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Wen J.,Jilin University | Wen J.,The Key Laboratory of Complex Condition Drilling and Mining Technology of Ministry of Land and Resources | Chen C.,Jilin University | Chen C.,The Key Laboratory of Complex Condition Drilling and Mining Technology of Ministry of Land and Resources
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | Year: 2013

Through the study on the series of problems of hot melt in the drilling process, the mathematical models were established, and by calculating the relevant parameters in hot melt drilling experiment, the corresponding analytical solutions was obtained, and its own laws and the produced reasons was analysed, the variation of the calculation results were basically the same with the actual process. It is shown that the constructed mathematical model is reliable. In addition, the study on the glassy crust formation around the hole wall in the process of the hot melt drilling was conducted and the hard shell thickness calculated reliable empirical formula was given. Genetic algorithm and BP algorithm integration were used to build the GA-BP neural network to predict the data of the hot melt and the influence on hot melt drilling speed of the effective thermal power and the WOB applied to the subterrene. The results show that the mean square errors based on GA-BP neural network to predict the velocity distribution of hot melt in annular clearance, the role of the hot melt pressure on the outer surface of the subterrene, the friction of the hot melt in the subterrene, the influence on the hot melt drilling speed of the effective thermal power on the subterrene, the influence on the hot melt drilling speed of the impose different WOB on the subterrene in different stratas are 0.449×10-6, 0.005 6, 0.001 1, 0.104, 0.136, respectively, which are lower than those based on BP neural network. And the computing time of GA-BP network are 7, 15, 2, 9 and 15 s, which are shorter than BP network.


Chen C.,Jilin University | Chen C.,State Key Laboratory of Superhard Materials | Chen C.,The Key Laboratory of complex condition Drilling and Mining Technology of Ministry of Land and Resources | Wen J.,Jilin University | And 6 more authors.
Jilin Daxue Xuebao (Diqiu Kexue Ban)/Journal of Jilin University (Earth Science Edition) | Year: 2012

Hot melt drilling is a new method for multi-purpose rock breacking. 70% to 80% of the thermal energy concentrated in the bottom of the hole when drilling, the heat generated by the bit as the heat source in the hot melt drilling process, the temperature field of the fuser and the rock and soil around it will directly determine the ability of hot melt drilling. Therefore it is extremely important to study the temperature field. In order to reveal the changes and the extent of the surrounding soil temperature field in the hot melt drilling, the steady and unsteady heat conduction equation of the soil temperature around the fuser along the radial distribution was established. Through measuring the soil temperature around the fuser in the experimental table of the hot melt drilling, the measurement results with theoretical calculation results are consistent, indicating that the establishment of the thermal conduction mathematical model is reliable. Using experimentally measured data to construct the GA-BP neural network to predict the temperature field of the surrounding soil in the hot melt drilling process, the results showed that the mean square error based on GA-BP neural network to predict the temperature change over time in sand, wet clay and wet sand were 0.027, 0.024, 0.011, which were lower than that based on BP neural network. The computing time of GA-BP network are 9 seconds, 4 seconds and 11 seconds, which were shorter than BP network. Using of finite element software ANSYS to simulate the temperature field in the hot melt drilling process to get the temperature field nephogram in the different soil with the time changes in the hot melt drilling experiment, the simulation results with the experimental data can be fit well.

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