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。     

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.     

某型装甲车驾驶员终端仿真训练系统设计与实现

针对部队、院校装备教学训练需求,以某型装甲车驾驶员终端为研究对象,研究并分析了某型装甲车辆驾驶员终端结构、工作原理以及对外的接口规范、标准,采用NI公司的虚拟仪表控件、MapInfo公司的MapX地图控件等控件,设计并实现了基于半实物的装甲车驾驶员终端训练系统。系统设计既能满足装备仿真训练,也可以接入实际装备,完成与实际装备的互联和通信,满足部队、院校教学训练的需要,达到了预期的目标。     

基于MATLAB环境的精确空投系统模型分析与仿真

在针对运动体运动规律仿真领域中,MathWorks公司推出的Matlab软件具有强大的功能。本文针对近年来国际上较为热门的"精确空投系统"进行了简化建模,并在Matlab/simulink环境中进行了仿真分析,得到了较为准确可靠的仿真结果。     

某型航空发动机燃油调节器改型设计研究

航空推进系统仿真技术在发动机预研和型号研制中具有重要的作用。某型现役飞机飞行高度增加,需对其燃油调节器进行改型设计,而关于航空发动机燃油调节仿真的问题,大多数是以传递函数及插值表等形式来描述的,计算量大,物理意义不直观,仿真效果不明显。针对以上不足,在对某型发动机燃油调节器的组成、功能、工作原理进行了详细分析的基础上,以流量连续方程及力平衡方程为基础,结合Adaptive Simpson积分方法,基于AMEsim仿真软件,采用图形化时域仿真建模方式建立了相关部件的数学模型,对其高空工作特性进行了仿真分析。结果表明,保证了发动机的正常工作,为燃油调节器的设计与改进提供了依据。     

C~4ISR系统需求仿真组件模型研究

研究面向C4ISR系统需求验证中的仿真支持过程,针对不同阶段、不同领域的需求仿真和重用问题,通过C4ISR系统需求的仿真概念模型,从系统功能、作战活动和系统状态等不同侧面抽象描述需求仿真的特性,建立了面向仿真的C4ISR系统需求组件模型,并用案例分析了C4ISR系统需求仿真组件模型的可行性。     

网络蠕虫传播模型的分析与仿真研究

研究网络安全问题,网络蠕虫是当前网络安全的重要威胁。网络蠕虫传播途径多样化、隐蔽性强、感染速度快等特点。蠕虫模型以简单传染病模型进行传播,无法准确描述网络蠕虫复杂变化特点,网络蠕虫检测正确率比较低。为了提高网络蠕虫检测正确率,提出一种改进的网络蠕虫传播模型。在网络蠕虫传播模型引入动态隔离策略,有效切断网络蠕虫传播途径,采用自适应的动态感染率和恢复率,降低网络蠕虫造成的不利影响。仿真结果表明,相对于经典网络蠕虫传播模型,改进模型有效地加低了网络蠕虫的传播速度,提高网络蠕虫检测正确率和整个网络安全性,为网络蠕虫传播研究提供重要指导。     

四旋翼无人机的系统模型与辨识

研究无人机系统优化控制问题,为实现四旋翼微型飞行器的自动控制飞行,需要得到正确的系统模型参数,实现精确控制。子空间辨识法可以直接地反应出多输出多输出系统模型,算法比较容易实现。首先从四旋翼的力学模型出发,建立了在小角度假设下的线性系统模型,并首次将子空间辨识方法运用到四旋翼飞行器的辨识中。用仿真实例实验,假设飞行器在缓慢上升过程,采用由子空间辨识方法获得的系统模型输出与原系统在同样输入条件下的输出进行了比较。结果表明在假设条件下的辨识具有可行性,为中旋翼无人机优化控制提供了依据。