特殊螺纹接头油套管油田验收公差的确定一直是油田和生产厂家争议的焦点。为解决特殊螺纹接头螺纹参数验收公差无科学计算方法的问题,根据尺寸链理论及统计学公差分析方法建立了一种特殊螺纹接头油套管螺纹顶径验收公差的确定方法,并以...特殊螺纹接头油套管油田验收公差的确定一直是油田和生产厂家争议的焦点。为解决特殊螺纹接头螺纹参数验收公差无科学计算方法的问题,根据尺寸链理论及统计学公差分析方法建立了一种特殊螺纹接头油套管螺纹顶径验收公差的确定方法,并以某扣型Φ88.90 mm×7.34 mm C110特殊螺纹油管为例,通过螺纹生产各阶段接箍螺纹顶径的统计分析,计算出该油管接箍螺纹顶径验收公差为±0.06 mm。研究成果为特殊螺纹接头油套管验收公差的确定提供了技术支持,对油田提高特殊螺纹接头油套管入库验收和质量控制水平具有指导意义。展开更多
Metallic glasses(MGs)have an amorphous atomic arrangement,but their structure and dynamics in the nanoscale are not homogeneous.Numerous studies have confirmed that the static and dynamic heterogeneities of MGs are vi...Metallic glasses(MGs)have an amorphous atomic arrangement,but their structure and dynamics in the nanoscale are not homogeneous.Numerous studies have confirmed that the static and dynamic heterogeneities of MGs are vital for their deformation mechanism.The“defects”in MGs are envisaged to be structurally loosely packed and dynamically active to external stimuli.To date,no definite structure-property relationship has been established to identify liquid-like“defects”in MGs.In this paper,we proposed a machine-learned“defects”from atomic trajectories rather than static structural signatures.We analyzed the atomic motion behavior at different temperatures via a k-nearest neighbors machine learning model,and quantified the dynamics of individual atoms as the machine-learned temperature.Applying this new temperature-like parameter to MGs under stress-induced flow,we can recognize which atoms respond like“liquids”to the applied loads.The evolution of liquid-like regions reveals the dynamic origin of plasticity(thermo-and acousto-plasticity)of MGs and the correlation between stress-induced heterogeneity and local environment around atoms,providing new insights into thermo-and acousto-plastic forming.展开更多
文摘特殊螺纹接头油套管油田验收公差的确定一直是油田和生产厂家争议的焦点。为解决特殊螺纹接头螺纹参数验收公差无科学计算方法的问题,根据尺寸链理论及统计学公差分析方法建立了一种特殊螺纹接头油套管螺纹顶径验收公差的确定方法,并以某扣型Φ88.90 mm×7.34 mm C110特殊螺纹油管为例,通过螺纹生产各阶段接箍螺纹顶径的统计分析,计算出该油管接箍螺纹顶径验收公差为±0.06 mm。研究成果为特殊螺纹接头油套管验收公差的确定提供了技术支持,对油田提高特殊螺纹接头油套管入库验收和质量控制水平具有指导意义。
基金supported by the National Natural Science Foundation of China(52071217)Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots。
文摘Metallic glasses(MGs)have an amorphous atomic arrangement,but their structure and dynamics in the nanoscale are not homogeneous.Numerous studies have confirmed that the static and dynamic heterogeneities of MGs are vital for their deformation mechanism.The“defects”in MGs are envisaged to be structurally loosely packed and dynamically active to external stimuli.To date,no definite structure-property relationship has been established to identify liquid-like“defects”in MGs.In this paper,we proposed a machine-learned“defects”from atomic trajectories rather than static structural signatures.We analyzed the atomic motion behavior at different temperatures via a k-nearest neighbors machine learning model,and quantified the dynamics of individual atoms as the machine-learned temperature.Applying this new temperature-like parameter to MGs under stress-induced flow,we can recognize which atoms respond like“liquids”to the applied loads.The evolution of liquid-like regions reveals the dynamic origin of plasticity(thermo-and acousto-plasticity)of MGs and the correlation between stress-induced heterogeneity and local environment around atoms,providing new insights into thermo-and acousto-plastic forming.
