Creep Model Analysis of Rock Salt Cavern Under Normal Operations

2013 ◽  
Vol 10 (12) ◽  
pp. 3815-3823
Author(s):  
Xinrong Liu
2016 ◽  
Vol 50 (1) ◽  
pp. 139-157 ◽  
Author(s):  
Elham Mahmoudi ◽  
Kavan Khaledi ◽  
Shorash Miro ◽  
Diethard König ◽  
Tom Schanz

2012 ◽  
Vol 500 ◽  
pp. 218-225
Author(s):  
Jian Zhong Chen ◽  
Ye Hua Sheng ◽  
Yong Zhi Wang

After analyzing the characteristics of rock salt and the feasibility and importance of using underground rock salt cavern as energy stockpiles, this paper explains in detail about the principle of sonar detection technology in cavern measuring and its data organization. This paper studies the technology of three-dimensional surface topological reconstruction of underground rock salt cavern based on sonar detection data with half-edge data structure and boundary representation models. Meanwhile, by conducting validity check for constructed surface model and repair of possible defects, the paper finds correct and effective three-dimensional surface models of underground rock salt cavern, and provides effective data base for the stability analysis, creep analysis and other numerical simulations of underground rock salt cavern. This method, with important economic and social significance, can provide a scientific basis and technological support for the construction of caverns, especially for the safe and rational use of underground rock salt cavern.


2016 ◽  
Vol 97 ◽  
pp. 478-485
Author(s):  
A.v. Blumenthal ◽  
E. Mahmoudi ◽  
K. Khaledi ◽  
D. König ◽  
T. Schanz

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Xinrong Liu ◽  
Xin Yang ◽  
Zuliang Zhong ◽  
Ninghui Liang ◽  
Junbao Wang ◽  
...  

Utilizing deep rock salt cavern is not only a widely recognized energy reserve method but also a key development direction for implementing the energy strategic reserve plan. And rock salt cavern adopts solution mining techniques to realize building cavity. In view of this, the paper, based on the dissolving properties of rock salt, being simplified and hypothesized the dynamic dissolving process of rock salt, combined conditions between dissolution effect and seepage effect in establishing dynamic dissolving models of rock salt under different flow quantities. Devices were also designed to test the dynamic dissolving process for rock salt samples under different flow quantities and then utilized the finite-difference method to find the numerical solution of the dynamic dissolving model. The artificial intelligence algorithm, Particle Swarm Optimization algorithm (PSO), was finally introduced to conduct inverse analysis of parameters on the established model, whose calculation results coincide with the experimental data.


Author(s):  
Xinming Zhao ◽  
Qianwen Wang ◽  
Yifei Gao ◽  
Tao Yang ◽  
Lichao He ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hao Tang ◽  
Dongpo Wang ◽  
Zhao Duan

Creep models are mainly used to describe the rheological behaviour of geotechnical materials. An important research focus for studying creep in geotechnical materials is the development of a model with few parameters and good simulation performance. Hence, in this study, by replacing the Newtonian dashpot and spring in the classical Maxwell model with fractional and elastic-plastic elements, a new Maxwell creep model based on fractional derivatives and continuum damage mechanics was developed. One- and three-dimensional (1D/3D) creep equations of the new Maxwell creep model were derived. The 1D creep equation of the new model was used to fit existing creep data of rock salt, and the 3D creep equation was used to fit the creep data of remolded loess. The model curves matched the creep data very well, showing considerably higher accuracy than other models. Furthermore, a sensitivity study was carried out, showing the effects of the fractional derivative order β and exponent α on the creep strain of rock salt. This new model is simple with few parameters and can effectively simulate the complete creep behaviour of geotechnical materials.


2022 ◽  
Vol 48 ◽  
pp. 103951
Author(s):  
Junbao Wang ◽  
Qiang Zhang ◽  
Zhanping Song ◽  
Shijin Feng ◽  
Yuwei Zhang

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