CN105629238A - Wind field digital reconstruction method through airborne radar - Google Patents

Abstract

The invention relates to a wind field reconstruction method, and particularly relates to a wind field digital reconstruction method through airborne radar. The wind field digital reconstruction method through airborne radar comprises the steps that airborne radar performs real-time measurement on a wind field of a target area so as to obtain spectral width data of the wind field and mean radial velocity of the wind field; the estimated value of turbulence intensity of the wind field is calculated by utilizing the spectral width data of the wind field, turbulence scale distribution of the wind field is obtained through radar airborne flight parameters, the estimated value of turbulence intensity is combined with turbulence scale distribution so as to obtain transfer functions of different positions of flight space, and a digital model of the turbulence wind field is established; a mean wind velocity wind field is established by utilizing mean radial velocity of the wind field; and the digital model of the turbulence wind field and the mean wind velocity wind field are linearly superposed according to spatial distribution so that digital reconstruction of the wind field is performed. Wind velocity and wind velocity direction signals according to spatial distribution or time distribution can be provided according to the requirements so that that limitation that existing airborne radar can only provide the wind field statistical characteristics can be overcome.

Description

Method for digitally reconstructing wind field by using airborne radar

Technical Field

The invention relates to a method for reconstructing a wind field. More specifically, the invention relates to a method for digitally reconstructing a wind farm using airborne radar.

Background

The flying through the dangerous wind field can cause the airplane to bump and the larger body to be overloaded, which not only seriously affects the riding quality of the passenger plane, but also causes dangerous events such as injuries caused by the passenger cabin, and the like, and the accidents of passenger injuries are occasionally happened when the passenger cabin encounters turbulent flow.

At present, detection and early warning of disturbance wind fields such as turbulence, micro-undershoot wind shear and the like of an in-service aircraft mainly depend on detection equipment taking a meteorological radar as a core. The Doppler coherent system is widely used by the existing meteorological radar, the Doppler system meteorological radar can measure target wind fields such as turbulence, wind shear and the like and provides three statistical characteristics of the wind fields, wherein radar spectrum width data provides root mean square information of wind field speed.

Although the radar spectrum width contains the speed root mean square information of the turbulent wind field, the radar spectrum width cannot provide a wind field digital model which meets the statistical characteristics. In order to research the flight safety of the flying through the wind field or develop a new generation of wind field detection equipment, a wind field digital model is required.

At present, other methods predict the flight safety of the wind field by means of an airborne radar, for example, in about 2001, rockwell-corils (rockwell collins) company develops a new turbulence detection and early warning system (TPAWS) by using an airborne doppler meteorological radar, research and development departments accumulate data in advance through a large number of flight experiments to establish a numerical correspondence between turbulence wind field statistical characteristics and airplane flight responses, and when a radar installation is used, the flight safety is predicted according to the detected wind field statistical characteristics, but the method has high test cost and certain danger. If the wind field can be reconstructed digitally using radar data, extensive flight tests can be avoided to save costs.

Based on the background, in order to meet the use requirement of a wind field digital model, a method for restoring and modeling a wind field based on radar detection data is needed, so that the flight safety of the wind field is conveniently researched, and secondary development of wind field detection equipment such as radars is facilitated.

Disclosure of Invention

An object of the present invention is to solve at least the above problems and to provide at least the advantages described later.

It is still another object of the present invention to provide a method for digitally reconstructing a wind field using an airborne radar, which can provide wind speed and wind speed direction signals distributed spatially or temporally according to needs, and overcome the limitation that the existing airborne radar can only provide statistical characteristics of the wind field.

To achieve these objects and other advantages and in accordance with the purpose of the invention, a method for digitally reconstructing a wind farm using airborne radar is provided, comprising the steps of:

firstly, measuring a wind field of a target area in real time by an airborne radar to obtain spectral width data of the wind field and an average radial speed of the wind field;

calculating to obtain an estimated value of the turbulence intensity of the wind field by using the spectral width data of the wind field obtained in the step one, obtaining the turbulence scale distribution of the wind field by using the flight parameters of the radar carrier, combining the estimated value of the turbulence intensity with the turbulence scale distribution to obtain transfer functions of different positions of a flight space, and further establishing a digital model of the turbulence wind field;

step three, establishing an average wind speed wind field by using the average radial speed of the wind field obtained in the step one;

and step four, linearly superposing the digital model of the turbulent wind field established in the step two and the average wind speed wind field established in the step three according to spatial distribution, thereby carrying out digital reconstruction on the wind field.

Preferably, in the method for digitally reconstructing a wind field by using an airborne radar, the flight parameters of the radar carrier in the second step include position information of the radar and flight parameters of the carrier.

Preferably, in the method for digitally reconstructing a wind field by using an airborne radar, the flight parameters of the aircraft include the flight altitude and the terrain roughness of the aircraft.

Preferably, in the method for digitally reconstructing a wind field by using an airborne radar, in the second step, a digital model of the turbulent wind field is established by passing zero-mean white noise through a filter with a transfer function of g(s).

Preferably, in the method for digitally reconstructing a wind field by using an airborne radar, the transfer function g(s) is calculated by combining an estimated value of the turbulence intensity of the wind field with a delaton model.

Preferably, in the method for digitally reconstructing a wind field by using an airborne radar, the spectrum width data of the wind field obtained in the step one is combined with a turbulent flow spectrum model to calculate an estimated value of the turbulence intensity of the wind field.

The invention at least comprises the following beneficial effects: according to the method, the wind field digital model with three dimensions is established by utilizing the statistical characteristics obtained by the airborne radar in detecting the wind field and combining the flight parameters of the radar carrier, the statistical characteristics of the established wind field digital model are consistent with the radar detection result, and the digital reconstruction of the wind field detected by the airborne radar is completed on the premise of meeting the research and use requirements. The invention can provide wind speed and wind speed direction signals distributed according to space or time according to requirements, and overcomes the limitation that the existing airborne radar can only provide wind field statistical characteristics. The obtained three-dimensional wind field digital model can be used for wind field evaluation, wind field flight simulation, wind field change rule and other researches, and provides a method basis for secondary development of radar and other wind field detection products.

Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.

Drawings

FIG. 1 is a schematic flow diagram of a method of digitally reconstructing a wind farm using airborne radar in accordance with the present invention;

fig. 2 is spectral width data obtained by detecting a wind field by an airborne radar in an embodiment of the present invention;

fig. 3 is a schematic diagram of a triaxial wind speed distribution on a central line of a wind farm where digital reconstruction is completed in an embodiment of the present invention.

Detailed Description

The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.

It will be understood that terms such as "having," "including," and "comprising," as used herein, do not preclude the presence or addition of one or more other elements or groups thereof.

As shown in FIG. 1, the present invention provides a method for digitally reconstructing a wind field by using airborne radar, wherein a general disturbance wind field is composed of turbulent wind field wind speed fluctuation and average wind speed, wherein the turbulent wind field needs to be established by means of a turbulent intensity value, and the average wind speed information can be given by the average radial velocity provided by Doppler radar. The main parameters of wind field modeling comprise a turbulence scale and turbulence intensity, the turbulence scale changes along with the flight height and terrain roughness of a radar carrier, the value of the turbulence scale has already been studied and obtained, and the turbulence intensity is based on the turbulence intensity estimated value of a Doppler radar. The method specifically comprises the following steps:

firstly, an airborne radar measures a wind field of a target area in real time to obtain spectral width data of the wind field and average radial velocity of the wind field. The airborne radar can be a Doppler meteorological radar, the spectral width data of the wind field comprises root mean square information of the speed of the wind field, and the average radial speed of the wind field reflects reference value information of random fluctuation of turbulent wind speed.

And step two, calculating to obtain an estimated value of the turbulence intensity of the wind field by using the spectrum width data of the wind field obtained in the step one, solving the estimated value of the turbulence intensity of the wind field, establishing a closed equation set by using the radar spectrum width data and a turbulence spectrum model, and performing numerical iteration solution. Obtaining the turbulence scale distribution of the wind field by the flight parameters of the radar carrier, and combining the estimated value of the turbulence intensity with the turbulence scale distribution to obtain the transfer functions of different positions of a flight space, so that a digital model of the turbulence wind field can be established under the condition of ensuring the spectral width/turbulence intensity equivalence.

And step three, establishing an average wind speed wind field by using the average radial speed of the wind field obtained in the step one.

And step four, linearly superposing the digital model of the turbulent wind field established in the step two and the average wind speed wind field established in the step three according to spatial distribution, and combining the two models, thereby digitally reconstructing the wind field and obtaining the triaxial wind speed distribution on the central line of the wind field.

In the method for digitally reconstructing the wind field by using the airborne radar, the flight parameters of the radar carrier in the second step include position information of the radar and flight parameters of the carrier.

In the method for digitally reconstructing the wind field by using the airborne radar, the flight parameters of the airborne machine comprise the flight height and the terrain roughness of the airborne machine.

In the second step, a digital model of the turbulent wind field is established by a method of passing zero-mean white noise through a filter with a transfer function of G(s).

In the method for digitally reconstructing the wind field by using the airborne radar, the transfer function G(s) is calculated by combining an estimated value of the turbulence intensity of the wind field with a Delauton model.

In the method for digitally reconstructing the wind field by using the airborne radar, the estimated value of the turbulence intensity of the wind field is calculated by combining the spectrum width data of the wind field obtained in the step one and a turbulence spectrum model.

Example 1

The method comprises the following steps that firstly, a Doppler radar measures a wind field of a target area in real time to obtain spectral width data of the wind field and average radial velocity of the wind field. Fig. 2 shows half-sector spectral width detection data of a doppler radar, wherein the selected region of the middle wire frame is a predetermined wind field reconstruction region.

Step two, calculating by using the spectral width data of the wind field obtained in the step one and combining a turbulent flow frequency spectrum model to obtain an estimated value of the turbulent flow intensity of the wind field; and obtaining the turbulence scale distribution of the wind field according to the position information of the radar, the flight height of the carrier and the terrain roughness. And combining the estimated value of the turbulence intensity with the turbulence scale distribution and the Delauton model to obtain transfer functions G(s) of different positions in the flight space, and establishing a digital model of the turbulence wind field by a method of passing zero-mean white noise through a filter with the transfer function G(s). The altitude of the radar antenna is 2400 m, the longitudinal (flight direction) turbulence scale Lu is 488 m, the lateral (span direction) turbulence scale Lv is 244 m, and the normal (downward in the plane of symmetry of the body) turbulence scale Lw is 244 m.

And step three, establishing an average wind speed wind field by using the average radial speed of the wind field obtained in the step one.

And step four, linearly superposing the digital model of the turbulent wind field established in the step two and the average wind speed wind field established in the step three according to spatial distribution, thereby carrying out digital reconstruction on the wind field. Estimating the wind speed by using the wind field which completes the digital reconstruction to obtain the three-axis wind speed distribution on the central line of the wind field, as shown in fig. 3, the abscissa is the flight distance and has the unit of Km, and the ordinate is the wind speed in the X-axis direction, the Y-axis direction and the Z-axis direction from top to bottom in sequence and has the unit of m · s^-1。

The specific algorithm for calculating the estimation value of the turbulence intensity of the wind field by combining the spectrum width data of the wind field obtained in the first step with the turbulence spectrum model is as follows:

step a, a turbulence intensity definition formula of the wind field and a spectrum width data definition formula of the wind field are combined to form an equation set.

The turbulence intensity of the wind field is defined as:

${\mathrm{\σ}}^2=\underset{T\→\mathrm{\∞}}{\lim }\frac{1}{2T}{\∫}_{-T}^T{(v-\stackrel{\‾}{v})}^{2}dt,$

wherein, sigma is the turbulence intensity of the wind field, T is the time scale of measuring and counting the average wind speed, v is the local wind degree,is the average wind speed.

The spectral width data of the wind field is defined as:

${\mathrm{\σ}}_v^2=\frac{1}{{\stackrel{\‾}{P}}_r}{\∫}_{-\mathrm{\∞}}^{\mathrm{\∞}}{(v-\stackrel{\‾}{v})}^2\mathrm{\φ}\left(v\right)dv,$

wherein σ_vIs the spectral width data of the wind field, phi (v) is the velocity spectral distribution density, is the power of the doppler velocity in the interval v to v + dv,is the average power of the echo signal, v is the local wind,is the average wind speed.

The definition of the average power of the echo signal is:

${\stackrel{\‾}{P}}_r={\∫}_{-\mathrm{\∞}}^{\mathrm{\∞}}\mathrm{\φ}\left(v\right)dv,$

wherein,is the average power of the echo signal, v is the local wind velocity, phi (v) is the velocity spectral distribution density, and is the power of the Doppler velocity in the interval v to v + dv.

And b, deriving a relation containing the spectral width data and the turbulence intensity according to a Von Karman turbulence wind field spectrum model so as to close the equation system in the step a.

The relationship derived based on the von karman model is:

when 0< R,

$\frac{<{\mathrm{\&sigma;}}_v^2\left(r\right)>}{{\mathrm{\&sigma;}}^2}=1-\frac{55}{27}\&CenterDot;\frac{1}{{\mathrm{\&pi;a}}^{\&prime;}}\&CenterDot;{\mathrm{\&mu;}}^5{\&Integral;}_0^{\mathrm{\&infin;}}\frac{x^{3/2}{e}^{-x}}{{\&lsqb;1+{\mathrm{\&mu;}}^{2}x\&rsqb;}^{17/6}}\&CenterDot;M\&lsqb;2,\frac{5}{2},(1-\frac{r^2}{R^2})x\&rsqb;dx,$

when the R < R is greater than the R,

$\frac{<{\mathrm{\&sigma;}}_v^2\left(r\right)>}{{\mathrm{\&sigma;}}^2}=1-\frac{55}{27}\&CenterDot;\frac{1}{{\mathrm{\&pi;a}}^{\&prime;}}\&CenterDot;{\mathrm{\&mu;}}^5{\&Integral;}_0^{\mathrm{\&infin;}}\frac{x^{3/2}{e}^{-{(r/R)}^{2}x}}{{\&lsqb;1+{\mathrm{\&mu;}}^{2}x\&rsqb;}^{17/6}}\&CenterDot;M\&lsqb;\frac{1}{2},\frac{5}{2},(\frac{r^2}{R^2}-1)x\&rsqb;dx,$

where R is the observed wind field position, R is a characteristic length of the turbulent wind field, equal to the ratio of the radial to tangential velocity variance, R ═ σ ∑_r/σ_θ(ii) a Sigma is the turbulence intensity of the wind field; mu is a dimensionless parameter, proportional to the turbulence scale and inversely proportional to the radial velocity variance, mu ═ a' L/sigma_rL is turbulence scale, a is a parameter related to radar and antenna, a' is the first derivative of a relative to radar detection position, M (a, gamma, ξ) is a structured composite hyper-geometric distribution function, and integral quantityIs a constructor of the spatial frequency k of the turbulence, k having the unit radian/meter.

And c, measuring the wind field of the target area in real time by the airborne radar to obtain the spectral width data of the wind field.

And selecting a target area to be observed, detecting the target area by using a Doppler radar, and acquiring the spectral width data of the wind field.

And d, solving the equation set closed in the step b by using the spectral width data of the wind field obtained in the step c based on a fourth-order Runge-Kutta method and combined with radar detection parameters to obtain an estimated value of the turbulence intensity of the wind field.

While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.

Claims (6)

1. A method for digitally reconstructing a wind field using airborne radar, comprising the steps of:

firstly, measuring a wind field of a target area in real time by an airborne radar to obtain spectral width data of the wind field and an average radial speed of the wind field;

calculating to obtain an estimated value of the turbulence intensity of the wind field by using the spectral width data of the wind field obtained in the step one, obtaining the turbulence scale distribution of the wind field by using the flight parameters of the radar carrier, combining the estimated value of the turbulence intensity with the turbulence scale distribution to obtain transfer functions of different positions of a flight space, and further establishing a digital model of the turbulence wind field;

step three, establishing an average wind speed wind field by using the average radial speed of the wind field obtained in the step one;

and step four, linearly superposing the digital model of the turbulent wind field established in the step two and the average wind speed wind field established in the step three according to spatial distribution, thereby carrying out digital reconstruction on the wind field.

2. The method for digitally reconstructing a wind farm using airborne radars according to claim 1, wherein the flight parameters of the radar vehicle in step two include position information of the radar and flight parameters of the vehicle.

3. The method for digitally reconstructing a wind farm using airborne radar as claimed in claim 2, characterized in that said aircraft flight parameters include the aircraft flight altitude and terrain roughness.

4. The method for digitally reconstructing a wind field using airborne radar as claimed in claim 1, wherein in the second step, the digital model of the turbulent wind field is created by passing zero-mean white noise through a filter with transfer function g(s).

5. The method of claim 4, wherein the transfer function G(s) is calculated from an estimate of turbulence intensity of the wind field in combination with a Delauton model.

6. The method of claim 1, wherein the spectral width data of the wind field obtained in the first step is combined with a turbulence spectrum model to calculate an estimate of the turbulence intensity of the wind field.