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Fine wind field simulation method based on spatial correlation and monitoring data

A technology for spatial correlation and wind field simulation, applied in the fields of meteorology and wind engineering, can solve problems such as inaccurate precision, and achieve the effect of improving simulation results, improving wind speed accuracy, and accurate numerical simulation results.

Active Publication Date: 2021-11-09
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention provides a fine wind field simulation method based on spatial correlation and monitoring data, aiming at the problem of inaccurate precision in the expression that ignores the influence of factors such as terrain, wind direction and atmospheric circulation in the existing method

Method used

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  • Fine wind field simulation method based on spatial correlation and monitoring data
  • Fine wind field simulation method based on spatial correlation and monitoring data
  • Fine wind field simulation method based on spatial correlation and monitoring data

Examples

Experimental program
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Effect test

Embodiment 1

[0041] Such as figure 1 As shown, a fine wind field simulation method based on spatial correlation and monitoring data, the fine wind field simulation method includes the following steps:

[0042] Step 1: Obtain the spatial correlation coefficient field, which is obtained by WRF simulation without combining observation data;

[0043] Step 2: Obtain observational data from traditional observations and non-traditional observations;

[0044] Step 3: Combined with the numerical simulation module of the observation data in step 2, consider fluid dynamics control equations and multi-physics processes, and obtain accurate space field data by running the WRF core module ARW in step 1. Both spatial and temporal scales of impact need to be considered.

[0045] Further, the step 1 specifically includes the following steps:

[0046] Step 1.1: WPS, the WRF pre-processing module, processes the terrain and the initial data field;

[0047] Step 1.2: ARW numerical simulation of WRF core ca...

Embodiment 2

[0063] Introduction to the WRF observation-nudging method based on spatial correlation:

[0064] The basic control equations of WRF are shown in equations (1)-(9):

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] μ=p hs -p ht (9)

[0074] The weight function expression in the original observation-nudging method is shown in formula (10):

[0075]

[0076] Physically, the weight function in the observation-nudging method represents the influence strength of the observation data on the surrounding space points, or it can be regarded as the correlation between the observation data and the surrounding space points. From this point of view, it can be found that the weight function in the current WRF is unreasonable for simulating the complex terrain wind field. The specific reason is that in the field of meteorology, the first consideration is a large scale, at least on the order of a few kilometers, so the impact of mo...

Embodiment 3

[0088] In order to verify the advantages of the proposed WRF observation-nudging method based on spatial correlation, the present invention conducts a comparative study of three methods. Case-0, WRF simulation without observation-nudging method; Case-1, original WRF observation-nudging method, using 10-minute time interval observation data and 1-minute time window; Case-2, WRF observation based on spatial correlation -nudging method, using 10-minute interval observation data and 1-minute time window.

[0089] For all simulation examples, the standard parameterization schemes are as follows: Yonsei University boundary layer scheme (YSU), WRF Single-Moment 5-class microphysics scheme (WSM5), Grell-Freitas ensemble cumulus scheme (GF), Rapid Radiative Transfer Model Longwave Radiation Scheme (RRTM), Rapid Radiative TransferModel for General Circulation Models Shortwave Radiation Scheme (RRTMG), and UnifiedNoah Surface Model (Noah). Table 1 and Table 2 show the basic parameters o...

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Abstract

The invention discloses a fine wind field simulation method based on spatial correlation and monitoring data. The method comprises the following steps: step 1, obtaining a spatial correlation coefficient field which is obtained through WRF simulation without combining observation data; step 2, obtaining observation data obtained by traditional observation and non-traditional observation; and step 3, considering a fluid mechanics control equation and a multi-physical process by combining a numerical simulation module of the observation data in the step 2, and obtaining accurate space field data by running the WRF core module ARW in the step 1. The method solves the problem that in an existing method, expression of influences of factors such as terrain, wind direction and atmospheric circulation is neglected, and the precision is not accurate.

Description

technical field [0001] The invention belongs to the fields of meteorology and wind engineering; in particular, it relates to a fine wind field simulation method based on spatial correlation and monitoring data. Background technique [0002] How to obtain accurate wind field information has an important impact on evaluating the wind effect of major infrastructure in large civil engineering. The mesoscale numerical model WRF can consider multiple physical processes (wind, temperature, humidity, water vapor, etc.) to simulate the regional wind field under the real atmosphere and terrain. The advantage of the WRF model is that it is multi-physics and can obtain the local space wind field; the disadvantage is that there is a certain deviation between the simulation results of the wind speed and the observation data. On the contrary, the advantage of observation data is its high reliability; however, the number of observation points is limited. If the respective advantages of WR...

Claims

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Application Information

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IPC IPC(8): G06F30/28G06F30/15G06F111/10G06F113/08
CPCG06F30/28G06F30/15G06F2113/08G06F2111/10
Inventor 赖马树金任贺贺李惠
Owner HARBIN INST OF TECH
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