基于问答驱动迭代的工程车辆仿真设计方法研究

为了实现基于问答驱动迭代的复杂产品的仿真设计,提出了一种在过程设计中,将制定问题、行为模型、仿真模型和决策依据作为独立对象来建立过程模型的方法。该方法能确保需求规范的属性与预估产品的属性之间的信息粒度级别,并使其达到对象到对象层面的可追溯性。将该方法应用于大型液压挖掘机工作装置铰接点的优化设计,以工作装置的工作范围最大和在挖掘、举升、平推等典型工况下的工作性能最佳为原则,对按照蒙特卡罗方法生成的5120组不同的工作装置铰接点设计方案进行了选优。结果表明,该方法设计的工作装置性能优良,满足设计需求,是进行复杂产品过程建模的一种切实可行的方法。     

Model selection in finite element model updating using the Bayesian evidence statistic

This paper considers the problem of finite element model (FEM) updating in the context of model selection. The FEM updating problem arises from the need to update the initial FE model that does not match the measured real system outputs. This inverse system identification-problem is made even more complex by the uncertainties in modeling some of the structural parameters. Such uncertainty often results in a number of competing forms of FE models being proposed which leads to lack of consensus in the field. A model can be formulated in a number of ways; by the number, the location and the form of the updating parameters. We propose the use of a Bayesian evidence statistic to help decide on the best model from any given set of models. This statistic uses the recently developed stochastic nested sampling algorithm whose by-product is the posterior samples of the updated model parameters. Two examples of real structures are each modeled by a number of competing finite element models. The individual model evidences are compared using the Bayes factor, which is the ratio of evidences. Jeffrey's scale is then used to determine the significance of the model differences obtained through the Bayes factor.     

基于神经网络的软测量模型及应用

本文提出了一种新的基于神经网络软测量模型及算法,为复杂测量系统中软测量建模提供了一种有效的途径。应用结果表明采用该算法可获得较高的测量精度。     

专家系统在发酵过程生物量智能检测中的应用

发酵过程是一个机理复杂的生化反应过程,对于其中无法在线检测而又起着关键作用的生物量参数,软测量技术是很好的解决途径.在生物量软测量建模的过程中,由于发酵过程分类繁杂,机理模型众多,因此适合用人工智能.专家系统来解决这类问题.本文给出了应用于发酵过程软测量混合模型划分的专家系统的结构,阐述了该专家系统知识库和推理机的构建,该系统为发酵过程软测量混合模型的划分提供了一种新的途径.     

Design of a closed-loop supply chain (CLSC) model using an interactive fuzzy goal programming

A closed-loop supply chain (CLSC) network is composed of both forward and reverse flows. An essential issue to be considered in designing any supply chain network is determination of number and locations of facilities in each layer of the network. Such a problem is a challenging job, since it contains sub-problems which are proven to be nondeterministic polynomial time complete. This paper proposes a CLSC distribution network design problem in which reverse flows are imported into forward model proposed by Selim and Ozkarahan (Int J Adv Manuf Technol 36:401–418, 1). Such a model is considered assuming forward covering (model I) and backward covering (model II) objectives, and then results are compared against the model incorporating covering of both forward and backward networks (model III). Our aim is to accentuate on the role of considering backward parameters in design of a CLSC network and to show how results differ from considering sub-problems separately. To model and solve the problem, a fuzzy goal programming approach is developed for network design in an interactive manner between decision maker and the model. To validate the presented model and the proposed solution approach, a test problem is presented and comparison of results is made using this problem. The results show that the proposed model can solve the CLSC problems in a manageable time. Moreover, outputs of the three models differ significantly. Therefore, the role of incorporating backward flows into the network design problem has been shown using our experiments.     

Optimum path-tracking control for inverse problem of vehicle handling dynamics

A method based on optimal control theory is presented in this paper to solve path-tracking problems in inverse vehicle handling dynamics. The idea behind is to identify the optimal steering torque input along a prescribed path to generate an expected trajectory that guarantees minimum clearance. Based on this purpose, the path-tracking problem, treated as an optimal control problem, is first converted into a nonlinear programming problem by Gauss pseudospectral method (GPM) and is then solved with Sequential quadratic programming (SQP). Finally, a real vehicle test is executed to verify the rationality of the proposed model and methodology. Results show that the minimum lateral position error of the generated path-tracking trajectory can be a good solution for path-tracking problem in inverse vehicle handling dynamics for GPM. The algorithm has higher calculation accuracy compared with other methods to solve path-tracking problems. The study could help drivers identify safe lane-keeping trajectories and areas easily.     

基于有限元的大型模锻压力机钢丝缠绕动梁预应力测试

预应力检测对重型压机钢丝缠绕工艺来说至关重要。钢丝缠绕压机动梁结构复杂,缠绕时表面应力场分布无明显规律,测试难度大,目前动梁缠绕工艺的应力测试尚属空白,给工艺质量评价及工艺控制带来不便。应用有限元法对400 MN航空模锻压机动梁缠绕进行模拟,研究动梁缠绕时端面的应力场分布特征,确定动梁端面存在环形的径向、周向单向应力区,据此制定应变测量贴片方案,解决了重型压机动梁缠绕过程中预应力测试的难题。有限元计算结果与试验结果基本吻合,确保动梁缠绕工艺质量达到设计要求,也证实了这种基于有限元的应力测试方法的可行性。该方法对于其他复杂工程结构的应力测试具有借鉴意义。     

大规模MCM基板互连探针测试和路径优化

已有的路径优化算法在MCM基板互连测试中已经发挥了一定的作用，但由于MCM的高密互连特性，使得测试变得更加复杂和困难，因此人们希望能引入新的方法与思路，以解决MCM基板互连测试的路径优化问题。将蚁群算法应用到互连测试探针路径优化问题当中，根据MCM基板互连测试的特点，建立探针路径优化的模型。提出一种针对大规模MCM基板互连探针测试的方法，首先将MCM基板进行分片，然后对每片进行优化，最后将优化结果连接在一起，成为一条完整的路径。实验结果表明，蚁群算法能在较短的时间内得到更优的路径。     

一种加筋板多裂纹应力强度因子的试验验证方法

给出一种加筋板多裂纹应力强度因子试验验证方法,是基于等幅载荷下裂纹扩展速率反推得到的.该方法不仅能确定复杂问题的应力强度因子,而且能验证确定加筋板多裂纹应力强度因子的类比法.进行LY12CZ铝合金加筋板多裂纹裂纹扩展试验,给出试验验证反推应力强度因子的方法及过程.并给出用类比法近似计算的结果和试验验证结果及平均值.同时也指出这一方法可解决复杂问题应力强度因子的确定,但裂纹扩展速率存在一定的分散性.得到的结果表明试验验证方法和类比法对于确定加筋板多裂纹应力强度因子是可用的.     

支持机床产品协同设计的信息模型研究

为解决处于不同地域的设计团队在协同设计过程中各阶段产品模型不一致的问题,文章对支持机床产品协同设计信息模型进行了研究,在此基础上,建立了机床产品功能零部件编码系统,实现了机床产品零件的设计和零部件的预装配.     

基于参考模型的自适应优化实时调节PID系数的方法及其仿真

提出一种基于参数模型的自适应优化实时调节PID系数的方法,并结合实际控制对象进行了计算机仿真,仿真结果表明,采用这种方法可以解决PID调节器系数难以确定的困难。     

融入运动信息和模型自适应的相关滤波跟踪

为解决余弦窗的影响和复杂场景中的目标遮挡问题,提出了一种融入运动信息和模型自适应的相关滤波跟踪算法。采用HOG特征和颜色直方图特征互补结合的框架,引入卡尔曼滤波和上下文感知滤波器,可以解决余弦窗的影响;引入一种高置信度检测方法和一种新的模型自适应更新方法,可以解决目标遮挡的问题。将提出的算法在OTB-2015测试集与其他6种相关滤波类算法进行比较,实验结果表明,该算法精确度和成功率分别为0.821和0.615。相对于Staple-CA算法,精确度提升了1.3%,成功率提升了2.8%,同时,算法速度为54.34 帧/s,满足实际工程实时性要求。     

求解单行布局问题的一种变邻域搜索算法

设施布局问题对于企业内部的物流合理化起着重要作用, 单行布局问题作为设施布局问题的一个典型特例,属NP-hard问题,常规方法难以精确求解。针对单行布局问题的 复杂性,提出了一种改进的变邻域搜索算法进行求解。首先构建单行布局问题的数学模型,然后分析模型并构造一种有效的初始解;针对问题特征,设计了4种邻域结构,该邻域结构采用了从简单到复杂的推进搜索机制;改进了局部搜索算法并对邻域结构进行搜索;最后进行了实例验证,并与多种经典算法进行了对比,结果表明,所提变邻域搜索算法 可在短时间内得到单行布局问题高质量的近优解。     

四辊冷带轧机三倍频颤振机理的研究

研究了四辊冷带轧机三倍频颤振的产生机理,发展了轧件振动模型,分析了由于轧件失稳所导致的轧机颤振现象,提出了轧机三倍频再生颤振模型,并对其进行了理论研究,仿真结果和现场测试进一步论证了三倍频再生颤振理论模型的正确性及其工业价值。     

基于转子端数据驱动LSTM-CNN模型的高速旋转系统运行状态识别方法

针对复杂结构高速转轴运行状态难以准确实时监测与识别的问题,提出了一种基于转子系统数据驱动的复合神经网络转轴工况识别方法。首先,提出了一种基于长短期记忆网络(Long short-term memory,LSTM)和卷积神经网络(Convolutional neural networks,CNN)的复合神经网络模型(LSTM-CNN)。然后,建立双盘转子动力学仿真模型,并利用Newmark-β法对转子系统进行数值求解,获得转子系统关键固定节点动力学响应特征;同时基于有限元仿真获得关键旋转节点的动力学响应特征,并将两类数据分别导入LSTM-CNN模型中进行工况识别,并对其准确率和效率进行比较分析。最后,设计搭建高速转子实验平台,获取转子端和固定端数据分别对模型进行训练与验证,比较不同模型对高速转轴运行状态的识别能力。仿真数据与实验验证分析结果均表明基于转子端数据驱动的LSTM-CNN模型识别比传统的基于固定端数据驱动的识别方法具有更优的识别精度和效率。     

The sensitivity method in finite element model updating: A tutorial

The sensitivity method is probably the most successful of the many approaches to the problem of updating finite element models of engineering structures based on vibration test data. It has been applied successfully to large-scale industrial problems and proprietary codes are available based on the techniques explained in simple terms in this article. A basic introduction to the most important procedures of computational model updating is provided, including tutorial examples to reinforce the reader’s understanding and a large scale model updating example of a helicopter airframe.     

Upright orientation of 3D shapes via tensor rank minimization

In general, the upright orientation of a model is beneficial for human to recognize this model and is widely used in geometry processing and computer graphics. However, the orientation of the model obtained by existing technologies, such as 3D scanning systems or modeling, may be far away from the right orientation. In order to solve this problem, a robust and efficient upright method is needed. We observe that when the model is aligned with the three axes, the rank of the three-order tensor constructed by this model is the lowest usually. Inspired by this observation, we formulate the alignment of the 3D model with axes as a low-rank tensor optimization problem which is a global and unsupervised method and the genetic algorithm (GA) is used to solve this optimization problem. After the 3D model has been aligned with the three axes, some geometric properties can be used to pick out the best upright orientation from the six candidate supporting bases easily. The three-order tensor is constructed by voxelizing the bounding box of the 3D model, and then filling the voxel element with zero or one based on whether it contains the points of the model or not. The experimental results demonstrate that our method is robust, efficient and effective for all kinds of the models (manifold or non-manifold, man-made or non-artificial, or point cloud).     

Design of a robust model predictive controller with reduced computational complexity

The practicality of robust model predictive control of systems with model uncertainties depends on the time consumed for solving a defined optimization problem. This paper presents a method for the computational complexity reduction in a robust model predictive control. First a scaled state vector is defined such that the objective function contours in the defined optimization problem become vertical or horizontal ellipses or circles, and then the control input is determined at each sampling time as a state feedback that minimizes the infinite horizon objective function by solving some linear matrix inequalities. The simulation results show that the number of iterations to solve the problem at each sampling interval is reduced while the control performance does not alter noticeably.     

基于粒子群算法的摄像机标定过程优化

摄像机标定为机器视觉在物体位姿与姿态的测量过程中最重要一环,其映射物体三维空间与二维图像之间关系是一个复杂非线性最优化问题。为了更好地解决这一复杂优化问题,阐述了利用粒子群优化(PSO)算法计算摄像机标定过程的一种优化方法,重点描述了PSO算法的原理,单目视觉测量系统,以及基于CMOS摄像机的成像模型及其原理和算法。通过图像软件提取靶体模型上特征控制点,及摄像机标定算法建立了相应的计算公式。结合PSO算法优化像机外参,实验结果表明,PSO算法计算准确、速度快,具有很强的工程应用价值。     

A particle-image velocimetry system for measurement of velocity flow fields for investigations of thermohydraulic processes on the large-scale benchmark of a promising fast-neutron reactor

The use of visualization techniques in the research on thermohydraulic processes using large-scale models of nuclear power plants is discussed. In particular, the original particle-image velocimetry (PIV) measurement system at the TISEI test bench was presented, which is a model of a proposed fast-neutron reactor. Illumination and video-filming systems with the simultaneous use of several lasers and camcorders, as well as image-processing algorithms that make it possible to carry out field measurements with high precision in a complex configuration of the reactor model, reflections and distortions of the laser knife section, and shadowing by obstacles are described. The developed methodology of conversion of the image coordinates and velocity field into the reference system of the reactor model using a virtual 3D simulation made it possible to significantly simplify the experiments.