Surface crack and sand inrush disaster induced by high-strength mining: example from the Shendong coal field, China

Weitao Yan,Huayang Dai,Junjie Chen 한국지질과학협의회 2018 Geosciences Journal Vol.22 No.2

Sand inrush disaster and ground destruction induced by high-strength mining in the Shendong coal field seriously threaten the normal operation of the mine and cause significant property losses and environmental disruption. The physical simulation experiment demonstrate that the roof of high-strength mining working face can be regarded as a “step beam” structure and broken by sliding instability. The vertical damage state of overlying strata is summarized into three types: slightly, severely and very severely damage. On the basis of in situ data of the working face with the mining height greater than 3 m, the prediction formulas of the caved and fractured zone heights are given. The vertical damage types of working faces 22407 and 22402 are analyzed. Owing to the sliding instability of the roof and the thin bedrock, the surface stepped crack has become widely distributed above the highstrength mining working face. The sand inrush of working face 22402 can be interpreted by the very severely damaged of overburden and the thick aeolian sand aquifer. This work can be used to improve the understanding of mining-induced disaster and establish a disaster prediction model.

A new model for predicting surface mining subsidence: the improved lognormal function model

Weitao Yan,Junjie Chen,Yueguan Yan 한국지질과학협의회 2019 Geosciences Journal Vol.23 No.1

Mining-induced problems in the coal field seriously threaten the normal operation of the mines and cause significant property losses and environmental disruption. Thus, high precision subsidence prediction is important on the processing of mining subsidence problems. In this paper, we analyzed the formation mechanism of skewed subsidence. The rock beam on the side of the gob and coal pillar presented different supporting reaction force, and the difference resulted in the asymmetric distribution of subsidence velocity, which further led to the formation of the surface skewed subsidence basin. The relationship between the wave curve and vibration curve was determined, and the skewed subsidence process of the surface point in the mining affected area was analyzed. The total duration of the initial and accelerated subsidence phases is smaller than that of the decelerated and end subsidence phases. Then, from the skewed subsidence characteristics, the skewed subsidence prediction model based on the lognormal function was built. An application example was selected to validate the feasibility and effectiveness of the proposed model. Results showed that the model has good prediction ability.

Improved Quantum-Behaved Particle Swarm Method for Optimizing Complex Thin Plate Structure

Weitao Cheng,Yixiao Qin,Jinpeng Gu,Haibiao Gao,Yue Yan,Junle Yang,Yang Chen,Shen Su,Kaiyao Yang 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.4

A large number of heavy-duty asymmetric thin-plate box girder structures exist in large equipment, and their optimization can reduce the amount of material used and increase their load-carrying capacity. A new optimization method based on the Improved Quantum-Behaved Particle Swarm Optimization method (IQBPSO) is proposed in order to efficiently solve the mathematical model for the rationalization and optimization design of structures. The penalty function and Lévy flight strategy are considered in the optimization design of the improved algorithm, thus transforming the constrained optimization problem into an unconstrained optimization problem and improving the diversity and local optimization search capability of the quantum particle swarm. A mathematical model for the optimal design of box girder section size is established with the reduction of beam cross-sectional area as the objective function and the thin plate strength, rigidity, and stability of the thin slab as the constraints. The rapid lightweight design of the thin plate box beam was achieved, resulting in a 9.6% reduction in the manufacturing cost of the thin plate box beam. The optimization results are compared with several solutions of the thin slab box beams to verify the reliability and validity of the proposed optimization method.

Crushing Characteristics on Square Tubes Under Progressive Buckling

Kaibo Yan,Sisi Lu,Pan Wang,Weitao Ni,Zhaowei Chen,Shuen Zhao,Shuguang Yao 한국강구조학회 2023 International Journal of Steel Structures Vol.23 No.1

This paper presented the research on crushing characteristics for progressive bucking square tubes under axial loading using the dimensional analysis method. According to the mechanical properties of mild steel and aluminium alloy, the finite element models of progressive buckling square tubes made up of different materials were established. Then dimensional analysis was developed to obtain the influence of inherent parameters on energy absorption characteristics of progressive buckling square tubes, and a theoretical prediction model was proposed to predict the energy absorption characteristics of progressive buckling square tubes subjected to axial loading. The theoretical prediction results were found to match well with finite element analysis and dynamic test results. Furthermore, the equivalence methods of square tubes made up of different materials and geometric parameters were discussed. The results showed that the equivalence of deformation displacement and equivalence of mean crushing force are better equivalence methods for lightweight design, and keeping a constant length and width to obtain an equivalent thickness for progressive buckling square tubes is a more stable equivalence method.

Torque Ripple Minimization of PMSM Using Parameter Optimization Based Iterative Learning Control

Changliang Xia,Weitao Deng,Tingna Shi,Yan Yan 대한전기학회 2016 Journal of Electrical Engineering & Technology Vol.11 No.2

In this paper, a parameter optimization based iterative learning control strategy is presented for permanent magnet synchronous motor control. This paper analyzes the mechanism of iterative learning control suppressing PMSM torque ripple and discusses the impact of controller parameters on steady-state and dynamic performance of the system. Based on the analysis, an optimization problem is constructed, and the expression of the optimal controller parameter is obtained to adjust the controller parameter online. Experimental research is carried out on a 5.2kW PMSM. The results show that the parameter optimization based iterative learning control proposed in this paper achieves lower torque ripple during steady-state operation and short regulating time of dynamic response, thus satisfying the demands for both steady state and dynamic performance of the speed regulating system.

A Component-Based Localization Algorithm for Sparse Sensor Networks Combining Angle and Distance Information

( Shigeng Zhang ),( Shuping Yan ),( Weitao Hu ),( Jianxin Wang ),( Kehua Guo ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.3

Location information of sensor nodes plays a critical role in many wireless sensor network (WSN) applications and protocols. Although many localization algorithms have been proposed in recent years, they usually target at dense networks and perform poorly in sparse networks. In this paper, we propose two component-based localization algorithms that can localize many more nodes in sparse networks than the state-of-the-art solution. We first develop the Basic Common nodes-based Localization Algorithm, namely BCLA, which uses both common nodes and measured distances between adjacent components to merge components. BCLA outperforms CALL, the state-of-the-art component-based localization algorithm that uses only distance measurements to merge components. In order to further improve the performance of BCLA, we further exploit the angular information among nodes to merge components, and propose the Component-based Localization with Angle and Distance information algorithm, namely CLAD. We prove the merging conditions for BCLA and CLAD, and evaluate their performance through extensive simulations. Simulations results show that, CLAD can locate more than 90 percent of nodes in a sparse network with average node degree 7.5, while CALL can locate only 78 percent of nodes in the same scenario.