农田土壤表层饱和导水率的条件模拟

本文简单地介绍了地统计模拟中的序贯高斯方法, 以农田土壤表层饱和导水率Ks为例, 对其分别进行了条件模拟和克立格插值, 并将条件模拟、克立格插值结果与实测数据进行了对比分析. 结果表明克立格插值具有明显的"平滑"效应, 改变了Ks数据的空间结构; 而条件模拟具有与实测数据相同的空间自相关结构, 并且对未知点的模拟值具有不确定性. 因此, 条件模拟对于研究那些具有空间不确定性且会对环境带来不良影响的风险性变量, 更具有实际意义.     

热带低丘地区农场尺度土壤质量指标的空间变异

本文以我国热带地区海南儋州一个典型农场为样区,采用地统计学方法研究土壤质量指标的空间变异.研究选择了土壤质量评价最小数据集中的7项土壤质量指标(总有机碳,容重,粘粒含量,速效P、K,阳离子交换量,pH)进行分析,统计分析的结果表明,研究区域土壤存在酸化和养分贫瘠化现象.地统计学分析结果表明,表层土壤基底值普遍高于表下层土壤,说明表层土壤由于受随机因素影响更为强烈导致空间变异性增强.在各项土壤质量指标中,pH和土壤容重主要表现为基底效应,总有机碳、阳离子交换量,速效P、K含量以及亚表层土壤粘粒含量表现出中等空间相关性,表层粘粒含量空间变异随距离增加而增加.在GIS支持下,采用块段克立格方法(block kriging)进行各项土壤质量指标的空间内插得到指标的等级分布图.研究结果可以为进一步的土壤质量综合评价提供方法和理论依据.     

Eigenfrequency optimization of stiffened plate using Kriging estimation

This paper describes the optimization of the beam stiffeners attached to plates in an eigenfrequency problem. The solution space is estimated using the Kriging method. A finite element analysis is carried out to evaluate the objective function at the sample points used for the estimation. The gradient method is used as a local optimizer. The Kriging estimation incurs relatively low cost, and is easy to combine with the gradient method. In this paper, we solve eigenfrequency optimization problems for a fully supported plate to maximize the minimum eigenfrequency and the difference between the 1st- and 2nd-order eigenfrequency. An optimization problem for an L-shaped plate with an eigenfrequency constraint is also solved. Good solutions are obtained for each example, and all the optimizations for these problems can be done at a lower computational cost. The results highlight the effectiveness of the method to solve the eigenfrequency optimization problems for the stiffened plate.     

苏州城市边缘带土壤综合肥力质量时空特征

对苏州市城市边缘带样区土壤进行系统的采样分析,应用一系列数理统计分析方法,阐述了研究区土壤主要肥力要素的空间变异特征及其影响因素;利用数值化土壤综合肥力评价手段,估算了研究区土壤数值化肥力质量指数及其空间分异规律;并通过与研究区历史数据对比,揭示了过去20年来苏州市城市边缘带土壤主要肥力要素及综合肥力质量的动态演变趋势。结果表明,研究区表层土壤电导率、CEC以及养分含量空间变异强烈,表现出城市边缘带土壤肥力分布的重要空间特征。经过过去20年的农业生产实践,研究区土壤主要养分含量总体上呈现增长趋势,土壤综合肥力质量明显提高。但土壤全K含量却呈下降趋势,表明土壤养分不均衡现象依然存在。土壤pH大幅下降,土壤酸化现象严重。选取的10个样本土属中,平均综合肥力质量指数增高和降低的土壤数量分别为5个和4个。同时,土壤综合肥力质量空间可变性显著增大,最大变幅与变异系数明显升高,反映出快速工业化、城市化与农业集约化背景下土壤资源利用方式与利用强度的变化对土壤质量结构及其空间变异特征的重要影响。     

黄河三角洲地区典型地块地下水特征的空间变异性研究

以黄河三角洲地区典型地块为研究区,运用经典统计学和地统计学相结合的方法研究了包括埋深和含盐量的地下水性质空间变异特征,绘制了各地下水特征的随机性和结构性的半方差图和空间分布图。结果表明:受不同尺度的结构性因素和随机性因素的共同作用,地下水埋深和含盐量均具有中等的变异强度和空间自相关性,地下水埋深的自相关距大于含盐量。对Kriging插值结果分析表明:地下水埋深的空间变异性受微地形因素及农田工程措施影响较大,长期的农业生产措施会影响地下水含盐量的空间分布,地下水含盐量空间变异性与埋深有一定的关联。地下水特征的空间结构性和定量化研究对黄河三角洲地区地下水资源评价、土壤的次生盐碱化防治及区域水盐运动调控有着直接的现实意义。     

基于Kriging的差分进化算法及其在苯乙烯流程优化中的应用

自适应差分进化算法基于个体生成策略和控制参数自适应,无须人为设置参数,对问题有较好的适应性,但其收敛速度和精度有待提高。将具有较高预测精度的Kriging模型应用于自适应差分进化算法中,建立跟随种群变化的Kriging模型,通过模型极值点与种群最优个体竞争,对种群产生扰动,影响种群进化过程,改善算法的收敛速度和寻优性能。对10个典型测试函数的测试结果表明,该算法较标准和自适应差分进化算法收敛速度加快,收敛精度提高,且具有更好的稳定性。将基于Kriging的差分进化算法应用于苯乙烯装置的流程优化,操作运行费用显著降低。     

基于Kriging改进响应面法的桥梁地震动力可靠度研究

鉴于桥梁结构对地震响应的非线性和复杂性等问题,提出了基于Kriging改进响应面法的桥梁结构地震动力可靠度分析方法。首先利用Kriging模型的优越模拟性能,将其作为响应面函数,并采取自适应策略加以改进,而后采取线性过滤器脉冲响应法对地震随机激励荷载进行了离散,并基于首次超越问题的定义建立了动力可靠度极限状态方程,最后对桥梁结构的地震可靠度问题进行了分析。计算分析结果表明:提出的基于Kriging模型的改进响应面法能有效完成桥梁地震动力可靠度的计算分析(包括结构参数随机性对桥梁动力可靠度的影响),且结果具有较高的准确性和高效性。     

Advanced Scientific Computing in BASIC With Applications in Chemistry,Biology, and Pharmacology

Tuning and calibration are processes for improving the representativeness of a computer simulation code to a physical phenomenon. This article introduces a statistical methodology for simultaneously determining tuning and calibration parameters in settings where data are available from a computer code and the associated physical experiment. Tuning parameters are set by minimizing a discrepancy measure while the distribution of the calibration parameters are determined based on a hierarchical Bayesian model. The proposed Bayesian model views the output as a realization of a Gaussian stochastic process with hyper-priors. Draws from the resulting posterior distribution are obtained by the Markov chain Monte Carlo simulation. Our methodology is compared with an alternative approach in examples and is illustrated in a biomechanical engineering application. Supplemental materials, including the software and a user manual, are available online and can be requested from the first author.     

A framework for design optimization using surrogates

Design optimization is a computationally expensive process as it requires the assessment of numerous designs and each of such assessments may be based on expensive analyses (e.g. computational fluid dynamics method or finite element based method). One way to contain the computational time within affordable limits is to use computationally cheaper approximations (surrogates) in lieu of the actual analyses during the course of optimization. This article introduces a framework for design optimization using surrogates. The framework is built upon a stochastic, zero-order, population-based optimization algorithm, which is embedded with a modified elitism scheme, to ensure convergence in the actual function space. The accuracy of the surrogate model is maintained via periodic retraining and the number of data points required to create the surrogate model is identified by a k-means clustering algorithm. A comparison is provided between different surrogate models (Kriging, radial basis functions (Exact and Fixed) and Cokriging) using a number of mathematical test functions and engineering design optimization problems. The results clearly indicate that for a given fixed number of actual function evaluations, the surrogate assisted optimization model consistently performs better than a pure optimization model using actual function evaluations.     

A comprehensive preference-based optimization framework with application to high-lift aerodynamic design

An integral component of transport aircraft design is the high-lift configuration, which can provide significant benefits in aircraft payload-carrying capacity. However, aerodynamic optimization of a high-lift configuration is a computationally challenging undertaking, due to the complex flow-field. The use of a designer-interactive multiobjective optimization framework is proposed, which identifies and exploits preferred regions of the Pareto frontier. Visual data mining tools are introduced to statistically extract information from the design space and confirm the relative influence of both variables and objectives to the preferred interests of the designer. The framework is assisted by the construction of time-adaptive Kriging models, which are cooperatively used with a high-fidelity Reynolds-averaged Navier–Stokes solver. The successful integration of these design tools is facilitated through the specification of a reference point, which can ideally be based on an existing design configuration. The framework is demonstrated to perform efficiently for the present case-study within the imposed computational budget.