Q
Qi Zhang
Researcher at China University of Mining and Technology
Publications - 31
Citations - 263
Qi Zhang is an academic researcher from China University of Mining and Technology. The author has contributed to research in topics: Coal & Coal mining. The author has an hindex of 7, co-authored 25 publications receiving 132 citations. Previous affiliations of Qi Zhang include Central South University.
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Coupled thermo-hydro-mechanical modelling for geothermal doublet system with 3D fractal fracture
TL;DR: In this article, the authors proposed a thermo-hydro-mechanical coupling model considering the deformation of fractal fractures, which is regarded as a thin elastic layer existed in the bedrock, whoes deformation depends to the bedrock and its own mechanical properties.
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Impact of particle transfer on flow properties of crushed mudstones
Dan Ma,Xiexing Miao,Haibo Bai,Hai Pu,Zhanqing Chen,Jiang-Feng Liu,Yan-Hua Huang,Guimin Zhang,Qi Zhang +8 more
TL;DR: In this paper, a self-designed particle transfer permeability testing system was used to test the crushed mudstones' flow properties, which included fine filling particle transfer rate, porosity increase rate and permeability under the conditions of varying pore pressure, particle size mixture and compaction level (initial porosity).
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Micro-cracking behavior of shale matrix during thermal recovery: Insights from phase-field modeling
TL;DR: In this article, a variational principle was used to formulate coupled thermo-mechanical phase-field method by the variational principles to estimate the effect of thermal contribution and initial stress field on the micro-cracking behavior of shale matrix.
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Coupled thermal-gas-mechanical (TGM) model of tight sandstone gas wells
TL;DR: In this paper, a coupled thermal gas-mechanical (TGM) model was proposed on the basis of the elastic damage principal, and the coupling relationships were expressed by the governing equations.
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A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor
TL;DR: A new forecast system, which is based on a hybrid genetic algorithm (GA) with the support vector machine (SVM) algorithm, a model structure and the related parameters are proposed simultaneously on water inrush prediction and testing results show that GA has a useful ability in finding optimal parameters of a SVM model.