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Elbow precision machining technology by abrasive flow based on direct Monte Carlo method 被引量:4
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作者 LI Jun-ye ZHU Zhi-bao +4 位作者 WANG Bin-yu ZHANG Xin-ming WANG Fei ZHAO Wei-hong XU Cheng-yu 《Journal of Central South University》 SCIE EI CAS CSCD 2020年第12期3667-3683,共17页
The investigation was carried out on the technical problems of finishing the inner surface of elbow parts and the action mechanism of particles in elbow precision machining by abrasive flow.This work was analyzed and ... The investigation was carried out on the technical problems of finishing the inner surface of elbow parts and the action mechanism of particles in elbow precision machining by abrasive flow.This work was analyzed and researched by combining theory,numerical and experimental methods.The direct simulation Monte Carlo(DSMC)method and the finite element analysis method were combined to reveal the random collision of particles during the precision machining of abrasive flow.Under different inlet velocity,volume fraction and abrasive particle size,the dynamic pressure and turbulence flow energy of abrasive flow in elbow were analyzed,and the machining mechanism of particles on the wall and the influence of different machining parameters on the precision machining quality of abrasive flow were obtained.The test results show the order of the influence of different parameters on the quality of abrasive flow precision machining and establish the optimal process parameters.The results of the surface morphology before and after the precision machining of the inner surface of the elbow are discussed,and the surface roughness Ra value is reduced from 1.125μm to 0.295μm after the precision machining of the abrasive flow.The application of DSMC method provides special insights for the development of abrasive flow technology. 展开更多
关键词 precision machining by abrasive flow direct simulation monte Carlo method abrasive particle collision processing technology
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Target reliability index for serviceability limit state of single piles 被引量:4
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作者 边晓亚 郑俊杰 +1 位作者 徐志军 章荣军 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第1期310-316,共7页
The objective is to develop an approach for the determination of the target reliability index for serviceability limit state(SLS) of single piles. This contributes to conducting the SLS reliability-based design(RBD) o... The objective is to develop an approach for the determination of the target reliability index for serviceability limit state(SLS) of single piles. This contributes to conducting the SLS reliability-based design(RBD) of piles. Based on a two-parameter,hyperbolic curve-fitting equation describing the load-settlement relation of piles, the SLS model factor is defined. Then, taking into account the uncertainties of load-settlement model, load and bearing capacity of piles, the formula for computing the SLS reliability index(βsls) is obtained using the mean value first order second moment(MVFOSM) method. Meanwhile, the limit state function for conducting the SLS reliability analysis by the Monte Carlo simulation(MCS) method is established. These two methods are finally applied to determine the SLS target reliability index. Herein, the limiting tolerable settlement(slt) is treated as a random variable. For illustration, four load test databases from South Africa are compiled again to conduct reliability analysis and present the recommended target reliability indices. The results indicate that the MVFOSM method overestimates βsls compared to that computed by the MCS method. Besides, both factor of safety(FS) and slt are key factors influencing βsls, so the combination of FS and βsls is welcome to be used for the SLS reliability analysis of piles when slt is determined. For smaller slt, pile types and soils conditions have significant influence on the SLS target reliability indices; for larger slt, slt is the major factor having influence on the SLS target reliability indices. This proves that slt is the most key parameter for the determination of the SLS target reliability index. 展开更多
关键词 single pile serviceability limit state target reliability index mean value first order second moment method monte Carlo simulation
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