Failure analysis on welded joints of 347H austenitic boiler tubes

Microstructural and thermo-mechanical analyses using the finite element method (FEM) were performed on the welded joint of 347H boiler tubes in a coal-fired power plant after 3600 h of operation. The cracks in the failed tube started on the inner surface of the heat-affected zone (HAZ). This area had a high hardness value and volume fraction of strain-induced martensite. The plastic deformation around the crack was concentrated near the grain boundaries. This failure occurred as the stabilizing effect disappeared due to carbide dissolution in the heat-affected zone, and plastic deformation and tensile residual stress were formed at the inner side due to the solidification contraction of the outer bead.     

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville–Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville–Thermalito Complex (OTC) – a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation–storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California.     

水库水温分层对浮式取水口前流速分布的影响

由于目前缺乏对浮式取水口前流场变化规律的研究,以分析水库水温分层对取水口前各断面流速分布影响为目标,依据某水库水温资料,采用k-ε紊流模型对浮式取水口前流场进行三维数值模拟,针对水库水温均一和水温分层的两种情况,研究取水口前流速分布,分析水库水温分层对取水口前各断面流速分布的影响。研究结果表明,在浮式取水口附近,水温均一和水温分层的流速分布存在着差异,这种差距随着取水流量和淹没深度的增大而减小。研究成果可为浮式管型取水设施的应用和运行提供技术支持,为取水水温预测提供理论依据。     

近海复杂环境因素对闸墩混凝土裂缝的影响

近海大体积混凝土结构所处的外界环境以及地质条件比较复杂,对于带裂缝水闸结构来说,因所处的地理位置、气候变化以及潮汐升降水位等因素的缘故,水闸闸墩裂缝处于张合状态,这降低了水闸结构的稳定性,甚至会影响水闸结构的寿命。沿海环境温度变化对混凝土裂缝宽度变化的实际监测分析,对于混凝土结构安全耐久性评价具有一定价值,依托典型软土地基永定新河防潮闸工程,利用测缝计对闸墩裂缝宽度变化进行健康监测。主要研究运行期环境因素(以空气温度、水温以及潮汐水位为主)对闸墩裂缝的影响,采用健康安全监测软件,通过对数据的挖掘处理,得出环境温度和水位是影响闸墩水下裂缝宽度变化的敏感性因素的结论,为以后水闸闸墩裂缝的安全维护和裂缝的预防提供思路。     

基于分形理论的坝基裂隙岩体注灰量与导水率关系研究

由于天然岩体裂隙分布的隐蔽性和不确定性,很难准确评价天然裂隙岩体的渗透性和可灌性。离散裂隙网络作为描述天然裂隙网络的有力工具,能在一定程度上反应裂隙岩体的某些特征,但都是基于统计意义上的概率分布,无法反应裂隙网络参数的内部联系以及微观特征。Mandelbrot提出的分形几何是描述不规则物体的重要工具,研究发现裂隙网络中的裂隙迹长分布服从分形特征,很多学者利用这一理论对裂隙网络的渗透性进行了研究,推导了裂隙网络渗透系数的表达式。然而缺乏裂隙网络的可灌性和灌浆量的研究。本文首先利用裂隙网络的分形特征和裂隙迹长与隙宽的关系,建立了裂隙岩体导水率的分形模型,推导了裂隙岩体导水率与分形维数的关系表达式,并与已有研究结果进行了对比,证实了该关系式的可靠性。其次,分析了裂隙岩体灌浆注灰量与分形维数的关系,进而推导了注灰量与导水率的关系式,根据注灰量与导水率的关系曲线对灌浆区域的划分,定义了3个不同的灌浆区域:正常区域、微小裂隙区域和扩展区域,分别对应于正常灌浆、微小裂隙较多和裂隙扩展变形这3种不同的灌浆状态。最后,通过与工程实测数据的对比,证实了本文注灰量与导水率关系式的可靠性和适用性,也说明了3个灌浆区域的划分是合理并且重要的。因此,该关系式和灌浆区域的划分可以用来预测和评价实际工程灌浆施工的合理性和可灌性。     

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.     

高含沙水流作用下水垫塘底板上举力特性试验

针对高含沙水流对水垫塘底板上举力特性的影响,通过模型试验,研究了不同含沙量下底板上举力的分布规律、幅值特性和频谱特性。结果表明:含沙水流作用下的上举力沿程分布规律与清水时相同;各流速下上举力时均值均随含沙量的增大而增大,且基本呈线性函数关系,斜率在0.17～0.22之间;上举力标准差随含沙量的增大先增大后减小,在含沙量为183 kg/m~3时出现峰值;上举力的脉动在0～3 Hz的低频范围内,与清水相比,水流含沙后功率谱的频带宽度增加,高频域内的功率谱密度值有所增加;水流的时间积分尺度随着含沙量的增大呈先增大后减小的趋势。     

平板闸门流激振动效应对启门力的影响

为探究流激振动对闸门启闭力的影响机理,通过对固定的上、下游水位和底缘形式的平板闸门进行流固耦合分析,结果表明引起闸门振动的激振力和由闸门振动位移产生的摩擦系数和摩擦力臂的变化是造成启闭力变化的主要原因。在前人统计的闸门振动较大的不利开度的基础上,利用波形合成法以及振动理论提出作用于闸门面板上的激振力的计算公式,并考虑其对闸门启门摩擦系数、摩擦力臂以及启门摩擦力的影响,总结出流激振动效应下平板闸门启门力的计算方法。通过与石泉水电站中孔平板闸门启门力实测资料对比发现,理论计算的结果与实际工程的实测值吻合良好,可为实际工程的启闭力计算提供一定的科学依据。