双排刚性桩屏障对平面SH波的隔离性状研究

基于Twersky的理论,引入圆柱体对声波和电磁波的多重散射解析解,对用排桩隔离环境振动的问题,提出了一种新的求解任意排列、任意直径刚性桩对平面SH波多重散射的理论方法,解决了传统的假设单重散射的计算方法中不考虑桩列作为整体屏障从而忽略桩间相互干涉关系的不足。随后,在数值计算分析中讨论了散射重数,排间距,桩间距等因素对双排刚性桩隔振效果的影响,对实际工程中利用排桩进行振动污染治理和屏障隔振的设计提出了有意义的结论。     

非纯CO_2对MMP和驱油效率影响的实验研究

提出以油井产出气(烃类与CO2混合气体)为对象,采用细管实验确定混相驱最小混相压力(MMP)及非混相驱过程中的驱油效率.研究了气体组成跨度较大情况下气体组成对MMP和驱油效率的影响,通过大量地实验观察和分析,将驱替过程分为3个阶段,并对相应过程的驱油机理、组成等对采收率的影响进行了分析,得出驱油效率随CO2摩尔分数、压力变化的趋势及数量关系.结果表明:CO2摩尔分数降低则混相压力提高。在非混相状态,CO2摩尔分数每提高10%,驱油效率提高3.0%～5.4%,平均为4.4%;注气压力对驱油效率有较大影响,注气压力每提高1MPa,驱油效率提高1.2%～3.0%,平均为1.8%.     

钢筋混凝土柱-H型钢斜梁结构有限元分析

钢筋混凝土柱-H型钢斜梁结构由混凝土和钢两种性能差异较大的材料构成,其受力性能相对复杂.针对大部分设计人员采用PKPM进行该结构设计的现状,笔者将ANSYS考虑大变形、非线性的计算结果引入.分析表明,因PKPM未考虑非线性等因素,故导致相同条件下水平推力和水平位移的计算结果存在较大偏差.     

倾斜疲劳裂纹在混合型应力下张开及滑移特性

对于存在裂纹的机械零件或者构件,它很有可能会受到来自不同方向的作用力, 因此有必要研究裂纹在混合型应力条件下的传播特性. 该文使用具有不同倾斜角度的裂纹--这些裂纹是在应力比R=0的Ⅰ型循环应力下制作的,通过测量裂纹的开口方向和滑移方向的位移量,计算了裂纹端部Ⅰ型应力强度因子(KⅠ)mes和Ⅱ型应力强度因子(KⅡ)mes.裂纹的倾斜角度是指载荷方向与裂纹间的夹角.结果表明,对于存在压缩残余应力的疲劳裂纹,当倾斜角度较大,即β=60°时,应力强度因子的实验值(KⅠ)mes小于理论计算值(KⅠ),但此时实验值(KⅡ)mes与理论值(KⅡ)基本一致.另一方面,对于倾斜角度较小β=45°的疲劳裂纹,其(KⅠ)mes减少的同时,由于裂纹上下表面相互接触,从而导致(KⅡ)mes同时下降.     

光照变化条件下的光流估计

目的 为了提高光流法在处理光照变化和大位移方面的稳健性。提出一种结合结构纹理分解预处理和加权中值滤波的光流场模型。方法 该方法数据项采用灰度守恒假设和梯度守恒假设相结合、局部约束与全局约束相结合的思想。同时采用结构纹理分解、加权中值滤波、金字塔结构等高效的光流估计技术,进一步增强了光流算法的精确性与实用性。结果 分别通过Middlebury光流数据库图像和真实场景图像对提出的光流估计算法进行了大量实验验证。实验结果表明,改进的光流估计法在处理光照变化方面表现不错,不仅获得稠密的光流场,而且提高了光流场准确提取目标边缘的能力。结论 和传统光流方法相比,所提方法在光照变化情况下能获得更加理想的结果,降低了实际场景中光线变化的干扰,能更好地适用于实际场景中。     

Experimental and Numerical Analysis of the Viscous Fingering Instability of Shear‐Thinning Fluids

Miscible flow displacements in a rectilinear Hele‐Shaw cell of Newtonian as well as rheologically well‐characterized shear‐thinning fluids are examined through experimental measurements and numerical modelling. Water is used as a displacing fluid while the displaced fluid consists of either a reference Newtonian glycerol solution or shear‐thinning solutions of Alcoflood? polymers of different molecular weights. The experimental measurements revealed that the shear‐thinning behaviour of the non‐Newtonian solutions resulted in more complex instability patterns and new finger structures not previously observed in the case of Newtonian displacements are identified and characterized. An analysis of the effects of the rheological behaviour of the shear‐thinning fluids on instability characteristics such as the finger width and finger tip velocity is presented. Numerical simulations using a pseudo‐spectral method are conducted and allowed to compare the predictions of the mathematical model based on an effective Darcy's law with the experimental measurements.     

基于高光谱数据和雷达融合的滑坡信息提取

为了改进微地形滑坡遥感影像分类技术,从而提高微地形滑坡遥感信息提取的精度,采用湖北宜昌部分地区的无人机航拍高光谱影像(HSI)和激光雷达(LiDAR)数据作为研究数据源,并对高光谱和LiDAR数据进行融合,最后采用结合注意力模块(CBAM)的卷积神经网络(CNN)方法,对融合后的数据进行滑坡信息提取。研究表明,利用高光谱和雷达数据的优势,可以更准确地提取滑坡信息。     

流体运动估计光流算法研究综述

对流体图像序列进行运动分析一直是流体力学、医学和计算机视觉等领域的重要研究课题。从图像对中提取的密集精确的速度矢量场能够为许多领域提供有价值的信息,基于光流法的流体运动估计技术因其独特的优势成为一个有前途的方向。光流法可以获得具有较高分辨率的密集速度矢量场,在小尺度精细结构的测量上有所改进,弥补了基于相关分析法的粒子图像测速技术的不足。此外,光流方法还可以方便的引入各种物理约束,获得较为符合流体运动特性的运动估计结果。为了全面反映基于光流法的流体运动估计算法的研究进展,本文在广泛调研相关文献的基础上,对国内外具有代表性的论文进行了系统阐述。首先介绍了光流法的基本原理,然后将现有算法按照要解决的突出问题进行分类：结合流体力学知识的能量最小化函数,提高对光照变化的鲁棒性,大位移估计和消除异常值。对每类方法,从问题解决过程的角度予以介绍,分析了各类突出问题中现有算法的特点和局限性。最后,总结分析了流体运动估计技术当前面临的问题和挑战,并对未来基于光流法的运动估计算法的研究方向和研究重点进行了展望。     

A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism

Dam displacements can effectively reflect its operational status, and thus establishing a reliable displacement prediction model is important for dam health monitoring. The majority of the existing data-driven models, however, focus on static regression relationships, which cannot capture the long-term temporal dependencies and adaptively select the most relevant influencing factors to perform predictions. Moreover, the emerging modeling tools such as machine learning (ML) and deep learning (DL) are mostly black-box models, which makes their physical interpretation challenging and greatly limits their practical engineering applications. To address these issues, this paper proposes an interpretable mixed attention mechanism long short-term memory (MAM-LSTM) model based on an encoder-decoder architecture, which is formulated in two stages. In the encoder stage, a factor attention mechanism is developed to adaptively select the highly influential factors at each time step by referring to the previous hidden state. In the decoder stage, a temporal attention mechanism is introduced to properly extract the key time segments by identifying the relevant hidden states across all the time steps. For interpretation purpose, our emphasis is placed on the quantification and visualization of factor and temporal attention weights. Finally, the effectiveness of the proposed model is verified using monitoring data collected from a real-world dam, where its accuracy is compared to a classical statistical model, conventional ML models, and homogeneous DL models. The comparison demonstrates that the MAM-LSTM model outperforms the other models in most cases. Furthermore, the interpretation of global attention weights confirms the physical rationality of our attention-based model. This work addresses the research gap in interpretable artificial intelligence for dam displacement prediction and delivers a model with both high-accuracy and interpretability.     

PIV技术在冰盖下复式断面渠道流速测量中的运用

在测定冰盖下复式断面流场、流速的模型试验中,水体中的运动粒子是重点研究对象。粒子图像测速(Particle Image Velocimetry,PIV)技术已在水力学研究领域得到普遍应用,但该技术在冰盖下复式断面渠道模型试验的应用为国内首次。基于试验特点,设计砖砌混凝土的循环水箱,采用变频器配合水泵、流量计实现对流量的定量控制,对不同流量下不同断面的流场、流速进行测定,采用PIV技术对水体中运动的粒子的位移场进行具体分析。试验结果表明,PIV分析结果能够直观反映流场中的状态,且可以避免测量盲区,其所测的流速与多普勒超声测速仪测得值吻合较好,并给出了横向流速分布图,弥补了传统方法无法获得冰盖下的流场分布的难题。