CN113868970A - Airport area multi-source wind field fusion method based on numerical simulation model and mesoscale meteorological model - Google Patents
Airport area multi-source wind field fusion method based on numerical simulation model and mesoscale meteorological model Download PDFInfo
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Abstract
A numerical simulation model and mesoscale meteorological model based multi-source wind field fusion method for an airport area comprises the steps of 1) wind field refinement simulation, wherein a CFD technology and WRF mode based airport area wind field refinement simulation technical process is adopted; secondly, generating a CFD fine grid based on the local elevation data and the landform characteristics, and importing the CFD fine grid into a CFD mode; writing a CFD initial file based on a boundary wind field generated by a WRF mode, and assigning a value to a side boundary grid of the CFD initial file; finally, calculating and outputting refined three-dimensional wind field data under different meteorological conditions in the research area; 2) and performing wind field data fusion calculation.
Description
Technical Field
The invention relates to an airport area multi-source wind field fusion technology based on a numerical simulation model and a mesoscale meteorological model, which can fuse wind measurement data of different devices at the same time to obtain a near-real-time three-dimensional refined wind field of an airport area.
Background
Wind fields are one of the important factors affecting the take-off and landing safety of an aircraft. According to the aircraft lift force calculation formula, the lift force applied to the aircraft in the flying process is in direct proportion to the airspeed of the aircraft, and the aircraft can be enabled to be subjected to larger lift force by selecting upwind takeoff and landing usually, so that the safety of the aircraft is guaranteed. However, atmospheric wind is the most unstable meteorological element, and has obvious paroxysmal under the combined action of atmospheric turbulence, fluctuation and the like, and when weather processes such as convection disturbance, gust front, downburst flow, gravitational wave, terrain flow, clear sky turbulence and the like occur, short-distance sudden change of wind in the horizontal direction and the vertical direction can be caused, the lift force change of the airplane is seriously influenced, the airplane is rapidly lifted or sunk, and flight accidents are easily caused. Therefore, the wind field is effectively detected, and the wind shear strength and the position are further identified, so that the method is an important ring for aviation flight safety guarantee. Wind field detection is the basis of aviation flight safety guarantee, although various wind measuring means have been developed, the wind field detection is limited by respective detection principles, each wind measuring means has own application conditions, and only a wind field in a certain small-range space can be detected. The existing common wind field detection equipment comprises a wind station consisting of a single-point wind direction anemometer, a Doppler weather radar, a wind profiler, a laser wind measuring radar and the like, and different equipment is long and insufficient respectively.
In a wind station, a wind direction anemoscope is usually erected at the top of a 10m high wind rod, the detection accuracy is high, but only near-stratum single-point horizontal wind can be detected, the detection area and height are limited, and the number of sites and the height of the wind rod to be built need to be increased when a wind field of a larger area needs to be detected. The tracer of the Doppler weather radar is cloud rain particles, so that the Doppler weather radar can only detect areas with cloud rain, has certain effect on wet low-altitude wind shear accompanied by downburst storm, and has more defects. The radar works in a microwave band, a blind area is large, the distance resolution is poor, the radar is difficult to aim at a glidepath and a runway area for observation, and the detection effect on small-microscale wind shear such as a micro downburst is poor; secondly, the Doppler weather radar does not have the detection capability of dry wind shear due to the limitation of the tracer. The tracer of the wind profiler is atmospheric turbulence, so the wind profiler is suitable for dry wind field detection; however, the single wind profiler can only detect the vertical wind field information of the headspace area, the detection result is easily interfered by ground clutter, the reliability of the near-stratum wind field is low, and a detection blind area exists, so that the low-altitude wind shear is not easy to identify. The principle of the laser wind-finding radar is similar to that of the Doppler weather radar, but the tracer is aerosol particles, so that the laser wind-finding radar is suitable for detecting dry wind fields. The laser radar has the characteristics of strong anti-interference capability, small blind area, high data spatial resolution, capability of quickly acquiring refined low-altitude three-dimensional wind field information and the like, and has unique advantages compared with the traditional wind measuring means. The laser is attenuated by air molecules, aerosol particles and cloud and rain particles when transmitted in the atmosphere, and the detection performance of the laser wind-measuring radar is influenced by weather conditions, so that the laser wind-measuring radar is not suitable for detecting a wet wind field, but is an optimal detection means for a dry wind field, and is particularly suitable for detecting wind fields and wind shear in key areas such as a glidepath, an airplane runway and the like.
Due to different detection principles of various wind measuring devices, the indexes such as detection accuracy, space-time resolution, detection range and detection capability under different weather conditions have large difference, so that all-weather full-time-space coverage of the important attention area of taking off and landing of the airplane at the airport is difficult to realize by any measuring means. To realize effective monitoring of wind fields in key areas of airports, a refined area simulation wind field is combined with an actual measurement wind field, which is a solution, but needs to combine various wind measuring means and fuse detection data thereof. The patent applicant is dedicated to solving the fusion technology based on multi-source wind field data, and realizes the reconstruction of a three-dimensional refined wind field in an airport area on the basis of acquiring wind measuring data of each device, so that the aim of effectively monitoring the wind field in the airport area under each weather condition is fulfilled.
Disclosure of Invention
The method comprises the steps of firstly, constructing a refined wind field numerical simulation model of an airport area by utilizing a CFD numerical simulation technology based on guaranteed topographic features of the airport area, analyzing historical wind field features of the airport area, generating wind field initial boundary conditions under all seasons and all weather conditions by utilizing a WRF (write random programming) mode, driving the CFD simulation model, simulating flow field distribution of the airport area under different seasons and different weather conditions, and generating a CFD flow field database; secondly, calculating the matching degree of the current multi-source wind field data and each group of CFD data, and finding the CFD data which is most matched with the current wind field; and finally, performing numerical correction on the CFD data based on the current multi-source wind field data to obtain a current three-dimensional refined wind field.
The technical scheme of the invention is an airport regional multi-source wind field fusion method based on a numerical simulation model and a mesoscale meteorological model, and comprises the following steps of (1) wind field refined simulation, wherein an airport regional wind field refined simulation technical process based on a CFD (computational fluid dynamics) technology and a WRF (finite random-write filter) mode is implemented, firstly, a boundary wind field of a CFD (computational fluid dynamics) grid is generated based on the WRF mode, and wind field information of a research region under different meteorological conditions is simulated; secondly, generating a CFD fine grid based on the local elevation data and the landform characteristics, and importing the CFD fine grid into a CFD mode; writing a CFD initial file based on a boundary wind field generated by a WRF mode, and assigning a value to a side boundary grid of the CFD initial file; and finally, calculating and outputting refined three-dimensional wind field data under different meteorological conditions in the research area. Because the flow field information calculation based on the CFD technology is complex and time-consuming, the CFD database needs to be stored and established after refined three-dimensional wind field information in a research area under different meteorological conditions is obtained through calculation, and the CFD database is convenient to directly call in the later period;
(2) wind field data fusion algorithm
The wind field data fusion algorithm is used for processing direct wind measurement results which are acquired by various means and have inconsistent space-time and different measurement precision, and all-weather non-difference airport area digital wind field data are obtained after fusion and are used for further wind shear identification;
and (3) carrying out wind field data fusion by combining actually measured wind field data with a CFD mode and a mesoscale WRF mode wind field simulation result: firstly, searching and calling three-dimensional wind field data which are consistent with the current wind field distribution of an airport from a CFD database according to actually measured wind field data; and then, correcting the numerical value of the called CFD simulation three-dimensional wind field data based on the actually measured wind field data. The flow for quantifying the distribution consistency of the CFD database and the current wind field of the airport is as follows: firstly, calculating the deviation between the current wind measuring data and the same-position CFD simulation data aiming at any one database; then, the deviation of each CFD simulation data relative to the current observation data is calculated in a traversing manner; and finally, selecting CFD simulation data with the minimum deviation as an initial field of the current three-dimensional wind field fusion of the airport area.
The numerical correction part adopts a data assimilation scheme and comprises the following steps: (1) for the area with credible actually measured wind field data, the actually measured data is taken as the main; (2) supplementing the area without actually measured wind field data based on the simulation data of the CFD under the current meteorological condition; (3) when more than two wind measuring results exist at the same position, performing weighted calculation, wherein the weight of each wind measuring device is determined by the weather condition, the wind measuring principle of each wind measuring device, the observation position and the like; (4) the area with the actually measured wind field data has influences of different weights on the wind field data in a certain peripheral range, and the specific influence range and influence weight need to be determined by combining a sensitivity experiment.
Has the advantages that: the invention relates to a technical scheme for multi-source wind field fusion in an airport area, which comprises the following steps: an airport area wind field simulation technology based on a CFD technology; generating a technical scheme based on the boundary condition of the CFD simulation model of the mesoscale meteorological model; and (3) fusion algorithm of multi-source measured wind field data and simulation data. The invention aims to solve the technical problem of airport regional wind field monitoring under various meteorological conditions, designs an airport regional multi-source wind field fusion technology based on a numerical simulation model and a mesoscale meteorological model, can fuse various wind measurement data, can be conveniently applied to airport regional wind field monitoring, can further serve the safety guarantee of later aviation flight, and has strong popularization and application requirements and prospects.
Drawings
FIG. 1 illustrates an airport regional wind shear identification technique based on multi-source wind field data;
FIG. 2 is a technological process of airport regional wind field refinement simulation based on CFD technology and WRF mode.
FIG. 3 is a technical flow of a wind field data fusion algorithm.
Detailed Description
The invention relates to an airport regional wind shear intelligent identification method based on multi-source wind field data, which refers to related documents applied by the applicant on the same day: comprises the following steps of (a) carrying out,
step 1: generating a three-dimensional refined wind field of the airport area based on the multi-source wind field data;
step 2: performing wind field data fusion based on a CFD technology and a WRF mode to obtain a three-dimensional wind field;
and step 3: performing numerical correction on the three-dimensional wind field obtained by fusion based on the current measured data to obtain all-weather non-difference airport regional digital wind field data;
and 4, step 4: the wind shear is intelligently identified based on the convolutional neural network to obtain the position and the shear strength of the wind shear, and wind shear marking is carried out in a three-dimensional wind field data graph by combining refined three-dimensional wind field data.
Further, in the present invention: the generating of the three-dimensional refined wind field further comprises the following steps,
step 1-1: constructing a refined wind field numerical simulation model of an airport area;
step 1-2: constructing a simulation wind field database based on the CFD model;
step 1-3: calculating the matching degree of the current measured multi-source wind field data and each group of CFD data, and finding the CFD simulation data with the highest matching degree with the measured multi-source wind field data;
step 1-4: performing numerical correction on the CFD simulation data based on the current actually measured multi-source wind field data;
step 1-5: and obtaining the current three-dimensional refined wind field after data reconstruction.
Further, in the present invention: the wind farm data fusion further comprises the steps of,
step 2-1: outputting and processing WRF data;
step 2-2: generating a CFD refinement network;
step 2-3: performing data exchange on the WRF and the CFD;
step 2-4: and finally outputting the WRF data and processing the WRF data.
Further, in the present invention: the numerical correction further comprises the steps of,
step 3-1: for the area with credible actually measured wind field data, the actually measured data is taken as the main;
step 3-2: supplementing the area without actually measured wind field data based on the simulation data of the CFD under the current meteorological condition;
step 3-3: when more than two wind measuring results exist at the same position, performing weighted calculation, wherein the weight of each wind measuring device is determined by the weather condition, the wind measuring principle of each wind measuring device, the observation position and the like;
step 3-4: the area with the actually measured wind field data has influences of different weights on the wind field data in a certain peripheral range, and the specific influence range and influence weight need to be determined by combining a sensitivity experiment.
Further, in the present invention: the intelligent identification of wind shear based on a convolutional neural network further comprises the steps of,
step 4-1: constructing a wind shear recognition convolutional neural network structure and establishing a wind field plane image set with wind shear characteristics;
step 4-2: extracting image characteristics, and training wind shear to identify convolutional neural network node parameters;
step 4-3: constructing a plane scanning scheme of regional refined three-dimensional wind field data;
step 4-4: outputting the position and the shear strength of wind shear;
and 4-5: and marking the shear position and the shear strength in the three-dimensional wind field data graph.
The wind shear identification method can detect wind shear information under different meteorological conditions based on multi-source wind field data and a wind shear identification algorithm of a convolutional neural network, and realizes accurate identification of wind shear under various meteorological conditions.
The related technical process is shown in the attached figure 1.
(1) Wind field fine simulation
The technological process of the airport regional wind field fine simulation based on the CFD technology and the WRF mode is shown in the attached figure 2, firstly, the WRF mode is used for generating a boundary wind field of a CFD grid, and wind field information of a research region under different meteorological conditions is simulated; secondly, generating a CFD fine grid based on the local elevation data and the landform characteristics, and importing the CFD fine grid into a CFD mode; writing a CFD initial file based on a boundary wind field generated by a WRF mode, and assigning a value to a side boundary grid of the CFD initial file; and finally, calculating and outputting refined three-dimensional wind field data under different meteorological conditions in the research area. Because the flow field information calculation based on the CFD technology is complex and time-consuming, the CFD database needs to be stored and established after the refined three-dimensional wind field information in the research area under different meteorological conditions is obtained through calculation, and the CFD database is convenient to directly call in the later period.
(2) Wind field data fusion algorithm
The wind field data fusion algorithm is used for processing direct wind measurement results which are obtained by various means and have inconsistent space-time and different measurement precision, and all-weather and non-difference airport area digital wind field data are obtained after fusion and are used for further wind shear identification. The invention utilizes the combination of actually measured wind field data and CFD mode and mesoscale WRF mode wind field simulation results to carry out wind field data fusion, and the flow is shown in figure 3. Firstly, searching and calling three-dimensional wind field data which are consistent with the current wind field distribution of an airport from a CFD database according to actually measured wind field data; and then, correcting the numerical value of the called CFD simulation three-dimensional wind field data based on the actually measured wind field data. The flow for quantifying the distribution consistency of the CFD database and the current wind field of the airport is as follows: firstly, calculating the deviation between the current wind measuring data and the same-position CFD simulation data aiming at any one database; then, the deviation of each CFD simulation data relative to the current observation data is calculated in a traversing manner; and finally, selecting CFD simulation data with the minimum deviation as an initial field of the current three-dimensional wind field fusion of the airport area.
The numerical correction part adopts a data assimilation scheme and comprises the following steps: (1) for the area with credible actually measured wind field data, the actually measured data is taken as the main; (2) supplementing the area without actually measured wind field data based on the simulation data of the CFD under the current meteorological condition; (3) when more than two wind measuring results exist at the same position, performing weighted calculation, wherein the weight of each wind measuring device is determined by the weather condition, the wind measuring principle of each wind measuring device, the observation position and the like; (4) the area with the actually measured wind field data has influences of different weights on the wind field data in a certain peripheral range, and the specific influence range and influence weight need to be determined by combining a sensitivity experiment.
The simulation data are corrected according to the values of the actually measured wind field data of each observation device, so that the simulation data are closer to the true values, and the historical wind field statistical characteristics are added for embedding; the revised principle is the assimilation step in the application, wherein two weight problems are mainly involved, 1) when more than two wind measurement results exist at the same position, weighted calculation is carried out, and the weight of each wind measurement device is determined by the weather condition, the wind measurement principle of each device, the observation position and the like; for example, the weather radar is suitable for measuring a wind field in cloud and rain weather, if there is cloud and rain in the same day, the weight of the weather radar data is high, and if it is clear, the weight of the laser radar is high, and the like; meanwhile, the closer the observation point is to the assimilation point, the greater the influence weight on the assimilation point. 2) The area with the actually measured wind field data has the influence of different weights on the wind field data in a certain range around the area; for example, if a certain position is provided with a wind pole observation point, data within 500 meters around the observation point can be specified to be related to the observation point (the distance can be further increased if the distance is combined with local terrain, and both are plains), simulation data within 500 meters can be corrected based on the observation data of the certain position, and the influence weight is influenced, and the principle is that the farther the distance is, the smaller the weight is.
If the wind speed of the observation point is 5m/s, a simulation data point to be assimilated exists nearby, the wind speed is 3m/s, and the distance is 200m, the corrected data of the simulation data point are as follows:
if the wind speed of the simulation data point is 2m/s, the corrected data of the simulation data point is as follows:
after revision, the database is not changed, and the current three-dimensional wind field data is obtained only by calculation based on the current measured data and the simulation database.
And wind field statistical characteristics used for CFD model boundary conditions. And if the wind field is obtained based on years of statistics, the average wind speed in summer is 8m/s, and the wind direction is 5 degrees, substituting the data into the CFD model to obtain summer simulation data.
Claims (5)
1. A numerical simulation model and mesoscale meteorological model-based airport regional multi-source wind field fusion method is characterized by comprising the following steps: (1) firstly, generating a boundary wind field of a CFD grid based on a WRF mode, and simulating wind field information of a research area under different meteorological conditions; secondly, generating a CFD fine grid based on the local elevation data and the landform characteristics, and importing the CFD fine grid into a CFD mode; writing a CFD initial file based on a boundary wind field generated by a WRF mode, and assigning a value to a side boundary grid of the CFD initial file; and finally, calculating and outputting refined three-dimensional wind field data under different meteorological conditions in the research area. Because the flow field information calculation based on the CFD technology is complex and time-consuming, the CFD database needs to be stored and established after refined three-dimensional wind field information in a research area under different meteorological conditions is obtained through calculation, and the CFD database is convenient to directly call in the later period;
(2) wind field data fusion algorithm the wind field data fusion algorithm is used for processing direct wind measurement results which are acquired by various means and have inconsistent space-time and different measurement precision, and all-weather non-difference airport area digital wind field data are obtained after fusion and are used for further wind shear identification;
and (3) carrying out wind field data fusion by combining actually measured wind field data with a CFD mode and a mesoscale WRF mode wind field simulation result: firstly, searching and calling three-dimensional wind field data which are consistent with the current wind field distribution of an airport from a CFD database according to actually measured wind field data; then, correcting the numerical value of the called CFD simulation three-dimensional wind field data based on the actually measured wind field data; the flow for quantifying the distribution consistency of the CFD database and the current wind field of the airport is as follows: firstly, calculating the deviation between the current wind measuring data and the same-position CFD simulation data aiming at any one database; then, the deviation of each CFD simulation data relative to the current observation data is calculated in a traversing manner; and finally, selecting CFD simulation data with the minimum deviation as an initial field of the current three-dimensional wind field fusion of the airport area.
2. The airport area multi-source wind field fusion method based on the numerical simulation model and the mesoscale meteorological model as claimed in claim 1, wherein a data assimilation scheme is adopted in a numerical correction part, and the method comprises the following steps: (1) for the area with credible actually measured wind field data, the actually measured data is taken as the main; (2) supplementing the area without actually measured wind field data based on the simulation data of the CFD under the current meteorological condition; (3) when more than two wind measuring results exist at the same position, performing weighted calculation, wherein the weight of each wind measuring device is determined by the weather condition, the wind measuring principle of each wind measuring device, the observation position and the like; (4) the area with the actually measured wind field data has influences of different weights on the wind field data in a certain peripheral range, and the specific influence range and influence weight need to be determined by combining a sensitivity experiment.
3. The airport regional multi-source wind field fusion method based on the numerical simulation model and the mesoscale meteorological model as claimed in claim 2, wherein the simulation data are corrected numerically by actually measured wind field data of each observation device to be closer to a true value, and are embedded with statistical features of a historical wind field; the revision principle is an assimilation step, wherein two weight problems are mainly involved, 1) when more than two wind measurement results exist at the same position, weighted calculation is carried out, and the weight of each wind measurement device is determined by the weather condition, the wind measurement principle of each device and the observation position; the closer the observation point is to the assimilation point, the greater the influence weight on the assimilation point is; 2) the area with the actually measured wind field data has different weight influences on the wind field data in a certain range around the area.
4. The airport area multi-source wind farm fusion method based on numerical simulation models and mesoscale meteorological models as claimed in claim 3, wherein after revision, the database is not changed, and only current three-dimensional wind farm data is obtained through calculation based on current measured data and a simulation database.
5. The airport area multi-source wind farm fusion method based on numerical simulation models and mesoscale meteorological models of claim 3, wherein wind farm statistical features are used for CFD model boundary conditions.
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