两种混合单元混合机理的数值模拟

为了研究SV型和SX型混合单元在全静态乳化器的混合机理,使用群体平衡模型对乳化器内部流场进行模拟分析。结果表明,SV型与SX型混合单元的混合机理是油相与水相材料通过混合单元时其空间流动方向改变,在流场内形成大量的漩涡,从而达到乳化的目的;通过比较流体在经过SV型和SX型混合单元后的水相索特平均直径(d_(32)),得出SV型混合单元的乳化效果更强,但同时消耗更多的能量,压降更大。     

瞬变电磁井间勘探的全空间几何因子

井间是剩余油的主要分布区域,为探测井间剩余油,提高采收率,提出了基于全空间几何因子的瞬变电磁井间勘探方法。在本井使用线圈发射、邻井使用线圈接收,根据瞬变电磁场理论,在阶跃信号的激励下发射线圈在地层中激发出沿圆周方向的闭合瞬变电场,该电场在导电地层中产生与地层电导率呈正比的涡流。由Doll地层环模型可知,地层中的涡流在空间任意点激发得到与地层电导率成正比的二次场响应信号(有用信号),并可表示为空间各点电导率的加权平均值,其权重即为井间瞬变电磁勘探的全空间几何因子;全空间几何因子集中分布在发射线圈和接收线圈附近,其它区域分布较少,在发射线圈和接收线圈两侧呈现不同的极性;对瞬变电磁响应与地层电导率、井间距和源距的变化规律研究可知,瞬变电磁井间勘探有用信号随着地层电导率的增大而增大,随着井间距的增加单调减小,在发射线圈和接收线圈处于同一深度时该响应信号幅度最大。     

结合边缘特征先验引导的深度卷积显著性检测

针对当前基于深度学习的显著性检测算法缺少利用先验特征和边缘信息，且在复杂场景中难以检测出鲁棒性强的显著性区域的问题，提出了一种结合边缘特征，利用先验信息引导的全卷积神经网络显著性检测算法。该算法利用三种被经常用到的先验知识结合边缘信息形成先验图，通过注意力机制将提取的先验特征与深度特征有效融合，最终通过提出的循环卷积反馈优化策略迭代地学习改进显著性区域，从而产生更可靠的最终显著图预测。经过实验定性定量分析，对比证明了算法的可靠性。     

全自动焊含缺陷环焊缝管段承载能力研究

为了研究全自动焊在役管线的真实承压能力，通过水压爆破试验对全自动焊含缺陷环焊缝在役管段的承载能力进行研究。研究结果表明，全自动焊焊接工艺下环焊缝缺陷主要是未熔合缺陷，且3种含未熔合缺陷管段起爆点、爆破压力、撕裂方向大致相同；含3种未熔合缺陷环焊缝管段的真实爆破压力均大于爆破压力的理论计算值，远远满足现场运行所需要的承载能力；含缺陷环焊缝的存在并没有对在役管段的整体承载能力造成影响；管段的承载能力主要取决于母材自身的材料性能。     

非分裂完全匹配层边界存储时间域全波形反演

正演波场的储存或重建是制约时间域全波形反演的关键问题，以计算代替存储的常规分裂完全匹配层（PML）边界存储策略可有效减少存储量。但常规分裂PML边界条件所需变量个数多、计算存储量大以及编程复杂。为此，采用非分裂复频移完全匹配层（CFS-NPML）有效边界存储策略重建正演波场实现时间域全波形反演。研究表明：CFS-NPML所需变量个数少，在边界吸收层网格点较少时吸收效果优于常规PML边界条件，波场重建过程中所需存储量少于常规PML边界条件。数值试验表明，该存储策略得到的重建波场与正向传播波场几乎无差别，其全波形反演结果与常规保存波场反演法一致；正演过程中引入并行计算技术未显著增加波场重建导致的额外正演时间。     

Performance analysis of large-scale MIMO system for wireless backhaul network

In this paper, we present a performance analysis of large-scale multi-input multi-output (MIMO) systems for wireless backhaul networks. We focus on fully connected N nodes in a wireless meshed and multi-hop network topology. We also consider a large number of antennas at both the receiver and transmitter. We investigate the transmission schemes to support fully connected N nodes for half-duplex and full-duplex transmission, analyze the achievable ergodic sum rate among N nodes, and propose a closed-form expression of the achievable ergodic sum rate for each scheme. Furthermore, we present numerical evaluation results and compare the resuts with closed-form expressions.     

多分量数据全波形反演在气藏检测中的关键问题——以苏里格气田为例

苏里格气田是中国典型的致密砂岩气藏，构造简单、平缓，横向非均质性强，有效储层与围岩声学特征差别小，地震响应不明显，常规地震监测方法预测难度大，但气田含气砂岩泊松比低，是地震气藏检测的有效参数。利用弹性全波形反演精度高和能处理复杂非均质介质的优势，反演地层拉梅常数、剪切模量和密度，并计算泊松比，从而进行气藏预测。重点阐述了苏里格气田多分量数据全波形反演初始模型建模、先验模型建模和地震数据预处理3个关键问题的处理方法。二维三分量数据反演和"甜点"预测结果表明：①对于具有强非均质性的苏里格气田，利用全波形反演获得精度较高的地层弹性参数能显著提高气藏预测的准确度；②苏里格地区构造简单、平缓，利用常规叠加速度并结合构造解释可以建立比较好的初始模型，从而有效地解决了周波跳跃和局部极小的难题；③先验知识的约束和地震数据的预处理是全波形反演成功应用于苏里格气田气藏检测的关键。     

智能车弯道行进算法的研究与实现

智能车在弯道处快速、平稳、准确的行进是循迹行进的关键问题。基于此提出了一种基于CCD摄像头进行路径识别的智能车分阶段全闭环控制方法,智能车在行进中依据导航线的曲率进行直道和弯道的辨识,并在直道和弯道部分采用不同的控制方法,实现弯道提前减速,直道加速通过,在直道与弯道的行进中均采用全闭环控制。视觉系统通过CCD摄像头提取路径中的导航线并计算导航线曲率以及智能车相对于导航线的平行偏移值和航向角偏差;主控系统依据导航线曲率选取控制策略,根据平行偏移值和航向角偏差计算转向角度与行进速度并通过控制执行机构完成转向与速度调节,控制结果通过视觉系统与测速装置反馈给主控系统,实现全闭环控制。以第十五届全国大学生机器人大赛为背景搭建实验系统,经过多次的调试与实验,智能车在赛道上最大运行速度可达3.5 m/s,全程平均速度约为2.8 m/s,实际测得最大偏离距离为35 mm,在允许误差范围之内。通过实际的实验系统,验证了智能车分阶段全闭环控制算法的可行性。     

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

Numerical verification of the continuous calculation method for tip stress during the driving process of the dynamic penetration test

This paper presents a calculation method for obtaining the continuous variation in stress between the tip and the soil during dynamic penetration tests, particularly in the case of using the Panda 3® penetration testing device. The originality of the method is that the tip stress can be computed continuously throughout the driving process. For each impact of the hammer on the penetrometer, data are recorded by sensors located at the top of the apparatus. Then, the stress at the tip and the displacement of the apparatus are calculated with a method based on the propagation of waves in the device. A three-dimensional numerical model of the penetration test, based on the Panda 3® specifications and using the discrete element method (DEM), is proposed in this paper. The purpose of the simulations is to validate the calculation method by comparing the curves of the tip stress versus the penetration distance obtained in two different ways, the first being the distance directly observed at the tip and the second being the distance calculated from the data recorded at the top of the penetrometer, as with the experimental device. The entire apparatus is represented, including the hammer, the rod, and the tip, and is driven into the model soil. The calculation method is applied, and the results are compared to the actual response of the soil to the driving of the penetrometer directly at the tip, which can be obtained with the numerical model. The responses are found to be very similar, confirming the theoretical framework and its underlying assumptions. This method is applied to dynamic penetration tests and provides the opportunity to obtain mechanical parameters other than the tip resistance from the tests.