To expand the detectable range of the linear array in the near-field acoustic emission source localization process to improve the localization accuracy and efficiency, this paper proposes a method of near-field acoustic emission source localization based on orthogonal matching pursuit under nonuniform linear array. Firstly, according to the propagation characteristics of acoustic emission signals, a narrowband signal decomposition method for broadband acoustic emission signals is proposed. Then, a near-field source dimension reduction method based on fourth-order cumulants under nonuniform array is proposed to realize the separation and estimation of source angle and distance parameters. Subsequently, an acoustic emission source localization method based on orthogonal matching pursuit is proposed to maximize the source localization performance of the nonuniform linear array. Finally, the performance comparison between three different nonuniform linear arrays and different methods is carried out. Meantime, the finite element simulation and acoustic emission localization experiments are used to analyze the localization accuracy law and verify the effectiveness of the method.

Throttle valves often experience erosion wear failures in the offshore oil and gas industry. This phenomenon arises due to the rapid fluid flow carrying solid particles in the pipelines, resulting in repeated impacts on the valve. Erosive damage to valves may lead to system malfunctions and significant economic losses. Therefore, comprehending the erosion mechanisms and influencing factors is of paramount significance. The work reviews common erosion equations, delineates the primary factors influencing throttle valve erosion, and analyses the underlying mechanisms. Furthermore, the numerical simulation method of throttle valve erosion is discussed, and the experimental research methods are summarized. Finally, possible limitations and gaps regarding throttle valve erosion are proposed. The current work can benefit petroleum and natural gas companies and research institutions by providing a comprehensive review of the erosion mechanisms and methodologies for throttle valves.

The data and information generated during the design process, such as part parameters, affect the efficiency and quality of product design. Knowledge graph (KG) is often used to express and reuse the above knowledge. However, due to the decentralised and complex nature of the KG, it lacks the quantitative knowledge required for part selection, e.g. the relationship between part type and performance. To complete the knowledge and meet the requirements for rapid and accurate performance evaluation, this paper proposes an automatic KGC method based on a surrogate model. Firstly, the schema layer is established based on the design knowledge ontology. For the sampling process, the query statement is applied to extract knowledge such as design parameters, which define the range of sample points. Secondly, the design of experiments (DOE) method can obtain the data set of the target performance through simulation. Surrogate models are built based on multiple machine learning algorithms. Meanwhile, the hyperparameter optimisation method can further improve the prediction accuracy of the model. Finally, the performance of the design scheme is predicted, and the results are used to complete the KG through knowledge extraction. The method is validated by the selection and evaluation of bogie parts.

The ultrasonic guided wave-based method for multi-damage localization has been widely proposed. However, the precision of this method is directly correlated with both the quantity of sensors employed and the intricacy of the implementation process. This relationship poses a challenge in striking a balance between the accuracy and efficiency. To improve the computational efficiency under the premise of ensuring the accuracy of multi-damage localization, this paper proposes a near-field weighted subspace fitting algorithm based on niche-particle swarm optimization. Firstly, the fitting relationship between the signal subspace of the diffraction wave and the array steering vector is established under the uniform linear array. Secondly, a multidimensional solution space search algorithm based on niche-particle swarm optimization is proposed to improve the search efficiency of damage. Finally, the algorithm is verified by performance comparison, finite element simulation and experiment. The results show that compared with the same type of method, the algorithm improves the computational efficiency by nearly threefold under the identifiable multi-damage conditions. Additionally, the angle error is 1 - 6 degrees, and the distance error is 1 - 20 mm.

This paper proposes a topology optimization method considering fatigue constraints for the jacket support structure of offshore wind turbine. Jacket support structure is an important component and supports the whole wind turbine weight under the dynamic environmental loads, which would induce serious fatigue problems. To obtain the optimal structural layout, the topology optimization method considering fatigue constrained is introduced for the jacket support structure under the dynamic load. The fatigue lifetime is constrained base on the Miner's cumulative damage rule. The design sensitivity of the objective and constraint functions are evaluated analytically, while the equivalent static load approach is applied. Some typical topology optimization problems are introduced to verify the effectiveness of the proposed method, and this method can obtain the appropriate layouts with stable convergence. A case study on the OC4 reference jacket indicates that the method can achieve a reasonable layout structure under the dynamic load, which meets the fatigue constraint. And the final design is reconstructed based on the topology optimization layout. Finally, a lighter jacket structure has good performance on the ultimate bearing capacity, eigenfrequency, and buckling, which is suited for long operation life.

轴箱轴承是高速动车组(EMU)走行部的关键部件,往往处于高速甚至超高速的运行状态,其轴承的磨损是一个非常突出的问题.建立转向架轴承的磨损模型,模拟在轨道激励载荷因素作用下滚动轴承的磨损状态,进行轴承的磨损损伤分析具有重要意义.针对动车组转向架轴箱轴承磨损监测难、分析难的问题,基于数值模拟方法,对轴箱轴承的磨损损伤分析方法进行了探究,为降低有限元模型的分析计算时间成本,提出了一种轴承切片式半解耦损伤分析方法.首先,将轴承三维模型径向等距切分成有限数量的二维切片,对二维轴承切片模型进行了磨损分析;然后,基于任意拉格朗日-欧拉(ALE)自适应网格偏移技术以及UMESHMOTION子程序,提出了轴承模型磨损有限元分析方法(流程);最后,利用高斯过程非线性拟合完成了三维轴承磨损状态复现,得到了轴承内圈的磨损分布规律,并对该模型的高效性与收敛性进行了对比分析.研究结果表明:利用二维切片拟合磨损分析方法的计算效率获得了大幅提高,同时该方法的准确性也较为可靠,与传统方法相比,其误差在10%以内;另外,轴承内圈磨损较为严重的区域出现在滚子与内滚道接触的边缘位置,而接触区域中心位置的磨损程度与边缘相比较轻.

螺栓连接在风力发电机等复杂装备中得到了广泛应用.设备的工作载荷及机械振动会引起螺栓松动,导致螺栓连接失效,严重影响设备的正常运行.为此,以海上风力发电机塔筒连接螺栓为研究对象,建立了螺栓结合面连接刚度模型,进行了塔筒法兰连接螺栓的松动状态预测研究.首先,建立了螺栓结合面刚度,通过虚拟材料模型对其进行了表征,探究了预紧力对虚拟材料层模型的影响特性,为有限元模型提供了参数;然后,通过力锤实验,并结合实测数据建立了高精度的有限元模型;最后,通过法兰连接件的前10阶扭转、弯曲固有频率,以任意两阶模态的频率变化平方比、预紧力变化前后频率相对变化比分别作为螺栓定位、定量指标,进行了螺栓松动预测.研究结果表明:随着预紧力的增加,固有频率呈现逐渐增大的趋势,但灵敏度稍有下降;通过对螺栓松动测试结果的统计可得,螺栓松动定位识别精度达到95.24％;单、多螺栓预紧力下降程度定量精度分别为93.4％、90.2％;基于虚拟材料模型的螺栓松动预测方法具备精度高、通用性强的特点,可为螺栓连接的数字孪生实时监测提供重要参考.

Due to the complex and diverse underwater environment, underwater acoustic transmission is easily disturbed, absorbing many noise components. To deal with the complex noise components is a challenge with the traditional signal processing method. Moreover, it is hard to diagnose the valve's leakage and the leakage degree in the subsea Christmas tree using underwater acoustic signals. This paper proposes a new non-contacting fault diagnosis method based on a deep neural network (DNN) with skip connections. First, we obtain the required time-domain data using acoustic sensors (a digital hydrophone) in experiments. Second, the time-domain data is preprocessed and is converted by the short-time Fourier transform into time-frequency domain signals. The time-frequency domain signals are then input into a constructed DNN model to minimize the noise components in the signal. After that, the denoised data are applied to a convolutional neural network for fault diagnosis. Finally, an underwater acoustic experiment is designed and performed to validate the proposed method's effectiveness. The experimental results demonstrate that our proposed method can effectively diagnose the leakage fault of the valve, and the diagnosis accuracy reaches 98.89%.

To satisfy the requirements of individual design and rapid performance evaluation of complex products, this paper proposes a hybrid approach to build a performance evaluation model and perform the rapid evaluation of design schemes. This approach consists of a surrogate model and knowledge graph (KG). Firstly, the KG of complex electromechanical products is established by Web Ontology Language to provide information about parts and evaluation indexes for the sampling process. It includes building ontology and writing inference and query rules at the framework level. Secondly, based on the sample points, a dynamics model is built and used for simulation. Using the Design of Experiments, the variables that have the greatest impact are found. The relevant variables will be input into the model to obtain the data set. According to the data set, a surrogate model based on the radial basis function is built as a performance evaluation model, which can improve computing efficiency to achieve evaluation results rapidly. In this study, the bogie design is used as a test case to evaluate the proposed method. And the results show that it can improve design efficiency for design issues such as part selection.

This paper proposes a digital twin method based on multi-source crack growth prediction data fusion. In this method, two different prediction methods based on theoretical model correction and machine learning model correction are constructed, which avoids the inapplicability of a single method in practical applications. Meantime, based on the consistency retention method corresponding to each model, the influence of uncertainty factors on crack growth prediction is gradually reduced by inputting crack detection data. Subsequently, by fusing the historical data and prediction data, the crack growth prediction result with the smallest deviation and higher reliability is output. The verification results show that the digital twin model proposed in this paper can effectively reduce the influence of uncertainty factors on crack growth prediction and realize the dynamic prediction of crack growth.

自主学习和创新能力的培养一直是我国高等教育的重点,课程教学是其中重要环节之一.探讨在机械类研究生课程教学中存在的问题,提出以项目设计为导向的课程教学模式,并以振动分析与动态测试课程为例,进行具体的项目设计和教学实施,利用具体的项目设计为导向,与课程理论知识内容融会贯通.在实际教学中该教学形式取得良好的教学成果,能够激发学生学习创新的积极性和主动性,实现预期效果.

As a technology for the interaction and integration of products and simulation models, the digital twin can achieve accurate prediction and evaluation of product performance. However, the accurate model base is computationally complex, has a long iteration time, and is unable to perceive changes in the operating state in time. This leads to poor adaptability of the model and low efficiency of performance evaluation. The surrogate model can simplify the above model and improve computational efficiency. Based on this, this paper proposes a digital twin modelling and updating approach. The surrogate model is applied to the digital twin modelling process, which can accurately describe the physical mechanism and achieve interaction with the physical world. Then, this paper defines the consistency metric function, which achieves the rapid perception of the operation state and follows the physical world. Meanwhile, an improved LHS-Adam model update algorithm is used to adaptively update the model structure, improving the efficiency of the model parameters adjustment. Finally, experiments are conducted on the bogie suspension system to verify the feasibility and effectiveness of the update method in practical applications. The experimental results show that the established digital twin model has good updating performance and more efficient performance evaluation capability.

At present, ML has become an effective method to solve the prediction problem of fatigue crack growth. To reduce the inaccurate prediction caused by uncertain factors in crack growth, this paper proposes a fatigue crack growth prediction method based on the ML model correction. This method improves the accuracy of crack growth prediction by using real crack data to correct the ML model. In the research process, the prediction performance of the three ML methods is compared, and the CGR-ML model for crack growth is established. Subsequently, dynamic correction strategy for the CGR-ML model is proposed while selecting crack detection points by using the nonlinear crack length selection method. Finally, the effectiveness of the method is verified by the central crack growth and the crack growth experiment under mixed-mode multi-step loading. It can be seen from the comparison with the previously proposed fatigue crack growth prediction method based on the theoretical model correction that the method proposed in this paper can achieve a better prediction effect.

为了探究分度圆裂纹对齿轮传动系统产生的影响,利用改进能量法计算分度圆裂纹齿轮的啮合刚度,结合动力学模型对齿轮传动系统进行了动态特性分析.首先,将齿轮的齿根圆视为悬臂梁起点,把分度圆裂纹按照其齿廓投影位置分为了 3种情况;然后,利用改进能量法理论计算了不同裂纹情况下的齿轮啮合刚度;最后,在啮合刚度的基础上,建立了齿轮系统的六自由度动力学模型,分析了不同裂纹情况下的齿轮系统的动态特性.仿真结果表明:相比于正常齿轮,分度圆裂纹为2.5 mm时,齿轮的啮合刚度最多下降14.3％,传递误差最多升高12.2％,齿间啮合力最多下降3.6％,啮合摩擦力最多下降14.8％;随着裂纹长度增大,齿轮啮合刚度逐渐降低,传递误差随之增大,齿间啮合力和啮合摩擦力逐渐降低.研究结果表明:由于裂纹的影响,齿轮传动系统会受到周期性冲击.

Jack-up platforms are widely used for the offshore oil and gas exploration, constructions and offshore wind farms. The jack-up platform legs are import component, which not only support the whole weight but also withstands various external loads, such as wind, current and wave load. A topology optimization algorithm-BESO (bi-directional evolutionary structural optimization) is proposed in this paper for the jack-up leg optimization. Based on the sensitivity analysis, the optimization strategy with sensitivity filtering and updated have been applied. And a simple numerical example is used to verify the proposed optimization method. A case jack-up platform is introduced to implement the leg structure optimization. After a series of iterations, a new leg structure is obtained. And the ultimate analysis, Eigen analysis and buckling of optimized leg structure optimized structure are all discussed compared with the traditional structures (K-type, the reverse(rev) K-type and X-type). To further evaluate the performance of the optimized leg structures, an experiment test is designed in the wave tank considering the current and wave load. Also, the optimized leg structure and the traditional ones are all manufactured to perform the experiment. The results show that the optimized leg structure has better ultimate bearing capacity, Eigen analysis and dynamic response with lighter weight under the same environmental loads. It is indicated that the BESO topology method is superior in finding best structure of the jack-up platform leg.

针对近海风机在风、波浪和地震3种载荷作用下的振动控制问题,提出了基于磁流变弹性体(magnetorheological elastomer,MRE)调谐质量阻尼器(tuned mass damper,TMD)的海上风机半主动控制方法.首先,介绍了MR E的变刚度磁致力学特性及面向海上减振需求的MR E-T MD结构设计原理;其次,建立了海上风机-T MD动力学模型,计算了风、波浪及地震激励载荷;再次,应用半主动控制算法跟踪识别风机塔筒顶端响应的频率,实时调节MRE-TMD的刚度,进而对海上风机进行振动控制.通过分析导管架式近海风机在多种载荷作用下的动力响应可知,采用基于MRE-TMD的控制方法能够有效衰减导管架式海上风机在多种载荷作用下的振动响应.与被动TMD相比,MRE-TMD具有较好的减振效果,为海上风机振动控制提供了一种新的解决思路.

With the development of artificial intelligence, big data, Internet of Things, and other technologies, digital twin has gained great attention and become a current research topic. Using digital twin technology, the digital twin model can be constructed in the cyber space that is fully equivalent to the physical entity. It is always consistent with the physical entity in the operation process, which greatly improves the dynamic perception and prediction ability of the real world. After the development in recent years, digital twin has gradually changed from the initial concept discussion to the study of model framework and implementation method. However, because the research objects in different industries have great differences in their own composition, service conditions, and application scenarios, they have personalized characteristics in modeling strategies and usage methods. Therefore, based on different industries, this paper reviews the current articles on digital twins and distinguishes the focus of digital twin modeling research; subsequently, the relevant supporting techniques and methods are summarized according to their different importance for digital twin modeling. Based on the review in this paper, future researchers can conduct targeted research on digital twin technology in term of the characteristics of the objects in their industry.

To accurately predict the crack growth trend of marine structures, this paper proposes a fatigue crack growth prediction method for offshore platforms based on digital twin. First, a digital twin model for offshore platforms is established, and the key technical procedures required for each part of the model are given. Subsequently, to implement consistency maintenance between the virtual model and the physical entity during the digital twin usage, a finite element surrogate model approach based on Gaussian process is performed, which integrates with the crack growth consistency maintenance strategy using dynamic Bayesian network. Finally, according to service condition characteristics of the offshore platform, a crack growth experiment under mixed-mode multi-step loading is designed to verify the effectiveness of the proposed method. The results show that the method not only reduces the influence of uncertain factors on crack growth prediction under complex loads, but also achieves accurate of crack growth through dynamic tracking.

Jacket support structure is the main support form in medium water depth for bottom fixed offshore wind turbine. Topology optimization is presented in this paper for an efficient method to optimize the jacket structure by altering the structural layout to improve its design, minimize weight and ultimately reduce the cost. Together with size and shape optimization, an innovative jacket structure is obtained with constraints on strength, stiffness, stability, ultimate strength, and natural frequency. The optimization has been solved and the overall mass of the optimized structure decreases by 38.24% compared with the primary structure. Moreover, ultimate limit state analysis, eigenanalysis and buckling are used to evaluate the optimized structure. The results indicate that the proposed optimized method is an effective way to reduce the weight and stress concentration. It also shows that topology optimization provides useful insights for the conceptual design phase and provides a better starting point for the further size and shape optimization.

To reduce the influence of uncertain factors, a three-dimensional fatigue crack growth prediction method based on consistency maintenance is proposed. Firstly, the crack growth update under complex loads is realized based on XFEM. Subsequently, the life correction method based on the improved particle filter and the path correction method based on the improved maximum tangential stress criterion are proposed. Taking real crack data as input, the accuracy of prediction results is improved from two dimensions of life and path. Finally, five cases are used to verify the proposed framework, which proves that it can realize dynamic tracking of crack growth.