Acquiring pristine deep lunar regolith cores with appropriate drilling tools is crucial for deciphering the lunar geological history.Conventional thick-walled drill bits are inherently limited in obtaining deep lunar ...Acquiring pristine deep lunar regolith cores with appropriate drilling tools is crucial for deciphering the lunar geological history.Conventional thick-walled drill bits are inherently limited in obtaining deep lunar regolith samples,whereas thin-walled coring bits offer a promising solution for lunar deep drilling.To support future lunar deep exploration missions,this study systematically investigates the failure mechanisms of lunar regolith induced by thin-walled drilling tools.Firstly,five thin-walled bit configurations were designed and evaluated based on drilling load,coring efficiency,and disturbance minimization,with Bit D demonstrating optimal overall performance.And the interaction mechanisms between differently configured coring bits and large-particle lunar regolith were elucidated.Coring experiments under critical drilling parameters revealed an operational window for the feed-to-rotation ratio(FRR of 2.0–2.5),effectively balancing drilling load and core recovery rate.Furthermore,a novel theoretical framework was developed to characterize dynamic drilling load parameters,supported by experimental validation.Based on these findings,practical strategies are proposed to mitigate drilling-induced disturbances,including parameter optimization and bit structural improvements.This research could provide valuable insights for designing advanced lunar deep drilling tools and developing drilling procedures.展开更多
在风光等清洁能源渗透率及能源低碳化需求不断提高的背景下,如何精确模拟新能源出力不确定性及引导负荷侧柔性资源参与需求响应显得尤为重要。针对上述问题,本文提出一种计及源荷不确定性及阶梯型碳交易的虚拟电厂优化调度模型。首先,...在风光等清洁能源渗透率及能源低碳化需求不断提高的背景下,如何精确模拟新能源出力不确定性及引导负荷侧柔性资源参与需求响应显得尤为重要。针对上述问题,本文提出一种计及源荷不确定性及阶梯型碳交易的虚拟电厂优化调度模型。首先,源侧基于Frank-Copula函数建立风光出力联合概率分布模型,采样约简得风光出力典型场景。其次,在多能耦合虚拟电厂中引入碳捕集与封存(carbon capture and storage,CCS)设备,降低碳排放,荷侧建立考虑柔性用户用能满意度的需求响应模型,以提升风光消纳。同时,引入阶梯型碳交易机制,建立源荷协同优化及低碳性改造的VPP日前优化调度模型。然后,采用梯形隶属度函数将多目标优化问题模糊化为单目标优化问题,调用CPLEX求解器求解。最后,通过算例分析验证本文方法的有效性。展开更多
基金supported by the National Natural Science Foundation of China(Nos.52225403,52434004,and 52404365)the National Key Research and Development Program of China(No.2023YFF0615404)the Scientific Instrument Developing Project of Shenzhen University.
文摘Acquiring pristine deep lunar regolith cores with appropriate drilling tools is crucial for deciphering the lunar geological history.Conventional thick-walled drill bits are inherently limited in obtaining deep lunar regolith samples,whereas thin-walled coring bits offer a promising solution for lunar deep drilling.To support future lunar deep exploration missions,this study systematically investigates the failure mechanisms of lunar regolith induced by thin-walled drilling tools.Firstly,five thin-walled bit configurations were designed and evaluated based on drilling load,coring efficiency,and disturbance minimization,with Bit D demonstrating optimal overall performance.And the interaction mechanisms between differently configured coring bits and large-particle lunar regolith were elucidated.Coring experiments under critical drilling parameters revealed an operational window for the feed-to-rotation ratio(FRR of 2.0–2.5),effectively balancing drilling load and core recovery rate.Furthermore,a novel theoretical framework was developed to characterize dynamic drilling load parameters,supported by experimental validation.Based on these findings,practical strategies are proposed to mitigate drilling-induced disturbances,including parameter optimization and bit structural improvements.This research could provide valuable insights for designing advanced lunar deep drilling tools and developing drilling procedures.
文摘在风光等清洁能源渗透率及能源低碳化需求不断提高的背景下,如何精确模拟新能源出力不确定性及引导负荷侧柔性资源参与需求响应显得尤为重要。针对上述问题,本文提出一种计及源荷不确定性及阶梯型碳交易的虚拟电厂优化调度模型。首先,源侧基于Frank-Copula函数建立风光出力联合概率分布模型,采样约简得风光出力典型场景。其次,在多能耦合虚拟电厂中引入碳捕集与封存(carbon capture and storage,CCS)设备,降低碳排放,荷侧建立考虑柔性用户用能满意度的需求响应模型,以提升风光消纳。同时,引入阶梯型碳交易机制,建立源荷协同优化及低碳性改造的VPP日前优化调度模型。然后,采用梯形隶属度函数将多目标优化问题模糊化为单目标优化问题,调用CPLEX求解器求解。最后,通过算例分析验证本文方法的有效性。
