This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
Cavitation is a complex multiphase flow phenomenon with an abrupt transient phase change between the liquid and the vapor, including multiscale vortical motions. The transient cavitation dynamics is closely associated...Cavitation is a complex multiphase flow phenomenon with an abrupt transient phase change between the liquid and the vapor, including multiscale vortical motions. The transient cavitation dynamics is closely associated with the evolution of the cavitation vortex structures. The present paper investigates the cavitation vortex dynamics using different vortex identification methods, including the vorticity method, the Q criterion method, the Omega method (Ω), the method and the Rortex method. The Q criterion is an eigenvalue-based criterion, and in the Ω method, the parameter is normalized, is independent of the threshold value and in most conditions Ω= 0.52 . The Rortex method is based on an eigenvector-based criterion. Numerical simulations are conducted using the implemented compressible cavitation solver in the open source software OpenFOAM for the sheet/cloud cavitating flows around a NACA66 (mod) hydrofoil fixed at a = 6°,= 1.25 and Re = 7.96 × 10^5 . The flow is characterized by the alternate interactions of the re-entrant flow and the collapse induced shock wave. Results include the vapor structures and the vortex dynamics in the unsteady sheet/cloud cavitating flows, with emphasis on the vortex structures in thecavitation region, the cavity interface, the cavity closure, the cavity wakes, and the foil wakes with the shedding cavity. The comparisons of the various methods, including that the vorticity method, the Q criterion method, the Ω method, the λ2 method and the Rortex method, show the performances of different methods in identifying the cavitation vortex structures. Generally, during the attached cavity growth stage, the Q criteria can well predict the vortex structures in the cavitation region and at the foil trailing edge in the pure liquid region, while with the Ω method and the Rortex method, the vortex structures outside the attached cavity and on the foil pressure side can also be predicted. The λ2 method can well predict the vortex structures in the cavity closure region. During the re-entrant jet development stage, the vortex structures in the re-entrant jet region is weak. During the cavity cloud shedding stage, the vortex dynamics at the foil leading edge covered by newly grown cavity sheet is different from that during the attached cavity sheet growth stage. During the shock wave formation and propagation stage, strong vortex structures with both the size and the strength are observed owing to the cavity cloud shedding and collapse behavior. The influence of the small parameter ε in the Ω method on the cavitation vortex identification is discussed.展开更多
The sheet/cloud cavitation is of a great practical interest since the highly unsteady feature involves significant fluctuations around the body where the cavitation occurs. Moreover, the cavitating flows are complicat...The sheet/cloud cavitation is of a great practical interest since the highly unsteady feature involves significant fluctuations around the body where the cavitation occurs. Moreover, the cavitating flows are complicated due to the thermal effects. The present paper numerically studies the unsteady cavitating flows around a NACA0015 hydrofoil in the fluoreketone and the liquid nitrogen with particular emphasis on the thermal effects and the dynamic evolution. The numerical results and the experimental measurements are generally in agreement. It is shown that the temperature distributions are closely related to the cavity evolution. Meanwhile, the temperature drop is more evident in the liquid nitrogen for the same cavitation number, and the thermal effect suppresses the occurrence and the development of the cavitating flow, especially at a low temperature in the fluoroketone. Furthermore, the cavitating flows are closely related to the complicated vortex structures. The distributions of the pressure around the hydrofoil is a major factor of triggering the unsteady sheet/cloud cavitation. At last, it is interesting to find that one sees a significant thermal effect on the cavitation transition, a small value of σ/2ɑ is required in the thermo-sensitive fluids to achieve the similar cavitation transition that occurs in the water.展开更多
In order to solve the problem of poor formability caused by different materials and properties in the process of tailor-welded sheets forming,a forming method was proposed to change the stress state of tailor-welded s...In order to solve the problem of poor formability caused by different materials and properties in the process of tailor-welded sheets forming,a forming method was proposed to change the stress state of tailor-welded sheets by covering the tailor-welded sheets with better plastic properties overlapping sheets.At the same time,the interface friction effect between the overlapping and tailor-welded sheets was utilized to control the stress magnitude and further improve the formability and quality of the tailor-welded sheets.In this work,the bulging process of the tailor-welded overlapping sheets was taken as the research object.Aluminum alloy tailor-welded overlapping sheets bulging specimens were studied by a combination of finite element analysis and experimental verification.The results show that the appropriate use of interface friction between tailor-welded and overlapping sheets can improve the formability of tailor-welded sheets and control the flow of weld seam to improve the forming quality.When increasing the interface friction coefficient on the side of tailor-welded sheets with higher strength and decreasing that on the side of tailor-welded sheets with lower strength,the deformation of the tailor-welded sheets are more uniform,the offset of the weld seam is minimal,the limit bulging height is maximal,and the forming quality is optimal.展开更多
The formation of an embedded electron current sheet within the magnetotail plasma sheet has been poorly understood.In this article,we present an electron current layer detected at the edge of the magnetotail plasma sh...The formation of an embedded electron current sheet within the magnetotail plasma sheet has been poorly understood.In this article,we present an electron current layer detected at the edge of the magnetotail plasma sheet.The ions were demagnetized inside the electron current layer,but the electrons were still frozen in with the magnetic field line.Thus,this decoupling of ions and electrons gave rise to a strong Hall electric field,which could be the reason for the formation of the embedded thin current layer.The magnetized electrons,the absence of the nongyrotropic electron distribution,and negligible energy dissipation in the layer indicate that magnetic reconnection had not been triggered within the embedded thin current layer.The highly asymmetric plasma on the two sides of the current layer and low magnetic shear across it could suppress magnetic reconnection.The observations indicate that the embedded electric current layer,probably generated by the Hall electric field,even down to electron scale,is not a sufficient condition for magnetic reconnection.展开更多
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi...The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.展开更多
This article examines the influence of annealing temperature on fracture toughness and forming limit curves of dissimilar aluminum/silver sheets.In the cold roll bonding process,after brushing and acid washing,the pre...This article examines the influence of annealing temperature on fracture toughness and forming limit curves of dissimilar aluminum/silver sheets.In the cold roll bonding process,after brushing and acid washing,the prepared surfaces are placed on top of each other and by rolling with reduction more than 50%,the bonding between layers is established.In this research,the roll bonding process was done at room temperature,without the use of lubricants and with a 70%thickness reduction.Then,the final thickness of the Ag/Al bilayer sheet reached 350μm by several stages of cold rolling.Before cold rolling,it should be noted that to decrease the hardness created due to plastic deformation,the roll-bonded samples were subjected to annealing heat treatment at 400℃for 90 min.Thus,the final samples were annealed at 200,300 and 400℃for 90 min and cooled in a furnace to examine the annealing temperature effects.The uniaxial tensile and microhardness tests measured mechanical properties.Also,to investigate the fracture mechanism,the fractography of the cross-section was examined by scanning electron microscope(SEM).To evaluate the formability of Ag/Al bilayer sheets,forming limit curves were obtained experimentally through the Nakazima test.The resistance of composites to failure due to cracking was also investigated by fracture toughness.The results showed that annealing increases the elongation and formability of the Ag/Al bilayer sheet while reduces the ultimate tensile strength and fracture toughness.However,the changing trend is not the same at different temperatures,and according to the results,the most significant effect is obtained at 300℃and aluminum layers.It was also determined that by increasing annealing temperature,the fracture mechanism from shear ductile with small and shallow dimples becomes ductile with deep cavities.展开更多
Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of th...Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.展开更多
With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud...With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.展开更多
The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic.Cloud environments pose significant challenges in maintaining privacy and security.Global approach...The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic.Cloud environments pose significant challenges in maintaining privacy and security.Global approaches,such as IDS,have been developed to tackle these issues.However,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional data.In fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within it.The traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of features.The selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)module.In this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious traffic.The classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable decisions.With the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive measures.Extensive experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different types.Theproposedmodel outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%F1-score.Such results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.展开更多
Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various doma...Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.展开更多
In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tas...The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.展开更多
Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in we...Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in weather forecasting and climate prediction models.Hence,the latest activation and triple-moment condensation schemes were combined to simulate and analyze the evolution characteristics of a cloud droplet spectrum from activation to condensation and compared with a high-resolution Lagrangian bin model and the current double-moment condensation schemes,in which the spectral shape parameter is fixed or diagnosed by an empirical formula.The results demonstrate that the latest schemes effectively capture the evolution characteristics of the cloud droplet spectrum during activation and condensation,which is in line with the performance of the bin model.The simulation of the latest activation and condensation schemes in a parcel model shows that the cloud droplet spectrum gradually widens and exhibits a multimodal distribution during the activation process,accompanied by a decrease in the spectral shape and slope parameters over time.Conversely,during the condensation process,the cloud droplet spectrum gradually narrows,resulting in increases in the spectral shape and slope parameters.However,these double-moment schemes fail to accurately replicate the evolution of the cloud droplet spectrum and its multimodal distribution characteristics.Furthermore,the latest schemes were coupled into a 1.5D cumulus model,and an observation case was simulated.The simulations confirm that the cloud droplet spectrum appears wider at the supersaturated cloud base and cloud top due to activation,while it becomes narrower at the middle altitudes of the cloud due to condensation growth.展开更多
The impact of aerosols on clouds,which remains one of the largest aspects of uncertainty in current weather forecasting and climate change research,can be influenced by various factors,such as the underlying surface t...The impact of aerosols on clouds,which remains one of the largest aspects of uncertainty in current weather forecasting and climate change research,can be influenced by various factors,such as the underlying surface type,cloud type,cloud phase,and aerosol type.To explore the impact of different underlying surfaces on the effect of aerosols on cloud development,this study focused on the Yangtze River Delta(YRD)and its offshore regions(YRD sea)for a comparative analysis based on multi-source satellite data,while also considering the variations in cloud type and cloud phase.The results show lower cloud-top height and depth of single-layer clouds over the ocean than land,and higher liquid cloud in spring over the ocean.Aerosols are found to enhance the cumulus cloud depth through microphysical effects,which is particularly evident over the ocean.Aerosols are also found to decrease the cloud droplet effective radius in the ocean region and during the mature stage of cloud development in the land region,while opposite results are found during the early stage of cloud development in the land region.The quantitative results indicate that the indirect effect is positive(0.05)in the land region at relatively high cloud water path,which is smaller than that in the ocean region(0.11).The findings deepen our understanding of the influence aerosols on cloud development and the mechanisms involved,which could then be applied to improve the ability to simulate cloud-associated weather processes.展开更多
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51839001, 91752105).
文摘Cavitation is a complex multiphase flow phenomenon with an abrupt transient phase change between the liquid and the vapor, including multiscale vortical motions. The transient cavitation dynamics is closely associated with the evolution of the cavitation vortex structures. The present paper investigates the cavitation vortex dynamics using different vortex identification methods, including the vorticity method, the Q criterion method, the Omega method (Ω), the method and the Rortex method. The Q criterion is an eigenvalue-based criterion, and in the Ω method, the parameter is normalized, is independent of the threshold value and in most conditions Ω= 0.52 . The Rortex method is based on an eigenvector-based criterion. Numerical simulations are conducted using the implemented compressible cavitation solver in the open source software OpenFOAM for the sheet/cloud cavitating flows around a NACA66 (mod) hydrofoil fixed at a = 6°,= 1.25 and Re = 7.96 × 10^5 . The flow is characterized by the alternate interactions of the re-entrant flow and the collapse induced shock wave. Results include the vapor structures and the vortex dynamics in the unsteady sheet/cloud cavitating flows, with emphasis on the vortex structures in thecavitation region, the cavity interface, the cavity closure, the cavity wakes, and the foil wakes with the shedding cavity. The comparisons of the various methods, including that the vorticity method, the Q criterion method, the Ω method, the λ2 method and the Rortex method, show the performances of different methods in identifying the cavitation vortex structures. Generally, during the attached cavity growth stage, the Q criteria can well predict the vortex structures in the cavitation region and at the foil trailing edge in the pure liquid region, while with the Ω method and the Rortex method, the vortex structures outside the attached cavity and on the foil pressure side can also be predicted. The λ2 method can well predict the vortex structures in the cavity closure region. During the re-entrant jet development stage, the vortex structures in the re-entrant jet region is weak. During the cavity cloud shedding stage, the vortex dynamics at the foil leading edge covered by newly grown cavity sheet is different from that during the attached cavity sheet growth stage. During the shock wave formation and propagation stage, strong vortex structures with both the size and the strength are observed owing to the cavity cloud shedding and collapse behavior. The influence of the small parameter ε in the Ω method on the cavitation vortex identification is discussed.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51709042,11672094,51522902,51639003 and 51679037)the Fundamental Research Funds for the Central Universities(Grant Nos.DUT16RC(3)085,DUT17ZD233)the Natural Science Foundation of Heilongjiang Province(Grant No.A201409)
文摘The sheet/cloud cavitation is of a great practical interest since the highly unsteady feature involves significant fluctuations around the body where the cavitation occurs. Moreover, the cavitating flows are complicated due to the thermal effects. The present paper numerically studies the unsteady cavitating flows around a NACA0015 hydrofoil in the fluoreketone and the liquid nitrogen with particular emphasis on the thermal effects and the dynamic evolution. The numerical results and the experimental measurements are generally in agreement. It is shown that the temperature distributions are closely related to the cavity evolution. Meanwhile, the temperature drop is more evident in the liquid nitrogen for the same cavitation number, and the thermal effect suppresses the occurrence and the development of the cavitating flow, especially at a low temperature in the fluoroketone. Furthermore, the cavitating flows are closely related to the complicated vortex structures. The distributions of the pressure around the hydrofoil is a major factor of triggering the unsteady sheet/cloud cavitation. At last, it is interesting to find that one sees a significant thermal effect on the cavitation transition, a small value of σ/2ɑ is required in the thermo-sensitive fluids to achieve the similar cavitation transition that occurs in the water.
基金Funded by the National Natural Science Foundation of China(Nos.52075347,51575364)and the Natural Science Foundation of Liaoning Provincial(No.2022-MS-295)。
文摘In order to solve the problem of poor formability caused by different materials and properties in the process of tailor-welded sheets forming,a forming method was proposed to change the stress state of tailor-welded sheets by covering the tailor-welded sheets with better plastic properties overlapping sheets.At the same time,the interface friction effect between the overlapping and tailor-welded sheets was utilized to control the stress magnitude and further improve the formability and quality of the tailor-welded sheets.In this work,the bulging process of the tailor-welded overlapping sheets was taken as the research object.Aluminum alloy tailor-welded overlapping sheets bulging specimens were studied by a combination of finite element analysis and experimental verification.The results show that the appropriate use of interface friction between tailor-welded and overlapping sheets can improve the formability of tailor-welded sheets and control the flow of weld seam to improve the forming quality.When increasing the interface friction coefficient on the side of tailor-welded sheets with higher strength and decreasing that on the side of tailor-welded sheets with lower strength,the deformation of the tailor-welded sheets are more uniform,the offset of the weld seam is minimal,the limit bulging height is maximal,and the forming quality is optimal.
基金the National Natural Science Founda-tion of China(NSFC,Grant No.42174181)and the Key Research Program of Frontier Sciences,CAS(Grant No.QYZDJ-SSW-DQC010).
文摘The formation of an embedded electron current sheet within the magnetotail plasma sheet has been poorly understood.In this article,we present an electron current layer detected at the edge of the magnetotail plasma sheet.The ions were demagnetized inside the electron current layer,but the electrons were still frozen in with the magnetic field line.Thus,this decoupling of ions and electrons gave rise to a strong Hall electric field,which could be the reason for the formation of the embedded thin current layer.The magnetized electrons,the absence of the nongyrotropic electron distribution,and negligible energy dissipation in the layer indicate that magnetic reconnection had not been triggered within the embedded thin current layer.The highly asymmetric plasma on the two sides of the current layer and low magnetic shear across it could suppress magnetic reconnection.The observations indicate that the embedded electric current layer,probably generated by the Hall electric field,even down to electron scale,is not a sufficient condition for magnetic reconnection.
文摘The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.
基金Project(4013311)supported by the National Science Foundation of Iran(INSF)。
文摘This article examines the influence of annealing temperature on fracture toughness and forming limit curves of dissimilar aluminum/silver sheets.In the cold roll bonding process,after brushing and acid washing,the prepared surfaces are placed on top of each other and by rolling with reduction more than 50%,the bonding between layers is established.In this research,the roll bonding process was done at room temperature,without the use of lubricants and with a 70%thickness reduction.Then,the final thickness of the Ag/Al bilayer sheet reached 350μm by several stages of cold rolling.Before cold rolling,it should be noted that to decrease the hardness created due to plastic deformation,the roll-bonded samples were subjected to annealing heat treatment at 400℃for 90 min.Thus,the final samples were annealed at 200,300 and 400℃for 90 min and cooled in a furnace to examine the annealing temperature effects.The uniaxial tensile and microhardness tests measured mechanical properties.Also,to investigate the fracture mechanism,the fractography of the cross-section was examined by scanning electron microscope(SEM).To evaluate the formability of Ag/Al bilayer sheets,forming limit curves were obtained experimentally through the Nakazima test.The resistance of composites to failure due to cracking was also investigated by fracture toughness.The results showed that annealing increases the elongation and formability of the Ag/Al bilayer sheet while reduces the ultimate tensile strength and fracture toughness.However,the changing trend is not the same at different temperatures,and according to the results,the most significant effect is obtained at 300℃and aluminum layers.It was also determined that by increasing annealing temperature,the fracture mechanism from shear ductile with small and shallow dimples becomes ductile with deep cavities.
基金supported By Grant (PLN2022-14) of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University)。
文摘Well logging technology has accumulated a large amount of historical data through four generations of technological development,which forms the basis of well logging big data and digital assets.However,the value of these data has not been well stored,managed and mined.With the development of cloud computing technology,it provides a rare development opportunity for logging big data private cloud.The traditional petrophysical evaluation and interpretation model has encountered great challenges in the face of new evaluation objects.The solution research of logging big data distributed storage,processing and learning functions integrated in logging big data private cloud has not been carried out yet.To establish a distributed logging big-data private cloud platform centered on a unifi ed learning model,which achieves the distributed storage and processing of logging big data and facilitates the learning of novel knowledge patterns via the unifi ed logging learning model integrating physical simulation and data models in a large-scale functional space,thus resolving the geo-engineering evaluation problem of geothermal fi elds.Based on the research idea of“logging big data cloud platform-unifi ed logging learning model-large function space-knowledge learning&discovery-application”,the theoretical foundation of unified learning model,cloud platform architecture,data storage and learning algorithm,arithmetic power allocation and platform monitoring,platform stability,data security,etc.have been carried on analysis.The designed logging big data cloud platform realizes parallel distributed storage and processing of data and learning algorithms.The feasibility of constructing a well logging big data cloud platform based on a unifi ed learning model of physics and data is analyzed in terms of the structure,ecology,management and security of the cloud platform.The case study shows that the logging big data cloud platform has obvious technical advantages over traditional logging evaluation methods in terms of knowledge discovery method,data software and results sharing,accuracy,speed and complexity.
基金supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00399401,Development of Quantum-Safe Infrastructure Migration and Quantum Security Verification Technologies).
文摘With the rise of remote collaboration,the demand for advanced storage and collaboration tools has rapidly increased.However,traditional collaboration tools primarily rely on access control,leaving data stored on cloud servers vulnerable due to insufficient encryption.This paper introduces a novel mechanism that encrypts data in‘bundle’units,designed to meet the dual requirements of efficiency and security for frequently updated collaborative data.Each bundle includes updated information,allowing only the updated portions to be reencrypted when changes occur.The encryption method proposed in this paper addresses the inefficiencies of traditional encryption modes,such as Cipher Block Chaining(CBC)and Counter(CTR),which require decrypting and re-encrypting the entire dataset whenever updates occur.The proposed method leverages update-specific information embedded within data bundles and metadata that maps the relationship between these bundles and the plaintext data.By utilizing this information,the method accurately identifies the modified portions and applies algorithms to selectively re-encrypt only those sections.This approach significantly enhances the efficiency of data updates while maintaining high performance,particularly in large-scale data environments.To validate this approach,we conducted experiments measuring execution time as both the size of the modified data and the total dataset size varied.Results show that the proposed method significantly outperforms CBC and CTR modes in execution speed,with greater performance gains as data size increases.Additionally,our security evaluation confirms that this method provides robust protection against both passive and active attacks.
文摘The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic.Cloud environments pose significant challenges in maintaining privacy and security.Global approaches,such as IDS,have been developed to tackle these issues.However,most conventional Intrusion Detection System(IDS)models struggle with unseen cyberattacks and complex high-dimensional data.In fact,this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system,named INTRUMER,which offers balanced accuracy,reliability,and security in cloud settings bymultiplemodulesworking together within it.The traffic captured from cloud devices is first passed to the TC&TM module in which the Falcon Optimization Algorithm optimizes the feature selection process,and Naie Bayes algorithm performs the classification of features.The selected features are classified further and are forwarded to the Heterogeneous Attention Transformer(HAT)module.In this module,the contextual interactions of the network traffic are taken into account to classify them as normal or malicious traffic.The classified results are further analyzed by the Explainable Prevention Module(XPM)to ensure trustworthiness by providing interpretable decisions.With the explanations fromthe classifier,emergency alarms are transmitted to nearby IDSmodules,servers,and underlying cloud devices for the enhancement of preventive measures.Extensive experiments on benchmark IDS datasets CICIDS 2017,Honeypots,and NSL-KDD were conducted to demonstrate the efficiency of the INTRUMER model in detecting network trafficwith high accuracy for different types.Theproposedmodel outperforms state-of-the-art approaches,obtaining better performance metrics:98.7%accuracy,97.5%precision,96.3%recall,and 97.8%F1-score.Such results validate the robustness and effectiveness of INTRUMER in securing diverse cloud environments against sophisticated cyber threats.
文摘Cloud detection is a critical preprocessing step in remote sensing image processing, as the presence of clouds significantly affects the accuracy of remote sensing data and limits its applicability across various domains. This study presents an enhanced cloud detection method based on the U-Net architecture, designed to address the challenges of multi-scale cloud features and long-range dependencies inherent in remote sensing imagery. A Multi-Scale Dilated Attention (MSDA) module is introduced to effectively integrate multi-scale information and model long-range dependencies across different scales, enhancing the model’s ability to detect clouds of varying sizes. Additionally, a Multi-Head Self-Attention (MHSA) mechanism is incorporated to improve the model’s capacity for capturing finer details, particularly in distinguishing thin clouds from surface features. A multi-path supervision mechanism is also devised to ensure the model learns cloud features at multiple scales, further boosting the accuracy and robustness of cloud mask generation. Experimental results demonstrate that the enhanced model achieves superior performance compared to other benchmarked methods in complex scenarios. It significantly improves cloud detection accuracy, highlighting its strong potential for practical applications in cloud detection tasks.
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
文摘The ease of accessing a virtually unlimited pool of resources makes Infrastructure as a Service (IaaS) clouds an ideal platform for running data-intensive workflow applications comprising hundreds of computational tasks. However, executing scientific workflows in IaaS cloud environments poses significant challenges due to conflicting objectives, such as minimizing execution time (makespan) and reducing resource utilization costs. This study responds to the increasing need for efficient and adaptable optimization solutions in dynamic and complex environments, which are critical for meeting the evolving demands of modern users and applications. This study presents an innovative multi-objective approach for scheduling scientific workflows in IaaS cloud environments. The proposed algorithm, MOS-MWMC, aims to minimize total execution time (makespan) and resource utilization costs by leveraging key features of virtual machine instances, such as a high number of cores and fast local SSD storage. By integrating realistic simulations based on the WRENCH framework, the method effectively dimensions the cloud infrastructure and optimizes resource usage. Experimental results highlight the superiority of MOS-MWMC compared to benchmark algorithms HEFT and Max-Min. The Pareto fronts obtained for the CyberShake, Epigenomics, and Montage workflows demonstrate closer proximity to the optimal front, confirming the algorithm’s ability to balance conflicting objectives. This study contributes to optimizing scientific workflows in complex environments by providing solutions tailored to specific user needs while minimizing costs and execution times.
基金supported by the National Natural Science Foundations of China(Grant Nos.42305163 and U22A20577)the Construction Project of Weather Modification Ability in Central China(Grant No.ZQC-H22256)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0760300)the Projects of the Earth System Numerical Simulation Facility(Grant Nos.2024-EL-PT-000707,2023-ELPT-000482,2023-EL-ZD-00026,and 2022-EL-PT-00083)the STS Program of the Inner Mongolia Meteorological Service,Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,and Institute of Atmospheric Physics,Chinese Academy of Sciences(Grant No.2021CG0047)。
文摘Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in weather forecasting and climate prediction models.Hence,the latest activation and triple-moment condensation schemes were combined to simulate and analyze the evolution characteristics of a cloud droplet spectrum from activation to condensation and compared with a high-resolution Lagrangian bin model and the current double-moment condensation schemes,in which the spectral shape parameter is fixed or diagnosed by an empirical formula.The results demonstrate that the latest schemes effectively capture the evolution characteristics of the cloud droplet spectrum during activation and condensation,which is in line with the performance of the bin model.The simulation of the latest activation and condensation schemes in a parcel model shows that the cloud droplet spectrum gradually widens and exhibits a multimodal distribution during the activation process,accompanied by a decrease in the spectral shape and slope parameters over time.Conversely,during the condensation process,the cloud droplet spectrum gradually narrows,resulting in increases in the spectral shape and slope parameters.However,these double-moment schemes fail to accurately replicate the evolution of the cloud droplet spectrum and its multimodal distribution characteristics.Furthermore,the latest schemes were coupled into a 1.5D cumulus model,and an observation case was simulated.The simulations confirm that the cloud droplet spectrum appears wider at the supersaturated cloud base and cloud top due to activation,while it becomes narrower at the middle altitudes of the cloud due to condensation growth.
基金supported by the National Natural Science Foundation of China(Grant No.42230601).
文摘The impact of aerosols on clouds,which remains one of the largest aspects of uncertainty in current weather forecasting and climate change research,can be influenced by various factors,such as the underlying surface type,cloud type,cloud phase,and aerosol type.To explore the impact of different underlying surfaces on the effect of aerosols on cloud development,this study focused on the Yangtze River Delta(YRD)and its offshore regions(YRD sea)for a comparative analysis based on multi-source satellite data,while also considering the variations in cloud type and cloud phase.The results show lower cloud-top height and depth of single-layer clouds over the ocean than land,and higher liquid cloud in spring over the ocean.Aerosols are found to enhance the cumulus cloud depth through microphysical effects,which is particularly evident over the ocean.Aerosols are also found to decrease the cloud droplet effective radius in the ocean region and during the mature stage of cloud development in the land region,while opposite results are found during the early stage of cloud development in the land region.The quantitative results indicate that the indirect effect is positive(0.05)in the land region at relatively high cloud water path,which is smaller than that in the ocean region(0.11).The findings deepen our understanding of the influence aerosols on cloud development and the mechanisms involved,which could then be applied to improve the ability to simulate cloud-associated weather processes.
