The simulation of the whole ship-bridge collision process can be effectively carried out by nonlinear dynamic finite element method. Based on the simple description of the theory, a scenario of a 40000 DWT oil tanker ...The simulation of the whole ship-bridge collision process can be effectively carried out by nonlinear dynamic finite element method. Based on the simple description of the theory, a scenario of a 40000 DWT oil tanker colliding with a bridge across the Yangtze River is designed for simulation. The technology of structure modeling and the determination of related parameters are introduced. The deformation of the bulb bow, the history,of collision force change, the exchange of collision energy and the stress distribution. of the bridge pier-are described in detail, which: are of great value to bridge design and bridge pier damage estimation. mechanical characters in the process of ship-bridge collision are described. More accurate results can be produced by finite element method than that by empirical formulas and simplified analytical methods.展开更多
Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent st...Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method (FEM) which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network (ANN). The radial basis function neural network (RBFNN) employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation efficiency in application using MATLAB software.展开更多
文摘The simulation of the whole ship-bridge collision process can be effectively carried out by nonlinear dynamic finite element method. Based on the simple description of the theory, a scenario of a 40000 DWT oil tanker colliding with a bridge across the Yangtze River is designed for simulation. The technology of structure modeling and the determination of related parameters are introduced. The deformation of the bulb bow, the history,of collision force change, the exchange of collision energy and the stress distribution. of the bridge pier-are described in detail, which: are of great value to bridge design and bridge pier damage estimation. mechanical characters in the process of ship-bridge collision are described. More accurate results can be produced by finite element method than that by empirical formulas and simplified analytical methods.
基金the National Natural Science Foundation of China (No. 50778131)the National key Technology R&D Pro-gram, Ministry of Science and Technology (No. 2006BAG04B01), China
文摘Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method (FEM) which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network (ANN). The radial basis function neural network (RBFNN) employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation efficiency in application using MATLAB software.
