2015年5月大气环流和天气分析

对2015年3—5月T639、ECMWF（文中简称EC）及日本数值模式中期预报产品进行了检验和对比。结果表明：三个模式对亚洲中高纬地区大尺度环流和850 hPa温度的演变和调整均具有较好的预报性能,EC模式对西风指数、850 hPa温度转折过程和变化幅度的预报略优于其他两个模式。对2015年3月31日至4月1日北方地区强沙尘暴天气过程的主要影响天气系统,三个模式预报能力相当,121 h时效都较好地预报出了地面冷高压,但强度和范围与实况有一定偏差。     

气体、泡沫等欠平衡钻井条件下感应测井应用分析

对气体、泡沫等欠平衡钻井条件下测井方式进行阐述,通过不同钻井液电阻率下对感应测井进行数字模拟,发现气体、泡沫等介质对感应测井影响较小。天然气介质和原油介质条件下感应测井现场实验的资料分析说明,感应测井在气体、泡沫等欠平衡钻井条件下可行。     

Mode of a spherical cavity’s thermo-elastodynamic response in a saturated porous medium for non-torsional loads

The thermo-hydro-elastodynamic model (THED), in which the thermo-osmosis and thermal-filtration phenomenon in a two-phase porous thermoelastic medium can be considered, was previously presented by the authors [Ganbin Liu, Kanghe Xie, Rongyue Zheng. Model of nonlinear coupled thermo-hydro-elastodynamics response for a saturated poroelastic medium. Sci China (Ser E) 2009;52(8):2373–83] and is used in this paper to investigate the thermo-elastodynamic response of a spherical cavity in a saturated poroelastic medium when subjected to a time-dependent non-torsional thermal/mechanical source. The Separated Variable Method is introduced, and the non-zero displacement potentials are expanded in terms of the Legendre polynomial. Solutions for the displacement, temperature increment, pore pressure and stress are obtained in the domain of the Laplace transform. Numerical results are also performed for different modes and are compared with the results of the thermoelastic model (TED) to ascertain the validity and the difference between these two models.     

海底双相随机介质声反射的SVM神经网络分类识别研究

考虑海底沉积介质为双相介质,为了更好地模拟实际海底底质的不均匀性,将随机介质理论引入双相介质理论。首先,通过基于随机-双相介质理论的高阶有限差分数值技术模拟计算海底底质分别为泥质砂、泥、泥质砾时的地震反射波信号。然后利用小波变换分别求取不同底质的一次反射波的包络作为其特征向量,最后利用基于粒子群智能算法优化的支持向量机神经网络对这些反射波信号进行分类识别。为了进一步考察所用方法的抗噪能力,对正演得到的海底底质反射波信号分别加入10%、30%、50%的高斯白噪音之后再进行分类,支持向量机仍然取得了较好的分类预测效果。基于上述正演模拟及分类识别方法的论证,提出了一套行之有效的微机软件模拟海底沉积物分类识别的一般化流程,这将有利于开展海底沉积物反射特征的进一步研究。     

城市中小河道水工建（构）筑物调查测绘技术方法的探讨

结合具体工程实例介绍了当前城市中小河道测量工程中常见水工建（构）筑物的类型、调查量测内容及对应精度指标、外业测绘取舍及内业成图表示方法等,特别是根据实地水工建（构）筑物的规模大小、复杂程度,探讨了对其进行调查测绘的技术方法,并结合CAD绘图系统提出了相应的解决办法.     

基于互信息量与BP神经网络的中长期径流预报方法研究

针对中长期径流预报因子的选择问题,采用互信息量方法筛选预报模型输入因子,在BP神经网络模型中,分别用均方误差和互信息量作为目标函数,衡量因子复合相关关系,优化选择最终预报因子并应用于碧流河汛期径流预报中。结果表明,基于互信息量筛选的预报因子与BP神经网络模型相结合,可有效识别多个预报因子与预报量间的复合相关性,对中长期径流预报因子的选择有很好参考价值。     

Two methods for pore network of porous media

Two approaches of generating pore networks of porous media are presented to capture the pore fabric. The first methodology extracted pore structure from a computer simulated packing of spheres. The modified Delaunay tessellation was used to describe the porous media, and modified Nelder–Mead method in conjunction with three pore‐merging algorithms was used to generate the pore size and coordination number distributions of the randomly packed spheres. The Biconical Abscissa Asymmetric CONcentric bond was used to describe the connection between two adjacent voids. This algorithm was validated by predicting pore structure of a cubic array of spheres of equal radius with known pore sizes, throat sizes and coordination number distributions. The predicted distributions of pore structure agreed well with the measured. Then, the algorithm was used to predict pore structure and permeability of randomly packed spherical particles, and predicted permeability values were compared with published experimental data. The results showed that the predicted permeability values were in good agreement with those measured, confirming the proposed algorithm can capture the main flow paths of packed beds. The second methodology generated an equivalent pore network of porous media, of which the centers of voids were located in a regular lattice with constant pore center distance. However, this network allowed for matching both main geometrical and topological characteristics of the porous media. A comparison of the two approaches suggested that the second approach can also be used as a predictive tool to quantitatively study the microscopic properties of flow through porous media. Copyright © 2012 John Wiley & Sons, Ltd.