Adding nanoparticles can significantly improve the tribological properties of lubricants.However,there is a lack of understanding regarding the influence of nanoparticle shape on lubrication performance.In this work,t...Adding nanoparticles can significantly improve the tribological properties of lubricants.However,there is a lack of understanding regarding the influence of nanoparticle shape on lubrication performance.In this work,the influence of diamond nanoparticles(DNPs)on the tribological properties of lubricants is investigated through friction experiments.Additionally,the friction characteristics of lubricants regarding ellipsoidal particle shape are investigated using molecular dynamics(MD)simulations.The results show that DNPs can drastically lower the lubricant's friction coefficientμfrom 0.21 to 0.117.The shearing process reveals that as the aspect ratio(α)of the nanoparticles approaches 1.0,the friction performance improves,and wear on the wall diminishes.At the same time,the shape of the nanoparticles tends to be spherical.When 0.85≤α≤1.0,rolling is ellipsoidal particles'main form of motion,and the friction force changes according to a periodic sinusoidal law.In the range of 0.80≤α<0.85,ellipsoidal particles primarily exhibit sliding as the dominant movement mode.Asαdecreases within this range,the friction force progressively increases.The friction coefficientμcalculated through MD simulation is 0.128,which is consistent with the experimental data.展开更多
In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow tasks.In cloud data centers,fog computing takes more time to run workflow applications...In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow tasks.In cloud data centers,fog computing takes more time to run workflow applications.Therefore,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing environments.Effective task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog nodes.This process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource bottlenecks.In this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local exploitation.This balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization algorithms.The FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response time.In relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.展开更多
基金Supported by National Natural Science Foundation of China (Grant No.52275178)Fujian industry university cooperation project (Grant No.2020H6025)。
文摘Adding nanoparticles can significantly improve the tribological properties of lubricants.However,there is a lack of understanding regarding the influence of nanoparticle shape on lubrication performance.In this work,the influence of diamond nanoparticles(DNPs)on the tribological properties of lubricants is investigated through friction experiments.Additionally,the friction characteristics of lubricants regarding ellipsoidal particle shape are investigated using molecular dynamics(MD)simulations.The results show that DNPs can drastically lower the lubricant's friction coefficientμfrom 0.21 to 0.117.The shearing process reveals that as the aspect ratio(α)of the nanoparticles approaches 1.0,the friction performance improves,and wear on the wall diminishes.At the same time,the shape of the nanoparticles tends to be spherical.When 0.85≤α≤1.0,rolling is ellipsoidal particles'main form of motion,and the friction force changes according to a periodic sinusoidal law.In the range of 0.80≤α<0.85,ellipsoidal particles primarily exhibit sliding as the dominant movement mode.Asαdecreases within this range,the friction force progressively increases.The friction coefficientμcalculated through MD simulation is 0.128,which is consistent with the experimental data.
基金This work was supported in part by the National Science and Technology Council of Taiwan,under Contract NSTC 112-2410-H-324-001-MY2.
文摘In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow tasks.In cloud data centers,fog computing takes more time to run workflow applications.Therefore,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing environments.Effective task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog nodes.This process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource bottlenecks.In this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local exploitation.This balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization algorithms.The FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response time.In relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.
