CO2吞吐工艺操作参数的整体优化设计

针对ZY油田原油CO2吞吐的工艺过程，建立了CO2吞吐数值模拟模型。应用数值模拟方法对CO2吞吐进行了实例分析，首先对该井的CO2吞吐参数进行了单因素敏感性分析，分析了CO2周期注入量、注入速度、焖井时间、采液速度等因素对CO2吞吐效果的影响。在此基础上进一步运用正交试验方法对CO2吞吐参数进行了优化，通过计算表明用正交试验优选法得到的优化方案比单因素敏感性分析所得的优化结果更为合理。     

波动方程数值模拟的隐式差分法

在有限差分波动方程数值模拟中,通常采用高阶差分方法来提高空间导数的数值逼近精度,以实现降低数值频散,提高数值模拟精度的目的。首先对差分频散进行了理论分析;然后讨论了估计一阶空间导数的隐式差分格式,并与通常采用的高阶精度显式差分格式进行了对比分析,结果表明,隐式差分格式能够在更宽的波数范围使差分频散控制在可接受的水平,如8阶精度的显式差分格式所适应的波数带宽约为O．55kmax,而隐式差分格式所适应的波数带宽约为0．7kmax;最后通过模型试算,对隐式差分格式的有效性进行了验证。模拟结果表明,用隐式差分格式在一定程度上降低了差分频散,提高了模拟精度。     

重复压裂裂缝转向时油藏数值模拟研究

裂缝转向的实现及选井、选层、优化设计等的数值模拟和机理是转向重复压裂需进一步研究解决的问题,为此,采用黑油模型,研究了在反九点注采井网中,不同储集层渗透率下,转向重复压裂的裂缝穿透率和导流能力等对采油动态的影响。研究表明,不论是渗透率高的地层还是渗透率低的地层,不利裂缝方位的边井最适合实施转向重复压裂且需要比常规重复压裂更大的缝长,而有利裂缝方位的边井和角井只需要与常规重复压裂相当的缝长;同时,转向重复压裂需要比常规重复压裂更高的裂缝导流能力。     

结构形式对椭圆扁管管内传热与流阻性能影响

利用数值模拟方法结合场协同理论,对直椭圆扁管、单向螺旋椭圆扁管和变向螺旋椭圆扁管管内传热与流动情况进行了数值模拟计算和理论分析,比较研究了不同的Re、Pr下,3种椭圆扁管的管内传热与流阻特性。结果表明,单向螺旋椭圆扁管和变向螺旋椭圆扁管与直椭圆扁管相比有很好的强化传热性能,而变向螺旋椭圆扁管要比单向螺旋椭圆扁管的强化传热性能更好。在管内流体具有高Pr及低Re的条件下,2种螺旋椭圆扁管的强化传热效果显著,可作为很好的强化传热元件应用于工程实际中。     

数控技术及产业发展

简要介绍了我国数控技术和产业发展的状况及当今世界数控技术的发展趋势,对我国发展自己的数控技术及产业途径提出了一些建议。     

弯曲性分叉河道中污染物扩散性的初步研究

弯曲性分叉河道中水流特征复杂,对污染物扩散有很大影响。为了定量分析其水流特征对污染物扩散的影响,采用有限元方法求解弯曲性分叉河道中的污染物扩散数学模型,研究其中污染物扩散的状况。主要针对河道回流、绕流对污染物扩散的影响进行了详细数值模拟计算,并分别给出了可视化结果及平均的扩散距离。计算结果表明:边界层稍微减弱污染物的扩散,弯道分离使污染物扩散呈现多向扩散,而回流和绕流尾迹区使污染物很难扩散,从而有利于泄漏污染物的收集和集中治理。     

有限元技术在数值试井中的应用

分析了试井技术的发展趋势，对有限元分析技术及其发展作了较为全面的介绍，重点阐述了利用有限元技术进行三维建模的方法和步骤，尤其是对复杂的非对称边界和非均质油藏的建模方法和技巧进行了深入探讨。利用数值试井技术，石油工程师可以方便准确地对复杂油藏和边界进行建模，试井的模拟过程和油藏任意位置的压力瞬间变化都可以通过图形和颜色的变化显示出来，解释的精度和速度都得到了大大地提高。     

GaN-MOCVD设备反应室流场的CFD数值仿真

采用计算流体力学方法对生长半导体材料GaN的重要设备MOCVD(金属有机物化学气相沉积)反应室中的流场结构进行了三维数值仿真.数值模拟采用基于非交错网格系统的SIMPLE算法,用有限体积方法对控制方程进行离散,并采用改进的压力-速度耦合方法进行求解.数值仿真给出了具有复杂几何结构和运动方式的GaN-MOCVD反应室中的流场结构,研究了改变几何尺寸和运行参数对MOCVD反应室流场结构的影响,对正在试制开发中的MOCVD设备的几何结构的改进和运行参数的优化提出了指导性建议.     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto     

The Influence and Interpretation of Surface Parameters for Wetting Transitions in Ternary Mixtures

Starting from a microscopic Hamiltonian defined on a semi-infinite cubic lattice, and employing a mean-field approximation, the surface parameters relevant for wetting in confined ternary mixtures are derived. These are found in terms of the microscopic coupling constants, and yield a physical interpretation of their origins. In comparison with the standard expression for the surface free-energy density, several new terms arising from the derivation are identified. The influence of the surface parameters on a predicted unbinding transition in a mixture of oil, water, and amphiphile demonstrate that existing results are robust to the addition of the extra surface terms.