In this paper,we implement three scales of fracture integrated prediction study by classifying it to macro-（ 1/4/λ）,meso-（ 1/100λ and 1/4λ） and micro-（ 1/100λ） scales.Based on the multi-scales rock physics ...In this paper,we implement three scales of fracture integrated prediction study by classifying it to macro-（ 1/4/λ）,meso-（ 1/100λ and 1/4λ） and micro-（ 1/100λ） scales.Based on the multi-scales rock physics modelling technique,the seismic azimuthal anisotropy characteristic is analyzed for distinguishing the fractures of meso-scale.Furthermore,by integrating geological core fracture description,image well-logging fracture interpretation,seismic attributes macro-scale fracture prediction and core slice micro-scale fracture characterization,an comprehensive multi-scale fracture prediction methodology and technique workflow are proposed by using geology,well-logging and seismic multi-attributes.Firstly,utilizing the geology core slice observation（Fractures description） and image well-logging data interpretation results,the main governing factors of fracture development are obtained,and then the control factors of the development of regional macro-scale fractures are carried out via modelling of the tectonic stress field.For the meso-scale fracture description,the poststack geometric attributes are used to describe the macro-scale fracture as well,the prestack attenuation seismic attribute is used to predict the meso-scale fracture.Finally,by combining lithological statistic inversion with superposed results of faults,the relationship of the meso-scale fractures,lithology and faults can be reasonably interpreted and the cause of meso-scale fractures can be verified.The micro-scale fracture description is mainly implemented by using the electron microscope scanning of cores.Therefore,the development of fractures in reservoirs is assessed by valuating three classes of fracture prediction results.An integrated fracture prediction application to a real field in Sichuan basin,where limestone reservoir fractures developed,is implemented.The application results in the study area indicates that the proposed multi-scales integrated fracture prediction method and the technique procedureare able to deal with the strong heterogeneity and multi-scales problems in fracture prediction.Moreover,the multi-scale fracture prediction technique integrated with geology,well-logging and seismic multi-information can help improve the reservoir characterization and sweet-spots prediction for the fractured hydrocarbon reservoirs.展开更多
Solid-state lithium battery(SSLB)is considered as one of the promising candidates for next-generation power batteries due to high safety,unprecedented energy density and favorable adaptability to high pression and tem...Solid-state lithium battery(SSLB)is considered as one of the promising candidates for next-generation power batteries due to high safety,unprecedented energy density and favorable adaptability to high pression and temperature.However,the system of solid electrolyte(SE),as one of the most important components in SSLB,is usually plagued by clumsy ionic transport,leading to poor rate performance of the SSLBs.Herein,a unique perspective is proposed to re-examine the ion-transport behavior in lithium conductors by tracing Liþat multiscale,including microscopic,mesoscopic and macroscopic scales.The multi-scale ion-transport mechanisms and corresponding characterization techniques are analyzed in depth.Furthermore,some strategies of structure design to improve ion-transport kinetics at corresponding scales are elaborated systematically,involving the modulation of microscopic homogeneous structure,mesoscopic heterogeneous structure and macroscopic structures,etc.The proposed generalized rules for SEs are expected to construct a close link from mechanism-structure-characterization to high performances for SSLBs.展开更多
In order to characterize different damage modes, real-time detection of the tensile cracking process for AZ31 magnesium alloy was performed using acoustic emission （AE） technique. Results showed that elastic deforma...In order to characterize different damage modes, real-time detection of the tensile cracking process for AZ31 magnesium alloy was performed using acoustic emission （AE） technique. Results showed that elastic deformation, plastic deformation, microcracking, stable and unstable propagation occurred during crack damage. Four damage modes were determined using AE multiparameter analysis. Dislocation motion signals with amplitudes 〈70 dB and twinning signals with 70-100 dB were found. Microcrack signal energy was concentrated from 2400 aJ to 4100 aJ, mainly at a rise time of less than 800 gs. A stable crack propagation signal had high peak to counts in the 20 to 50 range, whereas its ring count was in the 20 to 2000 range. The average frequency of unstable propagation signals was approximately 100 kHz, with duration from 2000 gs to 10s gs. The damage mechanisms and AE resources from different crack propagation steps were discussed. Various damage modes could be characterized by different AE signal parameters when they appeared simultaneously during crack propagation.展开更多
Previous studies on river health evaluation mainly focused on characterizations at a river-corridor scale and ignored the complex interactions between the river ecosystem and other components of the river basin.Based ...Previous studies on river health evaluation mainly focused on characterizations at a river-corridor scale and ignored the complex interactions between the river ecosystem and other components of the river basin.Based on the consideration of the interactions among rivers,associated river basin and habitats,an assessment framework with multi-scale indicators was developed.An index system divided among these three scales to characterize the health of river ecosystems in China’s Liao River Basin was established.Set pair analysis was applied to integrate the multi-scale indicators and determine the health classes.The evaluation results indicated that the rivers in the western and eastern zones of the Liao River were classified as sick,and rivers in the main stream of the Liao and Huntai rivers were classified as unhealthy.An excessive level of disturbances,such as large pollution loads and dense construction of water conservation projects within the river basin,were the main causes of the river health deterioration.展开更多
A fast knowledge based recognition method of the harbor target in large gray remote-sensing image is presented. First, the distributed features and the inherent feature are analyzed according to the knowledge of harbo...A fast knowledge based recognition method of the harbor target in large gray remote-sensing image is presented. First, the distributed features and the inherent feature are analyzed according to the knowledge of harbor targets; then, two methods for extracting the candidate region of harbor are devised in accordance with different sizes of the harbors; after that, thresholds are used to segment the land and the sea with strategies of the segmentation error control; finally, harbor recognition is implemented according to its inherent character （semi-closed region of seawater）.展开更多
Granular size segregation is an inevitable phenomenon in both natural and industrial processes.To understand the underlying mechanisms and develop effective optimization strategies,it is essential to employ robust met...Granular size segregation is an inevitable phenomenon in both natural and industrial processes.To understand the underlying mechanisms and develop effective optimization strategies,it is essential to employ robust methodologies that can quantitatively characterize and evaluate size segregation behaviors in granular systems.This review critically examines a wide variety of state-of-the-art methodologies from recent studies to quantify granular size segregation.The features of these methodologies are extracted and organized into a comprehensive framework.Four key questions are thoroughly discussed:evaluation criteria for identical segregation states,sensitivity to sample size,the influence of sampling division pattern,and the capability of handling multiple-component system.Finally,we provide an outlook on the future development of advanced and effective methodologies for granular size segregation characterization.展开更多
Machine learning(ML)is transforming material research and development(R&D),driving a fundamental shift from experience-driven approaches to data-driven frameworks.This review systematically highlights the transfor...Machine learning(ML)is transforming material research and development(R&D),driving a fundamental shift from experience-driven approaches to data-driven frameworks.This review systematically highlights the transformative breakthroughs brought by machine learning throughout the entire process of intelligent material innovation.And it provides a comprehensive full chain analysis,from atomic scale design to macroscopic applications,emphasizing multi-scale modeling that combines physical mechanisms with data-driven methods,running through all stages of material innovation.In the design phase,ML promotes performance-oriented structural optimization through inverse design systems and generative models.For synthesis and processing,closed-loop autonomous systems and green controllable synthesis strategies significantly improve efficiency and sustainability.In terms of advanced representation,ML-powered techniques can help proactively tackle key challenges of complex structures.Performance prediction models enable precise correlations between material properties and extreme properties(such as auxiliary structures)by revealing catalytic descriptors and decoding biological interface mechanisms.Ultimately,these ML-driven advancements are unlocking practical applications in key fields,such as energy,biomedicine,environmental remediation,and structural engineering.This article aims to provide a comprehensive technological roadmap for the next generation of smart material development by integrating cross scale insights and autonomous strategies,and to outline future directions for this rapidly developing paradigm.展开更多
基金supported by the national oil and gas major project(No.2011ZX05019-008)National Natural Science Foundation of China(No.41574108 and U1262208)presented at the Exploration Geophysics Symposium 2015 of the EAGE Local Chapter China
文摘In this paper,we implement three scales of fracture integrated prediction study by classifying it to macro-（ 1/4/λ）,meso-（ 1/100λ and 1/4λ） and micro-（ 1/100λ） scales.Based on the multi-scales rock physics modelling technique,the seismic azimuthal anisotropy characteristic is analyzed for distinguishing the fractures of meso-scale.Furthermore,by integrating geological core fracture description,image well-logging fracture interpretation,seismic attributes macro-scale fracture prediction and core slice micro-scale fracture characterization,an comprehensive multi-scale fracture prediction methodology and technique workflow are proposed by using geology,well-logging and seismic multi-attributes.Firstly,utilizing the geology core slice observation（Fractures description） and image well-logging data interpretation results,the main governing factors of fracture development are obtained,and then the control factors of the development of regional macro-scale fractures are carried out via modelling of the tectonic stress field.For the meso-scale fracture description,the poststack geometric attributes are used to describe the macro-scale fracture as well,the prestack attenuation seismic attribute is used to predict the meso-scale fracture.Finally,by combining lithological statistic inversion with superposed results of faults,the relationship of the meso-scale fractures,lithology and faults can be reasonably interpreted and the cause of meso-scale fractures can be verified.The micro-scale fracture description is mainly implemented by using the electron microscope scanning of cores.Therefore,the development of fractures in reservoirs is assessed by valuating three classes of fracture prediction results.An integrated fracture prediction application to a real field in Sichuan basin,where limestone reservoir fractures developed,is implemented.The application results in the study area indicates that the proposed multi-scales integrated fracture prediction method and the technique procedureare able to deal with the strong heterogeneity and multi-scales problems in fracture prediction.Moreover,the multi-scale fracture prediction technique integrated with geology,well-logging and seismic multi-information can help improve the reservoir characterization and sweet-spots prediction for the fractured hydrocarbon reservoirs.
基金supported by the Ministry of Science and Technology of the People's Republic of China(2022YFB2402200 and 2019YFA0705600)the National Natural Science Foundation of China(22121005,22005155,52072186,52203066,51673148 and 51678411)+5 种基金the Fundamental Research Funds for the Central Universities of China(63233017,63231002 and 63231198)the Science and Technology Plans of Tianjin,China(19PTSYJC00010)the China Postdoctoral Science Foundation Grant(2023M742135)the National Innovation and Entrepreneurship Training Program for College Students,China(202110058017)Tianjin Natural Science Foundation(23JCYBJC00660)Tianjin Enterprise Science and Technology Commissioner Project(23YDTPJC00490).
文摘Solid-state lithium battery(SSLB)is considered as one of the promising candidates for next-generation power batteries due to high safety,unprecedented energy density and favorable adaptability to high pression and temperature.However,the system of solid electrolyte(SE),as one of the most important components in SSLB,is usually plagued by clumsy ionic transport,leading to poor rate performance of the SSLBs.Herein,a unique perspective is proposed to re-examine the ion-transport behavior in lithium conductors by tracing Liþat multiscale,including microscopic,mesoscopic and macroscopic scales.The multi-scale ion-transport mechanisms and corresponding characterization techniques are analyzed in depth.Furthermore,some strategies of structure design to improve ion-transport kinetics at corresponding scales are elaborated systematically,involving the modulation of microscopic homogeneous structure,mesoscopic heterogeneous structure and macroscopic structures,etc.The proposed generalized rules for SEs are expected to construct a close link from mechanism-structure-characterization to high performances for SSLBs.
基金Project(2213K3170027) supported by the Shenzhen Polytechnic Project Fund,China
文摘In order to characterize different damage modes, real-time detection of the tensile cracking process for AZ31 magnesium alloy was performed using acoustic emission （AE） technique. Results showed that elastic deformation, plastic deformation, microcracking, stable and unstable propagation occurred during crack damage. Four damage modes were determined using AE multiparameter analysis. Dislocation motion signals with amplitudes 〈70 dB and twinning signals with 70-100 dB were found. Microcrack signal energy was concentrated from 2400 aJ to 4100 aJ, mainly at a rise time of less than 800 gs. A stable crack propagation signal had high peak to counts in the 20 to 50 range, whereas its ring count was in the 20 to 2000 range. The average frequency of unstable propagation signals was approximately 100 kHz, with duration from 2000 gs to 10s gs. The damage mechanisms and AE resources from different crack propagation steps were discussed. Various damage modes could be characterized by different AE signal parameters when they appeared simultaneously during crack propagation.
基金This research was supported by the National Natural Science Foundation of China(Grant Nos.50979006 and 50939001)National Water Pollution Control Technology Major Projects(Grant No.2008ZX07526-001 and 2008ZX07209-009).
文摘Previous studies on river health evaluation mainly focused on characterizations at a river-corridor scale and ignored the complex interactions between the river ecosystem and other components of the river basin.Based on the consideration of the interactions among rivers,associated river basin and habitats,an assessment framework with multi-scale indicators was developed.An index system divided among these three scales to characterize the health of river ecosystems in China’s Liao River Basin was established.Set pair analysis was applied to integrate the multi-scale indicators and determine the health classes.The evaluation results indicated that the rivers in the western and eastern zones of the Liao River were classified as sick,and rivers in the main stream of the Liao and Huntai rivers were classified as unhealthy.An excessive level of disturbances,such as large pollution loads and dense construction of water conservation projects within the river basin,were the main causes of the river health deterioration.
文摘A fast knowledge based recognition method of the harbor target in large gray remote-sensing image is presented. First, the distributed features and the inherent feature are analyzed according to the knowledge of harbor targets; then, two methods for extracting the candidate region of harbor are devised in accordance with different sizes of the harbors; after that, thresholds are used to segment the land and the sea with strategies of the segmentation error control; finally, harbor recognition is implemented according to its inherent character （semi-closed region of seawater）.
基金support from the Natural Science Foundation of Chongqing,China (grant Nos.Cstc2021ycjhbgzxm0165,CSTB2023NSCQ-MSX0514)the Fundamental Research Funds for the Central Universities (grant No.2020CDJQY-A005).
文摘Granular size segregation is an inevitable phenomenon in both natural and industrial processes.To understand the underlying mechanisms and develop effective optimization strategies,it is essential to employ robust methodologies that can quantitatively characterize and evaluate size segregation behaviors in granular systems.This review critically examines a wide variety of state-of-the-art methodologies from recent studies to quantify granular size segregation.The features of these methodologies are extracted and organized into a comprehensive framework.Four key questions are thoroughly discussed:evaluation criteria for identical segregation states,sensitivity to sample size,the influence of sampling division pattern,and the capability of handling multiple-component system.Finally,we provide an outlook on the future development of advanced and effective methodologies for granular size segregation characterization.
基金Foundation of China(Nos.NSFC-22509178 and 52371240)the Changjiang Scholars Program of the Ministry of Education(No.Q2018270)+1 种基金China Postdoctoral Science Foundation(No.2025M770225)Yangzhou Innovation Capability Enhancement Program(No.YZ2022170).
文摘Machine learning(ML)is transforming material research and development(R&D),driving a fundamental shift from experience-driven approaches to data-driven frameworks.This review systematically highlights the transformative breakthroughs brought by machine learning throughout the entire process of intelligent material innovation.And it provides a comprehensive full chain analysis,from atomic scale design to macroscopic applications,emphasizing multi-scale modeling that combines physical mechanisms with data-driven methods,running through all stages of material innovation.In the design phase,ML promotes performance-oriented structural optimization through inverse design systems and generative models.For synthesis and processing,closed-loop autonomous systems and green controllable synthesis strategies significantly improve efficiency and sustainability.In terms of advanced representation,ML-powered techniques can help proactively tackle key challenges of complex structures.Performance prediction models enable precise correlations between material properties and extreme properties(such as auxiliary structures)by revealing catalytic descriptors and decoding biological interface mechanisms.Ultimately,these ML-driven advancements are unlocking practical applications in key fields,such as energy,biomedicine,environmental remediation,and structural engineering.This article aims to provide a comprehensive technological roadmap for the next generation of smart material development by integrating cross scale insights and autonomous strategies,and to outline future directions for this rapidly developing paradigm.
