个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:东亚大学
学位:博士
所在单位:机械工程学院
学科:机械设计及理论
办公地点:大方楼8021#
电子邮箱:sxg@dlut.edu.cn
Multidisciplinary design optimization of tunnel boring machine considering both structure and control parameters under complex geological conditions
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论文类型:期刊论文
发表时间:2016-10-01
发表刊物:STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
收录刊物:SCIE、EI、Scopus
卷号:54
期号:4
页面范围:1073-1092
ISSN号:1615-147X
关键字:Tunnel boring machine; Multidisciplinary design optimization; System decomposition; Complex geological condition
摘要:A Tunnel Boring Machine (TBM) is an extremely large and complex engineering machine that usually works under a complicated geological environment to excavate tunnel underground. Considering the large number of sub-systems that usually belong to different disciplines, it is a challenging task to define, model, and optimize the whole TBM from the perspective of system engineering. Also, due to the complex mechanism and geological environment, the flexibility and efficiency of existing TBM excavation strategies are generally limited. To address these challenges, a multidisciplinary modeling is presented so that corresponding analytical or empirical models of each sub-system are formulated, and a multidisciplinary design optimization (MDO) method is applied to the TBM system optimization. Four excavation strategies are studied and compared, including: (i) two existing excavation strategies, and (ii) two new proposed excavation strategies by making control and/or structure parameters adaptive to geological conditions. Two case studies with these four excavation strategies are presented to illustrate the effectiveness and benefits of designing TBM using MDO methodologies. Wherein, Case I aims to minimize the construction period taking into account the restriction of sub-systems, and Case II simultaneously minimizes construction period, cost, and energy consumption. Since the resulting MDO formulation is straightforward to be solved as a single problem, the All-At-Once (AAO) method is utilized in this paper. The optimization results obtained by modeling the problem as MDO show that the excavation strategy with adaptive control and structure parameters can significantly reduce the total construction time, with lower cost and energy consumption.