解剖学的数字化实验教学平台的构建探索

随着数字医学及数字医疗技术的迅速发展,针对高等医学院校解剖实验教学中存在的问题,如何提高解剖学教学质量,文章介绍了数字化解剖,并分析了数字化解剖实验室的特点,将云计算技术应用到管理数字化实验教学中。给出了基于云计算技术的数字化解剖实验教学平台解决方案,改革数字化解剖实验室,提出一种新的数字化解剖教学模式。     

SIMUWORKS仿真软件的应用研究

SIMUWORKS涵盖了系统优化设计与调试、过程仿真培训、故障诊断、复杂系统动态特性建模研究、专家系统等各个方向的应用,提供了一整套完备的全过程支撑、一体化、通用的应用环境,其根本是以微机环境为基础的应用平台。软件部分采用了面向对象的图形化建模方法、分布式实时数据库技术和动态内存机器码生成技术,在国内外仿真领域处于领先地位。     

中职学校的计算机上机实验课

通过分析当今社会对中职学校学生的要求和目前中职学校计算机上机实验课中存在的问题,探讨了如何提高中职学校的计算机上机实验课程的教学水平。并根据当前中职学生的状况和特点,结合平时的教学经验,提出一些教学观念。     

地震断裂带高铁大跨拱桥深基坑钢板桩围堰支护试验与监测

针对大跨度高速铁路拱桥建设对深基坑开挖施工的安全性要求,为了研究钢板桩围堰支护在大跨度高速铁路拱桥深基坑施工中的可行性,以新建徐盐高铁徐洪河特大桥工程项目为依托,通过数值模拟及现场试验监测,对深基坑钢板桩围堰支护在施工过程中的形变及受力情况进行分析。结果表明:通过有限元模拟分析,钢板桩围堰最危险受力点处于钢板桩中部、中上部及四角连接处,围堰第4道内支撑内力较其他3道内支撑大。整个施工过程中工况5为施工最不利工况。由监测结果可知,在施工过程中钢板桩单日最大位移值为2.8mm,最大累计位移值为24.0mm,均未达到预警值,施工过程中钢板桩形变稳定。钢支撑轴力受基坑周边荷载的影响明显,围堰钢支撑最大压力值为147.31kN,最大拉力值为24.95kN,未达到预警值,结构安全。研究结果可供同类型工程施工参考。     

含三维内裂纹透明类岩石材料破坏红外热像试验研究

由于目前针对含三维内裂纹的岩石破坏红外热像特征的研究较少,本文基于预埋浇注法制作含内裂纹类岩石试件,开展了单轴压缩块体试验和巴西圆盘劈拉试验,得到含内裂纹的岩石破坏过程,监测内裂纹扩展及试件破坏过程中的红外热像特征,得到以下结论:(1)含三维内裂纹的块体试件在单轴压缩下,在预制内裂纹面上发生剪切破坏;(2)块体试件在单轴压缩作用下红外热像特征明显,在破坏时,在预制内裂纹面处急剧升温;(3)含三维内裂纹巴西圆盘试件在预制内裂纹尖端发生翼型裂纹扩展,边缘带有针刺状Ⅲ型裂纹参与特征,最终翼型裂纹与上下表面贯穿;(4)巴西圆盘试验的红外热像特征与块体单轴压缩不同,在断裂面处未发生明显变化。研究结果为研究三维内裂纹的扩展规律及内裂纹扩展破裂的红外热像规律可提供一定的试验参考。     

基于降雨-径流试验的高寒草甸产流机制研究

对黄河源区高寒草甸产流机制进行研究,为河源区生态环境保护、水资源科学规划管理提供基础水文理论认知。基于野外降雨-径流试验,通过回归分析构建考虑坡度和覆被因子的暴雨和中小雨产流计算模式,精度良好,能够反映暴雨和中小雨2种降雨模式下高寒草甸产流的一般特征。试验结果表明,因草甸土-植体系蓄容能力较强,高寒草甸区地表产流主要发生在暴雨模式下,中小雨模式下的地表产流量一般很小。模式应用表明,草甸发生退化后,暴雨和中小雨入渗均有减少,暴雨模式下退化草甸比高覆草甸入渗减少约12%,中小雨模式下减少约3%。     

模型桥墩局部冲刷瞬时地形数据自动获取装备研制

模型试验中冲刷地形测绘装备相对落后,致使桥墩周围局部冲刷瞬时地形数据无法实时获取,冲刷动态发展过程及机理的模型试验研究工作难以开展。研制了一种模型桥墩局部冲刷瞬时地形数据等值线自动绘制装备,并利用室内水工模型试验优化了各组成的性能指标,量化了绘制装备的设计参数,分析了监测系统在模型应用中的测绘精度及在工程原型中的适用性。模型试验研究表明,该装备结构简单,操作方便,加工制作成本较低,精度及灵敏性较高,能够快速获取动态过程中局部冲刷瞬时地形等值线,适用于局部冲刷发展影响流场特性研究中的水下地形快速测绘,也可按放大比尺制作成型后应用于涉水建筑物周围水下地形监测。     

Review of digital twin about concepts,technologies, and industrial applications

Various kinds of engineering software and digitalized equipment are widely applied through the lifecycle of industrial products. As a result, massive data of different types are being produced. However, these data are hysteretic and isolated from each other, leading to low efficiency and low utilization of these valuable data. Simulation based on theoretical and static model has been a conventional and powerful tool for the verification, validation, and optimization of a system in its early planning stage, but no attention is paid to the simulation application during system run-time. With the development of new-generation information and digitalization technologies, more data can be collected, and it is time to find a way for the deep application of all these data. As a result, the concept of digital twin has aroused much concern and is developing rapidly. Dispute and discussions around concepts, paradigms, frameworks, applications, and technologies of digital twin are on the rise both in academic and industrial communities. After a complete search of several databases and careful selection according to the proposed criteria, 240 academic publications about digital twin are identified and classified. This paper conducts a comprehensive and in-depth review of these literatures to analyze digital twin from the perspective of concepts, technologies, and industrial applications. Research status, evolution of the concept, key enabling technologies of three aspects, and fifteen kinds of industrial applications in respective lifecycle phase are demonstrated in detail. Based on this, observations and future work recommendations for digital twin research are presented in the form of different lifecycle phases.     

Automated manufacturing system discovery and digital twin generation

The latest developments in industry involved the deployment of digital twins for both long and short term decision making, such as supply chain management, production planning and control. Modern production environments are frequently subject to disruptions and consequent modifications. As a result, the development of digital twins of manufacturing systems cannot rely solely on manual operations. Recent contributions proposed approaches to exploit data for the automated generation of the models. However, the resulting representations can be excessively accurate and may also describe activities that are not significant for estimating the system performance. Generating models with an appropriate level of detail can avoid useless efforts and long computation times, while allowing for easier understanding and re-usability. This paper proposes a method to automatically discover manufacturing systems and generate adequate digital twins. The relevant characteristics of a production system are automatically retrieved from data logs. The proposed method has been applied on two test cases and a real manufacturing line. The experimental results prove its effectiveness in generating digital models that can correctly estimate the system performance.     

A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.