Fast beam migration(FBM),characterized by its super-high efficiency in velocity model building,consists of three main steps:beam forming,beam propagation,and image forming.The super-high efficiency is achieved by beam...Fast beam migration(FBM),characterized by its super-high efficiency in velocity model building,consists of three main steps:beam forming,beam propagation,and image forming.The super-high efficiency is achieved by beam forming,as it needs only to be performed once for one dataset and is independent of velocity,and the other two steps take relatively little time.However,compared to the beam-propagation and image-forming steps,the beam-forming step is still quite time-consuming owing to the high-dimensional computing problem of estimating the source and receiver slope orientation of a beam.Furthermore,previous methods for estimating the source and receiver slope orientation of a beam struggled to deal with intersecting events,leading to poor imaging results for complex subsurface structures,such as unconformities or faults,where events often intersect.We propose the use of a three-step multimodal optimization method based on the neighborhood crowding differential evolution(NCDE)algorithm to estimate the source and receiver slope orientation of a beam during the beam-forming step,which can quickly and accurately obtain slope orientations when events intersect.We first test the three-step multimodal optimization algorithm on a 3D super-gather and provide the parameter criteria.We then apply the FBM based on the three-step multimodal optimization algorithm to the Marmousi 2 and 3D SEG/EAGE salt models.Both results demonstrate that the proposed method can image intersecting events well and that the imaging quality of complex zones is improved.We also apply the proposed method to a 2D offshore seismic dataset containing abundant intersecting events,which validates the practicality of the proposed method.展开更多
The development of fast ionic conductors to improve the performance of electrochemical devices relies on expensive high-throughput(HT)density functional theory(DFT)calculations of transport properties.Machine learning...The development of fast ionic conductors to improve the performance of electrochemical devices relies on expensive high-throughput(HT)density functional theory(DFT)calculations of transport properties.Machine learning(ML)can accelerate HT workflows but requires high-quality data to ensure accurate predictions from trained models.In this study,we introduce the LiTraj dataset,which comprises 13,000 percolation and 122,000 migration barriers,and 1700 migration trajectories,calculated for Li-ion in diverse crystal structures using empirical force fields and DFT,respectively.With LiTraj,we demonstrate that classicalMLmodels and graph neural networks(GNNs)for structureto-property prediction of percolation and migration barriers can distinguish between“fast”and“poor”ionic conductors.Furthermore,we evaluate the capability of GNN-based universal ML interatomic potentials(uMLIPs)to identify optimal Li-ion migration trajectories.Fine-tuned uMLIPs achieve near-DFT accuracy in predicting migration barriers,significantly accelerating HT screenings of new ionic conductors.展开更多
The vapor diffusion and transport resulting from steam generator tube rupture(SGTR)accidents are a major concern threatening lead-based reactor core safety.In this study,a high-parameter SGTR experimental platform and...The vapor diffusion and transport resulting from steam generator tube rupture(SGTR)accidents are a major concern threatening lead-based reactor core safety.In this study,a high-parameter SGTR experimental platform and the multi-phase multi-physics processes numerical simulation were developed to investigate the phase behavior and interaction mechanisms.This study revealed the interaction mechanisms of lead-bismuth liquid metal and water driven by flash vaporization,jet impingement boiling,and moderate boiling.The migration and evolution of the discrete phases(vapor-water mixture)were inferred from the temperature transient laws and a numerical simulation.The results revealed that the evolution of the discrete phases consists of three stages:cavity formation,flanking diffusion,and stable up-floating.The jet pressure significantly extended the disturbance period.Variations in the water temperature mainly affected the depressurization boiling process,altering the diffusion region of the discrete phases.The temperature of the liquid metal and the duration of the jet had a minimal impact on the behavior of the discrete phases.This study provides a crucial reference for constructing a complete picture of accident evolution.展开更多
基金Natural Science Foundation of China(42304128 and 42074150)the National Key Research and Development Program of China(2023YFC2906704-5 and 2023YFC370790)+1 种基金the Mount Tai Industry Leading Talent Project Special Fund Support(tscx202312018)the Jinan Science and Technology Innovation Development Plan(Social Livelihood Special Project)(202131001)。
文摘Fast beam migration(FBM),characterized by its super-high efficiency in velocity model building,consists of three main steps:beam forming,beam propagation,and image forming.The super-high efficiency is achieved by beam forming,as it needs only to be performed once for one dataset and is independent of velocity,and the other two steps take relatively little time.However,compared to the beam-propagation and image-forming steps,the beam-forming step is still quite time-consuming owing to the high-dimensional computing problem of estimating the source and receiver slope orientation of a beam.Furthermore,previous methods for estimating the source and receiver slope orientation of a beam struggled to deal with intersecting events,leading to poor imaging results for complex subsurface structures,such as unconformities or faults,where events often intersect.We propose the use of a three-step multimodal optimization method based on the neighborhood crowding differential evolution(NCDE)algorithm to estimate the source and receiver slope orientation of a beam during the beam-forming step,which can quickly and accurately obtain slope orientations when events intersect.We first test the three-step multimodal optimization algorithm on a 3D super-gather and provide the parameter criteria.We then apply the FBM based on the three-step multimodal optimization algorithm to the Marmousi 2 and 3D SEG/EAGE salt models.Both results demonstrate that the proposed method can image intersecting events well and that the imaging quality of complex zones is improved.We also apply the proposed method to a 2D offshore seismic dataset containing abundant intersecting events,which validates the practicality of the proposed method.
基金the financial support of Russian Science Foundation project No.24-73-10204.
文摘The development of fast ionic conductors to improve the performance of electrochemical devices relies on expensive high-throughput(HT)density functional theory(DFT)calculations of transport properties.Machine learning(ML)can accelerate HT workflows but requires high-quality data to ensure accurate predictions from trained models.In this study,we introduce the LiTraj dataset,which comprises 13,000 percolation and 122,000 migration barriers,and 1700 migration trajectories,calculated for Li-ion in diverse crystal structures using empirical force fields and DFT,respectively.With LiTraj,we demonstrate that classicalMLmodels and graph neural networks(GNNs)for structureto-property prediction of percolation and migration barriers can distinguish between“fast”and“poor”ionic conductors.Furthermore,we evaluate the capability of GNN-based universal ML interatomic potentials(uMLIPs)to identify optimal Li-ion migration trajectories.Fine-tuned uMLIPs achieve near-DFT accuracy in predicting migration barriers,significantly accelerating HT screenings of new ionic conductors.
基金supported by the National Natural Science Foundation of China(Nos.U20B2011 and 123B2086)。
文摘The vapor diffusion and transport resulting from steam generator tube rupture(SGTR)accidents are a major concern threatening lead-based reactor core safety.In this study,a high-parameter SGTR experimental platform and the multi-phase multi-physics processes numerical simulation were developed to investigate the phase behavior and interaction mechanisms.This study revealed the interaction mechanisms of lead-bismuth liquid metal and water driven by flash vaporization,jet impingement boiling,and moderate boiling.The migration and evolution of the discrete phases(vapor-water mixture)were inferred from the temperature transient laws and a numerical simulation.The results revealed that the evolution of the discrete phases consists of three stages:cavity formation,flanking diffusion,and stable up-floating.The jet pressure significantly extended the disturbance period.Variations in the water temperature mainly affected the depressurization boiling process,altering the diffusion region of the discrete phases.The temperature of the liquid metal and the duration of the jet had a minimal impact on the behavior of the discrete phases.This study provides a crucial reference for constructing a complete picture of accident evolution.
