In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the...In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation （BP） model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.展开更多
This study uses the digital image correlation technique to measure the crack tip displacement field at various crack lengths in U71MnG rail steel,and the interpolated continuous displacement field was obtained by fitt...This study uses the digital image correlation technique to measure the crack tip displacement field at various crack lengths in U71MnG rail steel,and the interpolated continuous displacement field was obtained by fitting with a back propagation(BP)neural network.The slip and stacking of dislocations affect crack initiation and growth,leading to changes in the crack tip field and the fatigue characteristics of crack growth.The Christopher-James-Patterson(CJP)model describes the elastic stress field around a growing fatigue crack that experiences plasticity-induced shielding.In the present work,this model is modified by including the effect of the dislocation field on the plastic zone of the crack tip and hence on the elastic field by introducing a plastic flow factorρ,which represents the amount of blunting of the crack tip.The Levenberg-Marquardt(L-M)nonlinear least squares method was used to solve for the stress intensity factors.To verify the accuracy of this modified CJP model,the theoretical and experimental plastic zone errors before and after modification were compared,and the variation trends of the stress intensity factors and the plastic flow factorρwere analysed.The results show that the CJP model,with the introduction ofρ,exhibits a good blunting trend.In the low plasticity state,the modified model can accurately describe the experimental plastic zone,and the modified stress intensity factors are more accurate,which proves the effectiveness of dislocation correction.This plastic flow correction provides a more accurate crack tip field model and improves the CJP crack growth relationship.展开更多
A back propagation （BP） neural network mathematical model was established to investigate the maneuvering control of an air cushion vehicle （ACV）. The calculation was based on four-freedom-degree model experiments ...A back propagation （BP） neural network mathematical model was established to investigate the maneuvering control of an air cushion vehicle （ACV）. The calculation was based on four-freedom-degree model experiments of hydrodynamics and aerodynamics. It is necessary for the ACV to control the velocity and the yaw rate as well as the velocity angle at the same time. The yaw rate and the velocity angle must be controlled correspondingly because of the whipping, which is a special characteristic for the ACV. The calculation results show that it is an efficient way for the ACV＇s maneuvering control by using a BP neural network to adjust PID parameters online.展开更多
文摘In order to increase drilling speed in deep complicated formations in Kela-2 gas field, Tarim Basin, Xinjiang, west China, it is important to predict the formation lithology for drilling bit optimization. Based on the conventional back propagation （BP） model, an improved BP model was proposed, with main modifications of back propagation of error, self-adapting algorithm, and activation function, also a prediction program was developed. The improved BP model was successfully applied to predicting the lithology of formations to be drilled in the Kela-2 gas field.
基金Supported by Sichuan Science and Technology Program of China (Grant No.2022YFH0075)Opening Project of State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure of China (Grant No.HJGZ2021113)Independent Research Project of State Key Laboratory of Traction Power of China (Grant No.2022TPL_T13)。
文摘This study uses the digital image correlation technique to measure the crack tip displacement field at various crack lengths in U71MnG rail steel,and the interpolated continuous displacement field was obtained by fitting with a back propagation(BP)neural network.The slip and stacking of dislocations affect crack initiation and growth,leading to changes in the crack tip field and the fatigue characteristics of crack growth.The Christopher-James-Patterson(CJP)model describes the elastic stress field around a growing fatigue crack that experiences plasticity-induced shielding.In the present work,this model is modified by including the effect of the dislocation field on the plastic zone of the crack tip and hence on the elastic field by introducing a plastic flow factorρ,which represents the amount of blunting of the crack tip.The Levenberg-Marquardt(L-M)nonlinear least squares method was used to solve for the stress intensity factors.To verify the accuracy of this modified CJP model,the theoretical and experimental plastic zone errors before and after modification were compared,and the variation trends of the stress intensity factors and the plastic flow factorρwere analysed.The results show that the CJP model,with the introduction ofρ,exhibits a good blunting trend.In the low plasticity state,the modified model can accurately describe the experimental plastic zone,and the modified stress intensity factors are more accurate,which proves the effectiveness of dislocation correction.This plastic flow correction provides a more accurate crack tip field model and improves the CJP crack growth relationship.
文摘A back propagation （BP） neural network mathematical model was established to investigate the maneuvering control of an air cushion vehicle （ACV）. The calculation was based on four-freedom-degree model experiments of hydrodynamics and aerodynamics. It is necessary for the ACV to control the velocity and the yaw rate as well as the velocity angle at the same time. The yaw rate and the velocity angle must be controlled correspondingly because of the whipping, which is a special characteristic for the ACV. The calculation results show that it is an efficient way for the ACV＇s maneuvering control by using a BP neural network to adjust PID parameters online.
