High-credibility atmospheric wind field real-time prediction engineering estimation method

Abstract

The invention discloses a high-credibility atmospheric wind field real-time prediction engineering estimation method, and belongs to the field of wind field real-time prediction. The method comprises the following steps: small-scale disturbance mathematical modeling adopts a first-order autoregression model and Gaussian distribution random number selection; in the large-scale disturbance mathematical modeling, a periodic sine model and Gaussian distribution random number selection are adopted, on the premise that a disturbance initial value and the number of operation times are given, large-scale disturbance quantity and small-scale disturbance quantity of the atmospheric wind field are obtained through Monte Carlo operation, and then summation operation is carried out on the large-scale disturbance quantity and the average value of the atmospheric wind field obtained through interpolation fairing modeling; and finally obtaining an atmospheric wind field real-time prediction value. According to the method, the atmospheric wind field characteristics of any longitude and latitude position can be quickly and accurately predicted, the confidence interval probability of a calculation result reaches 99.74%, the calculation time of 50 times of calculation only takes 2 minutes, and wind field load information can be efficiently provided for spacecraft launching system structure design and task trajectory planning.

Description

Technical Field

[0001] The invention belongs to the field of real-time prediction of wind fields, and in particular relates to a high-reliability engineering estimation method for real-time prediction of atmospheric wind fields. Background Art

[0002] When an aircraft flies in the Earth's atmosphere, it needs the help of aerodynamics. As a medium that provides power, the static physical properties of air, such as density, pressure, temperature, and dynamic physical properties such as atmospheric motion, are crucial to the motion characteristics of the aircraft. Any spacecraft that flies through the atmosphere to space or returns to the Earth will be affected by the atmosphere. The movement of the atmospheric wind field is the main source of disturbance for the flight trajectory and flight attitude of the aircraft. As the main manifestation of the atmospheric environment, the impact of the atmospheric wind field on the performance of the aircraft cannot be ignored. For example, when a hypersonic warhead reenters, in order to analyze the stability characteristics of the warhead during reentry and determine the stable altitude, improve the hit accuracy of the warhead reentry, and reduce the landing point deviation, the influence of the high-altitude atmospheric wind field needs to be considered.

[0003] There are three main methods for studying atmospheric wind field prediction at home and abroad, namely ① measured atmospheric wind field data ② numerical simulation of atmospheric dynamics ③ engineering estimation method. Among them, the cost of measured atmospheric wind field data is high and the data storage volume is large; the numerical simulation of atmospheric dynamics obtains the numerical solution of the flow field by solving the fluid dynamics equation, and the solution process is complex and the amount of calculation is large. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a high-reliability atmospheric wind field real-time prediction engineering estimation method, comprising:

[0005] Acquire lower atmospheric data and middle atmospheric data, and acquire atmospheric wind field data based on the lower atmospheric data and middle atmospheric data;

[0006] Performing interpolation and smoothing processing on the atmospheric wind field data to obtain average value information of the atmospheric wind field;

[0007] A mathematical model of small-scale disturbances is constructed based on the first-order autoregressive model, and a model of large-scale disturbances is constructed based on the sinusoidal model;

[0008] The Gaussian distribution random numbers of small-scale disturbances and the Gaussian distribution random numbers of large-scale disturbances are selected through Monte Carlo operation;

[0009] Substituting the Gaussian distribution random number of the small-scale disturbance into the small-scale disturbance mathematical model to obtain the small-scale disturbance amount;

[0010] Substituting the Gaussian distribution random number of the large-scale disturbance into the large-scale disturbance quantity model to obtain the large-scale disturbance quantity;

[0011] The atmospheric wind field average value information, the small-scale disturbance amount and the large-scale disturbance amount are calculated to obtain a final prediction result.

[0012] Preferably, the expression for interpolating and smoothing the atmospheric wind field data is:

[0013] ;

[0014] Among them, V ij Longitude and latitude The average value of atmospheric wind field parameters at a point, is the longitude, is the latitude, , , , , For any The average value of atmospheric wind field variables at a point, , indicating any Point to longitude The relative position of , indicating any Point to longitude The relative position of Indicates any Point to latitude The relative position of , indicating any Point to latitude relative position.

[0015] Preferably, the calculation expression for obtaining the small-scale disturbance amount is:

[0016] ;

[0017] in, is the next position disturbance, represents the longitude of the next location, represents the latitude of the next location, represents the normalized variable, which is the ratio of the actual difference to the standard deviation, q is a Gaussian distributed random number with a mean of 0 and a standard deviation of 1, and r is the autocorrelation.

[0018] Preferably, the calculation expression of r is:

[0019] ;

[0020] Among them, <> represents that r is the autocorrelation function of the disturbance.

[0021] Preferably, the expression for obtaining the large-scale disturbance amount is:

[0022] ;

[0023] Where m is the wave number in the latitude direction, n is the wave number in the longitude direction, z is the height, t is the flow time, and T is the random wave period. is the vertical wavelength, is the random wave phase, equal , is the magnification factor.

[0024] Preferably, the calculation expressions of the latitudinal wave number and the longitudinal wave number are:

[0025] ;

[0026] in, is a Gaussian distributed random number with a mean of 0 and a standard deviation of 1. It is a rounding operation that discards the decimal part of the expression in brackets and only retains the integer part.

[0027] Preferably, the calculation expression of the vertical wavelength is:

[0028] =a v +0.045 z 3 / 2 ;

[0029] Among them, the constant a v It is a random number between 0 and 1.

[0030] Preferably, the calculation expression for obtaining the final prediction result is:

[0031] V_Tot( )= +μ(θ, )+ ;

[0032] Among them, V_Tot( ) is the final prediction result.

[0033] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computing program stored in the memory and executable on the processor, wherein the method is implemented when the processor executes the computing program.

[0034] On the other hand, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the method when executed by a processor.

[0035] Compared with the prior art, the present invention has the following advantages and technical effects:

[0036] The present invention can provide global atmospheric wind field prediction information in real time, which is used to provide wind field load information for spacecraft launch system structure design and mission trajectory planning;

[0037] The present invention adopts a sinusoidal model and a first-order autoregressive model to model large and small scale disturbances, respectively, and screens out large and small scale disturbances through Monte Carlo calculation, and the confidence interval probability of the calculated results reaches 99.74%;

[0038] Compared with numerical simulation of atmospheric dynamics, the prediction efficiency of the model of the present invention is significantly improved, and 50 Monte Carlo operations only take 2 minutes. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0040] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of the comparison of east-west wind speeds in October 2009 according to an embodiment of the present invention;

[0042] Figure 3 This is a schematic diagram of the comparison of north-south wind speeds in October 2009 according to an embodiment of the present invention;

[0043] Figure 4 This is a schematic diagram of the comparison of east-west wind speeds in April 2012 according to an embodiment of the present invention;

[0044] Figure 5 Schematic diagram of comparison of north-south wind speeds in April 2012 according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0047] Embodiment 1

[0048] like Figure 1 As shown, this embodiment provides a high-reliability atmospheric wind field real-time prediction engineering estimation method, including:

[0049] Performing interpolation and smoothing processing on the atmospheric wind field data to obtain average value information of the atmospheric wind field;

[0050] A mathematical model of small-scale disturbances is constructed based on the first-order autoregressive model, and a model of large-scale disturbances is constructed based on the sinusoidal model;

[0051] The Gaussian distribution random numbers of small-scale disturbances and the Gaussian distribution random numbers of large-scale disturbances are selected through Monte Carlo operation;

[0052] Substituting the Gaussian distribution random number of the small-scale disturbance into the small-scale disturbance mathematical model to obtain the small-scale disturbance amount;

[0053] Substituting the Gaussian distribution random number of the large-scale disturbance into the large-scale disturbance quantity model to obtain the large-scale disturbance quantity;

[0054] The atmospheric wind field average value information, the small-scale disturbance amount and the large-scale disturbance amount are calculated to obtain a final prediction result.

[0055] Step 1: Atmospheric altitude and database:

[0056] The altitude range of the lower atmosphere is 0 to 20 km above the ground, and the measured atmospheric data is selected from the global reanalysis NCEP database. This database includes the mean and standard deviation of four hours in a day (0:00, 6:00, 12:00 and 18:00, UTC time) and the mean of all four times of the day.

[0057] The middle atmosphere is 20-120km, and the measured atmospheric data is the middle atmosphere MAP database, which is taken from rocket detection or remote sensing data. The data is divided into grids with 10° latitude and 5 km altitude increments, forming a monthly average data set (pressure, density, temperature and average easterly wind component).

[0058] Step 2: Interpolation and smoothing model:

[0059] In the previous step, the average data of the atmospheric wind field has been selected from the existing measured database, but the measured data is often limited, and it is necessary to interpolate to obtain the average data of the atmospheric wind field at any longitude and latitude point. The average values ​​of atmospheric wind field parameters at each point are , , , , then any Average value of atmospheric wind field variables at a point It can be obtained by interpolation:

[0060] ;

[0061] Among them, V ij Longitude and latitude The average value of atmospheric wind field parameters at a point, is the longitude, is the latitude, , , , , For any The average value of atmospheric wind field variables at a point, , indicating any Point to longitude The relative position of , indicating any Point to longitude The relative position of Indicates any Point to latitude The relative position of , indicating any Point to latitude relative position.

[0062] Step 3: Small-scale perturbation model

[0063] The small-scale perturbation is calculated using a first-order autoregressive model, which calculates the perturbation at the next new location based on the relevant perturbation value at the previous location. In addition to maintaining the continuity and correlation of these perturbation values, the impact of changes in the mean and standard deviation are also considered. Small disturbance at , then the next position disturbance for:

[0064] ;

[0065] Where q is a Gaussian distributed random number with mean 0 and standard deviation 1.

[0066] ;

[0067] Where “<>” represents that r is the autocorrelation function of the disturbance.

[0068] Step 4: Large-scale disturbance model:

[0069] Consider longitude and latitude Large-scale disturbances at , using the sinusoidal model gives:

[0070] ;

[0071] Where m is the wave number in the latitude direction, n is the wave number in the longitude direction, z is the height, t is the flow time, and T is the random wave period. is the vertical wavelength, is the random wave phase, equal . Amplification factor It is expressed as:

[0072] ;

[0073] in is a random number uniformly distributed between 0 and 1. For wave numbers m and n,

[0074] ;

[0075] in is a Gaussian distributed random number with a mean of 0 and a standard deviation of 1. Vertical wavelength is a function of height z:

[0076] =a v +0.045 z 3 / 2 ;

[0077] The constant a v It is a random number between 0 and 1.

[0078] Step 5: Monte Carlo calculation

[0079] Based on the wind field disturbance model method in steps 3 and 4 above, Monte Carlo numerical simulation is used to select Gaussian distribution random numbers of small-scale and large-scale disturbances, and substitute them into the small-scale and large-scale disturbance models for calculation, and then the magnitude of large-scale and small-scale disturbances is obtained. Finally, the predicted value of the atmospheric wind field at any longitude and latitude position is It is the sum of the statistical mean, small-scale disturbance and large-scale disturbance, that is:

[0080] ;

[0081] in, The final prediction result.

[0082] A high-reliability atmospheric wind field real-time prediction engineering estimation method of this embodiment adopts a serial architecture, and the developed program can be deployed on domestic computers, realizing flexible transplantation of software and hardware of high-performance systems and forming a fully autonomous and controllable software and hardware environment.

[0083] Embodiment 2

[0084] This embodiment provides a high-reliability atmospheric wind field real-time prediction engineering estimation method, including:

[0085] Taking a certain city as an example, we selected two time points, October 2009 and April 2012, to compare and analyze the accuracy of the current engineering estimation method and the actual wind field data. The main implementation process includes the following:

[0086] Step 1: Enter the latitude and longitude coordinates and altitude information of a city point (0-30km).

[0087] Step 2: Query the average value information of the atmospheric wind field parameters at the current point from the database corresponding to the atmospheric height (NCEP and MAP), and use the longitude and latitude interpolation model to calculate the average value information of the atmospheric wind field at a certain point in the city within the altitude range of 0-30km.

[0088] Step 3: Given the initial disturbance value information of the atmospheric wind field (including density, temperature, pressure, horizontal wind, vertical wind, all set to 1 by default), set the number of Monte Carlo runs (assuming 50 times), select Gaussian distribution random numbers for small-scale and large-scale disturbances, and substitute them into the small-scale and large-scale disturbance models for calculation, and then obtain the magnitude of the large-scale and small-scale disturbances.

[0089] Step 4: Add the average value of the atmospheric wind field to the disturbance to obtain the predicted value of the atmospheric wind field for all (50) Monte Carlo calculations.

[0090] 5. Comparison of calculation results

[0091] In order to test the accuracy of the calculation model, the real data of a certain city in October 2009 and April 2012 were selected as a comparison of the calculation accuracy, where CorrMonte represents the calculated value of atmospheric wind speed, CorrMean represents the average value of atmospheric wind speed calculation, and Wuhan represents the real wind field data of a certain city. In this verification, a total of 50 Monte Carlo calculations were run (the calculation time was 2 minutes), and each calculation outputted 30 height correlation quantities, that is, the disturbance values ​​of the two component winds were outputted 1500 data, and the probability of the normal distribution 3σ confidence interval was 99.74%, indicating that the prediction result has a high reliability. Figure 2 , Figure 3 , Figure 4 and Figure 5From the results of the selected two months of east-west wind and north-south wind, it can be seen that for east-west wind, the actual data curve of a certain city point basically falls within the range of the CorrMonte calculated value, and the CorrMonte average value basically coincides with the actual data trend within the range below 30km above sea level; for north-south wind, the actual data curve of a certain city point also basically falls within the range of the CorrMonte calculated value, and the CorrMonte average value and the actual data are very close at most altitudes. This result shows the effectiveness of the calculation model in predicting and calculating horizontal wind fields at different altitudes.

[0092] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A high-reliability atmospheric wind field real-time prediction engineering estimation method, characterized in that: include: Acquire lower atmospheric data and middle atmospheric data, and acquire atmospheric wind field data based on the lower atmospheric data and middle atmospheric data; Performing interpolation and smoothing processing on the atmospheric wind field data to obtain average value information of the atmospheric wind field; A mathematical model of small-scale disturbances is constructed based on the first-order autoregressive model, and a model of large-scale disturbances is constructed based on the sinusoidal model; The Gaussian distribution random numbers of small-scale disturbances and the Gaussian distribution random numbers of large-scale disturbances are selected through Monte Carlo operation; Substituting the Gaussian distribution random number of the small-scale disturbance into the small-scale disturbance mathematical model to obtain the small-scale disturbance amount; Substituting the Gaussian distribution random number of the large-scale disturbance into the large-scale disturbance quantity model to obtain the large-scale disturbance quantity; The atmospheric wind field average value information, the small-scale disturbance amount and the large-scale disturbance amount are calculated to obtain a final prediction result.

2. The method according to claim 1, characterized in that The expression for interpolating and smoothing the atmospheric wind field data is: ； Among them, V ij Longitude and latitude The average value of atmospheric wind field parameters at a point, is the longitude, is the latitude, , , , , For any The average value of atmospheric wind field variables at a point, , indicating any Point to longitude The relative position of , indicating any Point to longitude The relative position of Indicates any Point to latitude The relative position of , indicating any Point to latitude relative position.

3. The method according to claim 1, characterized in that The calculation expression for obtaining the small-scale disturbance is: ； in, is the next position disturbance, represents the longitude of the next location, represents the latitude of the next location, represents the normalized variable, which is the ratio of the actual difference to the standard deviation, q is a Gaussian distributed random number with a mean of 0 and a standard deviation of 1, and r is the autocorrelation.

4. The method according to claim 3, characterized in that The calculation expression of r is: ； Among them, <> represents that r is the autocorrelation function of the disturbance.

5. The method according to claim 1, characterized in that The expression for obtaining the large-scale disturbance is: ； Where m is the wave number in the latitude direction, n is the wave number in the longitude direction, z is the height, t is the flow time, and T is the random wave period. is the vertical wavelength, is the random wave phase, equal , is the magnification factor.

6. The method according to claim 5, characterized in that The calculation expressions of the latitude direction wave number and the longitude direction wave number are: ； in, is a Gaussian distributed random number with a mean of 0 and a standard deviation of 1. It is a rounding operation that discards the decimal part of the expression in brackets and only retains the integer part.

7. The method according to claim 5, characterized in that The calculation expression of the vertical wavelength is: =a v +0.045 z 3 / 2 ; Among them, the constant a v It is a random number between 0 and 1.

8. The method according to claim 1, characterized in that: The calculation expression for obtaining the final prediction result is: V_Tot( )= +μ(θ, )+ ; Among them, V_Tot( ) is the final prediction result.

9. An electronic device comprising a memory, a processor, and a computing program stored in the memory and executable on the processor, characterized in that: When the processor executes the computing program, the method described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method described in any one of claims 1 to 8 is implemented.

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