Data-derived normal mode extraction is an effective method for extracting normal mode depth functions in the absence of marine environmental data.However,when the corresponding singular vectors become nonunique when t...Data-derived normal mode extraction is an effective method for extracting normal mode depth functions in the absence of marine environmental data.However,when the corresponding singular vectors become nonunique when two or more singular values obtained from the cross-spectral density matrix diagonalization are nearly equal,this results in unsatisfactory extraction outcomes for the normal mode depth functions.To address this issue,we introduced in this paper a range-difference singular value decomposition method for the extraction of normal mode depth functions.We performed the mode extraction by conducting singular value decomposition on the individual frequency components of the signal's cross-spectral density matrix.This was achieved by using pressure and its range-difference matrices constructed from vertical line array data.The proposed method was validated using simulated data.In addition,modes were successfully extracted from ambient noise.展开更多
For computation of large amplitude motions of ships fastened to a dock, a fast evaluation scheme is implemented for computation of the time-domain Green function for finite water depth. Based on accurate evaluation of...For computation of large amplitude motions of ships fastened to a dock, a fast evaluation scheme is implemented for computation of the time-domain Green function for finite water depth. Based on accurate evaluation of the Green function directly, a fast approximation method for the Green function is developed by use of Chebyshev polynomials. Examinations are carried out of the accuracy of the Green function and its derivatives from the scheme. It is shown that when an appropriate number of polynomial terms are used, very accurate approximation can be obtained.展开更多
基金supported in part by the Young Scientists Fund of National Natural Science Foundation of China (No.42206226)the National Key Research and Development Program of China (No.2021YFC3101603)。
文摘Data-derived normal mode extraction is an effective method for extracting normal mode depth functions in the absence of marine environmental data.However,when the corresponding singular vectors become nonunique when two or more singular values obtained from the cross-spectral density matrix diagonalization are nearly equal,this results in unsatisfactory extraction outcomes for the normal mode depth functions.To address this issue,we introduced in this paper a range-difference singular value decomposition method for the extraction of normal mode depth functions.We performed the mode extraction by conducting singular value decomposition on the individual frequency components of the signal's cross-spectral density matrix.This was achieved by using pressure and its range-difference matrices constructed from vertical line array data.The proposed method was validated using simulated data.In addition,modes were successfully extracted from ambient noise.
文摘For computation of large amplitude motions of ships fastened to a dock, a fast evaluation scheme is implemented for computation of the time-domain Green function for finite water depth. Based on accurate evaluation of the Green function directly, a fast approximation method for the Green function is developed by use of Chebyshev polynomials. Examinations are carried out of the accuracy of the Green function and its derivatives from the scheme. It is shown that when an appropriate number of polynomial terms are used, very accurate approximation can be obtained.
