汽车耐撞性数值分析网格研究及应用
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摘要
相对于传统的物理碰撞试验,计算机数值分析方法以其准确的计算结果、高效率的执行,成为目前汽车耐撞性研究所采用的主要手段。同时,随着全球化发展的趋势,汽车制造业逐渐在向网络环境下移植。跨国、跨地区汽车制造商以及零部件供应商,发挥各自优势共同开发产品,对提高产品质量、提升产品竞争力都具有积极的意义。在此背景下,在集中地进行的,基于计算机数值分析的耐撞性研究也必须适应新的基于网络的制造环境。
本文基于网格和数值分析理论,研究并构建了汽车耐撞性数值分析网格平台;结合耐撞性理论,研究了耐撞性协同仿真和分布式耐撞性数据挖掘方法。在此基础上实现了两个应用系统,分别为汽车耐撞性协同仿真应用系统,以及汽车耐撞性数据挖掘应用系统。结合企业实际,分别在这两个应用系统上实施了典型应用,取得了良好的效果。具体内容包括:
依据分布式环境下汽车耐撞性数值分析研究的需求,研究了网格系统架构、安全策略、适用于人工资源的工作流集成方法以及人工服务的调用、通知方法,在此基础上构建了满足OGSA规范的网格平台。提出了一种面向汽车耐撞性数值分析的网格应用中间件的概念和技术,并将其应用在所提出的网格平台上。该技术实现了应用服务与基本网格服务的解耦与分离,增强了系统开发的灵活性及可扩展性。本文提出的网格平台,不仅可以运用于汽车耐撞性数值分析,也适用于其他类似工程领域。
针对汽车耐撞性数值分析研究的重要领域——耐撞性协同仿真,研究了基于显示非线性有限元算法及接触算法的耐撞性仿真算法、面向信息安全的有限元协同设计方法,提出了面向信息安全的有限元模型合成方法及数值计算结果的分离方法。基于此方法,开发了面向耐撞性协同仿真的应用服务。在此基础上,将所开发的应用服务部署到所构建的汽车耐撞性数值分析网格平台的应用中间件层,并借助Jsp、Servlet、工作流等技术以及具体的业务逻辑,在平台的应用层开发了面向最终用户的Web应用界面,从而实现了汽车耐撞性协同仿真应用系统。该系统实现了整车商与部件商在进行耐撞性协同仿真活动过程中对资源的共享,以及参与协同的各方对各自知识产权的有效保护。为了评估所构建系统的性能,在广域网内(上汽工程研究院、延峰江森、上海超级计算中心和上海交大)搭建了耐撞性协同仿真实验床,并根据企业实际案例,多方协同进行了耐撞性协同仿真。
耐撞性数据挖掘是另一个汽车耐撞性数值分析领域的重要应用,其目的是通过对已有耐撞性数据所挖掘得到的知识为汽车耐撞性结构设计,尤其是优化匹配设计提供技术支持。本文基于数据挖掘和汽车耐撞性理论研究了耐撞性数据的元数据计算技术、决策树分类器的合成技术,结合Stacking算法、Meta-learning算法以及反求策略,提出并实现了分布式耐撞性数据挖掘算法。提出并实现了前、后端合成的分布式数据挖掘服务。前端服务负责对数据挖掘项目总的管理,后端服务负责对存储在本地(或传输至本地)的原始数据进行元数据抽取及局部数据挖掘。在此基础上,将所开发的应用服务部署到所构建的汽车耐撞性数值分析网格平台的应用中间件层,并借助Jsp、Servlet、工作流等技术以及具体的业务逻辑,在平台的应用层开发了面向耐撞性数据挖掘用户的Web应用界面,从而实现了汽车耐撞性数据挖掘应用系统。在局域网内搭建了耐撞性数据挖掘试验床,对分布在四个节点的183GB的耐撞性数据进行知识发现,以实现对车辆的优化匹配设计。
Compared with traditional crash tests, numerical analysis methods become the principal means for the vehicle crashworthiness research because of its accurate calculations result and efficient implementation. However, with the trend of globalization, automobile industry gradually transplanted to the network environment. Transnational and trans-regional automotive manufacturers and suppliers collaborate to develop products which is helpful to improve product quality and enhance competitiveness of their products. So, the numerical analysis based crashworthiness researches which carry out at local site must adapt to the network manufacturing environment.
This paper researches and builds the grid platform for vehicle crashworthiness numerical analysis based on distributed computing and numerical analysis theory, and also study the crashworthiness collaborative simulation and distributed data-mining method based on the crashworthiness theory. Based on the research above, this paper builds two application systems including the application system for vehicle crashworthiness collaborative simulation and the application system for vehicle crashworthiness data-mining, and two typical applications which are business practice related were implemented on these two systems separately.
According to the need of the vehicle crashworthiness numerical analysis, this paper study the grid system architecture, security policy, human resource related workflow, as well as calling and notification method of the human services. Based on the research above, this paper constructs a grid platform comply with OGSA. This paper presents a concept and technique about grid application middleware for vehicle crashworthiness numerical analysis which enables the decoupling and separation between the application services and the basic grid services, and also enhances the flexibility and scalability of the system development. This proposed grid platform can be applied not only to vehicle crashworthiness numerical analysis, but also applies to other areas of similar works.
Vehicle crashworthiness collaborative simulation is an important part of the vehicle crashworthiness numerical analysis. This paper researches the crashworthiness algorithm which is based on the explicit nonlinear finite element method and contact algorithm, and collaborative design method for finite element analysis based on information safety. Presents the combine method for finite element models based on information safety, and the separation method of the simulation results, based on which, developed the application services for crashworthiness collaborative simulation. Based on the research mentioned above, this paper build the application system for vehicle crashworthiness collaborative simulation by deploying the services that we developed to the application grid middleware, and developing the Web pages for the end-user in the application layer of the plat form based on the technique of Jsp, Servlet, workflow, et al. as well as business logic. This system enables car makers and part suppliers to share resources and to protect their own secret information about vehicle during the collaboration. In order to assess the performance of the system, a test bed has been established in Networks (Shanghai Automotive Engineering Research Institute, Yanfeng Johnson, the Shanghai Supercomputer Center and the Shanghai Jiaotong University), and a real business case was study based on this test bed.
Data-mining for crashworthiness data is another important part of vehicle crashworthiness numerical analysis which aims to bring technique support for the vehicle crashworthiness structural design, especially the optimal design. This paper studied the meta-data computing technology, combining of the decision tree classifier based on data mining and vehicle crashworthiness theory, and proposed the distributed data-mining algorithm for crashworthiness data based on Stacking algorithm, Meta-learning algorithm and reverse strategies. This paper also proposed a distributed data-mining service which consisted of front-end services in charged of the whole project management and back-end services in charged of the calculation of meta-data and the local data-mining of the data stored in local host. Based on the research mentioned above, this paper built the application system for vehicle crashworthiness data-mining by deploying the services that we developed to the application grid middleware, and developing the Web pages for the end-user in the application layer of the plat form based on the technique of Jsp, Servlet, workflow, et al. as well as business logic. In order to assess the performance of the system, a test bed has been established in LAN, and a case about the data-mining to 183GB data stored in four nodes was studied to realize the vehicle optimal design based on this test bed.
本文基于网格和数值分析理论,研究并构建了汽车耐撞性数值分析网格平台;结合耐撞性理论,研究了耐撞性协同仿真和分布式耐撞性数据挖掘方法。在此基础上实现了两个应用系统,分别为汽车耐撞性协同仿真应用系统,以及汽车耐撞性数据挖掘应用系统。结合企业实际,分别在这两个应用系统上实施了典型应用,取得了良好的效果。具体内容包括:
依据分布式环境下汽车耐撞性数值分析研究的需求,研究了网格系统架构、安全策略、适用于人工资源的工作流集成方法以及人工服务的调用、通知方法,在此基础上构建了满足OGSA规范的网格平台。提出了一种面向汽车耐撞性数值分析的网格应用中间件的概念和技术,并将其应用在所提出的网格平台上。该技术实现了应用服务与基本网格服务的解耦与分离,增强了系统开发的灵活性及可扩展性。本文提出的网格平台,不仅可以运用于汽车耐撞性数值分析,也适用于其他类似工程领域。
针对汽车耐撞性数值分析研究的重要领域——耐撞性协同仿真,研究了基于显示非线性有限元算法及接触算法的耐撞性仿真算法、面向信息安全的有限元协同设计方法,提出了面向信息安全的有限元模型合成方法及数值计算结果的分离方法。基于此方法,开发了面向耐撞性协同仿真的应用服务。在此基础上,将所开发的应用服务部署到所构建的汽车耐撞性数值分析网格平台的应用中间件层,并借助Jsp、Servlet、工作流等技术以及具体的业务逻辑,在平台的应用层开发了面向最终用户的Web应用界面,从而实现了汽车耐撞性协同仿真应用系统。该系统实现了整车商与部件商在进行耐撞性协同仿真活动过程中对资源的共享,以及参与协同的各方对各自知识产权的有效保护。为了评估所构建系统的性能,在广域网内(上汽工程研究院、延峰江森、上海超级计算中心和上海交大)搭建了耐撞性协同仿真实验床,并根据企业实际案例,多方协同进行了耐撞性协同仿真。
耐撞性数据挖掘是另一个汽车耐撞性数值分析领域的重要应用,其目的是通过对已有耐撞性数据所挖掘得到的知识为汽车耐撞性结构设计,尤其是优化匹配设计提供技术支持。本文基于数据挖掘和汽车耐撞性理论研究了耐撞性数据的元数据计算技术、决策树分类器的合成技术,结合Stacking算法、Meta-learning算法以及反求策略,提出并实现了分布式耐撞性数据挖掘算法。提出并实现了前、后端合成的分布式数据挖掘服务。前端服务负责对数据挖掘项目总的管理,后端服务负责对存储在本地(或传输至本地)的原始数据进行元数据抽取及局部数据挖掘。在此基础上,将所开发的应用服务部署到所构建的汽车耐撞性数值分析网格平台的应用中间件层,并借助Jsp、Servlet、工作流等技术以及具体的业务逻辑,在平台的应用层开发了面向耐撞性数据挖掘用户的Web应用界面,从而实现了汽车耐撞性数据挖掘应用系统。在局域网内搭建了耐撞性数据挖掘试验床,对分布在四个节点的183GB的耐撞性数据进行知识发现,以实现对车辆的优化匹配设计。
Compared with traditional crash tests, numerical analysis methods become the principal means for the vehicle crashworthiness research because of its accurate calculations result and efficient implementation. However, with the trend of globalization, automobile industry gradually transplanted to the network environment. Transnational and trans-regional automotive manufacturers and suppliers collaborate to develop products which is helpful to improve product quality and enhance competitiveness of their products. So, the numerical analysis based crashworthiness researches which carry out at local site must adapt to the network manufacturing environment.
This paper researches and builds the grid platform for vehicle crashworthiness numerical analysis based on distributed computing and numerical analysis theory, and also study the crashworthiness collaborative simulation and distributed data-mining method based on the crashworthiness theory. Based on the research above, this paper builds two application systems including the application system for vehicle crashworthiness collaborative simulation and the application system for vehicle crashworthiness data-mining, and two typical applications which are business practice related were implemented on these two systems separately.
According to the need of the vehicle crashworthiness numerical analysis, this paper study the grid system architecture, security policy, human resource related workflow, as well as calling and notification method of the human services. Based on the research above, this paper constructs a grid platform comply with OGSA. This paper presents a concept and technique about grid application middleware for vehicle crashworthiness numerical analysis which enables the decoupling and separation between the application services and the basic grid services, and also enhances the flexibility and scalability of the system development. This proposed grid platform can be applied not only to vehicle crashworthiness numerical analysis, but also applies to other areas of similar works.
Vehicle crashworthiness collaborative simulation is an important part of the vehicle crashworthiness numerical analysis. This paper researches the crashworthiness algorithm which is based on the explicit nonlinear finite element method and contact algorithm, and collaborative design method for finite element analysis based on information safety. Presents the combine method for finite element models based on information safety, and the separation method of the simulation results, based on which, developed the application services for crashworthiness collaborative simulation. Based on the research mentioned above, this paper build the application system for vehicle crashworthiness collaborative simulation by deploying the services that we developed to the application grid middleware, and developing the Web pages for the end-user in the application layer of the plat form based on the technique of Jsp, Servlet, workflow, et al. as well as business logic. This system enables car makers and part suppliers to share resources and to protect their own secret information about vehicle during the collaboration. In order to assess the performance of the system, a test bed has been established in Networks (Shanghai Automotive Engineering Research Institute, Yanfeng Johnson, the Shanghai Supercomputer Center and the Shanghai Jiaotong University), and a real business case was study based on this test bed.
Data-mining for crashworthiness data is another important part of vehicle crashworthiness numerical analysis which aims to bring technique support for the vehicle crashworthiness structural design, especially the optimal design. This paper studied the meta-data computing technology, combining of the decision tree classifier based on data mining and vehicle crashworthiness theory, and proposed the distributed data-mining algorithm for crashworthiness data based on Stacking algorithm, Meta-learning algorithm and reverse strategies. This paper also proposed a distributed data-mining service which consisted of front-end services in charged of the whole project management and back-end services in charged of the calculation of meta-data and the local data-mining of the data stored in local host. Based on the research mentioned above, this paper built the application system for vehicle crashworthiness data-mining by deploying the services that we developed to the application grid middleware, and developing the Web pages for the end-user in the application layer of the plat form based on the technique of Jsp, Servlet, workflow, et al. as well as business logic. In order to assess the performance of the system, a test bed has been established in LAN, and a case about the data-mining to 183GB data stored in four nodes was studied to realize the vehicle optimal design based on this test bed.
引文
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