摘要
针对船舶的复杂管系布置设计工作量大、效率低下且依靠经验等问题,提出了一种船舶管线智能布置的优化Astar算法,建立了用于计算机识别的船舶虚拟布置空间模型,包括相关设备模型、管线简化模型、布置约束模型和栅格模型。对传统Astar算法进行优化,基于管线长度、拐角个数和栅格能量值对代价函数进行重新设计,引入方向概率系数和能量值提高管线布置结果的确定性。通过案例的仿真模拟,探究了方向概率系数、能量值对于管线布置速率和质量的影响。结果表明:与传统Astar算法以及遗传算法相比,所提优化Astar算法的拐角个数分别减少了87%、50%;路径搜寻时间分别降低了67.5%、51.5%,进一步提高了管线布置效率。与传统Astar算法相比,优化Astar算法生成的船舶管线长度减小了49.8%,降低了管路成本,说明了该智能布置算法的适配性和优越性,对于实际船舶管线布置具有一定的借鉴性和指导性。
Aiming at the problems of heavy workload,low efficiency and experience dependency in the layout design of complex ship pipeline,an optimized Astar algorithm for intelligent layout of ship pipelines is proposed.The virtual layout space model of the ship for computer identification is established,including:related equipment model,pipeline simplified model,layout constraint model and grid model.The traditional Astar algorithm is optimized:redesigning the cost function based on the pipeline length,the number of corners and the grid energy value,and introducing the directional probability coefficient and energy value to improve the certainty of pipeline layout results.The case simulation explores the influence of the directional probability coefficient and energy value on the pipeline layout rate and quality.The results show that compared with the traditional Astar algorithm and the genetic algorithm,the number of corners of the optimized Astar algorithm in this paper is reduced by 87%and 50%respectively;the path search time is reduced by 67.5%and 51.5%respectively,which further improves the pipeline layout efficiency.Compared with the traditional Astar algorithm,the length of the ship pipeline generated by the optimized Astar algorithm is reduced by 49.8%,which reduces the layout cost of the pipeline.It proves the adaptability and superiority of the intelligent layout method.It can provide reference and guidance for the actual ship pipeline layout.
作者
李喆
王顺森
李勇
吴君
颜晓江
徐耀博
LI Zhe;WANG Shunsen;LI Yong;WU Jun;YAN Xiaojiang;XU Yaobo(Institute of Turbomachinery,Xi’an Jiaotong University,Xi’an 710049,China;Science and Technology on Thermal Energy and Power Laboratory,Wuhan Second Ship Design and Research Institute,Wuhan 430205,China)
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2022年第12期108-117,共10页
Journal of Xi'an Jiaotong University
基金
热能动力技术国家重点实验室开放基金资助项目(TPL2020B03)。
