Consequently, the effects of dielectric constant can be rewritten as

Δεrd(r)1.552×106pa(Ta+4668qa)γRTa3I(r).E27

2.3. Effect of water vapour variation on the dielectric constant

Generally, the atmospheric water vapour inside the wake vortex can be modelled as a passive scalar, which is convected by the wake vortex velocity field and is governed by the convection-diffusion equation [4]:

qt+(V)qD2q=0,E28

where qis the water vapour concentration, Dis the diffusion coefficient for an air/water vapour system, and Vis the velocity filed of a wake.

In fluid dynamics, the Péclet number is a dimensionless number indicating the rates of convection and diffusion of a flow [5]:

Pe=ULDE29

where Lis the characteristic length, Uis the velocity, and Dis the mass diffusion coefficient. Generally, a flow is convection-dominated, if the Péclet number is large. In this study, the diffusion coefficient for air (D=2.42×105) is very small, which leads to a big Péclet number and the flow is convection dominated. In this manner, neglecting the impact of diffusion leads to a simplified governing equation:

qt+(V)q=0E30

As is known, the initial water vapour gradient is very important to characterize the equation. Here the stratified model is adopted in this work

q(r,t)|t=0=qa=mq(yy0)+q0E31

with q0and mqbeing the offset and gradient of water vapour content, respectively. Substituting the initial condition into the governing equation gives

q(r,0)t+(V)q(r,0)=VymqE32

Therefore, Eq. (30) minus Eq. (32) leads to a new equation:

Qt+(V)Q=VymqE33

with Q(r,t)=Δq(r,t)=q(r,t)q(r,0)being the water vapour variation. At the same time, the initial condition of Eq. (33) becomes Q(r,t)|t=0=q(r,t)|t=0q(r,0)=0; this is coincident with the physical image that the water vapour variation is initially zero.

Eq. (33) is a hyperbola differential equation, which is often numerically difficult to solve. In the present work, the leapfrog scheme is adopted to solve the target equation, and good convergence and stability are achieved. The scheme is as follows:

Qi,jn+1Qi,jn12Δt+uQi+1,jnQi1,jn2Δx+vQi,j+1nQi,j1n2Δx=vi,jmq,E34

with uand vbeing the velocity components in xand ydirection, respectively. In the process, the Von Neumann method leads to the following stable condition [6]:

Δt<12ΔVmaxE35

where Δtis the time step, Δis the minimum grid spacing, and Vmaxis the maximum velocity in the wake vortex.

In addition, non‐uniform grids and symmetric condition are used to reduce computational cost. On the one hand, the velocity distribution shows that the flow in and around the vortex core is relatively complex and the flow far from the vortex core is slowly variational. Typically, sparse grids are adequate to characterize the slowly variational zones, but complex zones require dense grids. In this manner, non‐uniform grid scheme can be adopted to reduce the total number of grids. On the other hand, the wake vortex is symmetric, so only half of wake vortex needs to be computed. Overall much computational cost can be saved through the above scheme.

If the water vapour variation, Q(r,t)is solved from Eq. (33), then Δεrvcan be immediately obtained according to Eq. (9).

In this above simulation, the moving coordinate system is also used. The dielectric constant distribution can be transformed into the stationary coordinate system if the following transform is used:

y=yVdtE36

where Vdis the descending velocity, tis the evolutional time, yand yare the coordinates in the moving coordinate system and stationary coordinate system, respectively.

2.4. Numerical examples

Here we give several numerical examples for the dielectric constant distribution due to different effects. The parameters of airplane and atmosphere are as shown in Table 1.

ParametersValue
Airplane mass M250,000 kg
Wingspan B68 m
Speed V133 m/s
Ambient pressure pa100,000 pa
Ambient absolute temperature Ta288 K
Diffusion constant D2.42×105 m2/s
Water vapour content offset q00.018 kg/kg
Water vapour content gradient mq8×108kg/(m kg)

Table 1.

Parameters of airplane and atmosphere.

According to the parameters, the distribution of I(r)can be worked out, and the intensity of dielectric constant due to density variation can be obtained as shown in Figure 3. It is observed that Δεrdin the vortex cores are much larger than that outside, so the vortex core could present a big contribution to the scattering of wake vortex.

Figure 3.

Dielectric constant distribution due to density variation.

The dielectric constant due to water vapour variation (Δεrv) can be obtained when the given partial equation (33) is solved, and Figure 4 presents Δεrvat t=40 s. It is observed that the convection effect of water vapour results in a non‐uniform laminar structure in and around the wake vortex cores, and these structures could be good contributor to the scattering in high frequency.

Figure 4.

Dielectric constant distribution due to water vapour variation.

Consequently, the sum of Figures 3 and 4 gives the total distribution of dielectric constant (see Figure 5). Comparing the magnitude of Δεrdand that of Δεrvshows that Δεrvis much less than Δεrd, and Δεrddominates the magnitude of Δεr. However, Δεrdand Δεrvhave different structures; they make different contribution to the scattering in different frequency bands. Typically, the scattering of clear air wake vortex at a frequency lower than 100 MHz is mainly determined by the density variation Δεrd; otherwise, the water vapour variation Δεrvmakes the major contribution.

Figure 5.

Total dielectric constant distribution.

2.5. Extrapolation of dielectric constant distribution

The extrapolation includes two parts: one is relate to the density distribution and another is related to the water vapour distribution.

2.5.1. Extrapolation of dielectric constant related to density distribution

As shown in Eq. (27), the key of density distribution is the integral I(r); this could be obtained with the normalized parameters.

First, if the space is normalized by vortex separation b0, say r˜=r/b0, the Rankine model can be normalized as

Vθ(r)=Γ0b0V˜θ(r˜),E37

with V˜θbeing the normalized velocity:

V˜θ(r˜)=12πr˜{r˜2/0.0522,r˜<0.052,1,r˜0.052.E38

Other velocity profile models have similar expressions. Substituting the normalized velocity into the integral Eq. (20) gives

I(r)=(Γ0b0)2I˜(r˜)E39

Where the normalized integral I˜(r˜)=C˜(V˜˜)V˜ds˜is only related to the velocity model, and ˜=(/x˜/y˜)T=b0, s¯=(x¯,y¯).

For a stably flying airplane, the lift force balances the weight, which leads to the following initial circulation expression:

Γ0=MgρaVab0E40

Consequently, substituting the circulation into the integral gives

I(b0r˜)=M2g2ρa2Va2b04I˜(r˜)E41

The dielectric constant related to density distribution is then rewritten as

Δεrd(b0r˜)1.552×106R[Ta(b0r˜)+4668qa(b0r˜)]γpa(b0r˜)Ta(b0r˜)M2g2Va2b04I˜(r˜)E42

Since the normalized integral I˜(r˜)=C˜(V˜˜)V˜ds˜is only related the velocity model, the following relationship can be obtained when the different airplane parameters and air conditions are introduced:

Δεr,2d(r2)Δεr,1d(r1)pa,1(r1)Ta,1(r1)[Ta,2(r2)+4668qa,2(r2)]pa,2(r2)Ta,2(r2)[Ta,1(r1)+4668qa,1(r1)]M22Va,12b0,14M12Va,22b0,24E43

with r1=b0,1r˜and r2=b0,2r˜.

2.5.2. Extrapolation of dielectric constant related to water vapour distribution

The key of water vapour distribution is to solve the partial differentiation equation (33).

Define some related normalized parameters as follows: Q˜=Q/b0,t˜=t/t0,r˜=r/b0,V˜=V/V0, where V0=Γ0/b0and t0=b0/V0. Consequently, the target partial differentiation equation is rewritten as

Q˜t˜+(V˜˜)Q˜=V˜ymq.E44

If the variable Q˜(r˜,t˜)is solved from above equation, the dielectric constant related to water vapour distribution becomes

Δεrv(b0r˜,t0t˜)=1.207×102paTa2b0Q˜(r˜,t˜).E45

In this manner, the dielectric constant for different airplane and air parameters is extrapolated as

Δεr,2v(r2,t2)Δεr,1v(r1,t1)pa,2(r2)pa,1(r1)[Ta,1(r1)Ta,2(r2)]2b0,2b0,1E46

where r1=b0,1r˜, r2=b0,2r˜=b0,2b0,1r1, t1=t0,1t˜, and t2=t0,2t˜=t0,2t0,1t1. For stably flying airplane, we have t0=ρab03Va/(Mg), and then the relationship becomes

t0,2t0,1=(b0,2b0,1)3p0,2p0,1T0,1T0,2V0,2V0,1M1M2E47

This is a very simple relationship.

With the combination of two extrapolation formulae, the dielectric constant distribution is then determined. This can save a lot of computation cost when different airplane wake vortices are to be analysed.

Another condition we always experience is wet weather condition (fog, rain, and snow). Here we mainly concern the rainy condition since it is the most common situation around airports.

3. Wake vortex generated in rainy weather

In still air, the raindrops fall vertically towards the ground and reach a constant terminal falling velocity. When an aircraft takes off or lands in rainy weather, the raindrops will be inevitably involved in the aircraft wake vortices. Raindrops’ motion in wake vortex is modified by the vortex flow. This modification of the trajectories of the raindrops may induce changes in raindrops’ number concentration and velocity distribution in wake vortices, therefore results in changes in the recorded radar signal. This section presents a modelling scheme for raindrops’ motion and distribution within the wake vortex.

3.1. Parameterization of raindrops

In still air, a falling raindrop reaches its terminal fall velocity VTwith the equilibrium between the inertial force and the drag force acting on it [7]. A widely used exponential expression between VT(m/s) and the diameter D(mm) is given by [8]

VT(D)=[α1α2exp(α3D)](ρ0ρ)0.4E48

where α1= 9.65 m/s, α2= 10.3 m/s, α3= 0.6 m/s, and (ρ0/ρ)0.4is a density ratio correction factor adjusting deviation of the terminal fall velocity due to the air density change with the fall altitude.

Drop size distributions (DSD) have been widely used by radar meteorologists as they are directly related to radar reflectivity [9]. In the following analysis, a suitable model to describe the size distribution of the rainfall in Europe is adopted [10]

N(D)=N0D2eDE49

where N0=64500R0.5(m-3 mm-3) with R (mm/h) being the considered rain rate, =7.09R0.27(mm1), N(D)(m3 mm1)represents the number of raindrops of the diameter D per unit volume per unit diameter class interval.

3.2. The motion equation of raindrops in wake vortices

Typically, the diameter of raindrops disperses between 0.5 and 4 mm. Usually their Stokes number in wake vortex flow is approximate to 1, which makes their motion trajectories significantly differ from the streamlines of total velocity field. To obtain those trajectories, the motion equation of the raindrops is studied [11].

When a raindrop enters into the wake vortex flow, its movement is governed by

a(t)=Fd(t)mp+gE50

where t is the time, a is the acceleration of the raindrop, Fdis the fluid drag force acting on the raindrop, mpis its mass, and g is the gravity acceleration. For a raindrop moving with velocity vpin the fluid whose velocity field is u[zp(t)], if its diameter Dranges from 0.5 to 4 mm, the drag force Fdcan be approximately considered in the Newton regime [12] and given by

Fd(t)=12Cdρaδv2(πD2)2, δv=u[zp(t)]vp(t) E51

where zp(t)denotes raindrops’ position, δvis the relative velocity between the vortex flow and the raindrop, and Cdis the fluid drag coefficient. The impact of air density variations in the vortex flow on Cdcan be neglected because the density of raindrops is much larger than that of air. Thus, Cdfor a raindrop of diameter D is derived by the equilibrium equation of its weight and the drag force when falling at terminal falling velocity in still air:

Cd=4ρwgD3ρaVT2E52

where ρwis the density of raindrops. Substituting Eqs. (51) and (52) into Eq. (50), the motion equation of raindrops within wake vortices can be further expressed as

{a(t)=g+gVT2|δv|δvdvp(t)dt=a(t)dzp(t)dt=vp(t)E53

The instantaneous position and velocity of raindrops can be obtained from the above equations, but it is not easy to obtain a simple and closed expression. Here, a fourth‐order four variables Runge‐Kutta algorithm is proposed to solve the equation of motion [11].

3.3. Examples of trajectories of raindrops in wake vortices

In still air, the raindrop falls along a vertical trajectory to the ground. In presence of wake vortices, the trajectory of a raindrop is depending on its diameter and the location where it enters into the wake vortex flow. Circulation is a very important parameter to characterize wake vortex since it describes the strength of wake vortex. For raindrops moving in the vortex flow, their motion characteristics, that is, trajectory and velocity also largely depend on the vortex circulation. For simplicity, only the impacted part of the wake vortex region is shown in Figure 6, where four sets of trajectories are illustrated for raindrops of diameters 0.5, 1.0, 2.0, and 4.0 mm. Each set of trajectories corresponds to a given circulation value of the wake vortices. For a given circulation, the corresponding trajectory and velocity of raindrops in the wake vortices are unique. Comparisons between different trajectories give the following conclusions: (1) the smallest raindrops are much more sensitive to the vortex circulation and (2) the motion characteristic of raindrops in wake vortices is representative of the vortex strength.

Figure 6.

Raindrops’ trajectories in wake vortices with different circulation: (a) D = 0.5 mm, (b) D = 1.0 mm, (c) D = 2.0 mm, (d) D = 4.0 mm. The solid line ‘‐', the dash‐dotted line ‘‐.', the dashed line ‘–’ and the dotted line ‘...’, correspond to the values of vortex circulation: 490 m2/s, 430 m2/s, 360 m2/s, and 300 m2/s, respectively.

3.4. Raindrops’ distribution in wake vortices

A Doppler radar will be very sensitive to raindrops’ motion and possibly enable the detection of wake vortices in rain. To better understand the impact of wake vortices on the raindrops’ motion, it is necessary to develop the methodology to quantitatively analyse the raindrops’ distribution in wake vortices.

3.4.1. Raindrops’ number concentration

The box counting method is adopted here to quantitatively compute raindrops’ distribution in wake vortices. For simplification, we consider the situation where the raindrops are falling into the two dimensional rectangular region of wake vortex in stable phase. Before entering into the wake vortex region, the raindrops are falling in still air with the constant terminal falling velocity, and they are named as “initial raindrops”. The number density of initial raindrops of diameter D (mm) is assumed to be N0(D)(m3mm1). In the wake vortex region, the raindrops’ trajectory and velocity are changed and they are denoted as “disturbed raindrops”. The number density of disturbed raindrops is assumed to be N(D,x,y)(m3mm1), where (x, y) are the coordinates in the wake vortex region. Obviously, for a given wake vortex pair, N(D, x, y) depends on both the diameters of raindrops and their locations in wake vortex. In order to better illustrate the influence of wake vortices on the raindrops’ distribution, the raindrops’ relative number concentration at a point (x, y) is defined as

ηN(D,x,y)=N(D,x,y)N0(D)E54

where ηN(D,x,y)depicts the change in raindrops’ concentration induced by the wake vortex. If ηN>1, the concentration of raindrops is enhanced, otherwise it is reduced.

In order to obtain the quantitative estimation of ηN, the wake vortex region is divided into nx×nygrid boxes with equal size. The size of each grid box in xy plane are Δx and Δy, respectively. Above the wake vortex region, there are nx×1grid boxes where the initial raindrops of diameter D are homogeneously distributed, and the number of initial raindrops in each grid box is recorded as Num0(D). At each time step, their positions and velocities are updated by computing the equation of motion. If some of the initial raindrops enter into the wake vortex region, the same number of new initial raindrops is added to the first row of the nx×nygrid boxes, say, the nx×1grid boxes above the wake vortex region. When all the raindrops released at initial time arrive at the bottom of wake vortex region, the number of disturbed raindrops Num(D, x, y)in each grid box in wake vortex region is counted. Thus, ηN(D,x,y)can be approximated by the ratio of the number of disturbed raindrops in a grid box centred at (x, y) to the number of initial raindrops in a grid box above the wake vortex region, that is

ηN(D,x,y)=N(D,x,y)N0(D)Num(D,x,y)Num0(D)E55

Obviously, the estimation accuracy of ηN(D,x,y)depends on the choice of the grid box size: Δx and Δy, and the number of initial raindrops in each grid box above the wake vortex region: Num0(D).

In Figure 7, the raindrops’ number concentration in wake vortices is illustrated. The simulation parameters are listed in Table 2. It can be noticed that in the wake vortex region, there are two columns where the raindrops’ concentration is very small, even to zero. These two columns appear symmetrically below the two vortex cores and the distance between them in (a) is much wider than the others. Between these two columns, there are two narrow regions where the number concentration of raindrops is enhanced. For the raindrops of 1 and 2 mm of diameter, the number concentration value exceeds 8 in some grid boxes.

Figure 7.

Raindrops’ number concentration in wake vortices: (a) D = 0.5 mm, (b) D = 1.0 mm, (c) D = 2.0 mm, (d) D = 4.0 mm. The colour bar indicates the value of raindrops’ number concentration in each grid box.

ParametersValues
Aircraft wingspan60.30 m
Aircraft maximum landing weight259,000 kg
Aircraft landing speed290 km/h
Grid box size1 m×1 m
Num0(D)100
Raindrops’ diameter0.5 mm, 1.0 mm, 2.0 mm, and 4.0 mm

Table 2.

Parameters for the computation of raindrops’ number concentration.

3.4.2. Raindrops’ velocity distribution

Besides the number concentration, the raindrops’ velocity distribution in each grid box is of great interest. If the grid box size used for the box counting method is small enough and the number of raindrops in each grid box is large enough, the velocity components of the raindrops in one grid box can be thought to obey Gaussian distributions. The mean value and variance of the velocity of the raindrops in each grid box are computed. If the number concentration of the grid box is zero, the raindrops’ velocity in this grid box is set to 0.

In Figures 8 and 9, the raindrops’ horizontal and vertical velocity distribution in wake vortices are illustrated, respectively. From Figure 8, it is interesting to find that the raindrops’ horizontal velocity field is similar to the wake vortex velocity field. From Figure 9, it is interesting to find that between the two vortex cores, the raindrops’ vertical velocity is speeded up. At the same time, the standard deviation of the raindrops’ velocity distribution in a grid box is sufficiently low to consider it as constant within each grid box. In fact, in wake vortex, the raindrops’ motion is affected by the vortex flow; the raindrops’ velocity field is not the same as the superimposition of the vortex flow velocity and raindrops’ terminal velocity, but it is representative of the wake vortex velocity characteristics.

Figure 8.

Raindrops’ horizontal velocity distribution in wake vortices: (a) D = 0.5 mm and (b) D = 1.0 mm.

Figure 9.

Raindrops’ vertical velocity distribution in wake vortices: (a) D = 0.5 mm and (b) D = 1.0 mm.

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Jianbing Li, Zhongxun Liu and Xuesong Wang (March 1st 2017). Modelling of Temporal‐Spatial Distribution of Airplane Wake Vortex for Scattering Analysis, Vortex Structures in Fluid Dynamic Problems, Hector Perez-de-Tejada, IntechOpen, DOI: 10.5772/66544. Available from:

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