本文選題：分形學　切入點：湍流風譜　出處：《太陽能學報》2017年11期 　論文類型：期刊論文

【摘要】：為避免由經(jīng)驗性地選擇湍流風譜模型導致特定風電場風速分布規(guī)律描述不準確的問題,基于Kaimal、Von Karman、SMOOTH和NWTCUP湍流風譜模型模擬在不同地表粗糙度和湍流強度條件下所建立風場的風速分布規(guī)律,通過計盒維數(shù)分形方法計算不同風速速度曲線的分形維數(shù),并與某風電場測風塔實測風速數(shù)據(jù)的分形維數(shù)進行比較。結(jié)果表明:風速曲線的分形維數(shù)均約為1.5,證明風速時域分布具有自相似特征。由于NWTCUP湍流風譜模型基于所選風電場的實測風速數(shù)據(jù)建立,因此NWTCUP模型在50 m高度和80 m高度處的風速分形維數(shù)與實測數(shù)據(jù)最為接近,表明分形維數(shù)可作為選擇湍流風譜模型的一個重要定量參數(shù)。
[Abstract]:In order to avoid the problem that the empirical selection of turbulent wind spectrum model leads to inaccurate description of wind speed distribution law in a particular wind farm, Based on Kaimalon Von Karmann SMOOTH and NWTCUP turbulent wind spectrum model, the wind velocity distribution law of wind field under different surface roughness and turbulence intensity is simulated, and the fractal dimension of different wind velocity curves is calculated by box-counting dimension fractal method. The results show that the fractal dimension of wind speed curve is about 1.5, which proves that the time domain distribution of wind speed is self-similar. Because the NWTCUP turbulence wind spectrum model is based on the. The establishment of the measured wind speed data of the selected wind farm, Therefore, the fractal dimension of wind velocity at 50 m and 80 m height of NWTCUP model is most close to the measured data, which indicates that fractal dimension can be used as an important quantitative parameter in selecting turbulent wind spectrum model.
【作者單位】： 上海理工大學能源與動力工程學院;上海市動力工程多相流動與傳熱重點實驗室;華創(chuàng)風能有限公司;
【分類號】：TM614

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