Development of Design and Calculation of Urban Drainage Pipe System

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Abstract: In the construction of municipal construction and environmental management projects, rainwater and sewer systems often occupy a large proportion of investment. Therefore, how to design urban drainage piping system rationally under the various technical conditions that meet the stipulations is an important issue in the design. The methods and the problems to be solved in the development of design and calculation of sewer system are discussed from the aspects of the optimized design under the fixed pipeline, the plane layout of the pipeline and the runoff model. It can be seen that in the future still need to devote a lot of energy to study and improve its design and calculation methods. Keywords drainage system optimization design layout layout runoff model Introduction The drainage system is an indispensable and important infrastructure in the modern city. It is also the backbone project of urban water pollution control and city drainage waterlogging prevention and flood control. Among them, the investment of rainwater and sewer systems in residential areas and industrial and mining enterprises generally accounts for about 70% of the total drainage system investment [1]. Therefore, it is an important issue in the design work to minimize the capital cost of the pipeline system under various technical conditions that meet the stipulations in the design. The design and calculation method of the traditional drainage system is as follows: After the designers have mastered the relatively complete and reliable design basis data, they can determine a more reasonable sewage pipeline layout according to the principle of pipeline alignment and layout. And then calculate the design flow of each design section, hydraulic calculation diagram or hydraulic calculation table and the relevant design requirements as the control conditions, from upstream to downstream, followed by hydraulic design of each pipe section, find the pipe diameter, slope and In the inspection wells at the end of the elevation and depth of embedding. Calculation, generally only by experience on the pipe diameter and slope, etc. for proper adjustment, in order to achieve the purpose of economic and reasonable, but the reasonable degree by the designer's personal capacity constraints; the other hand, the majority of calculations using repeated access Figure and table methods, work efficiency is low, a long time, is not conducive to the optimization of the design. Since the 1960s, the international community has gradually established mathematical models of various water supply and drainage engineering systems or processes on the basis of experience summaries and mathematical analyzes. As a result, water supply and drainage projects marked by quantitative and semi-quantitative " Reasonable design and management "stage. At the same time, optimized research and practice have been carried out for various types of water supply and drainage systems [2]. In order to explore the optimal design and calculation method of sewer system, many domestic and foreign scientific research, design, teaching units and individuals have done a lot of work and published a large number of articles. From the research results, the design and calculation of drainage pipes by computer not only free designers from the heavy labor of consulting charts, speed up the design progress, but also optimize the entire drainage pipe system and improve the design quality. Compared with the traditional method, the optimal solution determined can reduce the construction cost by more than 10% [3]. The drainage pipeline system is a huge and complex system. According to the existing research results, the design and calculation of the drainage pipeline system mainly involves three aspects: (1) Pipeline diameter-buried depth Optimization design; (2) optimization of pipeline layout; and (3) establishment of stormwater runoff model. Condensate drainage systems often have overflow facilities to limit the amount of water delivered to a local wastewater treatment plant. Since the overflowed rainwater is also discharged into the river nearby, the effect of the combined flow drainage system on the drainage area is actually the same from that of the split-flow rainwater system [4]. Optimized Design of Piping System under a Given Pipeline A great deal of groundbreaking work has been done at home and abroad for the optimization design of the pipe diameter-depth under the condition that the pipeline layout has been established. Optimization methods are generally divided into two types: indirect optimization and direct optimization. The indirect optimization method, also called analytic optimization, is based on the optimization mathematical model, and obtains the optimal solution through optimization calculation. The direct optimization method is based on the change of the performance index, Tunable parameters of choice, calculation and comparison, to get the optimal solution or satisfactory solution [5]. 1.1 Direct Optimization Method In the optimization design of drainage pipe, the direct optimization method is considered [6-8]: Although the hydraulic calculation formula for drainage pipe calculation is very simple, but the optional size of the pipe diameter is not continuous, can not be arbitrary The choice of pipe diameter; the maximum fullness limit is related to the pipe diameter; the minimum design flow rate, the change of the flow rate (which increases with the design flow rate), and its constraints on the pipe diameter, To use mathematical formulas to describe. Therefore, it is difficult to establish a complete mathematical model to solve the optimization problem to solve with indirect optimization method. In contrast, direct optimization method to solve this problem has the advantages of direct, intuitive and easy to verify. 1.2 Indirect optimization method Indirect optimization method is that: With the development of optimization technology, although there are a number of constraints in the design and calculation of drainage piping system, as long as the appropriate choice of some of the conditions, the rational use of mathematical tools It can be simplified, abstracted as easy to solve the mathematical model, by calculating the optimal solution. Depending on the time of day and the mathematical method used, the indirect optimization methods fall into the following categories: 1.2.1 Linear Programming The LFS is one of the most commonly used algorithms in optimization methods that solves many of the problems in the design of sewers , But also can be built on the drainage pipe sensitivity analysis. Its disadvantage is that the pipe diameter as a continuous variable to deal with, there is a calculated diameter and the diameter of the commercially available specifications of the contradiction [9]. And all the objective functions and constraints are linearized into a linear function, the pre-processing workload is large, the accuracy is difficult to be guaranteed. 1.2.2 Nonlinear programming method Dajani and Gemmell established a nonlinear programming model in 1972 in order to meet the nonlinear characteristics of the objective function and constraints in the optimal design of the drainage system [10]. The method is based on the principle of derivation, that is, the point at which the derivative of the objective function is zero is the optimal solution sought. It can handle commercially available pipe sizes, but when it can not be proved that the pipe cost function is a single-peak function, the result obtained may be a local optimal solution rather than a global optimal solution. 1.2.3 Dynamic Programming In 1975, Mays and Yen firstly introduced dynamic programming into the optimization design of sewer system [11]. At present, this method is still widely used at home and abroad. It is divided into two branches in the application: one is to use the buried depth of each node as a state variable, and conduct full-scale search by slope decision. The advantage of this method is that it directly uses the standard diameter and the optimization constraint has nothing to do with the initial solution, However, it is required that the interval of the state points is very small, which greatly increases the storage and calculation time [12]. In order to save computing time, Mays and Yen introduced the quasi-difference dynamic programming method in 1976. Pseudo-dynamic programming method is based on the dynamic programming method to reduce the scope of the introduction of the iterative process can significantly reduce the computational time and storage, but in the iterative process may omit the optimal solution, but also in complex terrain conditions Fell, gentle slope conditions are limited [13 ~ 14]. The other is based on the tube diameter as a state variable, through the flow rate and fullness decision-making search [15]. Due to the limited number of standard pipe diameters, there are significant advantages in terms of computer storage and calculation time compared with the method of embedding node depth into decision variables. Some of the standard pipe diameters selected for the pipe diameter of each pipe section in the initial dynamic planning are not necessarily feasible pipe diameters. Therefore, a feasible pipe diameter method is developed. Through mathematical analysis, this method uses the maximum and minimum pipe diameter and the standard pipe diameter between each of the pipe diameters satisfying the constraint conditions to form a feasible pipe diameter set, and then applies the dynamic programming Calculate. The feasible caliber method can improve the precision of calculation and reduce the computational workload and computer memory [16]. The dynamic programming method is an effective method to solve the optimization problem of multi-stage decision-making. There is not enough evidence to prove the "post-validation" state of the phase state whether using the node depth or the pipe diameter as the state variable. No post-effectiveness "refers to the fact that when given the state of a certain phase, the subsequent phases are not influenced by the state of the previous phases). Therefore, the optimal design of sewer system using dynamic programming is not necessarily the best solution. 1.2.4 Genetic Algorithm Genetic algorithm is an optimization technique developed rapidly in recent years. It is a stochastic optimization algorithm proposed by natural genetics in simulated biology [17]. It still uses the specification pipe diameter as the state variable, and searches for many points in the feasible solution space at the same time. Through the iterative operation factors such as selection, hybridization and mutation, the satisfactory solution is finally obtained. Generally, the optimal design scheme can be obtained when solving the optimal design of small and medium-sized pipeline system. Although the search method has a certain degree of randomness, when solving the problem of large-scale pipeline system, the genetic algorithm can still obtain the feasible solution approaching the optimal solution Program [18]. In short, in the process of the optimization design of drainage pipe system, both the indirect optimization method and the direct optimization method are applied and are constantly improving and perfecting. Common to both of these methods are the design specifications and requirements of the diameter, flow rate, gradient, fullness of the hydraulic relationship between the constraints, in order to achieve the minimum cost as the goal. 2 PLANES FOR PLANAR OPTIMIZATION OF PIPELINES Researchers have pointed out at the same time that they have solved the problem of drainage system optimization in a given pipeline and have found that they are more suitable for the optimization of different alignment schemes. However, since the design under the given pipeline is the basis of the pipeline layout, coupled with the immature design and optimization of the planned pipeline, there has been little progress in optimizing the layout of the system. The earliest study in this area is JCLiebman (1976). In his study, taking into account the hydraulic factor, assuming that each pipe diameter is the same, with the cost of excavation as the preferred basis, choose an initial arrangement, and then use the trial algorithm to gradually adjust. Since then Argaman (1973) and Mays (1976) have introduced the concept of a Drainage Line in a plan layout that uses a drain at a node in the drainage area that is equally spaced from the final outlet node (ie inspection well) Wire connection. For any drainage line, the upstream flow will flow downstream of the drainage line [19]. In this way, the optimal problem of pipeline layout is transformed into the shortest path problem, which can be solved by dynamic programming. This model has taken into account the hydraulic factors, but due to the introduction of drainage lines, the searching scope of the optimization process is limited to a small part of the feasible area of ​​the floorplan. Even those with rich design experience may be able to put the optimal The program is excluded. Coupled with its maximum storage and calculation of the characteristics of a long time, this method is still unable to achieve. In 1982, Walters made some improvements to this method, which was applied to the design of highway drainage system. Over time, the researchers found that the urban drainage system can be abstracted as a decision-making map made up of points and lines, and then turned to looking for a plane-optimized arrangement from the graph theory. In 1983, PRBhave and JF Borlow applied the algorithm of minimum generation in network graph theory to the optimization of the drainage system layout. Assuming that each pipe in the system has the same weight (Weight), to avoid the hydraulic factor, using the weight method to solve. In 1986, S. Tekel and H. Belkaya applied three kinds of weights to solve: (1) the reciprocal of the slope of each pipe section; (2) the pipe length of each pipe section; (3) , According to the smallest slope design when the amount of cut. The three kinds of weights are respectively calculated by using the shortest spanning tree algorithm to find the pipeline layout plan, then the pipe diameter, depth and the optimal design of pumping stations are optimized. Finally, the plane plan with the lowest investment cost is taken as the optimal design plan. For all feasible pipeline laying paths in drainage system, the actual weights of each pipe section can not be calculated until the scheme is determined, so it belongs to the variable weight problem in graph theory. However, until now, There is no effective solution to the problem. In China, Li Guiyi (1986) proposed a simple gradient method, and Chen Sen-fa (1988) proposed a hierarchical optimization method [20]. These methods also failed to achieve satisfactory results. Recently, the emergence of genetic algorithms has provided possible conditions for the optimal layout of drainage piping systems, because the computational mechanism of genetic algorithms has no special requirements on objective functions and constraints. GAWalters has applied genetic algorithms to study urban water supply and drainage, farmland irrigation, cables, and gas pipelines [21]. 3 Study on Rainfall Runoff Model The design of stormwater canal in China has been using inference formula method, which was piloted in 1974 and the outdoor drainage design code revised in 1987 are all the same. The reasoning formula method is based on the assumption that the water flow in the canal is uniform and the prevailing time of the water flow in the pipeline is obtained. Assuming that the flow velocity of rainwater on the ground is equal to the flow velocity of the water in the canal, the rainfall duration equals to the time of surface catchment, The maximum design flow of the next pipe section is obtained from the rainstorm formula. Select a feasible pipe diameter as the design pipe diameter, and obtain the required hydraulic gradient (or choose a feasible hydraulic gradient) from the hydraulic formula to find the feasible pipe diameter. Reasoning formula method using open channel uniform flow formula for hydraulic calculation, the biggest advantage is simple and rapid. Due to the use of the history of the largest rainfall data, can be biased in favor of security design. However, many researches have shown that the reasoning based on the derivation formula in the reasoning formula method is not reasonable and there are some imperfections, mainly in the following aspects: (1) The spatial variation of rainfall is not considered. Because the actual rainstorm intensity is unevenly distributed in the rainfall area, when the catchment area is larger, the rainfall taken takes longer, and the design flow of the downstream pipe section calculated by the formula will be greatly deviated. (2) In theory, we made a simplistic assumption that users may borrow parameters and constants published elsewhere to save time without checking. Due to the lack of sufficient examples of design information, there is a certain degree of blindness. (3) The peak flow can only be calculated, and the complete runoff process can not be deduced. For the design of rainwater regulating tank, the overflow flow calculation of the combined drainage pipe can not meet the requirements. (4) The assumption that the design recurrence period directly from design torrential rains is converted into the design recurrence period of drainage pipelines has not been fully substantiated. Marsalek (1978), Wenzel and Vookes (1978, 1979) pointed out that the selection of rainfall duration, duration distribution and early soil moisture content have a great impact on the relationship between peak flow rate and frequency. Some functions exist between these parameters relationship. (5) Can not meet the calculation requirements of runoff water quality. Because of the high pollution concentration of rainfall does not necessarily occur in the Gao Hongfeng process line. Even for confluent pipelines, there is still a significant amount of contaminants in the combined effluent from the system. In recent 20 years, with the increasingly prominent problems of urban runoff pollution, it is more and more important to establish all kinds of urban hydrological and hydraulic computational models with high accuracy. Much progress has been made abroad in this area and many models have been widely used in the planning, design and management of stormwater piping systems. Currently, the most famous programs in the West are [23] the Wallingford Procedure of the UK Department of Environment and the National Water Commission, the Storage, Treatment, Overflow, Runoff Mode of the Corps of Engineers Hydrographic Center, STORM), the EPA's Storm Water Management Mode SWMM and others. These models can simulate the rainfall and runoff processes in the city with relatively accurate quantities (rainfall and runoff) and quality (water quality of rainfall and runoff water and water quality of receiving water bodies). Their development is closely linked with the project. After a period After the accumulation of experience, the government departments will organize and coordinate the introduction of stereotypes software for design and management selection [24]. The study of urban runoff model in China started late, and at present there are some research results that are combined with the actual situation in our country. Such as the proliferation of rainwater pipe network simulation simplification and motion wave reduction [25], surface runoff system simulation techniques include: flow method, instantaneous unit line method and improved reasoning method [26]. 4 Conclusions Both domestic and foreign countries have made great achievements in the theoretical calculation and engineering application of the drainage piping system design, and many problems still waiting to be solved still exist. With the development of computing technology and system methods, it is an inevitable trend to study and design drainage pipe system design and calculation software better. ★ Correspondence: 200092 School of Environmental Science and Engineering, Tongji University Tel: (021) References 1 Gu Guowei. Research on Water Pollution Control Technology. Shanghai: Tongji University Press, 1997 2 Fu Guowei. Introduction to Water Supply and Drainage System Optimization (1). China Water and Wastewater, 1987, 3 (4): 45-50 50 James, S J. Optimal design of sanitary sewers. Computing in civil engineering proceeding of the fourth conference, Edited by W Tracy Lenocker, Published by the American Society of Civil Engineers 1986: 162 ~ 177 4 MJ Hall [English], translated by Zhan Daojiang etc. Urban Hydrology. Nanjing: Hohai University Press, 1989 5 Peng Yongzhen, Cui Foyi. Computer programming of water supply and drainage engineering. Beijing: China Building Industry Publishing House, 1994 6 Wang Boren. Calculation of sewer system and optimization options. China Water & Wastewater, 1985,1 (2): 1 ~ 5 7 Peng Yongzhen, Wang Shuying, Wang Fuzhen. Global Optimization of Drainage Network Calculation Program. , 1994,10 (5): 41 ~ 438 Zhang Lianmin. Flow rate control method for optimal design of sewer network. China Water & Wastewater, 199 4,10 (5): 41 ~ 43 9 Shen Yi.Application of Microcomputer in Sewage Pipeline Optimal Design. Ministry of Communications First Flight Engineering Prospecting and Design Institute, 1988 10 Li GY and Matthew, GS R. New approach for optimization of urban drainage systems. Journal of environmental engineering, ASCE, 1990, 116 (5): 927-944 11 Kuo JT and Yen B C. Hwang, GP P. Optimal design for storm sewer system with pumping stations. Journal of water resource planning and management, Journal of Xi'an Institute of Metallurgy and Architecture, 1993,25 (3): 305 ~ 310 13 Li Guiyi.Drainage network optimization design.China water supply and drainage (2): 18 ~ 23 14 Ouyang Jianxin, Chen Xinshang. Discrete Optimization of Penalty Function for Drainage System Design. Water Supply and Drainage, 1996,22 (5): 19-21 21 Ding Hongda. Dynamic Planning of Gravity Flow and Rainfall Water Pipe System Analysis. Water Supply and Drainage, 1983,9 (5): 2 ~ 7 16 Lu Shaoming, Liu Suiqing. Optimized Design of Feasible Pipe Diameter for Urban Sewer Network. Journal of Tongji University, 1996,24 (3): 275-280 17 Simpson AR, Dandy GC and Murphy L J. Genetic algorithms compared to other techniques for pipe optimization. Journal of water resource planning and management, 1994,120 (4): 423 ~ 443 18 Zhang Jinguo, Li Shuping. Genetic algorithm for drainage system optimization design. China Water and Wastewater, 1997 , 13 (3): 28 ~ 30 19 Li Guiyi. Optimization design of drainage channel system. Information Technology Station of Tongji University, 1986 20 Chen Senfa. Hierarchical Optimization Design of Urban Sewer Network System Layout. China Water Supply and Drainage, 1988,4 ( 3): 6 ~ 10 21 Walters GA and Lohbeck T K. Optimal layout of tree network using genetic algorithms. Engineering Optimization, 1993, 22: 27 ~ 48 22 Hydrology Bureau of Ministry of Water Conservancy and Electric Power. 23 Wang Wenyuan, Wang Chao. Enlightenment from the Development of Foreign Urban Drainage Systems. China Water and Wastewater, 1998,14 (2): 45 ~ 47 24 Harry van Mameren and Francois Clemens. Guidelines for hydrodynamic calculations on urban drainage in the and principles. Water science and technologies, 1997,36 (8): 247 ~ 252 25 Cen Guoping. DYNAMIC WAVE SIMULATION AND EXPERIMENT OF RAINWAY NETWORK Journal of Water Supply and Drainage, 1995,21 (10): 11 ~ 13 26 Zhou Yuwen, Meng Shaolu. Study on the process of in-line flow of rainwater network by instantaneous unit line method. In municipal construction and environmental management project construction, rainwater and sewer systems often occupy a larger proportion of investment. Therefore, how to design urban drainage piping system rationally under the various technical conditions that meet the stipulations is an important issue in the design. The methods and the problems to be solved in the development of design and calculation of sewer system are discussed from the aspects of the optimized design under the fixed pipeline, the plane layout of the pipeline and the runoff model. It can be seen that in the future still need to devote a lot of energy to study and improve its design and calculation methods. Keywords drainage system optimization design layout layout runoff model Introduction The drainage system is an indispensable and important infrastructure in the modern city. It is also the backbone project of urban water pollution control and city drainage waterlogging prevention and flood control. Among them, the investment of rainwater and sewer systems in residential areas and industrial and mining enterprises generally accounts for about 70% of the total drainage system investment [1]. Therefore, it is an important issue in the design work to minimize the capital cost of the pipeline system under various technical conditions that meet the stipulations in the design. The design and calculation method of the traditional drainage system is as follows: After the designers have mastered the relatively complete and reliable design basis data, they can determine a more reasonable sewage pipeline layout according to the principle of pipeline alignment and layout. And then calculate the design flow of each design section, hydraulic calculation diagram or hydraulic calculation table and the relevant design requirements as the control conditions, from upstream to downstream, followed by hydraulic design of each section of the pipe, calculate the pipe diameter, slope and In the inspection wells at the end of the elevation and depth of embedding. Calculation, generally only by experience on the pipe diameter and slope, etc. for proper adjustment, in order to achieve the purpose of economic and reasonable, but the reasonable degree by the designer's personal capacity constraints; the other hand, the majority of calculations using repeated access Figure and table methods, work efficiency is low, a long time, is not conducive to the optimization of the design. Since the 1960s, the international community has gradually established mathematical models of various water supply and drainage engineering systems or processes on the basis of experience summaries and mathematical analyzes. As a result, water supply and drainage projects marked by quantitative and semi-quantitative " Reasonable design and management "stage. At the same time, optimized research and practice have been carried out for various types of water supply and drainage systems [2]. In order to explore the optimal design and calculation method of sewer system, many domestic and foreign scientific research, design, teaching units and individuals have done a lot of work and published a large number of articles. From the research results, the design and calculation of drainage pipes by computer not only free designers from the heavy labor of consulting charts, speed up the design progress, but also optimize the entire drainage pipe system and improve the design quality. Compared with the traditional method, the optimal solution determined can reduce the construction cost by more than 10% [3]. The drainage pipeline system is a huge and complex system. According to the existing research results, the design and calculation of the drainage pipeline system mainly involves three aspects: (1) Pipeline diameter-buried depth Optimization design; (2) optimization of pipeline layout; and (3) establishment of stormwater runoff model. Condensate drainage systems often have overflow facilities to limit the amount of water delivered to a local wastewater treatment plant. Since the overflowed rainwater is also discharged into the river nearby, the effect of the combined flow drainage system on the drainage area is actually the same from that of the split-flow rainwater system [4]. Optimized Design of Piping System under a Given Pipeline A great deal of groundbreaking work has been done at home and abroad for the optimization design of the pipe diameter-depth under the condition that the pipeline layout has been established. Optimization methods are generally divided into two types: indirect optimization and direct optimization. The indirect optimization method, also called analytic optimization, is based on the optimization mathematical model, and obtains the optimal solution through optimization calculation. The direct optimization method is based on the change of the performance index, Tunable parameters of choice, calculation and comparison, to get the optimal solution or satisfactory solution [5]. 1.1 Direct Optimization Method In the optimization design of drainage pipe, the direct optimization method is considered [6-8]: Although the hydraulic calculation formula for drainage pipe calculation is very simple, but the optional size of the pipe diameter is not continuous, can not be arbitrary The choice of pipe diameter; the maximum fullness limit is related to the pipe diameter; the minimum design flow rate, the change of the flow rate (which increases with the design flow rate), and its constraints on the pipe diameter, To use mathematical formulas to describe. Therefore, it is difficult to establish a complete mathematical model to solve the optimization problem to solve with indirect optimization method. In contrast, direct optimization method to solve this problem has the advantages of direct, intuitive and easy to verify. 1.2 Indirect optimization method Indirect optimization method is that: With the development of optimization technology, although there are a number of constraints in the design and calculation of drainage piping system, as long as the appropriate choice of some of the conditions, the rational use of mathematical tools It can be simplified, abstracted as easy to solve the mathematical model, by calculating the optimal solution. Depending on the time of day and the mathematical method used, the indirect optimization methods fall into the following categories: 1.2.1 Linear Programming The LFS is one of the most commonly used algorithms in optimization methods that solves many of the problems in the design of sewers , But also can be built on the drainage pipe sensitivity analysis. Its disadvantage is that the pipe diameter as a continuous variable to deal with, there is a calculated diameter and the diameter of the commercially available specifications of the contradiction [9]. And all the objective functions and constraints are linearized into a linear function, the pre-processing workload is large, the accuracy is difficult to be guaranteed. 1.2.2 Nonlinear programming method Dajani and Gemmell established a nonlinear programming model in 1972 in order to meet the nonlinear characteristics of the objective function and constraints in the optimal design of the drainage system [10]. The method is based on the principle of derivation, that is, the point at which the derivative of the objective function is zero is the optimal solution sought. It can handle commercially available pipe sizes, but when it can not be proved that the pipe cost function is a single-peak function, the result obtained may be a local optimal solution rather than a global optimal solution. 1.2.3 Dynamic Programming In 1975, Mays and Yen firstly introduced dynamic programming into the optimization design of sewer system [11]. At present, this method is still widely used at home and abroad. It is divided into two branches in the application: one is to use the buried depth of each node as a state variable, and conduct full-scale search by slope decision. The advantage of this method is that it directly uses the standard diameter and the optimization constraint has nothing to do with the initial solution, However, it is required that the interval of the state points is very small, which greatly increases the storage and calculation time [12]. In order to save computing time, Mays and Yen introduced the quasi-difference dynamic programming method in 1976. Pseudo-dynamic programming method is based on the dynamic programming method to reduce the scope of the introduction of the iterative process can significantly reduce the computational time and storage, but in the iterative process may omit the optimal solution, but also in complex terrain conditions Fell, gentle slope conditions are limited [13 ~ 14]. The other is based on the tube diameter as a state variable, through the flow rate and fullness decision-making search [15]. Due to the limited number of standard pipe diameters, there are significant advantages in terms of computer storage and calculation time compared with the method of embedding node depth into decision variables. Some of the standard pipe diameters selected for the pipe diameter of each pipe section in the initial dynamic planning are not necessarily feasible pipe diameters. Therefore, a feasible pipe diameter method is developed. Through mathematical analysis, this method uses the maximum and minimum pipe diameter and the standard pipe diameter between each of the pipe diameters satisfying the constraint conditions to form a feasible pipe diameter set, and then applies the dynamic programming Calculate. The feasible caliber method can improve the optimization calculation accuracy and significantly reduce the computational workload and computer memory [16]. The dynamic programming method is an effective method to solve the optimization problem of multi-stage decision-making. There is not enough evidence to prove the "post-validation" state of the phase state whether using the node depth or the pipe diameter as the state variable. No post-effectiveness "refers to the fact that when given the state of a certain phase, the subsequent phases are not influenced by the state of the previous phases). Therefore, the optimal design of sewer system using dynamic programming is not necessarily the best solution. 1.2.4 Genetic Algorithm Genetic algorithm is an optimization technique developed rapidly in recent years. It is a stochastic optimization algorithm proposed by natural genetics in simulated biology [17]. It still uses the specification pipe diameter as the state variable, and searches for many points in the feasible solution space at the same time. Through the iterative operation factors such as selection, hybridization and mutation, the satisfactory solution is finally obtained. Generally, the optimal design scheme can be obtained when solving the optimal design of small and medium-sized pipeline system. Although the search method has a certain degree of randomness, when solving the problem of large-scale pipeline system, the genetic algorithm can still get the solution approaching the optimal solution Program [18]. In short, in the process of the optimization design of drainage pipe system, both the indirect optimization method and the direct optimization method are applied and are constantly improving and perfecting. Common to both of these methods are the design specifications and requirements of the diameter, flow rate, gradient, fullness of the hydraulic relationship between the constraints, in order to achieve the minimum cost as the goal. 2 PLANES FOR PLANAR OPTIMIZATION OF PIPELINES Researchers have pointed out at the same time that they have solved the problem of drainage system optimization in a given pipeline and have found that they are more suitable for the optimization of different alignment schemes. However, since the design under the given pipeline is the basis of the pipeline layout, coupled with the immature design and optimization of the planned pipeline, there has been little progress in optimizing the layout of the system. The earliest study in this area is JCLiebman (1976). In his study, taking into account the hydraulic factor, assuming that each pipe diameter is the same, with the cost of excavation as the preferred basis, choose an initial arrangement, and then use the trial algorithm to gradually adjust. Since then Argaman (1973) and Mays (1976) have introduced the concept of a Drainage Line in a plan layout that uses a drain at a node in the drainage area that is equally spaced from the final outlet node (ie inspection well) Wire connection. For any drainage line, the upstream flow will flow downstream of the drainage line [19]. In this way, the optimal problem of pipeline layout is transformed into the shortest path problem, which can be solved by dynamic programming. This model has taken into account the hydraulic factors, but due to the introduction of drainage lines, the searching scope of the optimization process is limited to a small part of the feasible area of ​​the floorplan. Even those with rich design experience may be able to put the optimal The program is excluded. Coupled with its maximum storage and calculation of the characteristics of a long time, this method is still unable to achieve. In 1982, Walters made some improvements to this method, which was applied to the design of highway drainage system. Over time, the researchers found that the urban drainage system can be abstracted as a decision-making map made up of points and lines, and then turned to looking for a plane-optimized arrangement from the graph theory. In 1983, PRBhave and JF Borlow applied the algorithm of minimum generation in network graph theory to the optimization of the drainage system layout. Assuming that each pipe in the system has the same weight (Weight), to avoid the hydraulic factor, using the weight method to solve. In 1986, S. Tekel and H. Belkaya applied three kinds of weights to solve: (1) the reciprocal of the slope of each pipe section; (2) the pipe length of each pipe section; (3) , According to the smallest slope design when the amount of cut. The three kinds of weights are respectively calculated by using the shortest spanning tree algorithm to find the pipeline layout plan, then the pipe diameter, depth and the optimal design of pumping stations are optimized. Finally, the plane plan with the lowest investment cost is taken as the optimal design plan. For all feasible pipeline laying paths in drainage system, the actual weights of each pipe section can not be calculated until the scheme is determined, so it belongs to the variable weight problem in graph theory. However, until now, There is no effective solution to the problem. In China, Li Guiyi (1986) proposed a simple gradient method, and Chen Sen-fa (1988) proposed a hierarchical optimization method [20]. These methods also failed to achieve satisfactory results. Recently, the emergence of genetic algorithms has provided possible conditions for the optimal layout of drainage piping systems, because the computational mechanism of genetic algorithms has no special requirements on objective functions and constraints. GAWalters has applied genetic algorithms to study urban water supply and drainage, farmland irrigation, cables, and gas pipelines [21]. 3 Study on Rainfall Runoff Model The design of stormwater canal in China has been using inference formula method, which was piloted in 1974 and the outdoor drainage design code revised in 1987 are all the same.推理公式法的计算方法是假定管渠中水流为均匀流，求得水流在管道中的流行时间；再假定雨水在地面的水流流速等于管渠中的水流流速，降雨历时等于地面集水时间，由暴雨公式求得下一管段的最大设计流量。选择一可行管径作为设计管径，由水力公式求得所需的水力坡度(或选择一可行的水力坡度，来求出所需的可行管径)。推理公式法应用明渠均匀流公式进行水力计算，其最大优点是简单迅速。由于使用了历史最大降雨资料，能够得到偏于安全的设计。但是，已有的许多研究表明，推理公式法中基于推导公式的假定不尽合理，存在一些不够完善的地方，主要表现在以下几个方面［22］ ：(1)没有考虑降雨的空间变化。由于实际暴雨强度在受雨面积上的分布不均匀，当汇水面积较大时，所取的降雨历时较长，按公式计算得出的下游管段的设计流量会出现较大的偏差。 (2)理论上作了过分简单的假设，使用者可能会不经检验地就借用其它地区公布的参数和常数，以便节省时间。设计因缺乏充分的实例资料，存在一定的盲目性。 (3)只能计算洪峰流量，无法推求完整的径流过程，对雨水调节池设计、合流制排水管道溢流流量计算无法适应要求。 (4)将直接来自设计暴雨的设计重现期，转化成排水管渠的设计重现期，这一假设并没有被充分证实。 Marsalek(1978)、Wenzel和Vookes(1978,1979)指出，降雨历时、时程分配和前期土壤含水量的选择，对洪峰流量～频率关系有很大影响，这些参数之间也存在着某种函数关系。 (5)不能满足对雨水径流水质方面的计算要求。因为高污染浓度的降雨并不一定发生在高洪峰过程线内。即使对于合流制管道，从系统溢流出的合流污水中仍然存在有大量污染物。近20年来，随着城市径流污染问题的日益突出，各种精度较高的城市水文、水力计算模型的建立显得越来越重要。国外在这方面取得很大进展，许多模型已广泛应用于雨水管道系统的规划、设计和管理。当前西方最著名的程序有［23］ ：英国环境部及全国水资源委员会的沃林福特程序(Wallingford Procedure)、美国陆军工程师兵团水文学中心的“暴雨”模型(Storage,Treatment,Overflow,Runoff Mode STORM)、美国环保局的雨水管理模型(Storm Water Management Mode SWMM)等。这些模型可对整个城市降雨、径流过程进行较为准确的量(降雨与径流量)和质(降雨与径流水的水质和接受水体的水质)的模拟，它们的开发与工程项目紧密结合，经过一段时期的经验积累后，政府主管部门便组织协调，推出定型软件供设计和管理人员选用［24］ 。我国对城市径流模型的研究起步较晚，目前已有一些结合我国实际的研究成果问世。如对雨水管网模拟的扩散波简化和运动波简化［25］ ，对地表径流系统的模拟技术包括：等流时线法、瞬时单位线法和改进推理法［26］ 。 4 结束语无论国内还是国外，在排水管道系统设计的理论计算和工程应用上均已取得很大的成果，也仍然存在着许多期待解决的问题。随着计算技术和系统方法的发展，更好地研究开发排水管道系统设计计算软件是必然的发展趋势。 ★作者通讯处：200092 同济大学环境科学与工程学院电话：(021)65986217 参考文献1 顾国维. 水污染治理技术研究. 上海：同济大学出版社，1997 2 傅国伟. 给水排水系统优化导论(一). 中国给水排水，1987，3(4)：45～50 3 James,S J. Optimal design of sanitary sewers. Computing in civil engineering proceeding of the fourth conference,Edited by W Tracy Lenocker,Published by the American Society of Civil Engineers,1986:162～177 4 MJ 霍尔［英］著，詹道江等译. 城市水文学. 南京：河海大学出版社，1989 5 彭永臻，崔福义. 给水排水工程计算机程序设计. 北京：中国建筑工业出版社，1994 6 王柏仁. 污水管道系统的计算程序与优化选择. 中国给水排水，1985，1(2)：1～5 7 彭永臻，王淑莹，王福珍. 排水管网计算程序的全局优化. 中国给水排水，1994，10(5)：41～43 8 张联民. 污水管网优化设计的流速控制法. 中国给水排水，199 4，10(5)：41～43 9 沈毅. 微机在污水管道优化设计中的应用. 交通部第一航务工程勘查设计院，1988 10 Li GY and Matthew,GS R. New approach for optimization of urban drainage systems. Journal of environmental engineering,ASCE,1990,116(5):927～944 11 Kuo JT and Yen B C. Hwang,G P. Optimal design for storm sewer system with pumping stations. Journal of water resource planning and management,ASCE,1991,117(1):11～27 12 张景国. 排水管道系统设计最优化. 西安冶金建筑学院学报，1993，25(3)：305～310 13 李贵义. 排水管网优化设计. 中国给水排水，1986，2(2)：18～23 14 欧阳建新，陈信常. 排水管系设计的罚函数离散优化法. 给水排水，1996，22(5)：19～21 15 丁宏达. 重力流雨水管道动态规划系统分析. 给水排水，1983，9(5)：2～7 16 陆少鸣，刘遂庆. 城市污水管网可行管径法优化设计. 同济大学学报，1996，24(3)：275～280 17 Simpson AR,Dandy GC and Murphy L J. Genetic algorithms compared to other techniques for pipe optimization. Journal of water resource planning and management,1994,120(4):423～443 18 张景国，李树平. 遗传算法用于排水管道系统优化设计. 中国给水排水，1997，13(3)：28～30 19 李贵义. 排水沟道系统的优化设计. 同济大学科技情报站，1986 20 陈森发. 城市污水管网系统布局的递阶优化设计. 中国给水排水，1988，4(3)：6～10 21 Walters GA and Lohbeck T K. Optimal layout of tree network using genetic algorithms. Engineering Optimization,1993,22:27～48 22 水利电力部水文局等. 城市雨洪水译文集. 北京：1987 23 王文远，王超. 国外城市排水系统的发展启示. 中国给水排水，1998，14(2)：45～47 24 Harry van Mameren and Francois Clemens. Guidelines for hydrodynamic calculations on urban drainage in the and principles. Water science and technologies,1997,36(8):247～252 25 岑国平. 雨水管网的动力波模拟及试验 证. 给水排水，1995，21(10)：11～13 26 周玉文，孟昭鲁. 瞬时单位线法推求雨水管网入流流量过程线的研究. 给水排水，1995，21(3)：5～9

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