Method for simulating wind field of extreme arid region based on wrf

Patent metadata

Field	Value
Publication number	US-2016203245-A1
Application number	US-201514597177-A
Country	US
Kind code	A1
Filing date	Jan 14, 2015
Priority date	Jan 14, 2015
Publication date	Jul 14, 2016
Grant date	—

Abstract

A method of simulating wind field of extreme arid region based on WRF includes following steps. A mode parameter optimization scheme for wind energy simulation is selected, wherein the mode parameter optimization scheme is selected by selecting a group of mode parameter optimization schemes of different ground floors, land process, and planet boundary layers having great influence on simulation of the boundary layer of wind field, and comparing the mode parameter optimization schemes. A wind energy simulation of the extreme arid region is performed during a preset length of time using the selected mode parameter optimization scheme. Simulation configuration of the wind field for the extreme arid region is obtained through results of the wind energy simulation.

First claim

What is claimed is:

1 . A method of simulating wind field of extreme arid region based on WRF, the method comprising:
selecting a mode parameter optimization scheme for wind energy simulation, wherein the mode parameter optimization scheme is selected by selecting a group of mode parameter optimization schemes of different ground floors, land process, and planet boundary layers having great influence on simulation of the boundary layer of wind field, and comparing the mode parameter optimization schemes; performing a wind energy simulation of the extreme arid region during a preset length of time using the selected mode parameter optimization scheme; and obtaining simulation configuration of the wind field for the extreme arid region through results of the wind energy simulation.

2 . The method of claim 1 , wherein the mode parameter optimization scheme is improved by utilizing the latest achievement of the observation data of the field observation experiment.

3 . The method of claim 2 , wherein the mode parameter optimization scheme is improved by:
a 1 , a roughness length of the extreme arid region is set to 0.002 meters in the mode parameter optimization scheme; a 2 , a soil volumetric heat capacity is set to 1.12×10 6 J·m 3 ·K −1 in the mode parameter optimization scheme; and a 3 , a surface classification adopts the detailed vegetation classification of NCAR in the mode parameter optimization scheme.

4 . The method of claim 2 , wherein the mode parameter optimization scheme is improved by introducing two sets of control tests and four sets of sensitivity test:
b 1 , the two sets of control tests are defined as CTL 1 and CTL 2 , and simulated via original mode and surface classification mode; and b 2 , the four sets of sensitivity tests are defined from STV 1 ˜STV 4 , wherein in the STV 1 ˜STV 2 , one parameter is optimized and another parameter is retained in two parameters to be optimized; in the STV 4 , both two parameters are optimized at the same time.

5 . The method of claim 4 , wherein in the control test, the two sets of control tests are simulated for 8 times, and an integration lasts 72 hours for each time to obtain a plurality of simulation results; the simulation results in former 12 hours are abandoned, the simulation results between 13 hours to 36 hours are selected to form 8 days' continuous simulation results with 193 wind speeds as samples.

6 . The method of claim 4 , wherein the sensitivity test comprises:
setting a surface roughness length at sparse vegetation region or bare vegetation region in the extreme arid region to 0.002 meters, and performing the sensitivity test STV 1 ; setting a soil volumetric heat capacity at the sparse vegetation region or bare vegetation region in the extreme arid region to 1.12×10 6 J·m −3 ·K −1 , and performing the sensitivity test STV 2 ; setting a surface roughness length to 0.002 meters and setting the soil volumetric heat capacity to 1.12×10 6 J·m 3 ·K −1 at the sparse vegetation region or bare vegetation region in the extreme arid region, and performing the sensitivity test STV 3 ; and adopting detailed surface classification, and setting the surface roughness length to 0.002 meters and setting the soil volumetric heat capacity to 1.12×10 6 J·m 3 ·K −1 at the same time at the sparse vegetation region or bare vegetation region in the extreme arid region, and performing the sensitivity test STV 4 .

7 . The method of claim 1 , wherein the performing a wind energy simulation comprises:
comparing observation data with simulation results via triple nested model during comparing the mode parameter optimization schemes; assessing a simulation ability of the mode parameter optimization scheme by interpolating grid point values of the same simulation results onto sites, and comparing the grid point values with site data; comprehensively assessing the simulation ability by following statistical parameters: average absolute error, mean relative error, root mean square error, correlation coefficient, and consistency index; selecting output of the National Weather Service T639 model as a simulated background field; comparing and analyzing the observation data with anemometer tower data; and obtaining a complete wind data during a presetting interval of one year by observing wind speeds and wind directions at the determined height.

8 . The method of claim 7 , wherein a resolution adopts 81 kilometers, 27 kilometers, and 9 kilometers respectively in the tripe nested mode.

9 . The method of claim 7 , wherein the simulated background field is selected from output of the National Weather Service T639 model, a resolution of the output is 0.3°×0.3°, a center of the background field is at 40.40° N, 96.15° E, the resolution of the triple nested mode adopts 81×81 km, 27×27 km, and 9×9 km, and a study region selected in the third nested mode (9×9 km) is (39° N˜42° N, 93° E˜99° E).

10 . The method of claim 7 , wherein an integration time step is set to 240 seconds, the integration is performed for 10 days, results are output per hour, the results within former 12 hours are taken as a rotational acceleration time of the mode; U, V components at height of 10 m, 30 m, 50 m, 70 m, and 100 m within the following 9 and a half days are taken as simulated results, wherein U, V is horizontal vector of wind speed respectively on different direction, and U and V are perpendicular to each other; and the presetting interval is 10 minutes.

Assignees

• State Grid Corp China
• Gansu Electric Power Company of State Grid
• Wind Power Technology Ct Of Gansu Electric Power Company

Inventors

• Wang ning-bo
• Zhang jian-mei
• He shi-en
• Ma yan-hong
• Zhao Long
• Zhou Qiang
• Ma Ming
• Liu Guang-Tu
• Wang Ding-Mei
• Lu Liang
• Lv qing-quan
• Wang Xiao-Yong
• Huang Rong
• Ding Kun
• Li Jin
• Zhou shi-yuan
• Zhang Jin-Ping

Classifications

• G01W1/10 Primary

  Devices for predicting weather conditions (computers per se G06; display devices G09) · CPC title

• G06F30/20 Primary

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G06F17/5009 Primary

  Physics · mapped topic

• G06F17/10

  Complex mathematical operations {(function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544)} · CPC title