During the launching stage,hydrodynamic pressure and adapters' reaction loads can influence the vehicle's rigid motion as well as cause its structural vibration,which is a typical rigid-flexible coupling dynam...During the launching stage,hydrodynamic pressure and adapters' reaction loads can influence the vehicle's rigid motion as well as cause its structural vibration,which is a typical rigid-flexible coupling dynamic problem. This paper presents a 2-D rigid-flexible coupling model to calculate the vehicle's dynamic responses in that period.The vehicle was equivalent to a flexure beam with axial deformation. Hybrid coordinate and modal superposition methods were used to describe its large rigid displacement and small deformation. By the second Lagrange equation,the vehicle centroid's displacements,rotational angle and modal coordinates were chosen as generalized coordinates and then the vehicle 's rigid-flexible coupling dynamic equations were obtained. By numerical simulation,the results of vehicle's motion parameters and transverse internal loads were acquired.The calculation results showed that differences of the vehicle's motion parameters between the rigid-flexible coupling model and the rigid body assumption are noticeable and the peak magnitude of the vehicle's transverse internal loads in the rigid-flexible coupling model is higher remarkably than that in the rigid body assumption.展开更多
The computational cost required by the Ensemble Kalman Filter （EnKF） is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation （OI） or three-dimension variational as- similation ...The computational cost required by the Ensemble Kalman Filter （EnKF） is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation （OI） or three-dimension variational as- similation （3DVAR）. Ensemble optimal interpolation （EnOI）, a crudely simplified implementation of EnKF, is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF. In this paper, to compromise between computational cost and dynamic covariance, we use the idea of ＂dressing＂ a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covariance. The term ＂dressing＂ means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles. This dressing EnKF （DrEnKF for short） scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model （HYCOM） over a four-year period. Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble. Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members. Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset. The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors （RMSE） at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.展开更多
文摘During the launching stage,hydrodynamic pressure and adapters' reaction loads can influence the vehicle's rigid motion as well as cause its structural vibration,which is a typical rigid-flexible coupling dynamic problem. This paper presents a 2-D rigid-flexible coupling model to calculate the vehicle's dynamic responses in that period.The vehicle was equivalent to a flexure beam with axial deformation. Hybrid coordinate and modal superposition methods were used to describe its large rigid displacement and small deformation. By the second Lagrange equation,the vehicle centroid's displacements,rotational angle and modal coordinates were chosen as generalized coordinates and then the vehicle 's rigid-flexible coupling dynamic equations were obtained. By numerical simulation,the results of vehicle's motion parameters and transverse internal loads were acquired.The calculation results showed that differences of the vehicle's motion parameters between the rigid-flexible coupling model and the rigid body assumption are noticeable and the peak magnitude of the vehicle's transverse internal loads in the rigid-flexible coupling model is higher remarkably than that in the rigid body assumption.
基金supported by the Knowledge Innovation Program of Chinese Academy of Sciences (Grant No. KZCX1-YW-12-03)National Basic Research Program of China (2006CB403600)+3 种基金Project of Young Scientists Fund by National Natural Sciences Foundation of China (Grant No. 40606008)National Science and Technology Infrastructure Program(2006BAC03B04)supported by National Natural Sciences Foundation of China (Grant No.40531006)supported by a private donation from Trond Mohn c/o Frank Mohn AS, Bergenand the MERSEA project from the European Commission (Grant No. SIP3-CT-2003-502885)
文摘The computational cost required by the Ensemble Kalman Filter （EnKF） is much larger than that of some simpler assimilation schemes, such as Optimal Interpolation （OI） or three-dimension variational as- similation （3DVAR）. Ensemble optimal interpolation （EnOI）, a crudely simplified implementation of EnKF, is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF. In this paper, to compromise between computational cost and dynamic covariance, we use the idea of ＂dressing＂ a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covariance. The term ＂dressing＂ means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles. This dressing EnKF （DrEnKF for short） scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model （HYCOM） over a four-year period. Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble. Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members. Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset. The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors （RMSE） at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.
