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Effects of wind speed,underlying surface,and seed morphological traits on the secondary seed dispersal in the Tengger Desert,China
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作者 QU Wenjie ZHAO Wenzhi +3 位作者 YANG Xinguo WANG Lei ZHANG Xue QU Jianjun 《Journal of Arid Land》 SCIE CSCD 2024年第4期531-549,共19页
The maintenance of sand-fixing vegetation is important for the stability of artificial sand-fixing systems in which seed dispersal plays a key role.Based on field wind tunnel experiments using 11 common plant species ... The maintenance of sand-fixing vegetation is important for the stability of artificial sand-fixing systems in which seed dispersal plays a key role.Based on field wind tunnel experiments using 11 common plant species on the southeastern edge of the Tengger Desert,China,we studied the secondary seed dispersal in the fixed and semi-fixed sand dunes as well as in the mobile dunes in order to understand the limitations of vegetation regeneration and the maintenance of its stability.Our results indicated that there were significant variations among the selected 11 plant species in the threshold of wind speed(TWS).The TWS of Caragana korshinskii was the highest among the 11 plant species,whereas that of Echinops gmelinii was the lowest.Seed morphological traits and underlying surface could generally explain the TWS.During the secondary seed dispersal processes,the proportions of seeds that did not disperse(no dispersal)and only dispersed over short distance(short-distance dispersal within the wind tunnel test section)were significantly higher than those of seeds that were buried(including lost seeds)and dispersed over long distance(long-distance dispersal beyond the wind tunnel test section).Compared with other habitats,the mobile dunes were the most difficult places for secondary seed dispersal.Buried seeds were the easiest to be found in the semi-fixed sand dunes,whereas fixed sand dunes were the best sites for seeds that dispersed over long distance.The results of linear mixed models showed that after controlling the dispersal distance,smaller and rounder seeds dispersed farther.Shape index and wind speed were the two significant influencing factors on the burial of seeds.The explanatory power of wind speed,underlying surface,and seed morphological traits on the seeds that did not disperse and dispersed over short distance was far greater than that on the seeds that were buried and dispersed over long distance,implying that the processes and mechanisms of burial and long-distance dispersal are more complex.In summary,most seeds in the study area either did not move,were buried,or dispersed over short distance,promoting local vegetation regeneration. 展开更多
关键词 seed dispersal seed morphological traits wind speed vegetation regeneration wind tunnel Tengger Desert
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Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model
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作者 Wenbo XUE Hui YU +1 位作者 Shengming TANG Wei HUANG 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第6期1161-1170,共10页
Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SM... Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study. 展开更多
关键词 surface wind speed terrain features error analysis MOS calibration model
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Experimental study on the variation of optical remote sensing imaging characteristics of internal solitary waves with wind speed
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作者 Zhe CHANG Lina SUN +4 位作者 Tengfei LIU Meng ZHANG Keda LIANG Junmin MENG Jing WANG 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2024年第2期408-420,共13页
Optical remote sensing has been widely used to study internal solitary waves(ISWs).Wind speed has an important effect on ISW imaging of optical remote sensing.The light and dark bands of ISWs cannot be observed by opt... Optical remote sensing has been widely used to study internal solitary waves(ISWs).Wind speed has an important effect on ISW imaging of optical remote sensing.The light and dark bands of ISWs cannot be observed by optical remote sensing when the wind is too strong.The relationship between the characteristics of ISWs bands in optical remote sensing images and the wind speed is still unclear.The influence of wind speeds on the characteristics of the ISWs bands is investigated based on the physical simulation experiments with the wind speeds of 1.6,3.1,3.5,3.8,and 3.9 m/s.The experimental results show that when the wind speed is 3.9 m/s,the ISWs bands cannot be observed in optical remote sensing images with the stratification of h_(1)∶h_(2)=7∶58,ρ_(1)∶ρ_(2)=1∶1.04.When the wind speeds are 3.1,3.5,and 3.8 m/s,which is lower than 3.9 m/s,the ISWs bands can be obtained in the simulated optical remote sensing image.The location of the band’s dark and light extremum and the band’s peak-to-peak spacing are almost not affected by wind speed.More-significant wind speeds can cause a greater gray difference of the light-dark bands.This provided a scientific basis for further understanding of ISW optical remote sensing imaging. 展开更多
关键词 internal solitary wave(ISW) optical remote sensing wind speed characteristics of ISWs bands
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A Study on Reconstruction of Surface Wind Speed in China Due to Various Climate Variabilities
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作者 Li Yancong Li Xichen +1 位作者 Sun Yankun Xu Jinhua 《Journal of Northeast Agricultural University(English Edition)》 CAS 2024年第2期53-65,共13页
Using European Centre for Medium-Range Weather Forecasts Reanalysis V5(ERA5)reanalysis data,this study investigated the reconstruction effects of various climate variabilities on surface wind speed in China from 1979 ... Using European Centre for Medium-Range Weather Forecasts Reanalysis V5(ERA5)reanalysis data,this study investigated the reconstruction effects of various climate variabilities on surface wind speed in China from 1979 to 2022.The results indicated that the reconstructed annual mean wind speed and the standard deviation of the annual mean wind speed,utilizing various climate variability indices,exhibited similar spatial modes to the reanalysis data,with spatial correlation coefficients of 0.99 and 0.94,respectively.In the reconstruction of six major wind power installed capacity provinces/autonomous regions in China,the effects were notably good for Hebei and Shanxi provinces,with the correlation coefficients for the interannual regional average wind speed time series being 0.65 and 0.64,respectively.The reconstruction effects of surface wind speed differed across seasons,with spring and summer reconstructions showing the highest correlation with reanalysis data.The correlation coefficients for all seasons across most regions in China ranged between 0.4 and 0.8.Among the reconstructed seasonal wind speeds for the six provinces/autonomous regions,Shanxi Province in spring exhibited the highest correlation with the reanalysis,with a coefficient of 0.61.The large-scale climate variability indices showed good reconstruction effects on the annual mean wind speed in China,and could explain the interannual variability trends of surface wind speed in most regions of China,particularly in the main wind energy provinces/autonomous regions. 展开更多
关键词 wind speed wind energy correlation method climate variability European Centre for Medium-Range Weather Forecasts Reanalysis V5(ERA5)
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Wind Speed Prediction Based on Improved VMD-BP-CNN-LSTM Model
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作者 Chaoming Shu Bin Qin Xin Wang 《Journal of Power and Energy Engineering》 2024年第1期29-43,共15页
Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind s... Amid the randomness and volatility of wind speed, an improved VMD-BP-CNN-LSTM model for short-term wind speed prediction was proposed to assist in power system planning and operation in this paper. Firstly, the wind speed time series data was processed using Variational Mode Decomposition (VMD) to obtain multiple frequency components. Then, each individual frequency component was channeled into a combined prediction framework consisting of BP neural network (BPNN), Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) after the execution of differential and normalization operations. Thereafter, the predictive outputs for each component underwent integration through a fully-connected neural architecture for data fusion processing, resulting in the final prediction. The VMD decomposition technique was introduced in a generalized CNN-LSTM prediction model;a BPNN model was utilized to predict high-frequency components obtained from VMD, and incorporated a fully connected neural network for data fusion of individual component predictions. Experimental results demonstrated that the proposed improved VMD-BP-CNN-LSTM model outperformed other combined prediction models in terms of prediction accuracy, providing a solid foundation for optimizing the safe operation of wind farms. 展开更多
关键词 wind speed Forecast Long Short-Term Memory Network BP Neural Network Variational Mode Decomposition Data Fusion
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Dividing the transit wind speeds into intervals as a favorable methodology for analyzing the relationship between wind speed and the aerodynamic impedance of vegetation in semiarid grasslands 被引量:1
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作者 LI Ruishen PEI Haifeng +4 位作者 ZHANG Shengwei LI Fengming LIN Xi WANG Shuai YANG Lin 《Journal of Arid Land》 SCIE CSCD 2023年第8期887-900,共14页
In grassland ecosystems,the aerodynamic roughness(Z0)and frictional wind speed(u*)contribute to the aerodynamic impedance of the grassland canopy.Thus,they are often used in the studies of wind erosion and evapotransp... In grassland ecosystems,the aerodynamic roughness(Z0)and frictional wind speed(u*)contribute to the aerodynamic impedance of the grassland canopy.Thus,they are often used in the studies of wind erosion and evapotranspiration.However,the effect of wind speed and grazing measures on the aerodynamic impedance of the grassland canopy has received less analysis.In this study,we monitored wind speeds at multiple heights in grazed and grazing-prohibited grasslands for 1 month in 2021,determined the transit wind speed at 2.0 m height by comparing wind speed differences at the same height in both grasslands,and divided these transit wind speeds at intervals of 2.0 m/s to analyze the effect of the transit wind speed on the relationship among Z0,u*,and wind speed within the grassland canopy.The results showed that dividing the transit wind speeds into intervals has a positive effect on the logarithmic fit of the wind speed profile.After dividing the transit wind speeds into intervals,the wind speed at 0.1 m height(V0.1)gradually decreased with the increase of Z0,exhibiting three distinct stages:a sharp change zone,a steady change zone,and a flat zone;while the overall trend of u*increased first and then decreased with the increase of V0.1.Dividing the transit wind speeds into intervals improved the fitting relationship between Z0 and V0.1 and changed their fitting functions in grazed and grazing-prohibited grasslands.According to the computational fluid dynamic results,we found that the number of tall-stature plants has a more significant effect on windproof capacity than their height.The results of this study contribute to a better understanding of the relationship between wind speed and the aerodynamic impedance of vegetation in grassland environments. 展开更多
关键词 transit wind speeds frictional wind speed aerodynamic roughness computational fluid dynamic(CFD) grazed grassland grazing-prohibited grassland
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Effect of photovoltaic panel electric field on the wind speed required for dust removal from the panels 被引量:1
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作者 李兴财 王娟 +1 位作者 刘滢格 马鑫 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第8期296-303,共8页
Methods to remove dust deposits by high-speed airflow have significant potential applications,with optimal design of flow velocity being the core technology.In this paper,we discuss the wind speed required for particl... Methods to remove dust deposits by high-speed airflow have significant potential applications,with optimal design of flow velocity being the core technology.In this paper,we discuss the wind speed required for particle removal from photovoltaic(PV)panels by compressed air by analyzing the force exerted on the dust deposited on inclined photovoltaic panels,which also included different electrification mechanisms of dust while it is in contact with the PV panel.The results show that the effect of the particle charging mechanism in the electric field generated by the PV panel is greatly smaller than the effect of the Van der Waals force and gravity,but the effect of the particle charged by the contact electrification mechanism in the electrostatic field is very pronounced.The wind speed required for dust removal from the PV panel increases linearly with the PV panel electric field,so we suggest that the nighttime,when the PV electric field is relatively small,would be more appropriate time for dust removal.The above results are of great scientific importance for accurately grasping the dust distribution law and for achieving scientific removal of dust on PV panels. 展开更多
关键词 photovoltaic power generation dust removal electrostatic force required wind speed contact electrification
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Performance of the CMA-GD Model in Predicting Wind Speed at Wind Farms in Hubei, China 被引量:1
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作者 许沛华 成驰 +3 位作者 王文 陈正洪 钟水新 张艳霞 《Journal of Tropical Meteorology》 SCIE 2023年第4期473-481,共9页
This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two win... This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1). 展开更多
关键词 CMA-GD wind speed prediction wind farm root mean square error performance evaluation
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Short‐time wind speed prediction based on Legendre multi‐wavelet neural network 被引量:1
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作者 Xiaoyang Zheng Dongqing Jia +3 位作者 Zhihan Lv Chengyou Luo Junli Zhao Zeyu Ye 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第3期946-962,共17页
As one of the most widespread renewable energy sources,wind energy is now an important part of the power system.Accurate and appropriate wind speed forecasting has an essential impact on wind energy utilisation.Howeve... As one of the most widespread renewable energy sources,wind energy is now an important part of the power system.Accurate and appropriate wind speed forecasting has an essential impact on wind energy utilisation.However,due to the stochastic and un-certain nature of wind energy,more accurate forecasting is necessary for its more stable and safer utilisation.This paper proposes a Legendre multiwavelet‐based neural network model for non‐linear wind speed prediction.It combines the excellent properties of Legendre multi‐wavelets with the self‐learning capability of neural networks,which has rigorous mathematical theory support.It learns input‐output data pairs and shares weights within divided subintervals,which can greatly reduce computing costs.We explore the effectiveness of Legendre multi‐wavelets as an activation function.Mean-while,it is successfully being applied to wind speed prediction.In addition,the appli-cation of Legendre multi‐wavelet neural networks in a hybrid model in decomposition‐reconstruction mode to wind speed prediction problems is also discussed.Numerical results on real data sets show that the proposed model is able to achieve optimal per-formance and high prediction accuracy.In particular,the model shows a more stable performance in multi‐step prediction,illustrating its superiority. 展开更多
关键词 artificial neural network neural network time series wavelet transforms wind speed prediction
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Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed 被引量:1
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作者 Neelam Mughees Mujtaba Hussain Jaffery +2 位作者 Abdullah Mughees Anam Mughees Krzysztof Ejsmont 《Computers, Materials & Continua》 SCIE EI 2023年第6期6375-6393,共19页
Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely h... Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions.In microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids.With this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in MATLAB.To create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the forecasts.Hyperparameters of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization algorithm.Moreover,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE model.All these models were also optimized and modeled in MATLAB.The results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting. 展开更多
关键词 Deep stacked autoencoder sequence to sequence autoencoder bidirectional long short-term memory network wind speed forecasting solar irradiation forecasting
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Deep Learning for Wind Speed Forecasting Using Bi-LSTM with Selected Features 被引量:1
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作者 Siva Sankari Subbiah Senthil Kumar Paramasivan +2 位作者 Karmel Arockiasamy Saminathan Senthivel Muthamilselvan Thangavel 《Intelligent Automation & Soft Computing》 SCIE 2023年第3期3829-3844,共16页
Wind speed forecasting is important for wind energy forecasting.In the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed accurately.The main objective of this research ... Wind speed forecasting is important for wind energy forecasting.In the modern era,the increase in energy demand can be managed effectively by fore-casting the wind speed accurately.The main objective of this research is to improve the performance of wind speed forecasting by handling uncertainty,the curse of dimensionality,overfitting and non-linearity issues.The curse of dimensionality and overfitting issues are handled by using Boruta feature selec-tion.The uncertainty and the non-linearity issues are addressed by using the deep learning based Bi-directional Long Short Term Memory(Bi-LSTM).In this paper,Bi-LSTM with Boruta feature selection named BFS-Bi-LSTM is proposed to improve the performance of wind speed forecasting.The model identifies relevant features for wind speed forecasting from the meteorological features using Boruta wrapper feature selection(BFS).Followed by Bi-LSTM predicts the wind speed by considering the wind speed from the past and future time steps.The proposed BFS-Bi-LSTM model is compared against Multilayer perceptron(MLP),MLP with Boruta(BFS-MLP),Long Short Term Memory(LSTM),LSTM with Boruta(BFS-LSTM)and Bi-LSTM in terms of Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Square Error(MSE)and R2.The BFS-Bi-LSTM surpassed other models by producing RMSE of 0.784,MAE of 0.530,MSE of 0.615 and R2 of 0.8766.The experimental result shows that the BFS-Bi-LSTM produced better forecasting results compared to others. 展开更多
关键词 Bi-directional long short term memory boruta feature selection deep learning machine learning wind speed forecasting
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Short-term wind speed forecasting bias correction in the Hangzhou area of China based on a machine learning model 被引量:1
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作者 Yi Fang Yunfei Wu +4 位作者 Fengmin Wu Yan Yan Qi Liu Nian Liu Jiangjiang Xia 《Atmospheric and Oceanic Science Letters》 CSCD 2023年第4期37-44,共8页
准确的风速预报具有重要的社会意义.在本研究中,使用名为WSFBC-XGB的XGBoost机器学习模型对中国浙江省杭州市自动气象站的短期风速预报误差进行校正.WSFBC-XGB使用本地数值天气预报系统的产品作为输入.将WSFBC-XGB校正的结果与传统MOS(... 准确的风速预报具有重要的社会意义.在本研究中,使用名为WSFBC-XGB的XGBoost机器学习模型对中国浙江省杭州市自动气象站的短期风速预报误差进行校正.WSFBC-XGB使用本地数值天气预报系统的产品作为输入.将WSFBC-XGB校正的结果与传统MOS(模型输出统计)方法校正的结果进行了比较.结果表明:WSFBC-XGB预报风速的均方根误差(RMSE)/准确率(ACC)分别比NWP和MOS降低/提高了26.1%和7.64%/35.6%和7.02%;对于90%的站点WSFBC-XGB的RMSE/ACC均小于/高于MOS.此外,采用平均杂质减少法对WSFBC-XGB的可解释性进行分析,以帮助用户增加对模型的信任.结果表明:10米风速(47.35%),10米风的经向分量(12.73%),日循环(9.97%)和1000百帕风的经向分量(7.45%)是前4个最重要的特征.WSFBC-XGB模型将有助于提高短期风速预报的准确性,为大型户外活动提供支持. 展开更多
关键词 机器学习 极端梯度提升算法 风速 后处理 平均杂质减少
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Variation in cross-shore wind speeds influenced by the morphology of nourished beach
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作者 Xu Chen Jianhui Liu +5 位作者 Feng Cai Yanyu He Bailiang Li Hongshuai Qi Shaohua Zhao Gen Liu 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2023年第7期149-159,共11页
The cross-shore variation in wind speeds influenced by beach nourishment,especially the dramatic changes at the nourished berm,is important for understanding the aeolian sand transport processes that occur after beach... The cross-shore variation in wind speeds influenced by beach nourishment,especially the dramatic changes at the nourished berm,is important for understanding the aeolian sand transport processes that occur after beach nourishment,which will contribute to better beach nourishment project design on windy coasts.In this paper,the influencing factors and potential mechanism of wind speed variation at the edge of a nourished berm were studied.Field observations,together with the Duna model,were used to study the cross-shore wind speed distribution for different nourishment schemes.The results show that the nourished berm elevation and beachface slope are the main factors controlling the increase in wind speed at the berm edge.When the upper beach slope is constant,the wind speed at the berm edge has a positive linear correlation with the berm elevation.When the berm elevation remains constant,the wind speed at the berm edge is also proportional to the upper beach slope.Considering the coupling effects of nourished berm elevation and beachface slope,a model for predicting the wind speed amplification rate at the nourished berm edge was established,and the underlying coupling mechanism was illustrated. 展开更多
关键词 beach nourishment nourished beach berm cross-shore wind speed Duna model wind speed amplification rate
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Joint Return Value Estimation of Significant Wave Heights and Wind Speeds with Bivariate Copulas
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作者 TAO Shanshan DONG Sheng +1 位作者 LIN Yifan GUEDES SOARES Carlos 《Journal of Ocean University of China》 SCIE CAS CSCD 2023年第5期1181-1192,共12页
The joint design criteria of significant wave heights and wind speeds are quite important for the structural reliability of fixed offshore platforms.However,the design method that regards different ocean environmental... The joint design criteria of significant wave heights and wind speeds are quite important for the structural reliability of fixed offshore platforms.However,the design method that regards different ocean environmental variables as independent is conservative.In the present study,we introduce a bivariate sample consisting of the maximum wave heights and concomitant wind speeds of the threshold by using the peak-over-threshold and declustering methods.After selecting the appropriate bivariate copulas and univariate distributions and blocking the sample into years,the bivariate compound distribution of annual extreme wave heights and concomitant wind speeds is constructed.Two joint design criteria,namely,the joint probability density method and the conditional probability method,are applied to obtain the joint return values of significant wave heights and wind speeds.Results show that(28.5±0.5)m s^(-1)is the frequently obtained wind speed based on the Atlantic dataset,and these joint design values are more appropriate than those calculated by univariate analysis in the fatigue design. 展开更多
关键词 joint design wave height wind speed compound distribution COPULA
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The Influence of Sea Sprays on Drag Coefficient at High Wind Speed
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作者 SHI Hongyuan LI Qingjie +4 位作者 WANG Zhaowei ZHANG Xuri LI Huaqing XING Hao ZHANG Kuncheng 《Journal of Ocean University of China》 SCIE CAS CSCD 2023年第1期21-27,共7页
Field and laboratory observations indicate that the variation of drag coefficient with wind speed at high winds is different from that under low-to-moderate winds.By taking the effects of wave development and sea spra... Field and laboratory observations indicate that the variation of drag coefficient with wind speed at high winds is different from that under low-to-moderate winds.By taking the effects of wave development and sea spray into account,a new parameterization of drag coefficient applicable from low to extreme winds is proposed.It is shown that,under low-to-moderate wind conditions so that the sea spray effects could be neglected,the nondimensional aerodynamic roughness first increases and then decreases with the increasing wave age;whereas under high wind conditions,the drag coefficient decreases with the increasing wind speed due to the modification of the logarithmic wind profile by the effect of sea spray droplets produced by bursting bubbles or wind tearing breaking wave crests.The drag coefficients and sea surface aerodynamic roughnesses reach their maximum values vary under different wave developments.Correspondingly,the reduction of drag coefficient under high winds reduces the increasing rate of friction velocity with increasing wind speed. 展开更多
关键词 sea spray wave age drag coefficient high wind speed
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Contrasting Trend of Wintertime Wind Speed Between Near-surface and Upper Air in China During 1979-2021
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作者 王淞秋 郭建平 +4 位作者 郑晓辉 毋婧炎 冼甜 陈田萌 臧增亮 《Journal of Tropical Meteorology》 SCIE 2023年第3期324-336,共13页
The long-term height-resolved wind trend in China under global warming still needs to be discovered.To fill this gap,in this paper we examined the climatology and long-term(1979-2021)trends of the wintertime wind spee... The long-term height-resolved wind trend in China under global warming still needs to be discovered.To fill this gap,in this paper we examined the climatology and long-term(1979-2021)trends of the wintertime wind speed at the near-surface and upper atmosphere in China based on long-term radiosonde measurements.At 700,500,and 400 hPa,much higher wind speed was found over eastern China,compared with western China.At 300,200,and 100 hPa,maximum wind speed was observed in the latitude zone of around 25-35°N.Furthermore,westerly winds dominated most parts of China between 20°N and 50°N at altitudes from 700 hPa to 100 hPa.A stilling was revealed for the near-surface wind from 1979-2003.From 2004 onward,the near-surface wind speed reversed from decreasing to increasing.This could be largely due to the joint impact of reduced surface roughness length,aerosol optical depth(AOD),and increased sensible heat flux in the ground surface.The decrease of AOD tended to reduce aerosol radiative forcing,thereby destabilizing the planetary boundary layer(PBL).By comparison,the wintertime wind in the upper atmosphere exhibited a significant monotonic upward trend,albeit with varying magnitude for different altitudes.In the upper troposphere,the wintertime maximum wind was observed along a westerly jet stream,with a pronounced upward trend within the zone approximately bounded by latitudes of 25-50°N,particularly above 500 hPa.This accelerating wind observed in the upper troposphere and lower stratosphere could be closely associated with the large planetary-scale meridional temperature trend gradient.Besides,the direction for the wind at the near-surface and lower troposphere(925 and 850 hPa)exhibited a larger variance over the period 1979-2021,which could be associated with the strong turbulence of PBL caused by the heterogeneous land surface.For those pressure levels higher than 850 hPa,large wind directional variance was merely found to the south of 25°N.The findings from long-term radiosonde measurements in winter over China shed light on the changes in wind speed on the ground and upper atmosphere under global warming from an observational perspective. 展开更多
关键词 RADIOSONDE wind speed long-term trend thermal wind balance China
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Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model
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作者 R.Surendran Youseef Alotaibi Ahmad F.Subahi 《Computer Systems Science & Engineering》 SCIE EI 2023年第9期3371-3386,共16页
High precision and reliable wind speed forecasting have become a challenge for meteorologists.Convective events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb... High precision and reliable wind speed forecasting have become a challenge for meteorologists.Convective events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb daily life.For accurate prediction of wind speed and overcoming its uncertainty of change,several prediction approaches have been presented over the last few decades.As wind speed series have higher volatility and nonlinearity,it is urgent to present cutting-edge artificial intelligence(AI)technology.In this aspect,this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning(IWSP-CSODL)method.The presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter optimizer.In the presented IWSP-CSODL model,the prediction process is performed via a convolutional neural network(CNN)based long short-term memory with autoencoder(CBLSTMAE)model.To optimally modify the hyperparameters related to the CBLSTMAE model,the chicken swarm optimization(CSO)algorithm is utilized and thereby reduces the mean square error(MSE).The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios.The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models. 展开更多
关键词 WEATHER wind speed predictive model chicken swarm optimization hybrid deep learning
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Wind Speed and Power Density Analysis for Sustainable Energy in Batouri, East Region of Cameroon
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作者 Pierre Pokem Elie Bertrand Kengne Signe +1 位作者 Jean Nganhou Yaouba 《Journal of Power and Energy Engineering》 2023年第6期44-55,共12页
This paper develops the modeling of wind speed by Weibull distribution in the intention to evaluate wind energy potential and help for designing small wind energy plant in Batouri in Cameroon. The Weibull distribution... This paper develops the modeling of wind speed by Weibull distribution in the intention to evaluate wind energy potential and help for designing small wind energy plant in Batouri in Cameroon. The Weibull distribution model was developed using wind speed data collected from a metrological station at the small Airport of Batouri. Four numerical methods (Moment method, Graphical method, Empirical method and Energy pattern factor method) were used to estimate weibull parameters K and C. The application of these four methods is effective using a sample wind speed data set. With some statistical analysis, a comparison of the accuracy of each method is also performed. The study helps to determine that Energy pattern factor method is the most effective (K = 3.8262 and C = 2.4659). 展开更多
关键词 Weibull Distribution Mean wind speed Statistical Tests wind Energy
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Improving Performance of Recurrent Neural Networks Using Simulated Annealing for Vertical Wind Speed Estimation
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作者 Shafiqur Rehman HilalH.Nuha +2 位作者 Ali Al Shaikhi Satria Akbar Mohamed Mohandes 《Energy Engineering》 EI 2023年第4期775-789,共15页
An accurate vertical wind speed(WS)data estimation is required to determine the potential for wind farm installation.In general,the vertical extrapolation of WS at different heights must consider different parameters ... An accurate vertical wind speed(WS)data estimation is required to determine the potential for wind farm installation.In general,the vertical extrapolation of WS at different heights must consider different parameters fromdifferent locations,such as wind shear coefficient,roughness length,and atmospheric conditions.The novelty presented in this article is the introduction of two steps optimization for the Recurrent Neural Networks(RNN)model to estimate WS at different heights using measurements from lower heights.The first optimization of the RNN is performed to minimize a differentiable cost function,namely,mean squared error(MSE),using the Broyden-Fletcher-Goldfarb-Shanno algorithm.Secondly,the RNN is optimized to reduce a non-differentiable cost function using simulated annealing(RNN-SA),namely mean absolute error(MAE).Estimation ofWS vertically at 50 m height is done by training RNN-SA with the actualWS data a 10–40 m heights.The estimatedWS at height of 50 m and the measured WS at 10–40 heights are further used to train RNN-SA to obtain WS at 60 m height.This procedure is repeated continuously until theWS is estimated at a height of 180 m.The RNN-SA performance is compared with the standard RNN,Multilayer Perceptron(MLP),Support Vector Machine(SVM),and state of the art methods like convolutional neural networks(CNN)and long short-term memory(LSTM)networks to extrapolate theWS vertically.The estimated values are also compared with realWS dataset acquired using LiDAR and tested using four error metrics namely,mean squared error(MSE),mean absolute percentage error(MAPE),mean bias error(MBE),and coefficient of determination(R2).The numerical experimental results show that the MSE values between the estimated and actualWS at 180mheight for the RNN-SA,RNN,MLP,and SVM methods are found to be 2.09,2.12,2.37,and 2.63,respectively. 展开更多
关键词 Vertical wind speed estimation recurrent neural networks simulated annealing multilayer perceptron support vector machine
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Prediction of Wind Speed Using a Hybrid Regression-Optimization Approach
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作者 Bhuvana Ramachandran Anbazhagan Swaminathan 《Journal of Power and Energy Engineering》 2023年第7期21-35,共15页
Predicting wind speed is a complex task that involves analyzing various meteorological factors such as temperature, humidity, atmospheric pressure, and topography. There are different approaches that can be used to pr... Predicting wind speed is a complex task that involves analyzing various meteorological factors such as temperature, humidity, atmospheric pressure, and topography. There are different approaches that can be used to predict wind speed, and a hybrid optimization approach is one of them. In this paper, the hybrid optimization approach combines a multiple linear regression approach with an optimization technique to achieve better results. In the context of wind speed prediction, this hybrid optimization approach can be used to improve the accuracy of existing prediction models. Here, a Grey Wolf Optimizer based Wind Speed Prediction (GWO-WSP) method is proposed. This approach is tested on the 2016, 2017, 2018, and 2019 Raw Data files from the Great Lakes Environmental Research Laboratories and the National Oceanic and Atmospheric Administration’s (GLERL-NOAA) Chicago Metadata Archive. The test results show that the implementation is successful and the approach yields accurate and feasible results. The computation time for execution of the algorithm is also superior compared to the existing methods in literature. 展开更多
关键词 wind speed Prediction Multiple Linear Regression Grey Wolf Optimizer Accuracy of Results wind Power
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