A solution of multidisciplinary collaborative simulation for complex engineering systems in a distributed heterogeneous environment

This paper presents an integrated approach to multidisciplinary collaborative simulation for complex engineering systems. The formulized paradigm of multidisciplinary collaborative simulation for com- plex engineering systems is principally analyzed. An IEEE HLA and web services based framework is proposed to provide a heterogeneous, distributed and collaborative running environment where multidisciplinary modeling, running management and post-processing of collaborative simulation are undertaken. The mecha...     

基于神经网络的电力系统暂态稳定分布式自适应控制

针对电力系统中普遍存在的系统非线性和参数不确定性等问题,提出一种基于径向基函数神经网络(RBFNN)的分布式自适应控制器,以提高多机电力系统的暂态稳定性.利用基于RBFNN的方法对系统中的未知非线性项和外部扰动进行补偿,设计相应的自适应参数估计方法,逼近未知非线性项的理想权值矩阵.该策略基于多智能体框架,分布式控制器通过通信网络接收测量装置测量的实时数据,并控制储能装置动作,使受到扰动后各发电机能够迅速实现频率同步.利用李雅普诺夫稳定性理论,证明所提出的分布式控制方法的收敛性.最后,通过仿真研究验证所提出的分布式控制方法的有效性.     

A big data-driven framework for sustainable and smart additive manufacturing

From the last decade, additive manufacturing (AM) has been evolving speedily and has revealed the great potential for energy-saving and cleaner environmental production due to a reduction in material and resource consumption and other tooling requirements. In this modern era, with the advancements in manufacturing technologies, academia and industry have been given more interest in smart manufacturing for taking benefits for making their production more sustainable and effective. In the present study, the significant techniques of smart manufacturing, sustainable manufacturing, and additive manufacturing are combined to make a unified term of sustainable and smart additive manufacturing (SSAM). The paper aims to develop framework by combining big data analytics, additive manufacturing, and sustainable smart manufacturing technologies which is beneficial to the additive manufacturing enterprises. So, a framework of big data-driven sustainable and smart additive manufacturing (BD-SSAM) is proposed which helped AM industry leaders to make better decisions for the beginning of life (BOL) stage of product life cycle. Finally, an application scenario of the additive manufacturing industry was presented to demonstrate the proposed framework. The proposed framework is implemented on the BOL stage of product lifecycle due to limitation of available resources and for fabrication of AlSi10Mg alloy components by using selective laser melting (SLM) technique of AM. The results indicate that energy consumption and quality of the product are adequately controlled which is helpful for smart sustainable manufacturing, emission reduction, and cleaner production.     

Separation force analysis and prediction based on cohesive element model for constrained-surface Stereolithography processes

Constrained-surface based Additive Manufacturing (AM) processes have been widely used in both academia and industry for the past few years. Despite the advantages of constrained-surface based AM processes, it has not been widely used in practice. A main reason for this is that a substantial separation force is required to separate the cured part from the material vat during the pulling-up stage, which may damage the cured part and reduce the reliability of the process. The solutions proposed previously to reduce this separation force recommend using an intermediate coating material (e.g., Teflon and silicone films) between the cured part and the vat. This, however, has only negligible effects in reducing the separation force. In this work, the pulling-up process is modeled within the framework of mechanics-based principles. In particular, the cohesive zone model (CZM) is adopted to characterize the separation mechanism, and finite element (FE) simulation is carried out to investigate the separation process using the commercially available FE software, Abaqus. A new simple optimization scheme is also proposed to estimate the constitutive cohesive stiffness parameters from experimental measurements. These constitutive parameters are very difficult to estimate using the standard mechanical tests. The proposed work based on sound mechanics-based principles can be used for reliable prediction of pulling-up speed, and thus, is likely to be useful in devising an adaptive closed-loop system to control the pulling-up process and achieve a reliable AM approach.     

Eco-friendly additive manufacturing of metals: Energy efficiency and life cycle analysis

Additive manufacturing (AM) of metal materials has attracted widespread attention and is shifting the conventional manufacturing landscape toward free-form processes. With increasing concerns about global sustainability, eco-consideration is highly encouraged to be integrated into AM processes. This review provides a comprehensive and timely discussion on the life cycle of metal parts fabricated through AM. The energy consumption required for raw metal material extraction and subsequent AM processes is analyzed. The eco-design and energy efficiency of metal AM are evaluated to reveal the role of manufacturing methods, machine subsystems, and post-processing modes in the eco-integration. AM-induced supply chain management, utilization, and recycling of the printed metal structure are also analyzed. Finally, a comprehensive life cycle assessment regarding the environmental, social, and economic impacts of metal AM is also addressed. Future directions of AM are also briefly discussed to provide insight and vision on the emerging field of additive eco-manufacturing.     

基于Hadoop的广域网分布式主题爬虫系统框架

广域网分布式爬虫与局域网爬虫相比有诸多的优势,而现有基于Hadoop分布式爬虫的设计主要是面向局域网环境的。为解决Hadoop分布式计算平台不适合部署于广域网的问题,设计了一个基于Hadoop的广域网分布式爬虫系统框架。爬虫系统利用消息中间件实现分布式可靠通信,数据存储采用可伸缩的Hadoop分布式文件系统HDFS,网页解析利用MapReduce并行处理,并基于模板匹配实现框架可定制。系统的性能仿真显示该框架具有支撑大规模爬虫并发工作的能力。     

基于高层体系结构的通用仿真数据服务

针对分布式交互仿真系统中数据密集型应用与分布式存储之间的矛盾,提出一个在高层体系结构(HLA)框架内提供通用数据服务功能的解决方案,通过扩展HLA对象模型模板使数据模型和仿真模型无缝地集成,数据操作和成员接口具有一致的形式。根据数据服务模块在分布式仿真系统中的特点提供必要的优化选项,依照联邦开发和执行过程模型给出成员设计和接口调用的一般流程。     

Development of a simulation framework and applications to new production processes in shipyards

Recently, a floating crane is frequently used for the block lifting, transportation, turn-over, and assembly processes in waves. For these production processes, it is important to detect collision in advance between assembly blocks or the block and the other facilities like the wire rope and the barge which are carrying the block. The tension of the wire rope also needs to be calculated to check that the maximum value is less than the safety criteria. In this paper, a mathematical model is constructed based on multibody system dynamics considering the external forces such as the hydrostatic, hydrodynamic, wind force, etc. To observe the dynamic motions of the floating crane and the block, and to calculate the tension of the wire rope, the time and event simulations are performed by solving the mathematical model in the computer. For applying the simulations to the various production processes in shipyards, a simulation framework is developed. The simulation framework consists of a simulation kernel, application-specific modules, a simulation coordinator, development tools, and post-processing tools. The simulation kernel manages both DEVS (discrete event system specification) and DTSS (discrete time system specification) to deal with various simulation requests. The application-specific modules provide the functions used in application systems, such as dynamic analysis, collision detection, visualization, wire rope force calculation, hydrostatic force calculation and hydrodynamic force calculation. The simulation coordinator manages the data of the simulation kernel and the application-specific modules. The development tools provide a development process, a scenario manager, and a simulation model generator. The post-processing tools are used to report the simulation results. The examples of block lifting, transportation, turn-over, and assembly simulations are developed based on the framework to show that the framework is useful for the simulations of the production processes using one or more floating cranes.     

Multi-source data analytics for AM energy consumption prediction

The issue of Additive Manufacturing (AM) system energy consumption attracts increasing attention when many AM systems are applied in digital manufacturing systems. Prediction and reduction of the AM energy consumption have been established as one of the most crucial research targets. However, the energy consumption is related to many attributes in different components of an AM system, which are represented as multiple source data. These multi-source data are difficult to integrate and to model for AM energy consumption due to its complexity. The purpose of this study is to establish an energy value predictive model through a data-driven approach. Owing to the fact that multi-source data of an AM system involves nested hierarchy, a hybrid approach is proposed to tackle the issue. This hybrid approach incorporates clustering techniques and deep learning to integrate the multi-source data that is collected using the Internet of Things (IoT), and then to build the energy consumption prediction model for AM systems. This study aims to optimise the AM system by exploiting energy consumption information. An experimental study using the energy consumption data of a real AM system shows the merits of the proposed approach. Results derived using this hybrid approach reveal that it outperforms pre-existing approaches.     

考虑共享电池站和温控负荷响应的综合能源系统优化调度

针对区域综合能源系统协同优化调度问题,构建基于能量枢纽(energy hub,EH)的分层能量管理框架,充分考虑新能源电厂、气-电、电-气能源转换装置调节能力,以共享电池站(shared battery station,SBS)替代传统储能电站,为能源系统提供电能存储功能,采用温控负荷为能源系统提供需求侧响应,保障异质...     

基于均衡数据放置策略的分布式网络存储编码缓存方案

为了保证网络存储的负载平衡并避免在节点或磁盘故障的情况下造成不可恢复的损失,提出一种基于均衡数据放置策略的分布式网络存储编码缓存方案,针对大型高速缓存和小型缓存分别给出了不同的解决办法。首先,将Maddah方案扩展到多服务器系统,结合均衡数据放置策略,将每个文件作为一个单元存储在数据服务器中,从而解决大型高速缓存问题;然后,将干扰消除方案扩展到多服务器系统,利用干扰消除方案降低缓存的峰值速率,结合均衡数据放置策略,提出缓存分段的线性组合,从而解决小型缓存问题。最后,通过基于Linux的NS2仿真软件,分别在一个和两个奇偶校验服务器系统中进行仿真实验。仿真结果表明,提出的方案可以有效地降低峰值传输速率,相比其他两种较新的缓存方案,提出的方案获得了更好的性能。此外,采用分布式存储虽然限制了将来自不同服务器的内容组合成单个消息的能力,导致编码缓存方案性能损失,但可以充分利用分布式存储系统中存在的固有冗余,从而提高存储系统的性能。     

Data Utilization-Based Adaptive Data Management Method for Distributed Storage System in WAN Environment

Recently, research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand increase. Physical expansion limits exist for traditional standalone storage systems, such as I/O and file system capacity. However, the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data location. And this leads to system performance degradation due to low locality occurring in a Wide Area Network (WAN) environment with high network latency. This problem hinders deploying distributed storage systems to multiple data centers over WAN. It lowers the scalability of distributed storage systems to accommodate data storage needs. This paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system performance. The proposed distributed storage method monitors data utilization and locality to classify data temperature as hot, warm, and cold. With assigned data temperature, the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment, leading to overall system performance degradation. This paper also conducts simulations to evaluate the proposed and existing distributed storage methods. The result shows that our proposed method reduced latency by 38% compared to the existing method. Therefore, the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods, such as consistent hashing.     

分布式流数据加载和查询技术优化

分布式流查询是一种基于数据流的实时查询计算方法,近年来得到了广泛的关注和快速发展。综述了分布式流处理框架在实时关系型查询上取得的研究成果;对涉及分布式数据加载、分布式流计算框架、分布式流查询的产品进行了分析和比较;提出了基于Spark Streaming和Apache Kafka构建的分布式流查询模型,以并发加载多个文件源的形式,设计内存文件系统实现数据的快速加载,相较于基于Apache Flume的加载技术提速1倍以上。在Spark Streaming的基础上,实现了基于Spark SQL的分布式流查询接口,并提出了自行编码解析SQL语句的方法,实现了分布式查询。测试结果表明,在查询语句复杂的情况下,自行编码解析SQL的查询效率具有明显的优势。     

Evaluation of thermal energy storage and recovery for an electrical energy mediator system

Energy storage both electrical and thermal is a rapidly emerging field of interest toward the development of more sustainable energy systems. The inherent inefficiencies associate with electrical storage can be partially overcome when thermal storage that collects and storage the waste thermal energy for alternative uses is integrated. Consequently, thermal energy storage systems are an enabling technology that will allow increased energy efficiency of a community, permit load levelling to reduce peak electricity demand. In order to facilitate a technology evaluation, a sizing strategy is developed for a phase change material (PCM) thermal storage system that determines system requirements under given thermal energy capture and recovery cycles. The sizing process utilizes a simplified one-dimensional heat transfer model that estimates melt times for a phase change material thickness without detailed geometry information. This melt time estimate allows the proportion of phase change material to fluid routing materials to be calculated, giving an estimate of material cost for the thermal storage cell to determine economic feasibility. The model is compared to both experimental data and computational fluid dynamics models in order to determine its limitations. Through a specific example of hydrogen based distributed electrical energy mediator system, the utility of the sizing model in determining the estimated cost of thermal energy storage is demonstrated.     

面向大数据存储的主动与被动相结合的性能评测方法体系结构与实现

随着数据量的日益增加,大数据存储在整个大数据应用框架体系中居于重要地位。对大数据存储系统进行性能评测可以指导大数据应用开发人员分析性能瓶颈,进行大数据系统的性能优化。在以往的工作中,通常使用基准测试的方式来对不同大数据框架进行性能评测,或者采用插桩并分析轨迹文件的方式对分布式文件系统进行性能分析。这2种方法采用的分析角度不同,并没有形成合理的评测体系来评价大数据分布式存储系统。本文提出主动与被动相结合的大数据存储系统性能评测方法体系结构及其具体实现。在主动性能评测方法方面,提供了6个领域,超过20个应用的基准测试程序,对大数据存储系统主动发起性能测试,分析大数据存储系统的基准性能指标;在被动性能评测方法方面,提供了对低效任务、低效算子、低效函数的分析及定位方法,通过分析运行在大数据存储系统之上的大数据应用,分析大数据应用程序低效的原因。通过实验表明,该大数据性能评测方法体系结构能够全面地对大数据存储系统进行性能评测。     

Data dissemination scheme for distributed storage for IoT observation systems at large scale

In the emerging field of the Internet of Things (IoT), Wireless Sensor Networks (WSNs) have a key role to play in sensing and collecting measures on the surrounding environment. In the deployment of large scale observation systems in remote areas, when there is not a permanent connection with the Internet, WSNs are calling for replication and distributed storage techniques that increase the amount of data stored within the WSN and reduce the probability of data loss. Unlike conventional network data storage, WSN-based distributed storage is constrained by the limited resources of the sensors. In this paper, we propose a low-complexity distributed data replication mechanism to increase the resilience of WSN-based distributed storage at large scale. In particular, we propose a simple, yet accurate, analytical modeling framework and an extensive simulation campaign, which complement experimental results on the SensLab testbed. The impact of several key parameters on the system performance is investigated.     

Distributed resilient estimation over sensor networks for nonlinear time‐delayed systems with stochastic perturbations

This paper is concerned with the distributed resilient estimation problem for a class of nonlinear time‐delayed systems subject to stochastic perturbations. The plant and the measurements are disturbed by two Gaussian white stochastic processes with known statistical information, respectively. In addition, a resilient estimator is designed for each node by means of the parameter uncertainties and Bernoulli‐distributed random variables. Then, a novel exponential‐bounded performance index is put forward to measure the disturbance rejection level of the distributed estimators against the external disturbances and the impact of the initial values. A new vector dissipation definition including multiple vectors of energy storage functions is established to deal with the time‐delay estimation error dynamics. Within the framework of local performance analysis inspired by this new definition of vector dissipation, sufficient conditions in terms of recursive linear matrix inequalities are constructed for each node to guarantee the desirable performance index. Next, a local optimization problem subject to a set of recursive linear matrix inequalities is presented for each node to minimize the upper bound in the performance index, where the calculations can be conducted on every node in a distributed manner and the estimator gains are also calculated. Finally, an illustrative simulation example is provided to verify the applicability of the proposed estimators.     

Multi-commodity flow dynamic resource assignment and matrix-based job dispatching for multi-relay transfer in complex material handling systems (MHS)

Mass management and production of customized products requires material handling systems (MHS) which are flexible and responsive enough to accommodate dynamic and real-time changes in material handling tasks. Towards this goal, we develop a novel control framework to improve the flexibility and responsiveness of material handling systems. Flexibility is achieved by using multi-commodity flow network optimization to find the most optimized job sequence in terms of minimum transfer steps. Responsiveness is achieved by the use of a matrix-based discrete event (DE) supervisory controller to dispatch equipment control commands in real-time based on real-time sensor information, according to the optimized sequence. By modeling the MHS network as multi-commodity flow network to define job routes, and using the matrix-based DE controller to implement the job routes in real-time, the users achieve a seamlessly integrated solution to control the execution of transfer jobs that covers the supervisory planning stage through the real-time actual dispatching decisions. The proposed control framework is evaluated on an industrial case study of airfreight terminal material handling and simulation results show its effectiveness.     

Models, methods and middleware for grid-enabled multiphysics oil reservoir management

Meeting the demands for energy entails a better understanding and characterization of the fundamental processes of reservoirs and of how human made objects affect these systems. The need to perform extensive reservoir studies for either uncertainty assessment or optimal exploitation plans brings up demands of computing power and data management in a more pervasive way. This work focuses on high performance numerical methods, tools and grid-enabled middleware systems for scalable and data-driven computations for multiphysics simulation and decision-making processes in integrated multiphase flow applications. The proposed suite of tools and systems consists of (1) a scalable reservoir simulator, (2) novel stochastic optimization algorithms, (3) decentralized autonomic grid middleware tools, and (4) middleware systems for large-scale data storage, querying, and retrieval. The aforementioned components offer enormous potential for performing data-driven studies and efficient execution of complex, large-scale reservoir models in a collaborative environment.     

基于网格的煤矿安全应用网格体系研究

煤矿安全应用工作中数据信息分布、计算资源闲置、异构以及计算功能单一严重制约着行业应用和信息化的发展。网格（grid）是新一代高性能计算环境和信息服务基础设施,能够实现动态跨地域的资源共享和协同工作。该文基于新兴的网格技术,参照OGSA体系,提出并构建了从信息获取、处理到应用的完整的煤矿安全应用网格（CMSAG）体系结构。同时结合行业应用需求,着重研究了其资源表示、资源管理、存储访问以及数据缓存等关键技术,最后通过具体应用示例的工作流程论证了该网格体系的应用模式。