Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to...Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to be constant or parameterized by linear regression and other methods in existing studies,which may limit the accuracy of the oil spill simulation.A parameterization method for wind drift factor(PMOWDF)based on deep learning,which can effectively extract the time-varying characteristics on a regional scale,is proposed in this paper.The method was adopted to forecast the oil spill in the East China Sea.The discrepancies between predicted positions and actual measurement locations of the drifters are obtained using seasonal statistical analysis.Results reveal that PMOWDF can improve the accuracy of oil spill simulation compared with the traditional method.Furthermore,the parameteriza-tion method is validated with satellite observations of the Sanchi oil spill in 2018.展开更多
To study the unsteady aerodynamic loads of high-speed trains in fluctuating crosswinds, the fluctuating winds of a moving point shifting with high-speed trains are calculated in this paper based on Cooper theory and h...To study the unsteady aerodynamic loads of high-speed trains in fluctuating crosswinds, the fluctuating winds of a moving point shifting with high-speed trains are calculated in this paper based on Cooper theory and harmonic superposition method. The computational fluid dynamics method is used to obtain the aerodynamic load coefficients at different mean yaw angles, and the aero- dynamic admittance function is introduced to calculate unsteady aerodynamic loads of high-speed trains in fluctuating winds. Using this method, the standard deviation and maximum value of the aerodynamic force （moment） are simulated. The results show that when the train speed is fixed, the varying mean wind speeds have large impact on the fluctuating value of the wind speeds and aerodynamic loads; in contrast, when the wind speed is fixed, the varying train speeds have little impact on the fluctuating value of the wind speeds or aerodynamic loads. The ratio of standard deviation to 0.SpKU2, or maximum value to 0.5pKU2, can be expressed as the function of mean yaw angle. The peak factors of the side force and roll moment are the same （ - 3.28）, the peak factor of the lift force is - 3.33, and the peak factors of the yaw moment and pitch moment are also the same （- 3.77）.展开更多
基金funded by the Social Science Foundation of Shandong(No.20CXWJ08).
文摘Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to be constant or parameterized by linear regression and other methods in existing studies,which may limit the accuracy of the oil spill simulation.A parameterization method for wind drift factor(PMOWDF)based on deep learning,which can effectively extract the time-varying characteristics on a regional scale,is proposed in this paper.The method was adopted to forecast the oil spill in the East China Sea.The discrepancies between predicted positions and actual measurement locations of the drifters are obtained using seasonal statistical analysis.Results reveal that PMOWDF can improve the accuracy of oil spill simulation compared with the traditional method.Furthermore,the parameteriza-tion method is validated with satellite observations of the Sanchi oil spill in 2018.
基金supported by the 2013 Doctoral Innovation Funds of Southwest Jiaotong Universitythe Fundamental Research Funds for the Central Universities,the National Key Technology R&D Program of China (2009BAG12A01-C09)the High-Speed Railway Basic Research Fund Key Project (U1234208)
文摘To study the unsteady aerodynamic loads of high-speed trains in fluctuating crosswinds, the fluctuating winds of a moving point shifting with high-speed trains are calculated in this paper based on Cooper theory and harmonic superposition method. The computational fluid dynamics method is used to obtain the aerodynamic load coefficients at different mean yaw angles, and the aero- dynamic admittance function is introduced to calculate unsteady aerodynamic loads of high-speed trains in fluctuating winds. Using this method, the standard deviation and maximum value of the aerodynamic force （moment） are simulated. The results show that when the train speed is fixed, the varying mean wind speeds have large impact on the fluctuating value of the wind speeds and aerodynamic loads; in contrast, when the wind speed is fixed, the varying train speeds have little impact on the fluctuating value of the wind speeds or aerodynamic loads. The ratio of standard deviation to 0.SpKU2, or maximum value to 0.5pKU2, can be expressed as the function of mean yaw angle. The peak factors of the side force and roll moment are the same （ - 3.28）, the peak factor of the lift force is - 3.33, and the peak factors of the yaw moment and pitch moment are also the same （- 3.77）.
