基于ANSYS发动机曲轴有限元分析说明书论文
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基于ANSYS发动机曲轴有限元分析说明书论文,基于,ansys,发动机,曲轴,有限元分析,说明书,仿单,论文
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琛暟鎹 栧啓鎶鏈 枃 浠瓑鏂 鐨勫浣“兘鍔 浣垮 鐢熷 宸 佸 ,宸 浣 傚 鏃 琛 鍚戝 闄 鍚 敓浜 闈 宸鎶鏈 涔 宸 鏂 姣 涓 璁 璁紙璁 鏂囷級浠 鍔 涔 3锛 鏈瘯涓氳璁紙璁烘枃锛 棰 fl “ 琛 佸 瓑 細1 氳 Creo瀵 杞 鍔 満鏇茶酱 綋 烘 2 佸 ANSYS杞 涓 琛湁闄愬厓 鐨勫 fl3 佸fi 烘洸杞浣 佷 鍚” 搴“姏浜戝 骞 琛屼 鍖 璁 4 瘯涓氳 鏂涓囧瓧宸锛苟闄” 鍏 鍒嗘瀽鏁锛5 佸 鏂囧弬鑰冭鏂欒瘧鏂囷紙闄勫鏂囷級 3000瀛4锛 富佸弬鑰冩枃鐚細 1璁稿厗妫狅紝榛勯摱濞 姹借fl勯燵 M.鍖椾含锛氬浗闃插涓氬嚭 锛012.2浣欏織鐢 姹借 嗚 M.鍖椾含锛氭満姊板涓氬嚭 锛009.3鐜柊紝闄堟案娉 鏈 檺鍏冩虹 鍙夾 NSYS搴旂敤M.鍖椾含绉戝 虹増绀撅紝 2008.4鍚缓 姹借鍙戝姩鏈 哰 M. 鍖椾含锛氭満姊板涓氬嚭 锛013.5鍚崜锛 垬鏅撹姮.轰 Pro/E鍜孉NSYS Workbench鐨勫洓 稿 鍔 満鏇茶酱鏈 檺鍏冩 佸 fl怺J.鏂currency1鏈 柊宸 壓锛 014锛4):89-91.6鍒 尝.鏇茶酱鍙 暟鍖栧缓 鏈 檺鍏 fl怺D. 眽锛氭 姹夌悊宸 瀛紝 2006.7璧靛墤纾 鐩垪鍙戝姩鏈烘儻 姏鍒嗘瀽涓 钩琛 禰D.娌冲寳锛氱嚂灞卞瀛紝2014.8鐜 儨.轰 UG/ANSYS鐨 洸杞弬鏁板寲 烘 璁捐 鍙婃湁闄愬厓鍒嗘瀽 D.愰兘锛氳椾氦閫氬瀛紝2012.9閭撳彫鏂囷紝闄堟稕.轰 ANSYS鐨凚N492鍙戝姩鏈烘洸杞存湁闄愬厓鍒嗘瀽J.鍐滀瑁 涓庤 绋 紝2010锛29(8)锛9-33.10犲仴锛 忓繝锛岄儹 忓叞锛屼粯娲轰 ANSYS鐨勫 鍔 満鏇茶酱鏈 檺鍏 fl怺J.鏈虹數鎶鏈 紝 2013锛岋紙3锛細102-102.11鍒 稕.鍐呯噧鏈烘洸杞己搴 绠梉 D.娴 崡锛氬北涓滃瀛紝2008.12鑳粊鍠滐紝搴 ANSYS14.0鏈烘 涓庣粨fl勫 fl湁闄愬厓鍒嗘瀽浠 叆闂 绮鹃歔M.鍖椾含锛氭満姊板涓氬嚭 锛013.13寰愪腑庯紝犺寽锛岀浼 轰 UG鍜孉NSYS鐨勫洓 告洸杞存湁闄愬厓佸 fl怺J.鏈烘 宸涓庤嚜鍔寲锛 009锛55锛 锛細 17-19.14瀛欏啗锛 闀挎灄锛豹鏅嘲 . 圭晫鏉 ”悊瀵 洸杞存湁闄愬厓鍒嗘瀽鐨勫奖鍝嶇 禰J.姹借宸锛005锛7锛 锛細 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in the USA2.1 IntroductionThe field of intelligent vehicles is rapidly growing all over the world, both in the diversity of applications and research 3, 8, 18. Especially in the U.S., government agencies, universities, and companies working on this hope to develop autonomous driving entirely or in part for safety and for saving more energy. Many previous technologies, such as seat belts, air bags, work only after a traffic accident. Only intelligent vehicles can stop traffic accidents from happening in the first place. There- fore, DARPA has organized the Grand Challenges and the Urban Challenge from 2004 to 2007, which remarkably promoted the technologies of intelligent vehicles around the world. Hence, this chapter presents an overview of the most advanced intelligent vehicle projects which once attended either the Grand Challenges or the Urban Challenge supported by the DARPA in the USA.2.2 Carnegie Mellon UniversityBossThe research groups at Carnegie Mellon University had developed the Navlab series 8, 17, from Navlab 1 to 11, which include robot cars, tracks, and buses. The Navlabs applications have included Supervised Classification Applied to Road Following (SCARF) 6, 7, Yet Another Road Following (YARF) 12, Autonomous Land Vehicle In a Neural Net (ALVINN) 11, Rapidly Adapting Lateral Position Handler (RALPH) system 16. In addition, Sandstorm is an autonomous vehicle which was modified from the High Mobility Multipurpose Wheeled Vehicle (HMMWV) and competed in the DARPA Grand Challenge in 2005. The Highlander is another autonomous vehicle modified from HMMWV H1 which competed in same competition in 2005.Nevertheless, the latest intelligent vehicle is the Boss system (shown in Fig. 2.1) which won the first place in 2007 Grand Challenge 18. Boss combines various active and passive sensors to provide faster and safer autonomous driving in an urban environment. Active sensors include lidar and radar, and passive sensors include the Point Grey high-dynamic-range camera. The following functional modules were implemented on the Boss vehicle:Fig. 2.1 The intelligent vehicle, named Boss, developed by Carnegie Mellon Universitys Red Team (published courtesy of Carnegie Mellon University)1. Environment perception: Basically, the perception module provides a list of tracked moving objects, static obstacles in a regular grid, and vehicle localization relative to roads, road shape, etc. Furthermore, this module consists of four sub- systems, moving obstacle detection and tracking, static obstacle detection and tracking, roadmap localization, and road shape estimation.2. A three-layer planning system consisting of mission, behavioral, and motion planning is used to drive in urban environments. Mission planning is to detect obstacles and plan new route to its goal. Here, given Road Network Definition File (RNDF) encoding environment connectivity, a cost graph guides vehicles to travel on a road/lane planned by the behavioral subsystem. A value function is calculated to both provide the path from each way point to target way point, and allow the navigation system to respond when an error occurs. Furthermore, Boss is capable of planning another route if there is a blockage.The behavioral subsystem is in charge of executing the rules generated by the mission planning. In details, this subsystem makes decisions on lane-change, precedence, and safety decisions on different driving contexts, such as roads, intersections. Furthermore, this subsystem needs to complete the tasks, including carrying out the rules generated by the previous mission planner, responding to abnormal conditions, and identifying driving contexts, roads, interactions, and zones. Further- more, these driving contexts correspond to different behavior strategies consisting of lane driving, intersection handling, and achieving a zone pose. The third layer of the planning system is the motion planning subsystem which consists of trajec- tory generation, on-road navigation, and zone navigation. This layer is responsible for executing the current motion goal from the behavior subsystem. In general, this subsystem generates a path towards the target, and tracks the path.Fig. 2.2 The Stanford Universitys intelligent vehicle Junior that was the runner-up in the 2007 DARPA Urban Challenge (published courtesy of Stanford University)2.3 Stanford UniversityJuniorThe Stanford Universitys research team on intelligent vehicles has been one of the most experienced and successful research labs in the world. To better study and promote the applications of autonomous intelligent vehicles, the Volkswagen group founded the Volkswagen Automotive Innovation Laboratory (VAIL). Until now, Stanford University collaborated with the Volkswagen Group and built several intelligent vehicles, the Stanley (the autonomous Volkswagen Touareg that won the DARPA Grand Challenge in 2005 10), Junior (the autonomous Volkswagen Passat that was the runner-up in 2007 DARPA Urban Challenge 14). Moreover, Google has licensed the sensing technology from Stanley to map out 3D digital cities all over the world. We will introduce Junior that participated in the 2007 Urban Challenge below.Junior 14, shown in Fig. 2.2, is a modified 2006 Volkswagen Passat wagon, equipped with five laser range finders, a GPS/INS, five radars, two Intel quad core computer systems, and a custom drive-by-wire interface. Hence, this vehicle is capable of detecting an obstacle up to 120 m away.Juniors software architecture is designed as a data-driven pipeline and consists of five modules: Sensor interface: This interface provides data for other modules. Perception modules: These modules segment sensor data into moving vehicles and static obstacles, and also provide accurate position relative to the digital map of the environment. Navigation modules: These modules consist of motion planners, a hierarchical finite state machine, and generate the behavior of the vehicle. Drive-by-wire interface: This interface receives the control commands from navigation modules, and enables the control of throttles, brakes, steering wheels, gear shifting, turn signals, and emergency brake. Global services: The system can provide logging, time stamping, message- passing support, and watch-dog functions to keep the system running reliably.Furthermore, we introduce three fundamental modules: environment perception, precision localization, and navigation. In the perception module, there are two basic functions, static/dynamic obstacle detection and tracking, RNDF localization and update, where lasers implement primary scanning, and a radar system works as an early warning for moving objects in intersections as complement. After perceiving traffic environment, Junior estimates a local alignment between a digital map in the RNDF form and its current position from local sensors. In navigation module, the first task is to plan global paths, where there are two navigation cases, road navigation and free-style navigation. However, basic navigation modules do not include intersections. Furthermore, Junior strives to prevent itself from getting stuck in behavior hierarchy.Nowadays, researchers at Stanford University are still working on autonomous parking in tight parking spots1 and autonomous valet parking.2.4 Virginia Polytechnic Institute and State UniversityOdinThe team VictorTango formed by Virginia Tech and TORC Technologies developed Odin2 2, which took the third place in 2004 DARPA Grand Challenge. The Odin consists of three main parts: base vehicle body, perception, and planning.Now, we introduce the base vehicle platform. Odin is a modified 2005 Hybrid Ford Escape, shown in Fig. 2.3. Its main computing platform is a pair of HP servers, each with two quad-core processors.In the perception module, there are three submodules: object classification, localization, and road detection. Here, object classification first detects obstacles and then classifies them as either static or dynamic. The localization submodule yields the vehicle position and direction in the 3D world. The road detection submodule extracts a road coverage map and lane position.The planning module uses a Hybrid Deliberative-Reactive model, which consists of upper level decisions and lower level reactions as separate components. The coarsest level of planning is the route planner responsible for road segments and zones the vehicle should travel in. The driving behavior component takes care of obeying road rules. Motion planning is in charge of translating control commands into actuator control signals. Fig. 2.3 The intelligent vehicle Odin developed by the Team VictorTango (published courtesy of Virginia Polytechnic Institute and State University)2.5 Massachusetts Institute of TechnologyTalos Team MIT has developed an urban autonomous vehicle, called Talos3 (shown in Fig. 2.4) 1, 9, 13. There are three key novel features: (i) perception-based navigation strategy; (ii) a unified planning and control architecture; (iii) a powerful new software infrastructure. Moreover, this vehicle consists of various submodules: Road Paint Detector, Navigator, Lane Tracker, Driveability Map, Obstacle Detector, Motion Planner, Fast Vehicle Detector, Controller, Positioning Modules. The perception module includes obstacle detector, hazard detector and lane tracking sub- modules. Planning a control algorithm involves using a navigator, driveability map, motion planner, and a controller. The navigator plays an important role in mission- level behavior, and the rest of these submodules work together in a tight coupling to yield the desired motion control goal in complex driving conditions.Fig. 2.4 The intelligent vehicle Talos developed by the Team MIT (published courtesy of Massachusetts Institute of Technology)Fig. 2.5 The intelligent vehicle Skynet developed by Team Cornell (published courtesy of Cornell University)2.6 Cornell UniversitySkynetTeam Cornells Skynet4 is a modified Chevrolet Tahoe, shown in Fig. 2.5, and consists of two groups of sensors 15. One group is used for sensing vehicle itself, and the other group (laser, radar and vision) is for sensing the environment. Thanks to the above sensors, Skynet is capable of providing real-time position, velocity, and attitude for absolute positioning. Moreover, Skynets local map including obstacle detection information is the map of local environment surrounding Skynet. In many cases, autonomous driving in complex scenes is more than basic obstacle avoidance. Hence, the vehicle-centric local map is not enough for absolute positioning. We need to estimate environment structures using posterior pose and track generator algorithms.Skynet is using the probabilistic representation of the environment to plan mission paths within the context of the rule-based road network. One intelligent planner includes three primary layers: a behavioral layer, a tactical layer, and a operational layer. The goal of the behavior layer is to determine the fastest route to the next mission point. When there exist state transitions in the behavior layer, the corresponding component of the tactical layer is executed. Among the four tactical components, the road tactical component is to seek a proper lane and to monitor other agents in the same and neighboring lanes. The intersection tactical component handles intersection queuing behavior and safe merging. The zone tactical component takes care of basic navigation in unconstrained cases. The blockage tactical component implements obstacle detection and jud
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