碾压混凝土拱坝封拱温度场的仿真分析

混凝土坝的裂缝是一个带有普遍性的现象,因此有"无坝不裂"的说法。在大坝裂缝事故中,除少数裂缝是因结构不合理或地基不均匀沉降引起之外,大部分是由温度应力引起的。目前,普遍采用的拱梁分载法计算拱坝温度荷载难以描述拱坝在荷载作用下开裂状态。基于这一点,本文采用ANSYS有限元数值仿真技术、考虑混凝土材料的非线性,对某处于初设阶段的碾压混凝土拱坝采用混凝土多参数强度准则(非线性有限元法)进行计算。根据初设阶段资料和混凝土拱坝设计规范的要求对大坝关键部位进行等效应力计算并分析坝基面开裂状况,并对大坝封拱温度取值进行专门分析。在满足应力控制标准和施工可行的情况下提出合理的特征拱圈封拱温度值,以供设计人员参考。     

水电机组尾水系统流场数值模拟研究现状分析

水轮发电机组的稳定性能是衡量水轮发电机组的重要性能指标，国内外许多学者针对水轮发电机组稳定性的研究作了大量的工作，尤其以水电机组尾水系统内部流场的研究最为突出。本文分析了近年来尾水系统数值模拟现状，并针对其不足之处对今后的发展方向提出了看法。     

Optimal operation of multi-reservoir system by multi-elite guide particle swarm optimization

With increasingly scarce of fossil fuel resources required for energy demand, hydropower has become one of the most important energy resources. As a renewable and sustainable energy, hydropower system has developed rapidly during recent decades. The unprecedented rate of expansion and development scale of hydropower has posed a challenge to the operation of multi-reservoir system (OMRS). The optimal operation of multi-reservoir system is a large-scaled, non-linear and multi-stage problem which subject to a series of hydraulic constraints. The main objective of OMRS is to find out the optimal hourly water discharge rate of each hydro station in the multi-reservoir system to minimize the power deficit and distribute the uniformly deficit if any. In order to solve OMRS problem effectively, in this paper, a multi-elite guide particle swarm optimization (MGPSO) is proposed by introducing archive set into standard particle swarm optimization. External archive set which can preserve elite solutions along the evolution process is employed to provide multi-elite flying directions for particles. Meanwhile, an effective constrain handling method is proposed to handle the operational constraints of OMRS problem. This proposed method is applied to a multi-reservoir system consisting of 10 cascaded hydro plants for case study. Compared with several previous methods, the simulation results of MGPSO can get better solutions with smaller energy deficit, which proves it is an alternative method to deal with OMRS problem.     

Using Markov Chain Monte Carlo to quantify parameter uncertainty and its effect on predictions of a groundwater flow model

A statistical Bayesian framework is used to solve the inverse problem and develop the posterior distributions of parameters for a density-driven groundwater flow model. This Bayesian approach is implemented using a Markov Chain Monte Carlo (MCMC) sampling method. Three sets of data pertaining to the location of the freshwater–seawater transition zone exist for the site, including chemistry data, hydraulic head data and newly collected magnetotelluric (MT) data. A sequential conditioning approach is implemented where the chemistry data and MT-converted salinity are combined as a single data set and are used to first condition the parameter distributions. The head data are subsequently used as a second conditioning data set where the posterior distribution developed by the first conditioning is used as a prior for this second conditioning. Results of this analysis indicate that conditioning on the available data sets yields dramatic reduction of uncertainty compared to unconditioned simulations, especially for the recharge–conductivity ratio. This ratio controls the location of the transition zone, and the conditioning results in a smaller range of variability compared to the distribution used in previous modelling of the site. Using the conditioned distributions to solve the density-driven flow problem in a stochastic framework (i.e., model parameters are randomly sampled from the posterior distributions) results in a range of output flow fields that is much narrower than the previous model. The ensemble mean of these solutions and the uncertainty bounds expressed by the mean ± one standard deviation lie within the uncertainty bounds of the original model. For the case study shown here, the effect of conditioning data is dominant over the effect of prior information.     

Object oriented modelling and simulation of hydropower plants with run-of-river scheme: A new simulation tool

This paper presents the design of a component library for modelling hydropower plants, and describes the development of a new simulation tool for small hydropower plants with a run-of-river scheme. After reviewing the desirable features of simulation, an approach based on an object oriented modelling language, like EcosimPro, is presented. A general model of hydropower plant with run-of-river scheme is created with this component library. It provides the possibility of choosing a specific number of turbines and spillway gates. In this way, several hydropower stations of similar operating characteristics can be simulated using this same general model. It is expected to obtain interesting information in simulation like the reservoir level, water flows, turbine efficiencies, and so on. In addition, a graphical user interface has been designed in order to operate this general model more easily, to configure plant parameters and to simulate the plant behaviour under different conditions. Three real stations have been used as real examples for validating the model and testing the simulator: hydropower stations of Villafranca and El Carpio (Córdoba, Spain) and Marmolejo (Jaén, Spain).     

P507溶于煤油作为载体的一种新型分散组合液膜对Eu3+的分离

The separation of Eu3+ is studied with a dispersion combined liquid membrane (DCLM), in which polyvinylidene fluoride membrane (PVDF) is used as the liquid membrane support, dispersion solution containing HCl solution as the stripping solution, and 2-ethyl hexyl phosphonic acid-mono-2-ethyl hexyl ester (P507) dissolved in kerosene as the membrane solution. The effects of pH value, initial concentration of Eu3+ and different ionic strength in the feed phase, volume ratio of membrane solution to stripping solution, concentration of HCl solution, concentration of carrier, different stripping agents in the dispersion phase on the separation are investigated. The optimum condition for separation of Eu3+ is that concentration of HCl solution is 4.0 mol•L1, concentration of carrier is 0.16 mol•L1, and volume ratio of membrane solution to stripping solution is 30︰30 in the dispersion phase, and pH value is 4.2 in the feed phase. The ionic strength has no significant effect on separation of Eu3+. Under the optimum condition, when the initial concentration of Eu3+ is 0.8×104 mol•L1, the separation percentage of Eu3+ is 95.3% during the separation time of 130 min. The kinetic equation is developed in terms of the law of mass diffusion and the theory of interface chemistry. The diffusion coefficient of Eu3+ in the membrane and the thickness of diffusion layer between feed phase and membrane phase are obtained and their values are 1.48×107 m2•s1 and 36.6 μm, respectively. The results obtained are in good agreement with literature data.     

Hybrid neural network models for hydrologic time series forecasting

The need for increased accuracies in time series forecasting has motivated the researchers to develop innovative models. In this paper, a new hybrid time series neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks (ANNs). The proposed approach consists of an overall modelling framework, which is a combination of the conventional and ANN techniques. The steps involved in the time series analysis, e.g. de-trending and de-seasonalisation, can be carried out before gradually presenting the modified time series data to the ANN. The proposed hybrid approach for time series forecasting is tested using the monthly streamflow data at Colorado River at Lees Ferry, USA. Specifically, results from four time series models of auto-regressive (AR) type and four ANN models are presented. The results obtained in this study suggest that the approach of combining the strengths of the conventional and ANN techniques provides a robust modelling framework capable of capturing the non-linear nature of the complex time series and thus producing more accurate forecasts. Although the proposed hybrid neural network models are applied in hydrology in this study, they have tremendous scope for application in a wide range of areas for achieving increased accuracies in time series forecasting.     

变攻角垂直轴风力机三维定常流场数值模拟

应用Fluent软件求解三维定常不可压N-S方程,采用k-ε湍流模型和SIMPLE算法,结合MRF复合坐标系技术,模拟垂直轴风力机（VAWT）的流场特性.获得并分析了不同攻角下的速度场、压力场分布,得到风轮叶片压力分布和转矩值.结果表明绕流场模拟能有效地反应风力机不同攻角下的流场状况,可为垂直轴风力机的工程设计提供理论依据.     

两相更新支撑液膜中金属二价镍的迁移行为研究(英文)

A novel disphase supplying supported liquid membrane(DSSLM),containing supplying feed phase and supplying stripping phase for transport behavior of Ni(II),have been studied.The supplying supported feed phase included feed solution and di(2-ethyhexyl) phosphoric acid(HDEHP) as the carrier in kerosene,and supplying stripping phase included HDEHP as the carrier in kerosene and HCl as the stripping agent.The effects of volume ratio of membrane solution to feed solution(O/F),pH,initial concentration of Ni(II) and ionic strength in the feed solution,volume ratio of membrane solution to stripping solution(O/S),concentration of H2SO4 solution,HDEHP concentration in the supplying stripping phase on transport of Ni(II),the advantages of DSSLM compared to the traditional supported liquid membrane(SLM),the system stability,the reuse of membrane solution and the reten-tion of membrane phase were studied.Experimental results indicated that the optimum transport of Ni(II) was ob-tained when H2SO4 concentration was 2.00 mol·L-1,HDEHP concentration was 0.120 mol·L-1,and O/S was 4:1 in the supplying stripping phase,O/F was 1︰10 and pH was 5.20 in the supplying feed phase.The ionic strength in supplying feed phase had no obvious effect on transport of Ni(II).When initial Ni(II) concentration was 2.00×10?4 mol/L,the transport percentage of Ni(II) was up to 93.1 % in 250 min.The kinetic equation was deduced in terms of the law of mass diffusion and the interface chemistry.     

Study on a novel flat renewal supported liquid membrane with D2EHPA and hydrogen nitrate for neodymium extraction

The Nd(III) extraction in flat renewal supported liquid membrane(FRSLM),with polyvinylidene fluoride membrane and renewal solution including HNO3 solution as the stripping solution and di(2-ethylhexyl) phosphoric acid(D2EHPA) dissolved in kerosene as the membrane solution,was investigated.The effects of pH in the feed phase,volume ratio of membrane solution to stripping solution,concentra-tion of HNO3 solution and concentration of carrier in the renewal phase on extraction of Nd(III) were also studied,respectively.As a result,the optimum extraction conditions of Nd(III) were obtained when concentration of HNO3 solution was 4.00 mol/L,concentration of D2EHPA was 0.100 mol/L,and volume ratio of membrane solution to stripping solution was 1.00 in the renewal phase,and pH was 4.60 in the feed phase.When initial concentration of Nd(III) was 2.00×10-4 mol/L,the extraction percentage of Nd(III) was up to 92.9% in 75 min.