用最优化方法计算河流纵向离散系数

本文提出一种由示踪试验计算河流纵向离散系数的新方法——双站拟合法。大量的实测资料说明,瞬时面源在一维河流中形成的浓度过程是偏态的,Fick模型解析解与实际情况有一定偏差。作者选用皮尔逊Ⅲ型曲线,对实测浓度时间过程进行非线性拟合计算,得到了十分满意的拟合结果,说明可以用此曲线来描述浓度时间过程,采用最优化设计的方法,对双站进行同步拟合计算,由此计算出纵向离散系数。本文对各种方法进行了比较计算,说明了双站拟合法的优越性。     

河流污染物纵向离散系数确定的演算优化法

提出一个确定河流污染物纵向离散系数的新方法——演算优化法。该方法采用示踪剂在两个断面的时间浓度过程数据,将上游断面的实测浓度过程视作下游断面的连续投放源。在下游断面浓度计算值与实测值的平方误差达到最小的条件下,利用优化方法求出河段的平均流速和纵向离散系数。该方法摈弃了传统演算法采用的冻结云团假设,客观地反映了污染物上游浓度变化过程对下游浓度变化过程的影响,同时避免了演算法中繁琐的试算过程。采用Fischer的室内示踪试验数据,对演算优化法与传统演算法进行比较,结果表明演算优化法的计算过程比演算法更快捷,计算出的离散系数更精确可靠。     

河流纵向离散系数研究综述

综述了几种示踪试验中计算纵向离散系数的新方法,并分析了这些方法的特点。     

三峡库区江段纵向离散系数研究

本文通过对纵向离散特性的理论分析，确定河流纵向离散系数的一般计算公式。根据三峡库区江段现有六个代表性断面流速分布监测数据，采用积分公式计算各河段各个测次的纵向离散系数值，将这些纵向离散系数计算值作为一系列有代表性的样本，对纵向离散系数的一般计算公式进行回归分析计算，得到三峡库区江段的纵向离散系数预测公式。利用预测公式分析计算三峡库区江段纵向离散特征。     

桂江下游梧州河段纵向离散系数的选定

桂江下游梧州河段纵向离散系数的选定是利用梧州段的示踪实验数据,选用单站改进法和峰值反推法,推算其离散系数。把两种推算的纵向离散系数,分别代入公式C(x,t)=M/A4πD_tt~(1/2)exp[-(x-ut)~2/4D_tt]得时间t的示踪剂浓度值,该值与同一时刻实测值进行比较,计得平均偏差,根据平均偏差小的那种方法确定纵向离散系数,较能符合天然河流的水流特性,经计算结果表明,采用单站改进法计得的纵向离散系数的拟合浓度与实测的示踪剂浓度较为接近,为此,选用其计算结果,即桂江梧州段在枯水期流量为334m~3/s时;纵向离散系数为22.9m~2/s;当流量为398m~3/s时,纵向离散系数为29.7m~2/s。     

几种计算河流纵向弥散系数方法的比较

以水团示踪试验为例,介绍和比较了直线图解法、相关系数极值法和最优化计算法等不同的河流纵向弥散系数计算方法,指出:直线图解法在一定程度上克服了单站法和双站法的不足,但在分析河流水团示踪试验数据时,需用差分计算代替c-t曲线导数的计算,在这个过程中会产生一定的误差;相关系数极值法计算过程比较复杂,但与其他方法相比,可以避免图算过程,能够消除人为因素对计算结果精度的影响,而且还能够计算河流横断面面积;最优化计算方法多用于分析各种单站的实际观测资料.     

基于流速幂级数解的明渠纵向离散系数计算方法

针对纵向离散系数计算多直接采用经验公式或在流速分布的求解中引入经验公式,不能完全反映离散物理机制的问题,基于矩形断面一维水流控制方程,利用幂级数求解流速分布的理论解,提出了矩形断面明渠纵向离散系数的计算方法,并进一步将其推广至天然河流。利用该方法计算了104条天然河流的纵向离散系数,结果表明计算值与实测值处于同一量级,计算结果在可接受范围内。     

河流横向混合系数的研究进展

横向混合系数是河流中污染物横向混合特性的重要体现与反映。本文针对不同河道情况下横向混合系数的确定方法进行了分析。首先归纳不同河道的无量纲横向混合系数的量值分布及代表性公式,顺直室内水槽试验的无量纲横向混合系数值在0.1~0.26范围内,天然河道量值分布较为离散,基本大于室内水槽的数值,并对不同代表性公式的适用条件和范围进行阐述;之后总结了利用示踪试验数据计算横向混合系数的各种参数计算方法,矩法和直线图解法简便易操作,线性回归法可分析非惰性污染物,加速遗传算法、人工神经网络等优化算法提高了计算精度并降低了人为主观性;接着详细介绍了弯曲河道中利用横向速度公式带入三重积分来计算横向混合系数的理论方法,Odgaard和Rozovskii的断面横向速度表达式与横向混合系数存在较好的响应关系,量化了二次流对弯曲河道横向混合系数的影响;同时对含植物、有冰层覆盖明渠以及不规则河岸等其他情况下的横向混合系数的探索进行了总结,最后提出了天然河流中横向混合系数研究中有待进一步探讨的问题。     

基于POMgcs的复式断面河道纵向离散系数研究

天然河流中广泛存在由主槽、滩地构成的复式河道的水污染问题,纵向离散系数是河流水质预测的关键参数。通过建立三维数学模型,对物理水槽试验的流速分布特征进行验证,得到不同滩槽高差、水深条件下的纵向离散系数。基于复式断面各区域流速差异较大的特征,将断面划分为主槽中心区、滩地区和主槽底部区,由纵向离散系数与流速差异系数、断面几何参数的相关性分析,同时引入弗劳德数,建立了复式断面纵向离散系数计算公式。计算结果表明:公式计算值与模型计算值相对平均误差在10%以内,具有一定可靠性。     

紊流异重流纵向离散系数的研究

水库、湖泊和河中常见的异重流,大多数是紊流。以紊流异重流的形式挟带污染物质是水质污染的一种主要形式。本文建立了异重流的离散方程,导出了紊流异重流离散系数的理论解。研究结果表明:紊流异重流离散系数不仅是异重流厚度(h)、摩阻流速(V_*)的函数,而且还与交界面阻力与底部阻力比值(α),和异重流平均流速与摩阻流速比值(m)有关。     

室内模拟试验确定河流纵向扩散系数研究

采用几何相似原理,在不同流量、流速条件下,以室内模拟试验求得纵向扩散系数,所得结果与同条件下现场分析计算的结果基本上一致,与国内外相近条件和经验计算值接近。室内模拟试验法不仅克服野外条件下示踪剂投放和测试条件的不便,还能够更好地研究不同水动力条件下污染物的扩散运移规律。     

Estimation of longitudinal dispersion coefficient in rivers

The longitudinal dispersion coefficient is a crucial parameter for 1D water quality analyzing in natural rivers, and different types of empirical equations have been presented in the literature. To evaluate the precision of those commonly used equations, 116 sets of measured data for rivers in U.S. and UK have been collected for comparison. Firstly, the precisions of selected ten empirical equations under different aspect ratio (water surface width B/water depth H) have been compared, and calculation shows that most of the equations have underestimated the longitudinal dispersion when 20 < B/H < 100, in which most of the natural rivers located. The regression analysis on the collected data sets proved that the product of water depth H and the cross-sectional averaged velocity U has a higher linear correlation with the longitudinal dispersion coefficient than the product of H and shear velocity u1, and then a new expression of longitudinal dispersion coefficient, which is a combination of the product of HU and other two nondimensional hydraulic and geometric parameters, was deduced and the exponents were determined by the regression analysis. The comparison between the measured data and the predicted results shows that the presented equation has the highest precision for the studied natural rivers. To further evaluate the precision of the empirical formulae to artificial open channels, comparison was made between laboratory measuring data and empirical equation prediction, and the results have shown that the newly presented model is effective at predicting longitudinal dispersion in trapezoidal artificial channels too.     

Estimation of the transverse dispersion coefficient for two-dimensional models of mixing in natural streams

Existing equations to predict the transverse dispersion coefficient for the two-dimensional model of mixing and transport in natural streams use the channel aspect ratio and roughness factor as well as the sinuosity of the channel as the controlling parameters, and the performance of the equation varies largely according to the range of each parameter that was studied. To this end, this study suggests the criteria to select the proper method to calculate the transverse dispersion coefficient according to the availability and range of each parameter. The strengths and limitations of existing methodologies were compared against observed data acquired from tracer experiments conducted in natural streams. Further, a flow chart was proposed with the criteria to select a suitable method under specific hydraulic and geometric conditions of the stream.The results of the classification flow chart showed that, at the first step, in the cases where secondary currents data were available for natural streams, the theoretical equation by Baek and Seo (2011) could be used to estimate the transverse dispersion coefficient. At the second step, in the case of large value of P, i.e., P > 0.04, the equation by Baek and Seo (2013) was suitable to estimate the transverse dispersion coefficient, while in the case of a small value of P, equations by Yotukura and Sayer (1976) and Baek and Seo (2013), could be used with little differences. At the third step, for the narrow streams with W/h < 50, those proposed by Bansal (1971) and Deng et al. (2001) were preferable to estimate the transverse dispersion coefficient for two-dimensional mixing models. In wide streams with W/h > 50, the results of Jeon et al. (2007) showed much better agreement with the observed values than the others.     

天然河道水流挟沙能力研究

天然河道水流挟沙能力的确定对于利用数学模型来模拟泥沙运动和河床变形具有重要意义。本文在收集大量天然河流实测资料的基础上,对长江、汉江、塔里木河干流、黄河以及三门峡水库的水流挟沙能力进行了分析研究,选定了既适合于低含沙河流也适用于高含沙河流水流挟沙能力的计算公式,探讨了确定挟沙能力系数和指数的方法,避免了利用数学模型计算时系数取值的不确定性,提高了数学模型应用于不同天然河流的适应能力。研究表明,韩其为的高低含沙量统一的挟沙能力公式可以适用于各种不同的天然河流,系数取值稳定,且容易确定。     

A simple low-cost approach for transport parameter determination in mountain rivers

A simplified low-cost approach to experimentally determine transport parameters in mountain rivers is described, with an emphasis on the longitudinal dispersion coefficient (D_L). The approach is based on a slug injection of table salt (NaCl) as a tracer and specific conductance readings at different locations downstream of the injection spot. Observed specific conductance readings are fit using the advection-dispersion equation with OTIS-P, yielding estimates of cross-sectional area and longitudinal dispersion coefficient for various stream reaches. Estimates of the D_L are used to assess the accuracy of several empirical equations reported in the literature. This allowed the determination of complementary transport parameters related to transient storage zones. The empirical equations yielded rather high D_L values, with some reaching up an order of magnitude higher to those obtained from tracer additions and OTIS-P. Overall, the proposed approach seems reliable and pertinent for river reaches of ca. 150 m in length.     

近似拟合法确定离散系数的适用性和选点策略

通过随机数学试验的设计和运行,对近似拟合法的适用范围和选点策略进行探讨。试验结果表明:取C*(质量浓度与时间的乘积)最大的点作为t2,并取两边最接近的2点(最好等距)为t1和t3是一种较好的取点策略;计算出DL,tmax,u后,利用S值(顶端曲率相反数的自然对数),dt(t1,t2,t3时差)和t2′(t2与tmax时差)进行检验,可以判断计算结果的可靠性。     

中国西部河流自然风景价值保护的3种模式

为构建中国河流自然风景价值保护的可实施途径,通过剖析中美相关政策与案例,针对中国西部河流分类提出了3种模式:基于国家公园与自然保护地体系构建的"自然型"河流保护模式,在具有突出价值的已开发河流上的"共生型"河流保护模式,在已建水利工程面临退役的河流上的"修复型"河流保护模式。基于中国河流保护的特殊性提出以下保护策略:"自然型"河流应从国土空间规划角度,进行河流价值识别、环境影响评估与低影响自然体验教育项目引入;"共生型"河流应从低收益高消耗向高收益低影响发展方式转变,推进协调保护与开发矛盾的河流立法、基础科学研究与构建多方对话平台;"修复型"河流应从干流开发优先、支流保护优先角度,加强多学科参与河流修复试点项目的合作。