The phase field model can coherently address the relatively complex fracture phenomenon,such as crack nucleation,branching,deflection,etc.The model has been extensively implemented in the finite element package Abaqus...The phase field model can coherently address the relatively complex fracture phenomenon,such as crack nucleation,branching,deflection,etc.The model has been extensively implemented in the finite element package Abaqus to solve brittle fracture problems in recent studies.However,accurate numerical analysis typically requires fine meshes to model the evolving crack path effectively.A broad region must be discretized without prior knowledge of the crack path,further augmenting the computational expenses.In this proposed work,we present an automated framework utilizing a posteriori error-indicator(MISESERI)to demarcate and sufficiently refine the mesh along the anticipated crack path.This eliminates the need for manual mesh refinement based on previous experimental/computational results or heuristic judgment.The proposed Python-based framework integrates the preanalysis,sufficient mesh refinement,and subsequent phase-field model-based numerical analysis with user-defined subroutines in a single streamlined pass.The novelty of the proposed work lies in integrating Abaqus’s native error estimation and mesh refinement capability,tailored explicitly for phase-field simulations.The proposed methodology aims to reduce the computational resource requirement,thereby enhancing the efficiency of the phase-field simulations while preserving the solution accuracy,making the framework particularly advantageous for complex fracture problems where the computational/experimental results are limited or unavailable.Several benchmark numerical problems are solved to showcase the effectiveness and accuracy of the proposed approach.The numerical examples present the proposed approach’s efficacy in the case of a complex mixed-mode fracture problem.The results show significant reductions in computational resources compared to traditional phase-field methods,which is promising.展开更多
Triaxial testing serves as a fundamental method for evaluating the elastic and strength properties of rocks,crucial for developing accurate 3D geomechanical models.This paper presents a novel method for determining st...Triaxial testing serves as a fundamental method for evaluating the elastic and strength properties of rocks,crucial for developing accurate 3D geomechanical models.This paper presents a novel method for determining strength parameters by incorporating the dependence of uniaxial compressive strength(UCS)on P-wave velocity into the Hoek-Brown criterion.Additionally,a new approach is introduced to process triaxial test data efficiently using Python libraries such as SciPy,NumPy,Matplotlib,and Pandas.Furthermore,the paper addresses challenges in determining elastic parameters through triaxial testing.A Python script is developed to automate the calculation of elastic modulus and Poisson's ratio,over-coming subjectivity in selecting the linear portion of stress-strain curves.The script optimally identifies the linear region by minimizing the fit error with appropriate constraints,ensuring a more objective and standardized approach.The proposed methodologies are demonstrated using limestone specimens from Central Asian gas fields.These innovations offer faster,more reliable results,reducing error and enhancing the comparability of analyses in geomechanics,with potential applications across various geological settings.展开更多
Data are limitless. But those are usually not formed or created in our needs. Most of data providers deliver their data in Microsoft Excel spreadsheet, which is compatible with ArcGIS, the most widely used GIS （Geogr...Data are limitless. But those are usually not formed or created in our needs. Most of data providers deliver their data in Microsoft Excel spreadsheet, which is compatible with ArcGIS, the most widely used GIS （Geographic Information System） software in GIS sector. However, those table data contain much unnecessary information that do not need for a certain project. Using the raw data can increase processing times and reduce performance of geoprocessing tools. This study shows steps of how the raw data are being processed using ArcGIS ModelBuilder and Python script.展开更多
This study utilizes ML classifiers to estimate canopy density based on three decades of data (1990-2021). The Support Vector Machine (SVM) classifier outperformed other classifiers, such as Random Tree and Maximum Lik...This study utilizes ML classifiers to estimate canopy density based on three decades of data (1990-2021). The Support Vector Machine (SVM) classifier outperformed other classifiers, such as Random Tree and Maximum Likelihood. Satellite data from Landsat and Sentinel 2 was classified using a developed python model, providing an economical and time-saving approach. The accuracy of the classification was evaluated through a confusion matrix and area computation. The findings indicate a negative trend in the overall decadal change, with significant tree loss attributed to jhum cultivation, mining, and quarry activities. However, positive changes were observed in recent years due to the ban on illegal mining. The study highlights the dynamic nature of tree cover and emphasizes the need for biennial assessments using at least five time-series data. Micro-level analysis in Shallang, West Khasi hills, revealed a concerning trend of shortening jhum cycles. Automation in canopy change analysis is crucial for effective forest monitoring, providing timely information for law enforcement proposals and involving forest managers, stakeholders, and watchdog organizations.展开更多
文摘The phase field model can coherently address the relatively complex fracture phenomenon,such as crack nucleation,branching,deflection,etc.The model has been extensively implemented in the finite element package Abaqus to solve brittle fracture problems in recent studies.However,accurate numerical analysis typically requires fine meshes to model the evolving crack path effectively.A broad region must be discretized without prior knowledge of the crack path,further augmenting the computational expenses.In this proposed work,we present an automated framework utilizing a posteriori error-indicator(MISESERI)to demarcate and sufficiently refine the mesh along the anticipated crack path.This eliminates the need for manual mesh refinement based on previous experimental/computational results or heuristic judgment.The proposed Python-based framework integrates the preanalysis,sufficient mesh refinement,and subsequent phase-field model-based numerical analysis with user-defined subroutines in a single streamlined pass.The novelty of the proposed work lies in integrating Abaqus’s native error estimation and mesh refinement capability,tailored explicitly for phase-field simulations.The proposed methodology aims to reduce the computational resource requirement,thereby enhancing the efficiency of the phase-field simulations while preserving the solution accuracy,making the framework particularly advantageous for complex fracture problems where the computational/experimental results are limited or unavailable.Several benchmark numerical problems are solved to showcase the effectiveness and accuracy of the proposed approach.The numerical examples present the proposed approach’s efficacy in the case of a complex mixed-mode fracture problem.The results show significant reductions in computational resources compared to traditional phase-field methods,which is promising.
文摘Triaxial testing serves as a fundamental method for evaluating the elastic and strength properties of rocks,crucial for developing accurate 3D geomechanical models.This paper presents a novel method for determining strength parameters by incorporating the dependence of uniaxial compressive strength(UCS)on P-wave velocity into the Hoek-Brown criterion.Additionally,a new approach is introduced to process triaxial test data efficiently using Python libraries such as SciPy,NumPy,Matplotlib,and Pandas.Furthermore,the paper addresses challenges in determining elastic parameters through triaxial testing.A Python script is developed to automate the calculation of elastic modulus and Poisson's ratio,over-coming subjectivity in selecting the linear portion of stress-strain curves.The script optimally identifies the linear region by minimizing the fit error with appropriate constraints,ensuring a more objective and standardized approach.The proposed methodologies are demonstrated using limestone specimens from Central Asian gas fields.These innovations offer faster,more reliable results,reducing error and enhancing the comparability of analyses in geomechanics,with potential applications across various geological settings.
文摘Data are limitless. But those are usually not formed or created in our needs. Most of data providers deliver their data in Microsoft Excel spreadsheet, which is compatible with ArcGIS, the most widely used GIS （Geographic Information System） software in GIS sector. However, those table data contain much unnecessary information that do not need for a certain project. Using the raw data can increase processing times and reduce performance of geoprocessing tools. This study shows steps of how the raw data are being processed using ArcGIS ModelBuilder and Python script.
文摘This study utilizes ML classifiers to estimate canopy density based on three decades of data (1990-2021). The Support Vector Machine (SVM) classifier outperformed other classifiers, such as Random Tree and Maximum Likelihood. Satellite data from Landsat and Sentinel 2 was classified using a developed python model, providing an economical and time-saving approach. The accuracy of the classification was evaluated through a confusion matrix and area computation. The findings indicate a negative trend in the overall decadal change, with significant tree loss attributed to jhum cultivation, mining, and quarry activities. However, positive changes were observed in recent years due to the ban on illegal mining. The study highlights the dynamic nature of tree cover and emphasizes the need for biennial assessments using at least five time-series data. Micro-level analysis in Shallang, West Khasi hills, revealed a concerning trend of shortening jhum cycles. Automation in canopy change analysis is crucial for effective forest monitoring, providing timely information for law enforcement proposals and involving forest managers, stakeholders, and watchdog organizations.
