江苏辐射沙洲水道潮流及悬沙动力研究
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摘要
本论文根据“条子泥促淤并陆工程实验研究”课题1998年秋季部分站位的流速悬沙测量资料针对辐射沙洲海域水道的潮流、悬沙分布及泥沙悬浮输运机制等问题开展了下述研究。
本论文对辐射沙洲东大港水道4号站位连续2个潮次的水文、泥沙测量资料进行了深入的分析、研究，论述了在潮流作用下泥沙的悬浮与沉降变化过程，并对悬沙的垂线分布进行了拟合，给出了对数拟合关系式，并对该关系式进行了讨论和验证，初步明确了西洋水道主槽的悬沙垂线分布基本规律。本文对江苏省辐射沙洲海域东大港与豆腐渣腰门交汇水域5号站位连续两个潮次的悬沙分层测量资料进行研究、分析，对该交汇区域的悬沙特征和泥沙悬浮、输移规律有了进一步的认识。分析发现该站位悬沙垂线分布主要体现为两种分布形式，并给出了相应拟合表达式。作者根据辐射沙洲海域东大港水道错站位1998年秋季实测水文泥沙资料应用机制分解、谱分析、小波分析方法探讨潮流与含沙量的变化规律及相互关系，这对于揭示辐射沙洲海域水沙输移机制及沙洲水道动态演变具有重要意义。
本文在参考有关文献相关研究的基础上，对辐射沙洲海域东大港水道有关泥沙输运等方面进行BP神经网络模型预测方面的研究。根据东大港水道4号站位连续两个潮次的流速、悬沙含量以及水深的有关时序测量资料，取连续2个潮次26个有关测量(计算)数据中的前21组数据为学习样本，后5组数据为检验样本，建立典型的前向型BP网络模型。由学习样本得出各层间的的最优权值矩阵，并进行仿真模拟与验证。利用BP神经网络模型对下述问题进行模拟：1)垂线平均速度U_z，U_z／H作为输入向量，垂线平均悬沙含量值C_z作为输出向量；2)垂线平均流速U_z，临底层含沙量C_b作为输入向量，垂线平均含沙量C_z为输出向量；3)利用表层流速U_s，水深H，来仿真预测瞬时单宽悬沙通量值E；4)表层流速U_s为输入向量，单宽推移质通量q_b为输出向量；5)前时刻的U_s(n)／H(n)，垂线平均悬沙含量C_z(n)与该时刻的U_s(n+1)／H(n+1)作为输入变量，该时刻的垂线平均含沙量C_z(n+1)作为输出变量。模拟结果表明，不论从模拟输出结果与实测计算值的时间过程的曲线对比上，模拟输出结果的相对误差方面，模拟输出结果与测量计算值的线性回归相关程度来看，运用BP神经网络模型可以对上述有关输入输出问题建立精度较高的非线性关系。可以利用BP神经网络模型进行上述相关问题的预报。
本文对辐射沙洲海域东大港4号站位连续2个潮周期的流速测量资料进行了分析、研究，拟合给出了流速垂线分布关系，并根据实际测量资料确定了该站位6个水层的浑水水流尾流函数项表达式。这些表达式表明浑水水流尾流函数是相对深度和摩阻速度的函数。探讨分析了尾流函数的空间分布和潮周期过程。非恒定流的水流尾流函数项的准确表达，对于揭示含沙水流流速结构及水、沙相影响作用规律将起到启发和推动作用。进一步研究得出混水水流的尾流函数项与悬沙含量、悬沙含量梯度之间在潮周期大部分时间里有较好的对应关系。测量结果表明，该站位悬沙含量较小，所以悬沙的浓度和梯度对流体流速的分布结构的影响不是那么直接明显。尾流函数、悬沙含量及悬沙含量垂线梯度都是潮流动力及相应紊动作用的结果。本文同时利用BP神经网络模型对4号站位的流速分布的尾流函数与含沙量、含沙量垂线梯度、相对位置的函数关系进行了模拟。模拟结果表明，尾流函数与含沙量、含沙量梯度、相对位置可以建立对应关系，但关系较为复杂。
本文对辐射沙洲陈家坞槽14#站位连续2个潮次的流速测量资料进行了分析、研究。该站位体现为落强涨弱的潮流特点。对该站位的潮流特征、各层流速的时空过程进行了总结、论述。对各层潮流的椭圆分布、潮平均流速分布及其余流特征做了细致的分析。研究并拟合给出该站位潮流流速垂向分布关系，在此基础上推演出该站位潮流流速垂向分布实用计算关系式。
On the basis of analyzing the two tidal periods successive survey results on the tidal current and suspended sediment at 4 station in the tidal channel called DongDaGang , the characters of tidal current and the rule of suspended sediment change are further studied, the surveyed suspended sediment concentration data were fitted to the relative depth, the vertical distributing character of suspended sediment was discovered. Also on the basis of analyzing the two tidal periods successive survey results on the tidal current and suspended sediment at No.5 station in the tidal channel at the crunode of DongDaGang and Dayaomen tidal channel , the tidal characters and process of suspended sediments are further understood. The two main vertical distributing form of suspended sediments are discovered and the corresponding fitting relationships are made certain. The interaction between sea topography and tidal current is analyzed and studied in the article.
Suspended sediment concentration in the water exhibits a complicated non-linear dynamical process in the tidal channel.Seeing that artificial neural network possesses the capabilities of dealing with complex non-linear dynamial systems,this paper demonstrates how it can be used in suspended sediment concentration and sediment transport prediction as a new approach by considering the non-linear relationship between sediment,sediment transport and its affecting factors. The Back Propagation(BP) model of artifical neural net works is applied to predict sediment concentration and its transport in No.4 station.The simulating and predicting results show the BP model not only possesses high accuracy of fitness but also attains precise prediction as well.
Based on the field data of flow and sediment at No.4 station in Dongdagang tidal channel, the component items of suspended sediment transport are analysed and studied the by the mechanism decompose method, It comes to the conclusion that there exist significant tidal pumping effect and strong exchange between suspended load and bedload during the transport process.spectral analysis and wavelet analysis are also applied to analyse the fluctuations of the suspended sediment concentration,tidal flow velocity and sediment transporation. The result shows that the sediment transportation is primarily affected by tidal flow only in lower-frequency, and the closed correlation exists between velocity and suspended sediment in high-frequence, which is considered to be very important for sediment transportation.
On the basis of analyzing the two tidal periods successive survey results on the tidal current at No.4 station in the tidal channel called DongDaGang, the characters of tidal current and space distributing of current vector are further studied. The vertical distributing of current velocity is studied and the surveyed current velocity data are fitted to the relative depth. The vertical distributing relationship between velocity and relative depth is discovered. Based on the surveyed current velocity data, the remainder current functions of six water layers are calibrated by fitting method. The remainder current function term is the function of friction velocity and relative depth. The relationship between the remainder current functions and suspended sediment concentration & its vertical grads are discovered. The relationship is only formal connection not the direct dynamical consequence. The remainder current functions and the suspended sediment concentration & its vertical grads all are the results of hyd
rokinetics.
Based on the current velocity and corresponding current direction data of water flow at No.4 station in Dongdagang tidal channel, current velocity and its direction of each layer is simulated and predicted by the Back Propagation(BP) model of artificial neural net works. The simulated and predicted results show the distribution and tidal processes of current velocity and its direction are predictable with velocity and direction of surface layer & water depth by the BP model.
On the basis of analyzing the two tid
本论文对辐射沙洲东大港水道4号站位连续2个潮次的水文、泥沙测量资料进行了深入的分析、研究，论述了在潮流作用下泥沙的悬浮与沉降变化过程，并对悬沙的垂线分布进行了拟合，给出了对数拟合关系式，并对该关系式进行了讨论和验证，初步明确了西洋水道主槽的悬沙垂线分布基本规律。本文对江苏省辐射沙洲海域东大港与豆腐渣腰门交汇水域5号站位连续两个潮次的悬沙分层测量资料进行研究、分析，对该交汇区域的悬沙特征和泥沙悬浮、输移规律有了进一步的认识。分析发现该站位悬沙垂线分布主要体现为两种分布形式，并给出了相应拟合表达式。作者根据辐射沙洲海域东大港水道错站位1998年秋季实测水文泥沙资料应用机制分解、谱分析、小波分析方法探讨潮流与含沙量的变化规律及相互关系，这对于揭示辐射沙洲海域水沙输移机制及沙洲水道动态演变具有重要意义。
本文在参考有关文献相关研究的基础上，对辐射沙洲海域东大港水道有关泥沙输运等方面进行BP神经网络模型预测方面的研究。根据东大港水道4号站位连续两个潮次的流速、悬沙含量以及水深的有关时序测量资料，取连续2个潮次26个有关测量(计算)数据中的前21组数据为学习样本，后5组数据为检验样本，建立典型的前向型BP网络模型。由学习样本得出各层间的的最优权值矩阵，并进行仿真模拟与验证。利用BP神经网络模型对下述问题进行模拟：1)垂线平均速度U_z，U_z／H作为输入向量，垂线平均悬沙含量值C_z作为输出向量；2)垂线平均流速U_z，临底层含沙量C_b作为输入向量，垂线平均含沙量C_z为输出向量；3)利用表层流速U_s，水深H，来仿真预测瞬时单宽悬沙通量值E；4)表层流速U_s为输入向量，单宽推移质通量q_b为输出向量；5)前时刻的U_s(n)／H(n)，垂线平均悬沙含量C_z(n)与该时刻的U_s(n+1)／H(n+1)作为输入变量，该时刻的垂线平均含沙量C_z(n+1)作为输出变量。模拟结果表明，不论从模拟输出结果与实测计算值的时间过程的曲线对比上，模拟输出结果的相对误差方面，模拟输出结果与测量计算值的线性回归相关程度来看，运用BP神经网络模型可以对上述有关输入输出问题建立精度较高的非线性关系。可以利用BP神经网络模型进行上述相关问题的预报。
本文对辐射沙洲海域东大港4号站位连续2个潮周期的流速测量资料进行了分析、研究，拟合给出了流速垂线分布关系，并根据实际测量资料确定了该站位6个水层的浑水水流尾流函数项表达式。这些表达式表明浑水水流尾流函数是相对深度和摩阻速度的函数。探讨分析了尾流函数的空间分布和潮周期过程。非恒定流的水流尾流函数项的准确表达，对于揭示含沙水流流速结构及水、沙相影响作用规律将起到启发和推动作用。进一步研究得出混水水流的尾流函数项与悬沙含量、悬沙含量梯度之间在潮周期大部分时间里有较好的对应关系。测量结果表明，该站位悬沙含量较小，所以悬沙的浓度和梯度对流体流速的分布结构的影响不是那么直接明显。尾流函数、悬沙含量及悬沙含量垂线梯度都是潮流动力及相应紊动作用的结果。本文同时利用BP神经网络模型对4号站位的流速分布的尾流函数与含沙量、含沙量垂线梯度、相对位置的函数关系进行了模拟。模拟结果表明，尾流函数与含沙量、含沙量梯度、相对位置可以建立对应关系，但关系较为复杂。
本文对辐射沙洲陈家坞槽14#站位连续2个潮次的流速测量资料进行了分析、研究。该站位体现为落强涨弱的潮流特点。对该站位的潮流特征、各层流速的时空过程进行了总结、论述。对各层潮流的椭圆分布、潮平均流速分布及其余流特征做了细致的分析。研究并拟合给出该站位潮流流速垂向分布关系，在此基础上推演出该站位潮流流速垂向分布实用计算关系式。
On the basis of analyzing the two tidal periods successive survey results on the tidal current and suspended sediment at 4 station in the tidal channel called DongDaGang , the characters of tidal current and the rule of suspended sediment change are further studied, the surveyed suspended sediment concentration data were fitted to the relative depth, the vertical distributing character of suspended sediment was discovered. Also on the basis of analyzing the two tidal periods successive survey results on the tidal current and suspended sediment at No.5 station in the tidal channel at the crunode of DongDaGang and Dayaomen tidal channel , the tidal characters and process of suspended sediments are further understood. The two main vertical distributing form of suspended sediments are discovered and the corresponding fitting relationships are made certain. The interaction between sea topography and tidal current is analyzed and studied in the article.
Suspended sediment concentration in the water exhibits a complicated non-linear dynamical process in the tidal channel.Seeing that artificial neural network possesses the capabilities of dealing with complex non-linear dynamial systems,this paper demonstrates how it can be used in suspended sediment concentration and sediment transport prediction as a new approach by considering the non-linear relationship between sediment,sediment transport and its affecting factors. The Back Propagation(BP) model of artifical neural net works is applied to predict sediment concentration and its transport in No.4 station.The simulating and predicting results show the BP model not only possesses high accuracy of fitness but also attains precise prediction as well.
Based on the field data of flow and sediment at No.4 station in Dongdagang tidal channel, the component items of suspended sediment transport are analysed and studied the by the mechanism decompose method, It comes to the conclusion that there exist significant tidal pumping effect and strong exchange between suspended load and bedload during the transport process.spectral analysis and wavelet analysis are also applied to analyse the fluctuations of the suspended sediment concentration,tidal flow velocity and sediment transporation. The result shows that the sediment transportation is primarily affected by tidal flow only in lower-frequency, and the closed correlation exists between velocity and suspended sediment in high-frequence, which is considered to be very important for sediment transportation.
On the basis of analyzing the two tidal periods successive survey results on the tidal current at No.4 station in the tidal channel called DongDaGang, the characters of tidal current and space distributing of current vector are further studied. The vertical distributing of current velocity is studied and the surveyed current velocity data are fitted to the relative depth. The vertical distributing relationship between velocity and relative depth is discovered. Based on the surveyed current velocity data, the remainder current functions of six water layers are calibrated by fitting method. The remainder current function term is the function of friction velocity and relative depth. The relationship between the remainder current functions and suspended sediment concentration & its vertical grads are discovered. The relationship is only formal connection not the direct dynamical consequence. The remainder current functions and the suspended sediment concentration & its vertical grads all are the results of hyd
rokinetics.
Based on the current velocity and corresponding current direction data of water flow at No.4 station in Dongdagang tidal channel, current velocity and its direction of each layer is simulated and predicted by the Back Propagation(BP) model of artificial neural net works. The simulated and predicted results show the distribution and tidal processes of current velocity and its direction are predictable with velocity and direction of surface layer & water depth by the BP model.
On the basis of analyzing the two tid
引文
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林晖，闾国年，宋志尧，等著．东中国海潮波系统与海岸演变模拟研究[M]．北京：科学出版社，2000．
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