Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 19086-19098 |
| Seitenumfang | 13 |
| Fachzeitschrift | IEEE ACCESS |
| Jahrgang | 6 |
| Publikationsstatus | Veröffentlicht - 30 März 2018 |
Abstract
This paper is focused on developing an effective method to optimize the performance (e.g., advance rate and energy consumption) of a tunnel boring machine (TBM) by determining reasonable operating and structural parameters according to geological conditions. The TBM is a complex mechatronic system consisting of closely coupled subsystems. The changing of an operating or structural parameter can significantly influence the performances of several subsystems simultaneously in different manners. In addition, most of the subsystems of the TBM are subject to rock loads with server uncertainties, as a result of the probabilistic natures of rock characteristics. To overcome these challenges, multidisciplinary modeling of the subsystems of the TBM is performed to derive the performance functions and limit state functions of the whole machine. Based on design of experiment, sensitive analysis (SA) is carried out to detect the significant factors and their influences on the performances of the whole machine and the subsystems. Based on the SA results, the optimization efficiency can be improved by disregarding insignificant factors. Taking the stochastic properties of rocks into account, a reliability-based performance optimization strategy is proposed. Case studies are carried out and proved that with the proposed method, the performances of the TBM can be greatly improved while the system reliability is kept at high level.
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in: IEEE ACCESS, Jahrgang 6, 30.03.2018, S. 19086-19098.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Reliability-Based Performance Optimization of Tunnel Boring Machine Considering Geological Uncertainties
AU - Wang, Lintao
AU - Sun, Wei
AU - Long, Yangyang
AU - Yang, Xu
N1 - Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 51605071 and Grant U1608256 and in part by the Special Grade of the China Postdoctoral Science Foundation under Grant 2016T90218.
PY - 2018/3/30
Y1 - 2018/3/30
N2 - This paper is focused on developing an effective method to optimize the performance (e.g., advance rate and energy consumption) of a tunnel boring machine (TBM) by determining reasonable operating and structural parameters according to geological conditions. The TBM is a complex mechatronic system consisting of closely coupled subsystems. The changing of an operating or structural parameter can significantly influence the performances of several subsystems simultaneously in different manners. In addition, most of the subsystems of the TBM are subject to rock loads with server uncertainties, as a result of the probabilistic natures of rock characteristics. To overcome these challenges, multidisciplinary modeling of the subsystems of the TBM is performed to derive the performance functions and limit state functions of the whole machine. Based on design of experiment, sensitive analysis (SA) is carried out to detect the significant factors and their influences on the performances of the whole machine and the subsystems. Based on the SA results, the optimization efficiency can be improved by disregarding insignificant factors. Taking the stochastic properties of rocks into account, a reliability-based performance optimization strategy is proposed. Case studies are carried out and proved that with the proposed method, the performances of the TBM can be greatly improved while the system reliability is kept at high level.
AB - This paper is focused on developing an effective method to optimize the performance (e.g., advance rate and energy consumption) of a tunnel boring machine (TBM) by determining reasonable operating and structural parameters according to geological conditions. The TBM is a complex mechatronic system consisting of closely coupled subsystems. The changing of an operating or structural parameter can significantly influence the performances of several subsystems simultaneously in different manners. In addition, most of the subsystems of the TBM are subject to rock loads with server uncertainties, as a result of the probabilistic natures of rock characteristics. To overcome these challenges, multidisciplinary modeling of the subsystems of the TBM is performed to derive the performance functions and limit state functions of the whole machine. Based on design of experiment, sensitive analysis (SA) is carried out to detect the significant factors and their influences on the performances of the whole machine and the subsystems. Based on the SA results, the optimization efficiency can be improved by disregarding insignificant factors. Taking the stochastic properties of rocks into account, a reliability-based performance optimization strategy is proposed. Case studies are carried out and proved that with the proposed method, the performances of the TBM can be greatly improved while the system reliability is kept at high level.
KW - DOE analysis
KW - geological uncertainties
KW - performance optimization
KW - Tunnel boring machine
UR - http://www.scopus.com/inward/record.url?scp=85044723511&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2018.2821190
DO - 10.1109/ACCESS.2018.2821190
M3 - Article
AN - SCOPUS:85044723511
VL - 6
SP - 19086
EP - 19098
JO - IEEE ACCESS
JF - IEEE ACCESS
SN - 2169-3536
ER -