Graphene nanopore has been extensively employed in nanoscale sensing devices due to its outstanding properties.The understanding of its mechanical properties at nanoscale is crucial for sensing improvement.In this wor...Graphene nanopore has been extensively employed in nanoscale sensing devices due to its outstanding properties.The understanding of its mechanical properties at nanoscale is crucial for sensing improvement.In this work,the mechanical proper ties of graphene nanopore are t hus investigated using the atomistic finite element met hod.Four graphene models with different pore shapes(circle(CR),horizontal rec tangle(RH),vertical rec tangle(RV)and square(SQ))in sub-5nm size,which could be successfully fabricated experimentally,have been studied here.The force normal to a pore rim is applied to mimic the impact force due to a fluid flow.As expected,the strength of nanoholed graphene is pore size dependent.Increasing pore size results in the reduction in its str ength.Comparing bet ween different pore shapes with comparable sizes,the order of pore st rength is CR>RH>RV>SQ.In addition,two different corner st rue tu res(V-like or zigzag and C-like or armchair corners)are observed,where the V-like st rue ture causes higher tensile stress.Besides,we find that the highest tensile stress is produced at the corner in all cases.This finding suggests the corners as an origin of pore fracture.The results of RH and RV highlight the impact of a direction of pore orientation on mechanical properties.Aligning a long side of a pore along the zigzag direction gains more tensile stress,while aligning on an armchair side causes a deflection.Not only the pore geometry and size,but also the pore orientation is crucial for defining the mechanical properties of nanopores.展开更多
Thermal performance of a heat exchanger duct with punched winglets(PWs)mounted on the upper duct wall has been examined for Reynolds number(Re)ranging from 4100 to 25,500.In the present experiment,two types of PWs:pun...Thermal performance of a heat exchanger duct with punched winglets(PWs)mounted on the upper duct wall has been examined for Reynolds number(Re)ranging from 4100 to 25,500.In the present experiment,two types of PWs:punched delta-and elliptical-winglets(P-DW and P-EW)with four punched-hole sizes were tested at a fixed attack angle,optimal relative pitch and height.Also,data of solid delta-and elliptical-winglets(DW and EW)were included for comparison.The investigation has shown that the P-DW yields higher thermal-performance enhancement factor(η)than the P-EW.Although the solid DW and EW with no punch have the highest heat transfer and friction loss,the PWs yield betterηthan the solid ones.For PWs,the P-DW with smaller hole size has the peak heat transfer and friction loss around 5.7 and 40 times over the smooth duct,respectively but the optimumηof 2.17 is seen for the one with a certain hole size.The PWs provideηat about 5%–8%above the solid winglets.展开更多
This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis(FELA)and a hybrid machine learning framework.The FELA simulations inves-tigat...This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis(FELA)and a hybrid machine learning framework.The FELA simulations inves-tigate the influence of the radius ratio(R/B),anisotropic ratio(re),interface roughness factor(α),and inclination angle(β).Specifically,the results reveal that increasingβsignificantly enhances Nc,especially as soil behavior approaches isotropy.Higherαimproves resistance at steeper inclinations by mobilizing greater interface shear.Nc increases with re,reflecting enhanced strength under isotropic conditions.To enhance predictive accuracy and generalization,a hybrid machine learning model was developed by integrating Extreme Gradient Boosting(XGBoost)with Genetic Algorithm(GA)and Mutation-Based Genetic Algorithm(MGA)for hyperparameter tuning.Among the models,MGA-XGBoost outperformed GA-XGBoost,achieving higher predictive accuracy(R^(2)=0.996 training,0.993 testing).Furthermore,SHAP analysis consistently identified anisotropic ratio(re)as the most influential factor in predicting uplift capacity,followed by interface roughness factor(α),inclination angle(β),and radius ratio(R/B).The proposed framework serves as a scalable decision-support tool adaptable to various soil types and foundation geometries,offering a more efficient and data-driven approach to uplift-resistant design in anisotropic cohesive soils.展开更多
Purpose-Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking.Especially in a traffic video monitoring system,vehicle detection is an essential...Purpose-Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking.Especially in a traffic video monitoring system,vehicle detection is an essential and challenging task.In the previous studies,many vehicle detection methods have been presented.These proposed approaches mostly used either motion information or characteristic information to detect vehicles.Although these methods are effective in detecting vehicles,their detection accuracy still needs to be improved.Moreover,the headlights and windshields,which are used as the vehicle features for detection in these methods,are easily obscured in some traffic conditions.The paper aims to discuss these issues.Design/methodology/approach-First,each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model.Next,the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks.These feature blocks will be used to track the moving objects frame by frame.Findings-Using the proposed method,it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement(waving trees),which has to be deemed as background.In addition,the proposed method is able to deal with different vehicle shapes such as cars,vans,and motorcycles.Originality/value-This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.展开更多
文摘Graphene nanopore has been extensively employed in nanoscale sensing devices due to its outstanding properties.The understanding of its mechanical properties at nanoscale is crucial for sensing improvement.In this work,the mechanical proper ties of graphene nanopore are t hus investigated using the atomistic finite element met hod.Four graphene models with different pore shapes(circle(CR),horizontal rec tangle(RH),vertical rec tangle(RV)and square(SQ))in sub-5nm size,which could be successfully fabricated experimentally,have been studied here.The force normal to a pore rim is applied to mimic the impact force due to a fluid flow.As expected,the strength of nanoholed graphene is pore size dependent.Increasing pore size results in the reduction in its str ength.Comparing bet ween different pore shapes with comparable sizes,the order of pore st rength is CR>RH>RV>SQ.In addition,two different corner st rue tu res(V-like or zigzag and C-like or armchair corners)are observed,where the V-like st rue ture causes higher tensile stress.Besides,we find that the highest tensile stress is produced at the corner in all cases.This finding suggests the corners as an origin of pore fracture.The results of RH and RV highlight the impact of a direction of pore orientation on mechanical properties.Aligning a long side of a pore along the zigzag direction gains more tensile stress,while aligning on an armchair side causes a deflection.Not only the pore geometry and size,but also the pore orientation is crucial for defining the mechanical properties of nanopores.
文摘Thermal performance of a heat exchanger duct with punched winglets(PWs)mounted on the upper duct wall has been examined for Reynolds number(Re)ranging from 4100 to 25,500.In the present experiment,two types of PWs:punched delta-and elliptical-winglets(P-DW and P-EW)with four punched-hole sizes were tested at a fixed attack angle,optimal relative pitch and height.Also,data of solid delta-and elliptical-winglets(DW and EW)were included for comparison.The investigation has shown that the P-DW yields higher thermal-performance enhancement factor(η)than the P-EW.Although the solid DW and EW with no punch have the highest heat transfer and friction loss,the PWs yield betterηthan the solid ones.For PWs,the P-DW with smaller hole size has the peak heat transfer and friction loss around 5.7 and 40 times over the smooth duct,respectively but the optimumηof 2.17 is seen for the one with a certain hole size.The PWs provideηat about 5%–8%above the solid winglets.
文摘This study evaluates the undrained uplift capacity of open-caisson anchors embedded in anisotropic clay using Finite Element Limit Analysis(FELA)and a hybrid machine learning framework.The FELA simulations inves-tigate the influence of the radius ratio(R/B),anisotropic ratio(re),interface roughness factor(α),and inclination angle(β).Specifically,the results reveal that increasingβsignificantly enhances Nc,especially as soil behavior approaches isotropy.Higherαimproves resistance at steeper inclinations by mobilizing greater interface shear.Nc increases with re,reflecting enhanced strength under isotropic conditions.To enhance predictive accuracy and generalization,a hybrid machine learning model was developed by integrating Extreme Gradient Boosting(XGBoost)with Genetic Algorithm(GA)and Mutation-Based Genetic Algorithm(MGA)for hyperparameter tuning.Among the models,MGA-XGBoost outperformed GA-XGBoost,achieving higher predictive accuracy(R^(2)=0.996 training,0.993 testing).Furthermore,SHAP analysis consistently identified anisotropic ratio(re)as the most influential factor in predicting uplift capacity,followed by interface roughness factor(α),inclination angle(β),and radius ratio(R/B).The proposed framework serves as a scalable decision-support tool adaptable to various soil types and foundation geometries,offering a more efficient and data-driven approach to uplift-resistant design in anisotropic cohesive soils.
文摘Purpose-Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking.Especially in a traffic video monitoring system,vehicle detection is an essential and challenging task.In the previous studies,many vehicle detection methods have been presented.These proposed approaches mostly used either motion information or characteristic information to detect vehicles.Although these methods are effective in detecting vehicles,their detection accuracy still needs to be improved.Moreover,the headlights and windshields,which are used as the vehicle features for detection in these methods,are easily obscured in some traffic conditions.The paper aims to discuss these issues.Design/methodology/approach-First,each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model.Next,the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks.These feature blocks will be used to track the moving objects frame by frame.Findings-Using the proposed method,it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement(waving trees),which has to be deemed as background.In addition,the proposed method is able to deal with different vehicle shapes such as cars,vans,and motorcycles.Originality/value-This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.
