船舶专业外文翻译--船舶设计优化.doc

Ship Design Optimization This contribution is devoted to exploiting the analogy between a modern manufacturing plant and a heterogeneous parallel computer to construct a HPCN decision support tool for ship designers. The application is a HPCN one because of the scale of shipbuilding - a large container vessel is constructed by assembling about 1.5 million atomic components in a production hierarchy. The role of the decision support tool is to rapidly evaluate the manufacturing consequences of design changes. The implementation as a distributed multi-agent application running on top of PVM is described1 Analogies between Manufacturing and HPCN There are a number of analogies between the manufacture of complex products such as ships, aircraft and cars and the execution of a parallel program. The manufacture of a ship is carried out according to a production plan which ensures that all the components come together at the right time at the right place. A parallel computer application should ensure that the appropriate data is available on the appropriate processor in a timely fashion.It is not surprising, therefore, that manufacturing is plagued by indeterminacy exactly as are parallel programs executing on multi-processor hardware. This has caused a number of researchers in production engineering to seek inspiration in other areas where managing complexity and unpredictability is important. A number of new paradigms, such as Holonic Manufacturing and Fractal Factories have emerged 1,2 which contain ideas rather reminiscent of those to be found in the field of Multi- Agent Systems 3, 4.Manufacturing tasks are analogous to operations carried out on data, within the context of planning, scheduling and control. Also, complex products are assembled at physically distributed workshops or production facilities, so the components must be transported between them. This is analogous to communication of data between processors in a parallel computer, which thus also makes clear the analogy between workshops and processors.The remainder of this paper reports an attempt to exploit this analogy to build a parallel application for optimizing ship design with regard to manufacturing issues.2 Shipbuilding at Odense Steel ShipyardOdense Steel Shipyard is situated in the town of Munkebo on the island of Funen. It is recognized as being one of the most modern and highly automated in the world. Itspecializes in building VLCCs (supertankers) and very large container ships. The yard was the first in the world to build a double hulled supertanker and is currently building an order of 15 of the largest container ships ever built for the Maersk line. These container ships are about 340 metres long and can carry about 7000 containers at a top speed of 28 knots with a crew of 12.Odense Steel Shipyard is more like a ship factory than a traditional shipyard. The ship design is broken down into manufacturing modules which are assembled and processed in a number of workshops devoted to, for example, cutting, welding and surface treatment. At any one time, up to 3 identical ships are being built and a new ship is launched about every 100 days.The yard survives in the very competitive world of shipbuilding by extensive application of information technology and robots, so there are currently about 40 robots at the yard engaged in various production activities. The yard has a commitment to research as well, so that there are about 10 industrial Ph.D. students working there, who are enrolled at various engineering schools in Denmark.3 Tomorrows Manufacturing SystemsThe penetration of Information Technology into our lives will also have its effect in manufacturing industry. For example, the Internet is expected to become the dominant trading medium for goods. This means that the customer can come into direct digital contact with the manufacturer.The direct digital contact with customers will enable them to participate in the design process so that they get a product over which they have some influence. The element of unpredictability introduced by taking into account customer desires increases the need for flexibility in the manufacturing process, especially in the light of the tendency towards globalization of production. Intelligent robot systems, such as AMROSE, rely on the digital CAD model as the primary source of information about the work piece and the work cell 5,6.This information is used to construct task performing, collision avoiding trajectories for the robots, which because of the high precision of the shipbuilding process, can be corrected for small deviations of the actual world from the virtual one using very simple sensor systems. The trajectories are generated by numerically solving the constrained equations of motion for a model of the robot moving in an artificial force field designed to attract the tool centre to the goal and repell it from obstacles, such as the work piece and parts of itself. Finally, there are limits to what one can get a robot to do, so the actual manufacturing will be performed as a collaboration between human and mechatronic agents.Most industrial products, such as the windmill housing component shown in Fig. 1, are designed electronically in a variety of CAD systems.Fig. 1. Showing the CAD model for the housing of a windmill. The model, made using Bentley Microstation, includes both the work-piece and task-curve geometries.4 Todays Manufacturing SystemsThe above scenario should be compared to todays realities enforced by traditional production engineering philosophy based on the ideas of mass production introduced about 100 years ago by Henry Ford. A typical production line has the same structure as a serial computer program, so that the whole process is driven by production requirements. This rigidity is reflected on the types of top-down planning and control systems used in manufacturing industry, which are badly suited to both complexity and unpredictability.In fact, the manufacturing environment has always been characterized by unpredictability. Todays manufacturing systems are based on idealized models where unpredictability is not taken into account but handled using complex and expensive logistics and buffering systems.Manufacturers are also becoming aware that one of the results of the top-down serial approach is an alienation of human workers. For example, some of the car manufacturers have experimented with having teams of human workers responsible for a particular car rather than performing repetitive operations in a production line. This model in fact better reflects the concurrency of the manufacturing process than the assembly line.5 A Decision Support Tool for Ship Design OptimizationLarge ships are, together with aircraft, some of the most complex things ever built. A container ship consists of about 1.5 million atomic components which are assembled in a hierarchy of increasingly complex components. Thus any support tool for the manufacturing process can be expected to be a large HPCN application.Ships are designed with both functionality and ease of construction in mind, aswell as issues such as economy, safety, insurance issues, maintenance and even decommissioning. Once a functional design is in place, a stepwise decomposition of the overall design into a hierarchy of manufacturing components is performed. The manufacturing process then starts with the individual basic building blocks such as steel plates and pipes. These building blocks are put together into ever more complex structures and finally assembled in the dock to form the finished ship.Thus a very useful thing to know as soon as possible after design time are the manufacturing consequences of design decisions. This includes issues such as whether the intermediate structures can actually be built by the available production facilities, the implications on the use of material and whether or not the production can be efficiently scheduled 7.Fig.2. shows schematically how a redesign decision at a point in time during construction implies future costs, only some of which are known at the time. Thus a decision support tool is required to give better estimates of the implied costs as early as possible in the process.Simulation, both of the feasibility of the manufacturing tasks and the efficiency with which these tasks can be performed using the available equipment, is a very compute-intense application of simulation and optimization. In the next section, we describe how a decision support tool can be designed and implemented as a parallel application by modeling the main actors in the process as agents.Fig.2. Economic consequences of design decisions. A design decision implies a future commitment of economic resources which is only partially known at design time.6 Multi-Agent SystemsThe notion of a software agent, a sort of autonomous, dynamic generalization of an object (in the sense of Object Orientation) is probably unfamiliar to the typical HPCN reader in the area of scientific computation. An agent possesses its own beliefs, desires and intentions and is able to reason about and act on its perception of other agents and the environment.A multi-agent system is a collection of agents which try to cooperate to solve some problem, typically in the areas of control and optimization. A good example is the process of learning to drive a car in traffic. Each driver is an autonomous agent which observes and reasons about the intentions of other drivers. Agents are in fact a very useful tool for modeling a wide range of dynamical processes in the real world, such as the motion of protein molecules 8 or multi-link robots 9. For other applications, see 4.One of the interesting properties of multi-agent systems is the way global behavior of the system emerges from the individual interactions of the agents 10. The notion of emergence can be thought of as generalizing the concept of evolution in dynamical systems.Examples of agents present in the system are the assembly network generator agent which encapsulates knowledge about shipbuilding production methods for planning assembly sequences, the robot motion verification agent, which is a simulator capable of generating collision-free trajectories for robots carrying out their tasks, the quantity surveyor agent which possesses knowledge about various costs involved in the manufacturing process and the scheduling agent which designs a schedule for performing the manufacturing tasks using the production resources available.7 Parallel ImplementationThe decision support tool which implements all these agents is a piece of Object- Oriented software targeted at a multi-processor system, in this case, a network of Silicon Graphics workstations in the Design Department at Odense Steel Shipyard. Rather than hand-code all the communication between agents and meta-code for load balancing the parallel application, abstract interaction mechanisms were developed. These mechanisms are based on a task distribution agent being present on each processor. The society of task distribution agents is responsible for all aspects of communication and migration of tasks in the system.The overall agent system runs on top of PVM and achieves good speedup and load balancing. To give some idea of the size of the shipbuilding application, it takes 7 hours to evaluate a single design on 25 SGI workstations. From：Applied Parallel Computing Large Scale Scientific and Industrial Problems Lecture Notes in Computer Science, 1998, Volume 1541/1998, 476-482, DOI: 10.1007/BFb0095371 .中文翻译：船舶设计优化这一贡献致力于开拓类比现代先进制造工厂和一个异构并行计算机,构建了一种HPCN决策支援工具给船舶设计师。这个应用程序是一个HPCN的一个原因是造船的规模一个巨大的集装箱船通过装配了大约150万原子部件在生产的过程中。该决策支持工具的作用是迅速的评估修改设计导致的制造后果。这个应用程序可描述为一个实现在PVM上运行的分布式多智能体。1.制造业与HPCN的相似性制造复杂的产品有许多相似之处，如船舶、飞机、汽车和执行并行程式。制造了一艘船按照生产计划展开的,必须保证所有的部件在适当的时间、适当的地点结合在一起。类似的计算机应用应确保合适的数据可在适当的处理器下运行。这并不奇怪，因此，制造业的困扰是不确定性的和在多处理器硬件下执行的并行程序完全不一样。这已经引起了一部分研究人员在生产工程中在管理复杂性和不可预测性的重要领域寻求灵感的地方。很多新范例,如Holonic制造系统和分形工厂出现1,2包含着思想,而让人回忆起了多-代理领域系统3、4。生产任务,就像是在执行操作数据,在其职权范围内策划、调度和控制。同时,复杂的产品都聚集在身体上的分布式讨论会或生产设施,所以组件类必须被流放分给他们。这好比通信之间的数据在一个平行处理器的计算机上,它也明确了车间和处理器之间的相似性。本文的其余部分的报告，企图利用这个理论，建立一个并行应用程序优化船舶设计对于生产中出现的问题。2. 欧登塞钢船厂的造船欧登塞钢船厂位于Munkebo镇Funen岛上。它被公认为是世界上其中一个最现代化的，高度自动化的船厂。它专门建设超大型油轮（超级油轮）和超大型集装箱船。公司厂区世界上第一个建造双壳超级油轮，目前正在为马士基航运公司建造的船舶15份订单是有史以来建造的最大的集装箱。这些集装箱船约340米长，在28节的最高速度下可以携带12名船员与约7000箱集装箱。欧登塞钢船厂相比传统船厂建造更像是一个船厂。这艘船设计可以把制造模块分解为组装和加工,在许多车间投入生产,例如,切割、焊接和表面处理。不管什么时候,都有3个相同的船只在建, 约每100天建成一艘新船。这个公司能在世界上竞争非常激烈的船舶业生存，是因为广泛的应用信息技术和机械,目前有40台机器在这个公司从事各种生产活动。公司里的研究任务,约有十个工业博士学生组成;他们来自丹麦不同的注册工科院校。3.未来的制造系统信息科技渗透到我们生活中也将影响制造业。例如，互联网有望在商品交易中占主导地位。这意味着客户可以直接用数字与制造商联系。直接与客户接触数字将使他们能够参与在设计过程中，使他们得到了他们的产品有一定的影响。考虑到顾客的需求增加了灵活性，在生产过程中是不可预测的元素，尤其是在生产全球化的趋势下。智能机器人系统，如AMROSE，数字化CAD模型上信息的主要来源是工件和工作单元5,6。这些信息提供机械用来构建任务的执行，避免轨迹碰撞，由于造船工艺精度高，可以进行修正的偏差小，实际的世界使用一个虚拟的非常简单的传感器系统。求解约束的运动微分方程所产生的数值的运动轨迹模型,机器的移动，在人为设计引起刀心达到目标和疏去除障碍，如工件的一块或者本身的一部分。最后,有限制哪些是可以得到一个机器人做了,所以实际制造将履行的人类与机电合作代理。最后，机器能做的是有局限性的，所以实际的制造业将通过人与机电一体化合作完成。大多数工业产品，如在图1所示风房组成部分，在各种CAD系统下电子化设计。图.1 显示了一个风车房CAD模型。该模型，用本特利MicroStation制作，包括工件和任务曲线几何结构。4.当今的制造系统与上述情况相比，今天现实执行的大规模生产是根据传统的生产工程哲学观念100年前从亨利福特引进。一个典型的生产线作为一个序列电脑程序具有相同的结