自航耙吸挖泥船疏浚优化的机理分析.doc
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自航耙吸挖泥船疏浚优化的机理分析,摘要随着疏浚事业的蓬勃发展，自航耙吸挖泥船的使用越来越广泛，无论是河道清淤还是吹填工作都可以看到耙吸挖泥船的身影。近年来，耙吸挖泥船的发展趋势一直是自动化、高效化，而目前耙吸挖泥船的疏浚效率比较低下，使得对疏浚优化的研究变得尤为重要。本课题在“交通部疏浚重点实验室基金”的支持下，采用遗传算法对耙吸挖泥船的疏浚过程优化，...
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摘 要
随着疏浚事业的蓬勃发展，自航耙吸挖泥船的使用越来越广泛，无论是河道清淤还是吹填工作都可以看到耙吸挖泥船的身影。近年来，耙吸挖泥船的发展趋势一直是自动化、高效化，而目前耙吸挖泥船的疏浚效率比较低下，使得对疏浚优化的研究变得尤为重要。
本课题在“交通部疏浚重点实验室基金”的支持下，采用遗传算法对耙吸挖泥船的疏浚过程优化，以周期产能为优化目标，搜索最优控制参数，达到周期产能最大。并开发了参数控制以及疏浚结果的人机界面指导工程化应用。本文的主要工作如下：
针对耙吸挖泥船耙头工作的不同环境，建立了不含高压冲水和含高压冲水这两种类型的耙头数学模型，并分别采用不同海域的疏浚数据对这两种耙头数学模型进行验证，证明了耙头模型的正确性，可以利用此耙头数学模型估计耙头的吸入密度，为操船者提供便利。
采用上海长江口实船疏浚数据，结合本文建立验证的耙头吸入密度模型和泥舱沉积模型，通过遗传算法搜索最优的航速、泥浆进舱流量以及高压冲水的最优值，对周期产能优化。仿真结果显示，优化后的干土吨和周期产能均有所增长，可在工程化应用中提高疏浚效率以及经济效益。
基于Visual C++平台开发了指导工程化应用的人机界面。界面可随时显示疏浚的历史数据，在疏浚过程中，通过优化的控制参数不断调整实船疏浚的工作状态，提高疏浚效率。并显示疏浚的干土吨变化曲线以及周期产能的大小，为操船者进行余下的疏浚工程提供决策依据。
关键词 自航耙吸挖泥船；遗传算法；疏浚优化；人机界面
Abstract
With the vigorous development of the dredging business, it has become more and more preva lent to use the self-propelled trailing suction hopper dredger. The figure of trailing suction hopper dredger could be seen in whether dredging or hydraulic fill work. In recent years, the automation and high-efficiency has been the development trend of trailing suction hopper dredger. However, the dredging efficiency of the trailing suction hopper dredger is relatively low，which makes the study of dredging optimization particularly important.
With the support of the “Ministry Key Laboratory Fund for Dredging”, the project intends to adopt the genetic algorithms to optimize the dredging process of the trailing suction hopper dredger, and with the optimal target of the period capacity, The project targets at the searches for the optimal control parameters in order to achieve the maximum production cycle. Beside, it establishes the control of parameters and the human-machine interface reflecting the results of dredging to direct the application in engineering. The main contents of this paper are as follows:
Aiming at the work of trailing suction hopper dredger rake head in different environments, this paper sets up two types of mathematical models of the head of rake in a non-high-pressure flushing and pressure flushing, and checks the two models by using different dredging data of various waters. And then, it proves the correctness of the models used to estimate the inhaled density of the rake head, which will provide convenience for the operation of ships.
In this paper, the actual dredging data of Changjiang River in Shanghai are used in connection with the model of spoil hopper verified and the model of inhaled density in the head of rake is verified in the paper. It searches for the optimal value of the speed, slurry flow rate entering in the hopper and pressure flush by genetic algorithm and then optimizes the cycle production. The simulation results show that both the tonnage of the optimized dry soil and the cycle production have increased, which can improves the productivity of dredging and economic benefits in engineering applications.
Finally, the paper establishes a human-machine interface by using the development platform of Visual C++ to guide the engineering application. The interface can show the historical data of dredging at any time. During the actual dredging process, the working condition of dredging can be continuously adjusted with the optimal control parameters to improve the dredging efficiency. Meanwhile, the interface can also display the curve reflecting the change of the tonnage of the dry soil and the size of the periodic capacity, which provides the operators with the basis of decision making to operate the remaining dredging.
Keywords self-propelled trailing suction hopper dredger; genetic algorithm; dredging optimization; human-machine interface
目 录
摘 要 I
Abstract III
第1章 绪论 1
1.1 课题的研究背景及意义 1
1.1.1 课题的研究背景 1
1.1.2 课题的研究意义 2
1.2 课题的国内外现状和发展趋势 3
1.3 本文研究的主要内容 5
第2章 遗传算法优化疏浚的理论研究 7
2.1 引言 7
2.2 遗传算法的介绍 7
2.3 选用的遗传算法的流程 8
2.3.1 确定适应度函数 9
2.3.2 编码 9
2.3.3 产生初始种群 10
2.3.4 选择 10
2.3.5 个体交叉 11
2.3.6 个体变异 11
2.3.7 解码 12
2.4 遗传算法中参数的选定 12
2.5 耙吸挖泥船疏浚优化选用遗传算法的依据 13
2.6 本章小结 14
第3章 自航耙吸挖泥船耙头模型的分析和仿真验证 15
3.1 引言 15
3.2 耙头概述 15
3.3 不含高压冲水的耙头研究 17
3.3.1 不含高压冲水耙头的数学建模 17
3.3.2 模型的参数估计 18
3.3.3 模型..
随着疏浚事业的蓬勃发展，自航耙吸挖泥船的使用越来越广泛，无论是河道清淤还是吹填工作都可以看到耙吸挖泥船的身影。近年来，耙吸挖泥船的发展趋势一直是自动化、高效化，而目前耙吸挖泥船的疏浚效率比较低下，使得对疏浚优化的研究变得尤为重要。
本课题在“交通部疏浚重点实验室基金”的支持下，采用遗传算法对耙吸挖泥船的疏浚过程优化，以周期产能为优化目标，搜索最优控制参数，达到周期产能最大。并开发了参数控制以及疏浚结果的人机界面指导工程化应用。本文的主要工作如下：
针对耙吸挖泥船耙头工作的不同环境，建立了不含高压冲水和含高压冲水这两种类型的耙头数学模型，并分别采用不同海域的疏浚数据对这两种耙头数学模型进行验证，证明了耙头模型的正确性，可以利用此耙头数学模型估计耙头的吸入密度，为操船者提供便利。
采用上海长江口实船疏浚数据，结合本文建立验证的耙头吸入密度模型和泥舱沉积模型，通过遗传算法搜索最优的航速、泥浆进舱流量以及高压冲水的最优值，对周期产能优化。仿真结果显示，优化后的干土吨和周期产能均有所增长，可在工程化应用中提高疏浚效率以及经济效益。
基于Visual C++平台开发了指导工程化应用的人机界面。界面可随时显示疏浚的历史数据，在疏浚过程中，通过优化的控制参数不断调整实船疏浚的工作状态，提高疏浚效率。并显示疏浚的干土吨变化曲线以及周期产能的大小，为操船者进行余下的疏浚工程提供决策依据。
关键词 自航耙吸挖泥船；遗传算法；疏浚优化；人机界面
Abstract
With the vigorous development of the dredging business, it has become more and more preva lent to use the self-propelled trailing suction hopper dredger. The figure of trailing suction hopper dredger could be seen in whether dredging or hydraulic fill work. In recent years, the automation and high-efficiency has been the development trend of trailing suction hopper dredger. However, the dredging efficiency of the trailing suction hopper dredger is relatively low，which makes the study of dredging optimization particularly important.
With the support of the “Ministry Key Laboratory Fund for Dredging”, the project intends to adopt the genetic algorithms to optimize the dredging process of the trailing suction hopper dredger, and with the optimal target of the period capacity, The project targets at the searches for the optimal control parameters in order to achieve the maximum production cycle. Beside, it establishes the control of parameters and the human-machine interface reflecting the results of dredging to direct the application in engineering. The main contents of this paper are as follows:
Aiming at the work of trailing suction hopper dredger rake head in different environments, this paper sets up two types of mathematical models of the head of rake in a non-high-pressure flushing and pressure flushing, and checks the two models by using different dredging data of various waters. And then, it proves the correctness of the models used to estimate the inhaled density of the rake head, which will provide convenience for the operation of ships.
In this paper, the actual dredging data of Changjiang River in Shanghai are used in connection with the model of spoil hopper verified and the model of inhaled density in the head of rake is verified in the paper. It searches for the optimal value of the speed, slurry flow rate entering in the hopper and pressure flush by genetic algorithm and then optimizes the cycle production. The simulation results show that both the tonnage of the optimized dry soil and the cycle production have increased, which can improves the productivity of dredging and economic benefits in engineering applications.
Finally, the paper establishes a human-machine interface by using the development platform of Visual C++ to guide the engineering application. The interface can show the historical data of dredging at any time. During the actual dredging process, the working condition of dredging can be continuously adjusted with the optimal control parameters to improve the dredging efficiency. Meanwhile, the interface can also display the curve reflecting the change of the tonnage of the dry soil and the size of the periodic capacity, which provides the operators with the basis of decision making to operate the remaining dredging.
Keywords self-propelled trailing suction hopper dredger; genetic algorithm; dredging optimization; human-machine interface
目 录
摘 要 I
Abstract III
第1章 绪论 1
1.1 课题的研究背景及意义 1
1.1.1 课题的研究背景 1
1.1.2 课题的研究意义 2
1.2 课题的国内外现状和发展趋势 3
1.3 本文研究的主要内容 5
第2章 遗传算法优化疏浚的理论研究 7
2.1 引言 7
2.2 遗传算法的介绍 7
2.3 选用的遗传算法的流程 8
2.3.1 确定适应度函数 9
2.3.2 编码 9
2.3.3 产生初始种群 10
2.3.4 选择 10
2.3.5 个体交叉 11
2.3.6 个体变异 11
2.3.7 解码 12
2.4 遗传算法中参数的选定 12
2.5 耙吸挖泥船疏浚优化选用遗传算法的依据 13
2.6 本章小结 14
第3章 自航耙吸挖泥船耙头模型的分析和仿真验证 15
3.1 引言 15
3.2 耙头概述 15
3.3 不含高压冲水的耙头研究 17
3.3.1 不含高压冲水耙头的数学建模 17
3.3.2 模型的参数估计 18
3.3.3 模型..
