舰船摇荡混沌动力学分析及其时域预报研究
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摘要
舰船在波浪中六自由度摇荡瞬时值、幅值等的预报——摇荡时域预报,是一项为国际航运界、船舶工程界,尤其是各国海军所关注而至今未能很好解决的课题。但从公开发表的文献看,至今实船摇荡的时域预报仍限于10秒以内,严重制约了其应用。究其原因,在于对波浪中舰船摇荡机理认识不清。为此,本文力求发掘舰船摇荡运动中所蕴含的非线性动力学特性,并提出有针对性的预报方法,以期有效提高预报精度和增加预报时长,使之能用于舰船航海实践。
(1)详细分析了舰船摇荡运动的非线性机理：在规则波中,大幅非线性横摇及其耦合运动,是隐含有某种确定性内在规律的混沌运动；而在三维非规则波中的舰船摇荡运动,则由于风浪作用和舰船运动本身的复杂性,难以用确定的微分方程进行描述,只宜通过实测摇荡时历进行分析。
(2)搜集整理了四艘不同类型舰船的海上实测摇荡数据,在采样、滤波等预处理的基础上,按照本文提出的混沌属性判断准则,对这些数据逐一进行了分析,从定性和定量两方面证明,这些舰船摇荡运动中确实蕴涵着一定的混沌特性,从而为舰船摇荡运动预报开辟了新思路。
(3)介绍了以混沌理论为基础的加权一阶局域法和基于最大Lyapunov指数的预报模型。将混沌相空间重构技术用于RBF神经网络的结构优化,使网络本身融入了混沌的确定性规则,提高了神经网络用于混沌时间序列预报的效果。对所选的实测舰船摇荡时历的仿真预报表明,该方法有效预报时间能达到10秒以上
(4)基于混沌理论的预报方法会由于舰船摇荡时历的非平稳性而效果变差,为此,引入经验模式分解(EMD)方法用于降低非平稳性,并提出了基于EMD-RBF的预报方法。对舰船摇荡时历的仿真预测表明,这种方法能从整体上有效降低非平稳性的影响,获得更好的预报效果。
Ship motion prediction in time domain, namely prediction of real time value and amplitude of ship motion in six degrees of freedom, is still an open question which is much concerned by the domain of shipping and ship engineering especially by the navies of the world. But the data available shows that the prediction length of real ship motion is sill less than 10 seconds which limits its application seriously. The reason is the lack of clear understanding of the ship motion mechanism in waves. This paper therefore aims to find out the nonlinearity dynamics held in the ship motion and to present the corresponding prediction methods to prolong the prediction length which can be used in the practice of navigation.
(1) This paper analyzes the nonlinearity mechanism of ship motion which shows the large amplitude nonlinear rolling motion and its coupling motion in regular waves have inherent chaotic characteristic and the ship motion in irregular waves can only be analyzed through ship motion time series due to the effect of wind and waves and the complexity of the ship motion.
(2) This paper collects a large quantity of real ship swaying motion data. On the base of preprocessing of sampling and filtering, this paper analyzes all the data selected according to the criterion judging the chaotic characteristic. The results show that a certain chaotic characteristic is held in the ship sway motion which presents a new approach for ship motion prediction.
(3) This paper presents the Add-weighted One-rank Local-region Model and the add-weighted predicting model based on the largest Lyapunov exponent. The technique of phase space reconstruction is used to optimizing the structure of RBF neural network and thus improves the prediction performance to the chaotic time series. Prediction results on the real ship motion show that the effective prediction length can amount to above 10 seconds.
(4) The prediction methods based on chaotic theory will be less effective due to the nonstationarity of ship motion time series. This paper thus uses the Empirical Mode Decomposition (EMD) to reduce the non-stationary and presents the prediction method based on EMD-RBF. Prediction results on the real ship motion show that this method can obviously reduce the effect of nonstationarity and get better prediction precision.
(1)详细分析了舰船摇荡运动的非线性机理：在规则波中,大幅非线性横摇及其耦合运动,是隐含有某种确定性内在规律的混沌运动；而在三维非规则波中的舰船摇荡运动,则由于风浪作用和舰船运动本身的复杂性,难以用确定的微分方程进行描述,只宜通过实测摇荡时历进行分析。
(2)搜集整理了四艘不同类型舰船的海上实测摇荡数据,在采样、滤波等预处理的基础上,按照本文提出的混沌属性判断准则,对这些数据逐一进行了分析,从定性和定量两方面证明,这些舰船摇荡运动中确实蕴涵着一定的混沌特性,从而为舰船摇荡运动预报开辟了新思路。
(3)介绍了以混沌理论为基础的加权一阶局域法和基于最大Lyapunov指数的预报模型。将混沌相空间重构技术用于RBF神经网络的结构优化,使网络本身融入了混沌的确定性规则,提高了神经网络用于混沌时间序列预报的效果。对所选的实测舰船摇荡时历的仿真预报表明,该方法有效预报时间能达到10秒以上
(4)基于混沌理论的预报方法会由于舰船摇荡时历的非平稳性而效果变差,为此,引入经验模式分解(EMD)方法用于降低非平稳性,并提出了基于EMD-RBF的预报方法。对舰船摇荡时历的仿真预测表明,这种方法能从整体上有效降低非平稳性的影响,获得更好的预报效果。
Ship motion prediction in time domain, namely prediction of real time value and amplitude of ship motion in six degrees of freedom, is still an open question which is much concerned by the domain of shipping and ship engineering especially by the navies of the world. But the data available shows that the prediction length of real ship motion is sill less than 10 seconds which limits its application seriously. The reason is the lack of clear understanding of the ship motion mechanism in waves. This paper therefore aims to find out the nonlinearity dynamics held in the ship motion and to present the corresponding prediction methods to prolong the prediction length which can be used in the practice of navigation.
(1) This paper analyzes the nonlinearity mechanism of ship motion which shows the large amplitude nonlinear rolling motion and its coupling motion in regular waves have inherent chaotic characteristic and the ship motion in irregular waves can only be analyzed through ship motion time series due to the effect of wind and waves and the complexity of the ship motion.
(2) This paper collects a large quantity of real ship swaying motion data. On the base of preprocessing of sampling and filtering, this paper analyzes all the data selected according to the criterion judging the chaotic characteristic. The results show that a certain chaotic characteristic is held in the ship sway motion which presents a new approach for ship motion prediction.
(3) This paper presents the Add-weighted One-rank Local-region Model and the add-weighted predicting model based on the largest Lyapunov exponent. The technique of phase space reconstruction is used to optimizing the structure of RBF neural network and thus improves the prediction performance to the chaotic time series. Prediction results on the real ship motion show that the effective prediction length can amount to above 10 seconds.
(4) The prediction methods based on chaotic theory will be less effective due to the nonstationarity of ship motion time series. This paper thus uses the Empirical Mode Decomposition (EMD) to reduce the non-stationary and presents the prediction method based on EMD-RBF. Prediction results on the real ship motion show that this method can obviously reduce the effect of nonstationarity and get better prediction precision.
引文
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