连续油管作业过程中经历复杂工况,现有研究方法大多仅考虑了单一工况,使得连续油管的疲劳寿命难以准确预测。为提高连续油管疲劳寿命预测的准确性,通过考虑连续油管作业在多工况条件下的特点,依据Manson-Coffin模型、Miner法则及中性层...连续油管作业过程中经历复杂工况,现有研究方法大多仅考虑了单一工况,使得连续油管的疲劳寿命难以准确预测。为提高连续油管疲劳寿命预测的准确性,通过考虑连续油管作业在多工况条件下的特点,依据Manson-Coffin模型、Miner法则及中性层假设,建立含磨损、冲砂、疲劳损伤的连续油管疲劳寿命判断依据;开展连续油管疲劳性能试验,获得CT110连续油管的疲劳寿命模型关键参数;基于连续油管的使用档案,进行了某使用日历下连续油管疲劳寿命算例分析,形成多工况连续油管疲劳寿命预测方法。结果表明:在给定使用日历下,Ø50.8 mm×4.4 mm CT110连续油管的理论起下作业次数为11次,进行10次作业后连续油管极限载荷为645 MPa,与未使用时相比损失了17%;随着作业次数增多,连续油管寿命明显降低,其截面极限载荷呈下降趋势。本文所研究的多工况连续油管疲劳寿命预测方法为连续油管在实际使用过程中疲劳寿命预测及降级使用提供技术指导。展开更多
The process parameters of pulsed tungsten inert gas(PTIG)have a significant infuence on the forma-tion quality,mechanical properties and corrosion resistance of the weld overlay.The PTIG was utilized to deposit Incone...The process parameters of pulsed tungsten inert gas(PTIG)have a significant infuence on the forma-tion quality,mechanical properties and corrosion resistance of the weld overlay.The PTIG was utilized to deposit Inconel 625 clads with various combinations of the process parameters,which were determined by the central composite design(CCD)method.Based on the experimental results,the relationship between process parameters of PTIG and formation quality of the Inconel 625 clads was established using support vector regression(SVR)with different kernel functions,including polynomial kernel function,radial basis function(RBF)kernel function,and sigmoid kernel function.The results indicate that the kernel functions have a great influence on the prediction of height,width and dilution.The models with RBF kernel function feature the best goodness of fitting and the most accurate against the other SVR models for estimating the height and the dilution.However,the model with polynomial kernel function is superior to the other SVR models for predicting the width.Meanwhile,the prediction performance of the SVR models was compared with the general regression analysis.The results demonstrate that the optimized SVR model is much better than the general regression model in the prediction performance.展开更多
文摘连续油管作业过程中经历复杂工况,现有研究方法大多仅考虑了单一工况,使得连续油管的疲劳寿命难以准确预测。为提高连续油管疲劳寿命预测的准确性,通过考虑连续油管作业在多工况条件下的特点,依据Manson-Coffin模型、Miner法则及中性层假设,建立含磨损、冲砂、疲劳损伤的连续油管疲劳寿命判断依据;开展连续油管疲劳性能试验,获得CT110连续油管的疲劳寿命模型关键参数;基于连续油管的使用档案,进行了某使用日历下连续油管疲劳寿命算例分析,形成多工况连续油管疲劳寿命预测方法。结果表明:在给定使用日历下,Ø50.8 mm×4.4 mm CT110连续油管的理论起下作业次数为11次,进行10次作业后连续油管极限载荷为645 MPa,与未使用时相比损失了17%;随着作业次数增多,连续油管寿命明显降低,其截面极限载荷呈下降趋势。本文所研究的多工况连续油管疲劳寿命预测方法为连续油管在实际使用过程中疲劳寿命预测及降级使用提供技术指导。
基金the Natural Science Basic Research Plan in Shaanxi Province of China(Nos.2020JQ-780 and 2017JQ5106)the Open Foundation of Chongqing En-gineering Technology Research Center for Light Alloy Materials and Procesling(No.GCZX202001)the Young Teacher Research Project of Xi'an Shiyou Uni-versity(No.0104-134010025)。
文摘The process parameters of pulsed tungsten inert gas(PTIG)have a significant infuence on the forma-tion quality,mechanical properties and corrosion resistance of the weld overlay.The PTIG was utilized to deposit Inconel 625 clads with various combinations of the process parameters,which were determined by the central composite design(CCD)method.Based on the experimental results,the relationship between process parameters of PTIG and formation quality of the Inconel 625 clads was established using support vector regression(SVR)with different kernel functions,including polynomial kernel function,radial basis function(RBF)kernel function,and sigmoid kernel function.The results indicate that the kernel functions have a great influence on the prediction of height,width and dilution.The models with RBF kernel function feature the best goodness of fitting and the most accurate against the other SVR models for estimating the height and the dilution.However,the model with polynomial kernel function is superior to the other SVR models for predicting the width.Meanwhile,the prediction performance of the SVR models was compared with the general regression analysis.The results demonstrate that the optimized SVR model is much better than the general regression model in the prediction performance.
