A unilateral self-locking mechanism(USM) was proposed to increase the tractive ability of the inchworm in-pipe robots for pipeline inspection.The USM was basically composed of a cam,a torsional spring and an axis.The ...A unilateral self-locking mechanism(USM) was proposed to increase the tractive ability of the inchworm in-pipe robots for pipeline inspection.The USM was basically composed of a cam,a torsional spring and an axis.The self-locking and virtual work principles were applied to studying the basic self-locking condition of the USM.In order to make the cooperation between the crutch and telescopic mechanism more harmonical,the unlocking time of the USM was calculated.A set of parameters were selected to build a virtual model and fabricate a prototype.Both the simulation and performance experiments were carried out in a pipe with a nominal inside diameter of 160 mm.The results show that USM enables the robot to move quickly in one way,and in the other way it helps the robot get self-locking with the pipe wall.The traction of the inchworm robot can rise to 1.2 kN,beyond the limitation of friction of 0.497 kN.展开更多
Nowadays artificial neural networkS （ANNs） with strong ability have been applied widely for prediction of non- linear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature, pressure, cr...Nowadays artificial neural networkS （ANNs） with strong ability have been applied widely for prediction of non- linear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature, pressure, critical temperature, critical pressure, density, molecular weight and acentric factor has been used for solubility predic- tion of three disperse dyes in supercritical carbon dioxide （SC-C02） and ethanol as co-solvent. It was shown how a multi-layer perceptron network can be trained to represent the solubility of disperse dyes in SC-C02. Numeric Sensitivity Analysis and Garson equation were utilized to find out the degree of effectiveness of different input variables on the efficiency of the proposed model. Results showed that our proposed ANN model has correlation coefficient, Nash-Sutcliffe model efficiency coefficient and discrepancy ratio about 0.998, 0.992, and 1.053 respectively.展开更多
基金Project(2007AA04Z256) supported by the National High-Tech Research and Development Program of China
文摘A unilateral self-locking mechanism(USM) was proposed to increase the tractive ability of the inchworm in-pipe robots for pipeline inspection.The USM was basically composed of a cam,a torsional spring and an axis.The self-locking and virtual work principles were applied to studying the basic self-locking condition of the USM.In order to make the cooperation between the crutch and telescopic mechanism more harmonical,the unlocking time of the USM was calculated.A set of parameters were selected to build a virtual model and fabricate a prototype.Both the simulation and performance experiments were carried out in a pipe with a nominal inside diameter of 160 mm.The results show that USM enables the robot to move quickly in one way,and in the other way it helps the robot get self-locking with the pipe wall.The traction of the inchworm robot can rise to 1.2 kN,beyond the limitation of friction of 0.497 kN.
文摘Nowadays artificial neural networkS （ANNs） with strong ability have been applied widely for prediction of non- linear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature, pressure, critical temperature, critical pressure, density, molecular weight and acentric factor has been used for solubility predic- tion of three disperse dyes in supercritical carbon dioxide （SC-C02） and ethanol as co-solvent. It was shown how a multi-layer perceptron network can be trained to represent the solubility of disperse dyes in SC-C02. Numeric Sensitivity Analysis and Garson equation were utilized to find out the degree of effectiveness of different input variables on the efficiency of the proposed model. Results showed that our proposed ANN model has correlation coefficient, Nash-Sutcliffe model efficiency coefficient and discrepancy ratio about 0.998, 0.992, and 1.053 respectively.
