中国煤层气资源控制程度及可靠性分析

准确认识深部条件下气体和水分的赋存状态、相对含量及分布特征,对煤层气高效勘探开发具有重要指导意义。基于理论模型、分子模拟和气水演变分析,明确了煤层中气、水的赋存状态,揭示了气、水动态运聚界限和动态演化过程。考虑煤−水界面作用、水的可动性及赋存状态,煤中水可分为可动水(重力水和毛细水)、束缚水(吸附水、沸石水、结晶水和层间水)、结构水,其中毛细水、重力水和吸附水由孔隙主导,沸石水、结构水、结晶水和层间水由矿物主导。分子模拟结果显示,水分子在0.7 nm孔隙中可以饱和充填,吸附和解吸路径一致,在更大孔隙中出现弱吸附层和自由态。水分子吸附过程表现为单分子含氧基团吸附、单层强吸附、多层弱吸附、水团簇形成和充填孔隙等阶段。甲烷分子在1.5 nm孔隙可存在3层稳定充填吸附,在较大孔隙中(>1.5 nm)即以单层吸附和游离态共存,游离态在介孔及更大孔隙中普遍存在。结合上述吸附−游离气存在界限,改进了游离气和吸附气理论计算公式,为含气量计算提供新思路。深部热成因煤层气是煤大规模生排烃之后的残余气,在排烃过程中发生气驱水和水分蒸发扩散,残余水分为束缚水和结构水,后期无法改变。假定静水压力20 MPa,在0、5、10、15和20 MPa储层压力下,外来水分可入侵最大孔径为7、9、13、27 nm和不侵入。受差异保存条件控制,煤成气除了形成超压和欠压等差异含气系统外,还可能在煤系形成多类型含气模式。上述研究明确了煤层气、水微观赋存机制及形成演化模式,对深部煤层气富集特征及高效开发设计具有指导意义。

     

双平面T型方管加强节点的承载力性能研究

通过有限元软件ANSYS对双平面T型方管相贯焊接加强节点轴力和弯矩作用下的极限承载力进行了数值模拟研究，采用的加强方式在主管内部设置纵向加劲肋。研究结果表明这种加强方式对于双平面T型节点的单项承载力都有不同程度的提高作用，且提高幅度与几何参数有关。     

泥石流源地松散体起动人工降雨模拟及放水冲刷实验

以神府－东胜矿区人为泥石流及云南东川蒋家沟自然泥石流为研究对象,采用人工降雨及放水冲刷模拟实验方法,分析了不同类型,不同地区泥石流源地松散体起动条件及泥石流过程,得出一些有意义的结论。     

中国西北部“4.5”沙尘暴过程中尺度低压的数值模拟

利用改进型ＰＳＵ／ＮＣＡＲ中尺度数值模式（ＭＭ４标准版），取模式水平格距４０ｋｍ，４６×６１网格，垂直方向ａ取１５层，即从地面到模式顶（１００ｈＰａ），σ＝０．０、０．１、０．２、０．３、０．４、０．５、０．６、０．７、０．７８、０．８４、０．８９、０．９３、０．９６、０．９８、０．９９、１．００，采用ＮＣＡＲ的３０＇×３０＇地形资料，以常规观测资料作为初始场，较好地模拟了此次沙尘暴过程的海平面气压的演变和分布，特别是张掖、柴达本盆地以及敦煌附近的三个中低压。同时，模拟了张掖中低压与蒙古冷高压之间的甘肃河西沙尘暴东大风。敏感性试验表明，沙尘暴中低压的形成发展主要是受于物理过程制约；沙尘暴中尺度系统的研究与暴雨中尺度系统的研究是有区别的，积云对流参数化并不是特别重要，在设计研究沙尘暴的数值模式中，应当合理地处理其他的热力、动力过程及大气外强迫源的作用。模式水平格距、地形真实程度对模拟中低压的位置、中心强度有重要贡献；下垫面变化中低压强度有一定影响。张掖热低压的形成发展主要是在有利的环境形势下，特殊地势起了重要作用，表现为直接动力强迫和间接热力强迫。     

基于有限比较法研究万州区滑带土干重度的概率分布

对万州区滑坡滑带土干重度的86个测试数据进行拟合优度检验,发现多种传统的检验方法都既可以接受正态分布也可以接受对数正态分布.在传统检验方法失效的情况下,引入了有限比较法,确定了干重度的最优概率分布为正态分布,并用统计模拟的方法分析了有限比较法选中真实分布的概率.     

洪水演进三维模拟仿真系统可视化研究

洪水演进仿真系统的研制，是实施“数字流域”工程的重要组成部分；结合洪水演进可视化目标的分析，基于Visual C 系统开发平台，融GIS技术和Opengl开发技术，采用三角形逼近、光滑处理和加入法向量以控制光照的方式，实现了流域地形及河床的三维可视化仿真；应用广度优先搜索算法确定了运动水体与流域河床形态的自适应与自相依的关系，使流域洪水演进模拟具有真实自然的可视化效果。所研制的系统雏形，可有效的模拟流域洪水的三维演进过程；     

Traction image method for irregular free surface boundaries in finite difference seismic wave simulation

In this study, we propose a new numerical method, named as Traction Image method, to accurately and efficiently implement the traction-free boundary conditions in finite difference simulation in the presence of surface topography. In this algorithm, the computational domain is discretized by boundary-conforming grids, in which the irregular surface is transformed into a 'flat' surface in computational space. Thus, the artefact of staircase approximation to arbitrarily irregular surface can be avoided. Such boundary-conforming gridding is equivalent to a curvilinear coordinate system, in which the first-order partial differential velocity-stress equations are numerically updated by an optimized high-order non-staggered finite difference scheme, that is, DRP/opt MacCormack scheme. To satisfy the free surface boundary conditions, we extend the Stress Image method for planar surface to Traction Image method for arbitrarily irregular surface by antisymmetrically setting the values of normal traction on the grid points above the free surface. This Traction Image method can be efficiently implemented. To validate this new method, we perform numerical tests to several complex models by comparing our results with those computed by other independent accurate methods. Although some of the testing examples have extremely sloped topography, all tested results show an excellent agreement between our results and those from the reference solutions, confirming the validity of our method for modelling seismic waves in the heterogeneous media with arbitrary shape topography. Numerical tests also demonstrate the efficiency of this method. We find about 10 grid points per shortest wavelength is enough to maintain the global accuracy of the simulation. Although the current study is for 2-D P-SV problem, it can be easily extended to 3-D problem.     

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.     

Farmland Abandonment Research Progress: Influencing Factors and Simulation Model

Farmland abandonment is a global problem and considered one of the most important areas in land use change research. Farmland abandonment research currently focuses on understanding the factors that affect farmland abandonment and developing scientific models to simulate farmland abandonment. The study reviewed the natural and political factors driving farmland abandonment and summarized the main models for farmland abandonment simulation together with their advantages and disadvantages. We discuss the main ecological effects of farmland abandonment and propose farmland abandonment research directions. The study found that: (1) the influence of labor cost change and ageing labor force on farmland abandonment needs further investigation, (2) simulation models for farmland abandonment must include the decision-making mechanism of individual farmers and focus on macro large-scale abandonment prediction models, and (3) the influence of farmland abandonment on landscape culture must be investigated in detail.