期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
Enhancing the resolution of sparse rock property measurements using machine learning and random field theory 被引量:1
1
作者 Jiawei Xie Jinsong Huang +3 位作者 Fuxiang Zhang jixiang he Kaifeng Kang Yunqiang Sun 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第10期3924-3936,共13页
The travel time of rock compressional waves is an essential parameter used for estimating important rock properties,such as porosity,permeability,and lithology.Current methods,like wireline logging tests,provide broad... The travel time of rock compressional waves is an essential parameter used for estimating important rock properties,such as porosity,permeability,and lithology.Current methods,like wireline logging tests,provide broad measurements but lack finer resolution.Laboratory-based rock core measurements offer higher resolution but are resource-intensive.Conventionally,wireline logging and rock core measurements have been used independently.This study introduces a novel approach that integrates both data sources.The method leverages the detailed features from limited core data to enhance the resolution of wireline logging data.By combining machine learning with random field theory,the method allows for probabilistic predictions in regions with sparse data sampling.In this framework,12 parameters from wireline tests are used to predict trends in rock core data.The residuals are modeled using random field theory.The outcomes are high-resolution predictions that combine both the predicted trend and the probabilistic realizations of the residual.By utilizing unconditional and conditional random field theories,this method enables unconditional and conditional simulations of the underlying high-resolution rock compressional wave travel time profile and provides uncertainty estimates.This integrated approach optimizes the use of existing core and logging data.Its applicability is confirmed in an oil project in West China. 展开更多
关键词 Wireline logs Core characterization Compressional wave travel time Machine learning Random field theory
下载PDF
Energy saving design of the machining unit of hobbing machine tool with integrated optimization 被引量:2
2
作者 Yan LV Congbo LI +3 位作者 jixiang he Wei LI Xinyu LI Juan LI 《Frontiers of Mechanical Engineering》 SCIE CSCD 2022年第3期209-227,共19页
The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase.The optimization design is a practical means of energy saving and can reduce energy consumpt... The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase.The optimization design is a practical means of energy saving and can reduce energy consumption essentially.However,this issue has rarely been discussed in depth in previous research.A comprehensive function of energy consumption of the machining unit is built to address this problem.Surrogate models are established by using effective fitting methods.An integrated optimization model for reducing tool displacement and energy consumption is developed on the basis of the energy consumption function and surrogate models,and the parameters of the motor and structure are considered simultaneously.Results show that the energy consumption and tool displacement of the machining unit are reduced,indicating that energy saving is achieved and the machining accuracy is guaranteed.The influence of optimization variables on the objectives is analyzed to inform the design. 展开更多
关键词 energy saving design energy consumption machining unit integrated optimization machine tool
原文传递
上一页 1 下一页 到第
使用帮助 返回顶部