土工织物拉伸强度试验不确定度评定

本文根据水利部标准《土工合成材料测试规程》(SL 235—2012)对机织土工织物的拉伸强度进行测试,依据国家计量规范《测量不确定度的评定与表示》(JJF 1059.1—2012)对试验结果进行测量不确定度评估,对引起不确定度的来源进行分析并量化,求得扩展不确定度并给出不确定度分析结果。     

流体黏性对迎浪航行船舶运动和波浪增阻的影响

对在规则波中迎浪航行的大型油轮KVLCC2船模,为了研究流体黏性对船舶运动和水动力的影响,分别采用求解RANS方程和求解欧拉方程的方法,应用CFD软件STAR-CCM+对黏性流场和无黏流场进行了数值模拟。采用GCI方法对数值不确定度进行了分析,验证了数值方法的有效性;采用湍流模型和无黏流模型对船舶在一系列波长的波浪中的运动和水动力进行了数值预报,并将数值结果与现有实验数据进行了比较。结果表明,流体黏性对船舶运动影响很小,对共振状态附近的波浪增阻有一定的影响。总体上,对船舶迎浪航行流场的数值模拟,忽略黏性能保证一定的计算精度而提高计算效率。     

泥浆管道输送特性的CFD模拟

为防止挖泥船施工中泥浆泵能耗高、输送阻力大和排泥管道淤堵,以水平管道中泥浆流动过程为研究对象,采用以颗粒动力学为基础的欧拉双流体模型,对水平管道中泥浆输送的固液两相流进行了CFD数值模拟,并与Durand模型的计算值和文献中的试验数据进行了对比。结果表明:数值模拟得到的计算结果与实测值表现出良好的一致性;在管道内充分发展段,管道截面的垂直中心线上颗粒浓度呈不对称分布,并且不对称度随着颗粒浓度增大或泥浆流速变大而降低;粗颗粒在管道底部由于受到升力作用,因此其浓度曲线在管道底部近壁处出现反折;CFD模型得到的压降计算值较Durand模型更接近实测值。     

间隙空化流动的非定常特性研究及验证确认分析

该文利用数值模拟手段,对间隙空化流动进行了细致的数值计算与分析。为了对数值结果的可靠性进行定量化的评估,采用了基于Richardson外推法的多种CFD不确定度估计方法。在此基础上,该文利用基于拉格朗日观点的三维拉格朗日分析技术,对间隙空化流动的演变规律进行细致的分析,该研究表明,涡心内部粒子的运动在较大程度上受到梢涡空化周围流速分布及壁面效应的影响。该研究有助于加深对于间隙空化流动的进一步认识。     

官地水电站尾水调压室与尾水洞连接岔洞的体型优化研究

对于首部式开发的水电站,其尾水系统水头损失直接关系到长期运行的经济效益。本文应用计算流体力学技术(CFD)对官地水电站尾水系统局部布置与结构体型进行数值模拟,开展三维流场计算与分析,得到其水头损失和流态,确定设计方案;然后进行模型试验,并对比分析数值计算和模型试验结果,证明三维流场计算结果的可靠性。采用三维流场计算可进行方案对比及体型优化并得出较优的尾水(引水)系统体型,可以为一些无法进行模型试验的工程提供设计依据。     

基于重叠网格法数值分析塔架对风机气动性能的影响

随着计算机技术和数值模拟方法的迅速发展,计算流体力学方法(CFD)成为对浮式风机性能进行分析研究的重要途径。naoe-FOAM-os-SJTU是在原有CFD求解器naoe-FOAM-SJTU基础上,结合重叠网格技术开发的面向船舶与海洋工程新的CFD数值求解器。该文应用naoe-FOAM-os-SJTU求解器对塔架对风机气动性能的影响进行数值分析。模型网格采用重叠网格技术进行处理,计算模型选用美国国家可再生能源实验室的大型风机NREL-5MW风机。对带塔架NREL-5MW风机进行气动力数值模拟和分析,数值计算得到风机和塔架所受推力与扭矩的历时曲线,风机不同截面的压力分布,以及尾流场的信息。将计算结果与无塔架的数值模拟结果进行对比分析,分析塔架对风机气动性能的影响。     

流速面积法流量测验随机不确定度计算方法的探讨

对单次流量随机不确定度计算,提出了进行河宽分段,并计算每段随机不确定度(XQ)的方法。根据误差传播理论和公式对各分量的随机不确定度进行综合,从而计算出全断面的单次流量随机不确定度。     

连续流动分析法测定水中六价铬的不确定度评定

本文对连续流动分析法测定水中六价铬的不确定度进行分析计算,分析了标准溶液配制、校准曲线拟合、测量重复性等因素引入的不确定度,并找出影响测定方法不确定度的主要因素。测量实验水样中六价铬的浓度为(1.309±0.040)mg/L(k=2)。     

溢洪道三维CFD建模实践及面临的挑战

随着计算机计算能力的持续发展,复杂水流的数值模拟越来越高效且划算,不管是初步研究还是详细研究,数值模拟都为水力分析提供了强有力的工具。通过对大尺度水力现象的模拟,证实了三维CFD工具的有效性;将三维CFD工具应用于多个大型工程项目中,对水力结构设计及运行进行分析优化。指出了三维CFD工具用于水流计算方面面临的困难,尤其是在模拟气蚀影响方面。     

官地水电站尾水系统体型优化研究

应用计算流体力学技术对官地水电站尾水系统进行数值模拟,得到其水头损失和流态,确定设计方案.进行模型试验,并对比分析数值计算和模型试验结果,证明CFD计算结果的可靠性.通过方案对比及体型优化得出较优的尾水系统体型,该设计可供类似工程参考.     

A new methodology for the CFD uncertainty analysis

With respect to the measurement uncertainty, this paper discusses the definition, the sources, the classification and the expressions of the CFD uncertainty. Based on the orthogonal design and the statistics inference theory, a new verification and validation method and the related procedures in the CFD simulation are developed. With the method, two examples of the CFD verification and validation are studied for the drag coefficient and the nominal wake fraction, and the calculation factors and their interactions which would significantly affect the simulation results are obtained. Moreover, the sizes of all uncertainty components resulting from the controlled and un-controlled calculation factors are determined, and the optimal combination of the calculation factors is obtained by an effect estimation in the orthogonal experiment design. It is shown that the new method can be used for the verification in the CFD uncertainty analysis, and can reasonably and definitely judge the credibility of the simulative result. As for CFD simulation of the drag coefficient and the nominal wake fraction, the results predicted can be validated. Although there is still some difference between the simulation results and the experiment results, its approximate level and credibility can be accepted.     

Practical evaluation of the drag of a ship for design and optimization

We consider two major components of the drag of a ship, the“friction drag”and the“wave drag”, that are related to vis-cous friction at the hull surface and wavemaking, and mostly depend on the Reynolds...     

Predictive Analysis and Simulation Uncertainty of a Distributed Hydrological Model

Distributed hydrological models should pass through a careful calibration procedure backed by sensitivity, uncertainty and predictive analysis before they are utilized as a decision making aid in watershed management and scenario studies. This paper examines whether the uncertainty of the parameters of the spatially distributed hydrologic model WetSpa causes significant uncertainty in the model predictions. The WetSpa model is applied to the Torysa river basin, a rather large catchment located in Slovakia. Parameter estimation, sensitivity and predictive analysis of the model parameters are performed using a model-independent parameter estimator, PEST. It is found that the correction factor for measured evaporation data has the highest relative sensitivity. Parameter uncertainty and predictive analysis give an insight of a proper parameter set and parameter uncertainty intervals and prove that the parameter uncertainty of the model does not result in a significant level of predictive uncertainty.     

A one-dimensional polynomial chaos method in CFD-Based uncertainty quantification for ship hydrodynamic performance＊

A one-dimensional non-intrusive Polynomial Chaos (PC) method is applied in Uncertainty Quantification (UQ) studies for CFD-based ship performances simulations. The uncertainty properties of Expected Va...     

Uncertainty analysis at large scales: limitations and subjectivity of current practices--a water quality case study.

Uncertainty analysis for large-scale model studies is a challenging activity that requires a different approach to uncertainty analysis at a smaller scale. However, in river basin studies, the practice of uncertainty analysis at a large scale is mostly derived from practice at a small scale. The limitations and inherent subjectivity of some current practices and assumptions are identified, based on the results of a quantitative uncertainty analysis exploring the effects of input data and parameter uncertainty on surface water nutrient concentration. We show that: (i) although the results from small- scale sensitivity analysis are often applied at larger scales, this is not always valid; (ii) the current restriction of the uncertainty assessment to uncertainty types with a strong evidence base gives structurally conservative estimates; (iii) uncertainty due to bias is usually not assessed, but it may easily outweigh the effects of variability; (iv) the uncertainty bandwidth may increase for higher aggregation levels, although the opposite is the standard assumption.     

Uncertainty in predicting riverbed erosion caused by urban stormwater discharge.

Ecologically based criteria require an integrated modeling approach. Due to the complexity of the system, the stochastic nature of loads, and the model abstractions, many uncertainties are involved. In this study, a simple integrated model is applied, which Swiss engineers employ to assess the impact of urban stormwater discharges on riverbed stability. In the course of a case study, an uncertainty analysis is carried out focusing on parameter uncertainties. The underlying context of the uncertainties is evaluated, and a variance-based sensitivity analysis is presented estimating the local uncertainty contribution of each parameter. The results reveal that the largest contributions stem from the model components describing the natural system. An experimental design is proposed that manages to reduce the output uncertainty significantly. Finally, we discuss the benefits of following the proposed procedure.     

Predicting Variability in a Lake Ontario Phosphorus Model

The prediction of a model always has a degree of uncertainty. Because the level of uncertainty is inversely related to the value of information contained in the prediction, there is a need to quantify the uncertainty. One approach to estimate prediction uncertainty is first-order error analysis. In this method, the error in a characteristic (variable or parameter) is defined by its first nonzero moment (the variance). Errors are propagated through the model using first-order terms in the Taylor series, and the variances are then combined to yield the total prediction uncertainty. An alternative approach to model prediction error analysis is Monte Carlo simulation. In this technique, probability density functions are assigned to each characteristic (variable or parameter), reflecting the uncertainty in that characteristic. Then, values are randomly selected from the distribution for each term and inserted into the model, to calculate a prediction. Repeating this process a number of times produces a distribution of predicted values, which reflects the combined uncertainties. These two approaches (first-order error analysis and Monte Carlo simulation) are applied to Lake Ontario data using a steady state mass balance phosphorus model. Comparisons are made which suggest guidelines for the use of each.     

Effects of Pipe Roughness Uncertainty on Water Distribution Network Performance During its Operational Period

The design of new water distribution networks (WDNs) is an important social problem. Failures during an operational period provoke deficits in consumption nodes thus decreasing the performance of the network. WDN performance can be defined as the ability to sufficiently secure demand and desirable pressure in nodes based on changes in design parameters. This paper focuses on the evaluation of network performance during an operational period, taking into account pipe roughness uncertainty. A network analysis is performed by generating probabilistic series of pipe roughness using Monte Carlo simulation (MCS) in the operational period of the Two-loop WDN. Results show that an increase in pipe roughness uncertainty causes a decrease in network performance in the operational period. Furthermore, the network has a desirable efficiency only in the first 10 years. Thus, the proposed design methodology that considers the uncertainty of design variables is an effective procedure to evaluate network performance.     

Uncertainty-Driven Characterization of Climate Change Effects on Drought Frequency Using Enhanced SPI

The Standardized Precipitation Index (SPI) is a well-established drought index that is based on transforming the interannual distribution of precipitation to a standard normal distribution. Because of its robust statistical basis, SPI is readily applicable to different regions making comparisons between locations and time windows possible. Nevertheless, the usability of SPI results is undermined by shortcomings that are partly resultant from data and model uncertainties. One such shortcoming is the inability of the existing SPI model to include change in variability of interannual precipitation from non-stationary normal – mostly caused by climate change. In addition, epistemic uncertainty in the form of incompleteness in station-wide precipitation records results in heterogeneity and inconsistency in SPI results. The effects of such epistemic uncertainty on the accuracy of estimations of long-term changes in drought frequency are mostly unknown. Given such deficiency, SPI’s procedure and subsequent results remain deterministic and inadequately informative. Here, we introduce modifications to the traditional SPI using Dempster-Shafer theory (DST) to enable modeling and propagation of variability and epistemic uncertainty with the regular SPI procedure. By generalizing the SPI model from a deterministic setting to an “uncertainty-driven setting” provided by DST, this work makes possible: (a) efficiently propagating data uncertainty in interpolation of station-wide precipitation and SPI, and (b) modeling the effects of shift in precipitation normals (due to e.g., climate change) on drought frequency. In addition, the significance of this shift may then be evaluated with respect to the epistemic uncertainty by measuring how much of the surrounding epistemic uncertainty this shift encloses (i.e., “probability of enclosing”). The latter is especially important due to large unknowns already associated with climate change modeling. We implement the model on summer extreme drought for the Okanagan Basin, BC, Canada. For a single general circulation model and scenario (CGCM3 A2) a maximum 7 % increase in summer extreme drought (for 2080s, as per current definition) is estimated with a maximum probability of enclosing of 36 %.