动态精化的煤层三维建模方法

现有的煤层建模方法大都从多源数据中提取点或边界来构建实体模型，对开采过程中煤层动态变化的过程缺乏足够关注.提出一种逐步细化的煤层精细建模方法，首先，对开采边界内进行网格划分后将其转换为三角网，通过全局数据中提取的采样点和普通克里金法对三角网节点的高程及厚度属性进行插值，之后将其拓展为三棱柱作为初始煤层模型;其次，确认开采数据的影响范围，并对初始模型进行动态修正.在唐山市钱家营煤矿进行应用的结果表明，该方法充分运用了开采数据，使煤层的真实变化能够及时反映在煤层三维模型上，提高模型的精度.     

基于 GIS 的华东地区旅游气候舒适度分析

气候舒适度持续时间的长短,是影响旅游地旅游季节长短及旅游地开发的重要因素.利用了华东地区132个气象站点30年的月均气温、月均相对湿度、月均日照和风速等气候要素数据,利用ArcGIS平台,基于克里格和协同克里格插值方法,计算温湿指数和风效指数,获得华东地区栅格单元为1 km的气候舒适度评价图,并对其空间分布规律及各月份气候的舒适性进行了分析.结果表明：（1）我国华东地区温湿指数与风效指数的总体分布趋势是随纬度及海拔高度由南向北、由低到高而递减.风效指数的舒适区比温湿指数的偏大;（2）华东地区的旅游气候舒适期,地域差异显著,最高值分布在福建.气候舒适期为5个月的区域最大,占华东地区总面积的62.7%.舒适期在3个月的地区最少,占华东地区面积的比例不足1%.     

离心压气机叶片扩压器气动优化设计

本文以某高速离心压气机叶片扩压器为研究对象,采用Kriging模型在设计工况下对其进行气动优化设计,以获取较高的"叶轮 扩压器"等熵效率,同时保证扩压器出口静压不降.Kriging模型大大加速了设计过程,得到的优化结果和原始设计相比,在性能上有了较大改善.     

析取克立格理论及其在品位估计中的应用

非线性现象普遍存在于许多自然科学的研究之中,而线性地质统计局限于对有效数据的线性组合,无法解决很多非线性问题;但是,一些非线性方法不容易处理,如条件期望的前提条件苛刻,计算复杂耗费大.析取克立格法(DK)介于两者之间,它利用二元概率分布函数进行估计,不仅有效、准确、易实现,更能很好地估计Z(x)的任意函数,解决可回采储量估计等现实问题.本文介绍了DK方法的基本理论和方法,对Z(x)的正态变换,厄米特多项式展开的过程进行详细讨论,并给出了计算实例,最后利用指示克立格对转换函数计算析取克立格估值,很容易解决可回采储量等非线性问题.     

Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes

Modeling and forecasting spatiotemporal patterns of precipitation is crucial for managing water resources and mitigating water-related hazards. Globally valid spatiotemporal models of precipitation are not available. This is due to the intermittent nature, non-Gaussian distribution, and complex geographical dependence of precipitation processes. Herein we propose a data-driven model of precipitation amount which employs a novel, data-driven (non-parametric) implementation of warped Gaussian processes. We investigate the proposed warped Gaussian process regression (wGPR) using (i) a synthetic test function contaminated with non-Gaussian noise and (ii) a reanalysis dataset of monthly precipitation from the Mediterranean island of Crete. Cross-validation analysis is used to establish the advantages of non-parametric warping for the interpolation of incomplete data. We conclude that wGPR equipped with the proposed data-driven warping provides enhanced flexibility and—at least for the cases studied– improved predictive accuracy for non-Gaussian data.     

A Multiresolution Gaussian Process Model for the Analysis of Large Spatial Datasets

We develop a multiresolution model to predict two-dimensional spatial fields based on irregularly spaced observations. The radial basis functions at each level of resolution are constructed using a Wendland compactly supported correlation function with the nodes arranged on a rectangular grid. The grid at each finer level increases by a factor of two and the basis functions are scaled to have a constant overlap. The coefficients associated with the basis functions at each level of resolution are distributed according to a Gaussian Markov random field (GMRF) and take advantage of the fact that the basis is organized as a lattice. Several numerical examples and analytical results establish that this scheme gives a good approximation to standard covariance functions such as the Matérn and also has flexibility to fit more complicated shapes. The other important feature of this model is that it can be applied to statistical inference for large spatial datasets because key matrices in the computations are sparse. The computational efficiency applies to both the evaluation of the likelihood and spatial predictions.     

Realistic sampling of amino acid geometries for a multipolar polarizable force field

The Quantum Chemical Topological Force Field (QCTFF) uses the machine learning method kriging to map atomic multipole moments to the coordinates of all atoms in the molecular system. It is important that kriging operates on relevant and realistic training sets of molecular geometries. Therefore, we sampled single amino acid geometries directly from protein crystal structures stored in the Protein Databank (PDB). This sampling enhances the conformational realism (in terms of dihedral angles) of the training geometries. However, these geometries can be fraught with inaccurate bond lengths and valence angles due to artefacts of the refinement process of the X‐ray diffraction patterns, combined with experimentally invisible hydrogen atoms. This is why we developed a hybrid PDB/nonstationary normal modes (NM) sampling approach called PDB/NM. This method is superior over standard NM sampling, which captures only geometries optimized from the stationary points of single amino acids in the gas phase. Indeed, PDB/NM combines the sampling of relevant dihedral angles with chemically correct local geometries. Geometries sampled using PDB/NM were used to build kriging models for alanine and lysine, and their prediction accuracy was compared to models built from geometries sampled from three other sampling approaches. Bond length variation, as opposed to variation in dihedral angles, puts pressure on prediction accuracy, potentially lowering it. Hence, the larger coverage of dihedral angles of the PDB/NM method does not deteriorate the predictive accuracy of kriging models, compared to the NM sampling around local energetic minima used so far in the development of QCTFF. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.     

Prediction of conformationally dependent atomic multipole moments in carbohydrates

The conformational flexibility of carbohydrates is challenging within the field of computational chemistry. This flexibility causes the electron density to change, which leads to fluctuating atomic multipole moments. Quantum Chemical Topology (QCT) allows for the partitioning of an “atom in a molecule,” thus localizing electron density to finite atomic domains, which permits the unambiguous evaluation of atomic multipole moments. By selecting an ensemble of physically realistic conformers of a chemical system, one evaluates the various multipole moments at defined points in configuration space. The subsequent implementation of the machine learning method kriging delivers the evaluation of an analytical function, which smoothly interpolates between these points. This allows for the prediction of atomic multipole moments at new points in conformational space, not trained for but within prediction range. In this work, we demonstrate that the carbohydrates erythrose and threose are amenable to the above methodology. We investigate how kriging models respond when the training ensemble incorporating multiple energy minima and their environment in conformational space. Additionally, we evaluate the gains in predictive capacity of our models as the size of the training ensemble increases. We believe this approach to be entirely novel within the field of carbohydrates. For a modest training set size of 600, more than 90% of the external test configurations have an error in the total (predicted) electrostatic energy (relative to ab initio) of maximum 1 kJ mol^?1 for open chains and just over 90% an error of maximum 4 kJ mol^?1 for rings. © 2015 Wiley Periodicals, Inc.     

A Derivative Based Surrogate Model for Approximating and Optimizing the Output of an Expensive Computer Simulation

Approximation methods have found an increasing use in the optimization of complex engineering systems. The approximation method provides a 'surrogate' model which, once constructed, can be called instead of the original expensive model for the purposes of optimization. Sensitivity information on the response of interest may be cheaply available in many applications, for example, through a pertubation analysis in a finite element model or through the use of adjoint methods in CFD. This information is included here within the approximation and two strategies for optimization are described. The first involves simply resampling at the best predicted point, the second is based on an expected improvement approach. Further, the use of lower fidelity models together with approximation methods throughout the optimization process is finding increasing popularity. Some of these strategies are noted here and these are extended to include any information which may be available through sensitivities. Encouraging initial results are obtained.     

近水平煤层群矿床模型的建立

矿床模型是用计算机进行辅助设计和编制生产矿井采掘计划的基础，本文探讨了地下矿井近水平煤层群矿床模型的建立方法，并编制了相应的计算机软件。为矿井设计和生产管理的科学化提供了一种新途径。