Reconstruction and short-term prediction of wind pressure field on cylindrical coal sheds under undisturbed conditions using dynamic mode decomposition

被引:0
|
作者
Liu, Qingkuan [1 ,2 ,3 ]
Huo, Jing [2 ]
Liu, Shijie [2 ]
Qin, Zejun [2 ]
Li, Haohan [2 ]
Zhang, Zhen [1 ,2 ,3 ]
机构
[1] Shijiazhuang Tiedao Univ, Minist Educ, Key Lab Rd & Railway Engn Safety Control, Shijiazhuang 050043, Peoples R China
[2] Shijiazhuang Tiedao Univ, Sch Civil Engn, Shijiazhuang 050043, Peoples R China
[3] Innovat Ctr Wind Engn & Wind Energy Technol Hebei, Shijiazhuang 050043, Peoples R China
基金
中国国家自然科学基金;
关键词
Coal shed; Wind tunnel test; Dynamic modal decomposition; Pressure field reconstruction; Wind pressure coefficients; Short-term prediction; PROPER ORTHOGONAL DECOMPOSITION; FLOW;
D O I
10.1016/j.jweia.2025.106017
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Dynamic Mode Decomposition (DMD) accurately captures growth rates and frequency characteristics of each mode, establishing a reduced-order model for the evolution of the flow field to reconstruct or predict the flow dynamics process. This study applies DMD to analyze the wind pressure distribution problem of cylindrical coal sheds, investigating its accuracy in analyzing pressure fields. First, wind tunnel tests were conducted on cylindrical coal sheds to examine the mean wind pressure coefficient distribution under typical wind directions (defined as B). Subsequently, the wind pressure field of the coal shed was decomposed using the DMD at B = 0 degrees. Reconstruction with the first 15 DMD modes can better characterize the wind pressure distribution. Therefore, reconstruction with the first 15 DMD modes was chosen to reconstruct the pressure fields conducted at B = 30 degrees, 60 degrees, and 90 degrees. The DMD decomposition performance is optimal at B = 0 degrees for the cylindrical surface. For pressure field reconstruction in the same wind direction, as the number of modes increases, the reconstruction relative error of the pressure field decreases, thereby enhancing reconstruction accuracy and the ability to capture details. Finally, short-term predictions of the coal shed's wind pressure distribution were conducted, revealing better predictive performance near the incoming flow position. DMD can provide a basis for studying the wind pressure distribution on large-span structures such as coal sheds.
引用
收藏
页数:16
相关论文
共 50 条
  • [21] Short-term wind speed forecasting using variational mode decomposition and support vector regression
    Wang, Xiaodan
    Yu, Qibing
    Yang, Yi
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2018, 34 (06) : 3811 - 3820
  • [22] Short-Term Wind Power Prediction Based on a Variational Mode Decomposition-BiTCN-Psformer Hybrid Model
    Xu, Wu
    Dai, Wenjing
    Li, Dongyang
    Wu, Qingchang
    ENERGIES, 2024, 17 (16)
  • [23] Short-term wind speed prediction: Hybrid of ensemble empirical mode decomposition, feature selection and error correction
    Jiang, Yan
    Huang, Guoqing
    ENERGY CONVERSION AND MANAGEMENT, 2017, 144 : 340 - 350
  • [24] Extreme learning machine based short-term wind power prediction framework with adaptive variational mode decomposition
    Yang, Wei
    Jia, Li
    Xu, Yue
    2021 POWER SYSTEM AND GREEN ENERGY CONFERENCE (PSGEC), 2021, : 395 - 399
  • [25] Short-Term Photovoltaic Output Prediction Based on Decomposition and Reconstruction and XGBoost under Two Base Learners
    Xu, Weihui
    Wang, Zhaoke
    Wang, Weishu
    Zhao, Jian
    Wang, Miaojia
    Wang, Qinbao
    ENERGIES, 2024, 17 (04)
  • [26] Short-term electrical load forecasting based on error correction using dynamic mode decomposition
    Kong, Xiangyu
    Li, Chuang
    Wang, Chengshan
    Zhang, Yusen
    Zhang, Jian
    APPLIED ENERGY, 2020, 261 (261)
  • [27] LSTM Model Combined with Rolling Empirical Mode Decomposition and Sample Entropy Reconstruction for Short-Term Wind Speed Forecasting
    Yao, Sen
    Zhu, Hong
    Zhou, Xin
    Peng, Tingxin
    Zhang, Jingrui
    PROCESSES, 2025, 13 (03)
  • [28] Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short-term memory neural network
    Duan, Jiandong
    Wang, Peng
    Ma, Wentao
    Tian, Xuan
    Fang, Shuai
    Cheng, Yulin
    Chang, Ying
    Liu, Haofan
    ENERGY, 2021, 214
  • [29] Short-Term Wind Speed Prediction Based on Variational Mode Decomposition and Linear-Nonlinear Combination Optimization Model
    Sun, Wei
    Gao, Qi
    ENERGIES, 2019, 12 (12)
  • [30] SHORT-TERM WIND SPEED COMBINATION PREDICTION MODEL BASED ON COMPLEMENTARY ENSEMBLE EMPIRICAL MODE DECOMPOSITION AND STACKING FUSION
    Fei, Tang
    Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2024, 45 (07): : 735 - 744