摘要
为了准确分析内河航道中船行波的时频特性,将小波变换理论应用于内河航道船行波频谱分析中.通过建立水槽试验获得船舶以不同条件航行产生的水位波动数据,对8种工况下船舶航行产生的船行波,从时间尺度解析船行波波列结构特征,从频率尺度探究船行波能量分布特征及船舶航行条件的影响.结果表明,船行波小波能谱呈现局部突出的特点,小波谱能量主要集中在船行波低频主波段,对应的频率范围为0~0.35 Hz,与时间尺度上的水位波动过程相对应.当船舶航速、吃水深度增大时,船行波全局小波能量峰值显著增大,且同一位置处受吃水深度影响更大;当船舶航速、吃水深度相同时,同一位置处船行波全局小波能量峰值随离岸距离增大而减小.该研究可为内河航道中船行波频谱特征分析提供新途径.
To accurately analyze the time-frequency characteristics of ship waves in inland waterways,a wavelet transform theory was applied to the frequency spectrum analysis of ship waves.The flume tests were established to obtain water-level fluctuation data caused by ship sailing under different conditions.For the waves generated by the ship under 8 working conditions,the structural characteristics of the ship wave train were analyzed in the time domain,and the energy distribution of ship waves and the influences of ship sailing conditions on it were explored in the frequency domain.The results show that the wavelet energy spectrum of ship waves is locally prominent,and the energy of the wavelet spectrum is mainly concentrated in the main band of the water-level drop and the low frequency,and the corresponding frequency range is 0-0.35 Hz corresponding to the process of water-level fluctuation on time scale.When the ship speed and the draft increase,the global wavelet energy peak of ship waves increases significantly,and the influence on the draft at the same position is greater.When the ship speed and the draft are the same,the global wavelet energy peak of ship waves at the same position decreases with the increase of the offshore distance.The study provides a new way to analyze the frequency spectrum characteristics of ship waves in inland waterways.
作者
毛礼磊
陈一梅
李鑫
Mao Lilei;Chen Yimei;Li Xin(School of Transportation, Southeast University, Nanjing 211189, China)
出处
《东南大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2020年第6期1115-1122,共8页
Journal of Southeast University:Natural Science Edition
基金
国家自然科学基金资助项目(51479035)
东南大学优秀博士学位论文培育基金资助项目(YBPY1883).
关键词
小波变换
内河航道
船行波
小波能谱
全局小波能谱
wavelet transform
inland waterways
ship waves
wavelet energy spectrum
global wavelet energy spectrum