低温试验能力验证中的不确定度分析评估

本文简述电气领域如何评定测量不确定度过程，并结合CNAS T0346低温试验能力验证计划，识别和具体分析低温试验中各测量不确定度的来源，对各种影响因素进行评价，论述人、机、料、法、环、测各环节对测量结果的影响，给出了低温试验测量结果不确定度的评估过程。     

INVESTMENT SEQUENCING RECOGNIZING EXTERNALITIES IN WATER DESALTING1

ABSTRACT. The failure to recognize the learning process in new technologies such as desalting may lead to incorrect water resource investment decisions for two reasons. First, to neglect cost reductions stemming from “learning by doing” implies an overestimation of desalting costs. Second, since learning in a particular plant may result in external (learning) benefits to other plants, these may serve as the basis for a subsidy intended to internalize such benefits. Accordingly, the research reported below includes an estimation of learning functions for desalting and the results of a formulation designed to measure external benefits on the basis of these learning functions. These results are then incorporated into a decision framework for water resource investments which recognizes uncertainty in determining optimal timing of desalting construction.     

公路网规划环境影响评价中的困难和不确定性分析

首先分析了公路网规划环境影响评价中的困难,即评价方法的不成熟和获取资料的困难;然后重点分析了评价中的公路网规划方案的不确定、环境信息的不确定、空间信息的不确定和环境影响程度的不确定;最后提出了 4种解决困难和降低不确定的方法,即制定公路网规划环境影响评价技术导则,使用基于情景分析的预测方法,广泛开展公众参与以及通过多方协作的方式开展环境影响评价工作.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

环境模型参数识别与不确定性分析

在对水文模型实例的参数不确定性分析基础上,分别采用传统灵敏度分析方法、HSY算法、线性回归等方法对模型参数特性进行了识别与比较研究.结果表明参数优化算法与传统灵敏度分析方法不能解释模型结构复杂性特征,采用不确定性分析方法对环境模型参数进行识别提供了深入分析与理解模型系统的有效途径.     

Factors affecting the quantitative uncertainty of the estimated short-term intake. Part I—Calculation methods

ABSTRACT

The calculation of the combined uncertainty of the international estimated short-term intake (IESTI) of ethephon residues in apples is shown as an example. The ethephon residues in apples were reported by the Joint FAO (Food and Agriculture Organization of the United Nations)/WHO (World Health Organization) Meeting on Pesticide Residues (JMPR). The apple consumption data were taken from the IESTI (international short-term intake) calculation template used by the JMPR. The IESTI was calculated with the currently used method (case 2a) and a proposed one recommended by the EFSA (European Food Safety Authority)/RIVM (Dutch National Institute for Public Health) Scientific Workshop co-sponsored by FAO and WHO. In this example, the ratio of IESTI_proposed/IESTI_current and their combined relative uncertainty are about 2.8, and 1.7, respectively. The larger IESTI and uncertainty obtained with the proposed equation are the consequence of calculation only with the large portion (LP) instead of its combination with unit mass, and the MRL instead of the highest residue (HR). The LP is the major contributor to the combined uncertainty. Both the calculated IESTI and its combined uncertainty depend on the actual food – pesticide residue combination, and should be calculated for each case.     

Irrigation Water Allocation Using an Inexact Two‐Stage Quadratic Programming with Fuzzy Input under Climate Change

Agricultural irrigation accounts for nearly 70% of the total water use around the world. Uncertainties and climate change together exacerbate the complexity of optimal allocation of water resources for irrigation. An interval‐fuzzy two‐stage stochastic quadratic programming model is developed for determining the plans for water allocation for irrigation with maximum benefits. The model is shown to be applicable when inputs are expressed as discrete, fuzzy or random. In order to reflect the effect of marginal utility on benefit and cost, the model can also deal with nonlinearities in the objective function. Results from applying the model to a case study in the middle reaches of the Heihe River basin, China, show schemes for water allocation for irrigation of different crops in every month of the crop growth period under various flow levels are effective for achieving high economic benefits. Different climate change scenarios are used to analyze the impact of changing water requirement and water availability on irrigation water allocation. The proposed model can aid the decision maker in formulating desired irrigation water management policies in the wake of uncertainties and changing environment.     

非饱和土壤渗透系数空间不确定性对溶质运移的影响

包气带渗透系数的不确定性是影响非饱和带溶质运移的主要因素。应用贝叶斯方法对非饱和土壤渗透系数进行前处理,使用Monte-Carlo方法模拟其空间不确定性,并通过HYDRUS-1D模型对溶质运移进行数值模拟,研究包气带渗透系数的空间不确定性对溶质运移的影响。结果表明,由于包气带渗透性的不确定性使得溶质浓度分布呈现明显的不确定性,包气带内不同点的浓度值相差很大。与忽略包气带土性参数空间不确定性的模拟结果相对比,考虑包气带渗透系数不确定性的模拟结果与实际情况更加接近,更具合理性和科学性。同时,根据模拟结果,对实际工作中进行地下水数值模拟时溶质初始浓度输入值的确定提出相应建议。     

考虑爆裂影响的钢筋混凝土梁火灾温度场数值分析

为了描述混凝土高温爆裂对构件热传导过程的影响,结合钢筋混凝土梁火灾试验,考虑爆裂时间和区域的不确定性,建立了火灾下钢筋混凝土梁数值分析模型,分析爆裂深度、爆裂面积比、爆裂位置等爆裂参数对梁跨中截面温度场的影响规律。研究结果表明:当钢筋混凝土梁发生受火爆裂,梁截面的温度显著升高,并随爆裂深度的增大而进一步增强;爆裂面积比对截面温度场影响不显著,当爆裂深度、爆裂位置一定,爆裂面积比增加达到13%左右时,截面温度场基本上不再变化;爆裂深度、爆裂面积比一定,梁跨中爆裂对截面温度场影响最大,但是底部纵筋处温度较顶部纵筋处温度升高较快。