Steel catenary riser represents the pioneering riser technology implemented in China’s deep-sea oil and gas opera-tions.Given the complex mechanical conditions of the riser,extensive research has been conducted on it...Steel catenary riser represents the pioneering riser technology implemented in China’s deep-sea oil and gas opera-tions.Given the complex mechanical conditions of the riser,extensive research has been conducted on its dynamic analysis and structural design.This study investigates a deep-sea oil and gas field by developing a coupled model of a semi-submersible platform and steel catenary riser to analyze it mechanical behavior under extreme marine condi-tions.Through multi-objective optimization methodology,the study compares and analyzes suspension point tension and touchdown point stress under various conditions by modifying the suspension position,suspension angle,and catenary length.The optimal configuration parameters were determined:a suspension angle of 12°,suspension position in the southwest direction of the column,and a catenary length of approximately 2000 m.These findings elucidate the impact of configuration parameters on riser dynamic response and establish reasonable parameter layout ranges for adverse sea conditions,offering valuable optimization strategies for steel catenary riser deployment in domestic deep-sea oil and gas fields.展开更多
The performance and efficiency of hydraulic excavators heavily depend on the design and optimization of their working devices.The working device,which consists of the boom,arm,and bucket,plays a crucial role in determ...The performance and efficiency of hydraulic excavators heavily depend on the design and optimization of their working devices.The working device,which consists of the boom,arm,and bucket,plays a crucial role in determining the machine's digging capacity,stability,and overall operational efficiency.This paper presents a comprehensive study on the dynamics simulation and optimization of hydraulic excavator working devices.The paper outlines the fundamental principles of dynamic modeling,incorporating multi-body dynamics and hydraulic system analysis.It further explores various simulation techniques to evaluate the performance of the working device under varying operational conditions,including load and hydraulic system effects.The study also addresses performance optimization,focusing on multi-objective optimization methods that balance multiple factors such as energy efficiency,speed,and load capacity.Additionally,the paper discusses key factors influencing performance,such as mechanical design,material properties,and operational conditions.The results of the dynamic simulations and optimization analyses demonstrate potential improvements in operational efficiency and system stability,providing a valuable framework for the design and enhancement of hydraulic excavator working devices.展开更多
Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple dat...Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.展开更多
Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service lif...Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service life,and high reliability.However,in practical design processes,topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions,which traditional methods often struggle accommodate.Therefore,this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization(DHPSO)algorithm.By leveraging the Kriging model as a surrogate,the high cost associated with repeatedly running finite element analysis processes is reduced,addressing the issue of minimizing structural compliance.Meanwhile,the DHPSO algorithm enables a better balance between the population’s developmental and exploratory capabilities,significantly accelerating convergence speed and enhancing global convergence performance.Finally,the proposed method is validated through three different structural examples,demonstrating its superior performance.Observed that the computational that,compared to the traditional Solid Isotropic Material with Penalization(SIMP)method,the proposed approach reduces the upper bound of structural compliance by approximately 30%.Additionally,the optimized results exhibit clear material interfaces without grayscale elements,and the stress concentration factor is reduced by approximately 42%.Consequently,the computational results fromdifferent examples verify the effectiveness and superiority of this study across various fields,achieving the goal of providing more precise optimization results within a shorter timeframe.展开更多
Combinatorial optimization problems and ground state problems of spin glasses are crucial in various fields of science and technology.However,they often belong to the computational class of NP-hard,presenting signific...Combinatorial optimization problems and ground state problems of spin glasses are crucial in various fields of science and technology.However,they often belong to the computational class of NP-hard,presenting significant computational challenges.Traditional algorithms inspired by statistical physics like simulated annealing have been widely adopted.Recently,advancements in Ising machines,such as quantum annealers and coherent Ising machines,offer new paradigms for solving these problems efficiently by embedding them into the analog evolution of nonlinear dynamical systems.However,existing dynamics-based algorithms often suffer from low convergence rates and local minima traps.In this work,we introduce the dual mean-field dynamics into Ising machines.The approach integrates the gradient force and the transverse force into the dynamics of Ising machines in solving combinatorial optimization problems,making it easier for the system to jump out of the local minimums and allowing the dynamics to explore wider in configuration space.We conduct extensive numerical experiments using the Sherrington–Kirkpatrick spin glass up to 10000 spins and the maximum cut problems with the standard G-set benchmarks.The numerical results demonstrate that our dual mean-field dynamics approach enhances the performance of base Ising machines,providing a more effective solution for large-scale combinatorial optimization problems.展开更多
The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous flui...The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.展开更多
In this study,we construct a bi-level optimization model based on the Stackelberg game and propose a robust optimization algorithm for solving the bi-level model,assuming an actual situation with several participants ...In this study,we construct a bi-level optimization model based on the Stackelberg game and propose a robust optimization algorithm for solving the bi-level model,assuming an actual situation with several participants in energy trading.Firstly,the energy trading process is analyzed between each subject based on the establishment of the operation framework of multi-agent participation in energy trading.Secondly,the optimal operation model of each energy trading agent is established to develop a bi-level game model including each energy participant.Finally,a combination algorithm of improved robust optimization over time(ROOT)and CPLEX is proposed to solve the established game model.The experimental results indicate that under different fitness thresholds,the robust optimization results of the proposed algorithm are increased by 56.91%and 68.54%,respectively.The established bi-level game model effectively balances the benefits of different energy trading entities.The proposed algorithm proposed can increase the income of each participant in the game by an average of 8.59%.展开更多
To efficiently search out the optimal cam contour,a software integrated optimization method considering cam mechanism’s kinematic and dynamic characteristics was presented,and its effectiveness was demonstrated by a ...To efficiently search out the optimal cam contour,a software integrated optimization method considering cam mechanism’s kinematic and dynamic characteristics was presented,and its effectiveness was demonstrated by a case study of the cam contour optimization for an offset press open-close gripper mechanism.The acceleration curve and the residual vibration model of the follower were separately studied.A symmetric harmonic trapezoidal curve was designed to control the follower’s acceleration,and single-DOF lumped parameter torsional vibration model was proposed to describe the follower’s residual vibration.Accordingly,corresponding motion curve design software and Simulink vibration model of the follower were developed respectively,and they were integrated into an automatic optimization platform with iSIGHT.The multi-objective optimization with objectives of minimizing both the acceleration and the residual vibration of the follower was completed within the platform by using NSGA-II algorithm.An appropriate point with lower acceleration and residual vibration was chosen from Pareto front as an optimal solution of the follower’s motion curve.Based on the follower’s new motion curve,the actual cam contour was generated by inverse kinematic simulation in COSMOSMotion.The offset press that installed our new designed cam exhibited a lower vibration than the previous machine,and the maximum measured acceleration of the offset press at a printing speed of 15000 r/h is reduced by 7.7%.展开更多
Double-shaft-driven needle punching machine is a specialized equipment designed for processing C/C crucible preforms.Its main needle punching module is operated by two sets of reciprocating crank-slider mechanisms.The...Double-shaft-driven needle punching machine is a specialized equipment designed for processing C/C crucible preforms.Its main needle punching module is operated by two sets of reciprocating crank-slider mechanisms.The intense vibration during needle punching not only generates huge noise,but also substantially reduces the quality of the preform.It is imperative to perform a dynamic analysis and optimization of the entire needle punching machine.In this paper,the three-dimensional(3D)model of the entire double-shaft-driven needle punching machine for C/C crucible preforms is established.Based on the modal analysis theory,the modal characteristics of the needle punching machine under various operating conditions are analyzed and its natural frequencies and vibration modes are determined.The harmonic response analysis is then employed to obtain the amplitude of the needle plate at different frequencies,and the structural weak points of the needle punching machine are identified and improved.The feasibility of the optimized scheme is subsequently reevaluated and verified.The results indicate that the first six natural frequencies of the machine increase,and the maximum amplitude of the needle plate decreases by 70.3%.The enhanced dynamic characteristics of the machine significantly improve its performance,enabling more efficient needle punching of C/C crucible preforms.展开更多
Web pillars enduring complex coupled loads are critical for stability in high-wall mining.This study develops a dynamic failure criterion for web pillars under non-uniform loading using catastrophe theory.Through the ...Web pillars enduring complex coupled loads are critical for stability in high-wall mining.This study develops a dynamic failure criterion for web pillars under non-uniform loading using catastrophe theory.Through the analysis of the web pillar-overburden system’s dynamic stress and deformation,a total potential energy function and dynamic failure criterion were established for web pillars.An optimizing method for web pillar parameters was developed in highwall mining.The dynamic criterion established was used to evaluate the dynamic failure and stability of web pillars under static and dynamic loading.Key findings reveal that vertical displacements exhibit exponential-trigonometric variation under static loads and multi-variable power-law behavior under dynamic blasting.Instability risks arise when the roof’s tensile strength-to-stress ratio drops below 1.Using catastrophe theory,the bifurcation setΔ<0 signals sudden instability.The criterion defines failure as when the unstable web pillar section length l1 exceeds the roof’s critical collapse distance l2.Case studies and simulations determine an optimal web pillar width of 4.6 m.This research enhances safety and resource recovery,providing a theoretical framework for advancing highwall mining technology.展开更多
The design and optimization of nonlinear fiber laser sources,such as soliton self-frequency shift(SSFS)tunable sources and supercontinuum(SC)sources,have traditionally relied on manual tuning and simulations,posing ch...The design and optimization of nonlinear fiber laser sources,such as soliton self-frequency shift(SSFS)tunable sources and supercontinuum(SC)sources,have traditionally relied on manual tuning and simulations,posing challenges for real-time applications.Machine learning has shown promise in fiber nonlinear propagation characterization,but the optimization and design of nonlinear systems remain relatively unexplored,especially under multitarget optimization conditions.In this paper,we propose a method that combines deep reinforcement learning(DRL)and deep neural network(DNN)to achieve fast synchronization optimization of ultrafast pulse nonlinear propagation in optical fibers under multitarget optimization tasks,with applications demonstrated in complex SSFS and SC generation systems in the mid-infrared band.The results indicate that a set of optimization parameters can be obtained in a few seconds,enabling rapid,automated tuning of pulse parameters in pursuit of diverse optimization objectives.This integration of DRL and DNN models holds transformative potential for the real-time optimization of not only fiber lasers but also a wide variety of complex photonic systems,paving the way for intelligent,adaptive optical system design and operation.展开更多
This study discusses a machine learning‑driven methodology for optimizing the aerodynamic performance of both conventional,like common research model(CRM),and non‑conventional,like Bionica box‑wing,aircraft configurat...This study discusses a machine learning‑driven methodology for optimizing the aerodynamic performance of both conventional,like common research model(CRM),and non‑conventional,like Bionica box‑wing,aircraft configurations.The approach leverages advanced parameterization techniques,such as class and shape transformation(CST)and Bezier curves,to reduce design complexity while preserving flexibility.Computational fluid dynamics(CFD)simulations are performed to generate a comprehensive dataset,which is used to train an extreme gradient boosting(XGBoost)model for predicting aerodynamic performance.The optimization process,using the non‑dominated sorting genetic algorithm(NSGA‑Ⅱ),results in a 12.3%reduction in drag for the CRM wing and an 18%improvement in the lift‑to‑drag ratio for the Bionica box‑wing.These findings validate the efficacy of machine learning based method in aerodynamic optimization,demonstrating significant efficiency gains across both configurations.展开更多
Investors are always willing to receive more data.This has become especially true for the application of modern portfolio theory to the institutional asset allocation process,which requires quantitative estimates of r...Investors are always willing to receive more data.This has become especially true for the application of modern portfolio theory to the institutional asset allocation process,which requires quantitative estimates of risk and return.When long-term data series are unavailable for analysis,it has become common practice to use recent data only.The danger is that these data may not be representative of future performance.Although longer data series are of poorer quality,are difficult to obtain,and may reflect various political and economic regimes,they often paint a very different picture of emerging market performance.This paper presents an application of a stochastic non-linear optimization model of portfolios including transaction costs in the Brazilian financial market.In order to have that,portfolio theory and optimal control were used as theoretical basis.The first strategy tries to allocate the whole available wealth,not considering the risk associated to portfolio(deterministic result).In this case the investor obtained profits of 7.23%a month,taking into account the three risk aversion levels during the whole planning period.On the contrary,the results from the stochastic algorithm obtain profits of 1.34%a month and 18.06%a year,if the investor has low risk aversion.The profits would be 0.88%a month and 11.02%a year for a medium risk aversion investor.And with high risk aversion,the investor obtains 0.62%a month and 7.68%a year.展开更多
The solenoid switching valve(SSV)is the key control component of heavy equipment such as continuous casting machines.However,the incompatibility of structural parameters increases the opening and closing time of the S...The solenoid switching valve(SSV)is the key control component of heavy equipment such as continuous casting machines.However,the incompatibility of structural parameters increases the opening and closing time of the SSV.Therefore,this study proposes an optimized design method for an SSV to improve its dynamic performance.First,a multi-physics field-coupling model of the SSV is built,and the effects of different structural parameters on the electromagnetic characteristics are analyzed.After identifying the key influencing parameters,second-order response surface models are established to efficiently predict the opening and closing time.Subsequently,based on the nondominated sorting genetic algorithmⅡ(NSGA-Ⅱ),multi-objective optimization is applied to obtain the Pareto optimal solution of the structural parameters under the double-voltage driving strategy.The structure of the solenoid and valve as well as the dynamic characteristics of the valve are improved.Compared with those before optimization,the optimization results show that the opening and closing time of the optimized SSV are reduced by 24.38%and 51.8%,respectively,and the volume is reduced by 19.7%.The research results and the influence of the solenoid structural parameters on the electromagnetic force provide significant guidance for the design of this type of valve.展开更多
The rapid advancement of technology and the increasing speed of vehicles have led to a substantial rise in energy consumption and growing concern over environmental pollution.Beyond the promotion of new energy vehicle...The rapid advancement of technology and the increasing speed of vehicles have led to a substantial rise in energy consumption and growing concern over environmental pollution.Beyond the promotion of new energy vehicles,reducing aerodynamic drag remains a critical strategy for improving energy efficiency and lowering emissions.This study investigates the influence of key geometric parameters on the aerodynamic drag of vehicles.A parametric vehicle model was developed,and computational fluid dynamics(CFD)simulations were conducted to analyse variations in the drag coefficient(C_(d))and pressure distribution across different design configurations.The results reveal that the optimal aerodynamic performance—characterized by a minimized drag coefficient—is achieved with the following parameter settings:engine hood angle(α)of 15°,windshield angle(β)of 25°,rear window angle(γ)of 40°,rear upwards tail lift angle(θ)of 10°,ground clearance(d)of 100 mm,and side edge angle(s)of 5°.These findings offer valuable guidance for the aerodynamic optimization of vehicle body design and contribute to strategies aimed at energy conservation and emission reduction in the automotive sector.展开更多
The noise generated by high-speed hair dryers significantly affects user experience,with aerodynamic design playing a crucial role in controlling sound emissions.This study investigates the aerodynamic noise character...The noise generated by high-speed hair dryers significantly affects user experience,with aerodynamic design playing a crucial role in controlling sound emissions.This study investigates the aerodynamic noise characteristics of a commercial high-speed hair dryer through Computational Fluid Dynamics(CFD)analysis.The velocity field,streamline patterns,and vector distribution within the primary flow path and internal cavity were systematically examined.Results indicate that strong interactions between the wake flow generated by the guide vanes and the straight baffle in the rear flow path induce vortex structures near the outlet,which are primarily responsible for highfrequency noise.To address this,the guide vanes and rear flow path geometry were redesigned and optimized for improved acoustic and aerodynamic performance.Underrated operating conditions(28 V,20,000 rpm),the optimized configuration achieves a noise reduction of more than 2.2 dB while increasing outlet wind speed by over 9%.Moreover,the noise suppression effect becomes more pronounced at lower rotational speeds.展开更多
This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints.They pose a challenge as their optimal solutions evolve with time.Traditional iteration-base...This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints.They pose a challenge as their optimal solutions evolve with time.Traditional iteration-based methods that exactly solve the problem at each time instant,fail to precisely and realtime track the solution due to computational and communication bottlenecks.Our model,through rigorous theoretical analyses,is able to reduce the optimality gap(i.e.,the difference between the model state and optimal solution)to zero in a finite time,and thus,track the solution online.Besides,perturbance is taken into account.We prove that under certain conditions,our model can totally tolerate an important kind of noise that we call“errorrelated noise”.In numerical experiments,compared with six existing methods,our model exhibits superior robustness when contaminated by the error-related noise.The key techniques in the model design involve employing the zeroing neural network to leverage time-derivative information,and introducing an integral term as well as the class C_(L)^(0)functions to enhance convergence and noise resistance.Finally,we establish a model-free control framework for a surgical manipulator with the remote-center-of-motion constraint and compare the performances of the framework based on different models in simulations.The results indicate that our model achieves the best performance among various models employed within the framework.展开更多
Considering the self-excited and forced vibrations in high-speed milling processes, a novel method for dynamic optimization of system stability is used to determine the cutting parameters and structural parameters by ...Considering the self-excited and forced vibrations in high-speed milling processes, a novel method for dynamic optimization of system stability is used to determine the cutting parameters and structural parameters by increasing the chatter free material removal rate （CF-MRR） and surface finish. The method is hased on the theory of the chatter stability and the semi-bandwidth of the resonant region. The objective function of the method is material removal rate（MRR）,the constraints are chatter stability and surface finish, and the optimizing variables are cutting and structural parameters. The optimization procedure is stated. The method is applied to a milling system and CF-MRR is increased 18.86%. It is shown that the influences of the chatter stability and the resonance are simultaneously considered in the dynamic optimization of the milling system for increasing CF-MRR and the surface finish.展开更多
The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system perf...The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.展开更多
The goal of this effort was to provide a static and dynamic collaborative optimization （CO） model for the design of ship hull structure. The CO model integrated with static, mode and dynamic analyses. In the system-...The goal of this effort was to provide a static and dynamic collaborative optimization （CO） model for the design of ship hull structure. The CO model integrated with static, mode and dynamic analyses. In the system-level optimization model, a new objective function was advised, integrating all the subsystem-levels＇ objective functions, so as to eliminate the effects of dimensions and magnitude order. The proposed CO architecture enabled multi-objectives of the system and subsystem-level to be considered at both levels during optimization. A bi-level optimization strategy was advised, using the multi-island genetic algorithm. The proposed model was demonstrated with a deck optimization problem of container ship stern. The analysis progress and results of example show that the CO strategy is not only feasible and reliable, but also well suited for use in actual optimization problems of ship design.展开更多
基金financially supported by the National Key Research and Development Program of China(Grant No.2022YFC2806100)the National Natural Science Foundation of China(Grant Nos.U22B20126 and 52374020)+1 种基金Science Foundation of China University of Petroleum,Beijing(Grant No.2462025QNXZ009)Beijing Nova Program(Grant No.20250484913).
文摘Steel catenary riser represents the pioneering riser technology implemented in China’s deep-sea oil and gas opera-tions.Given the complex mechanical conditions of the riser,extensive research has been conducted on its dynamic analysis and structural design.This study investigates a deep-sea oil and gas field by developing a coupled model of a semi-submersible platform and steel catenary riser to analyze it mechanical behavior under extreme marine condi-tions.Through multi-objective optimization methodology,the study compares and analyzes suspension point tension and touchdown point stress under various conditions by modifying the suspension position,suspension angle,and catenary length.The optimal configuration parameters were determined:a suspension angle of 12°,suspension position in the southwest direction of the column,and a catenary length of approximately 2000 m.These findings elucidate the impact of configuration parameters on riser dynamic response and establish reasonable parameter layout ranges for adverse sea conditions,offering valuable optimization strategies for steel catenary riser deployment in domestic deep-sea oil and gas fields.
文摘The performance and efficiency of hydraulic excavators heavily depend on the design and optimization of their working devices.The working device,which consists of the boom,arm,and bucket,plays a crucial role in determining the machine's digging capacity,stability,and overall operational efficiency.This paper presents a comprehensive study on the dynamics simulation and optimization of hydraulic excavator working devices.The paper outlines the fundamental principles of dynamic modeling,incorporating multi-body dynamics and hydraulic system analysis.It further explores various simulation techniques to evaluate the performance of the working device under varying operational conditions,including load and hydraulic system effects.The study also addresses performance optimization,focusing on multi-objective optimization methods that balance multiple factors such as energy efficiency,speed,and load capacity.Additionally,the paper discusses key factors influencing performance,such as mechanical design,material properties,and operational conditions.The results of the dynamic simulations and optimization analyses demonstrate potential improvements in operational efficiency and system stability,providing a valuable framework for the design and enhancement of hydraulic excavator working devices.
文摘Cloud computing has become an essential technology for the management and processing of large datasets,offering scalability,high availability,and fault tolerance.However,optimizing data replication across multiple data centers poses a significant challenge,especially when balancing opposing goals such as latency,storage costs,energy consumption,and network efficiency.This study introduces a novel Dynamic Optimization Algorithm called Dynamic Multi-Objective Gannet Optimization(DMGO),designed to enhance data replication efficiency in cloud environments.Unlike traditional static replication systems,DMGO adapts dynamically to variations in network conditions,system demand,and resource availability.The approach utilizes multi-objective optimization approaches to efficiently balance data access latency,storage efficiency,and operational costs.DMGO consistently evaluates data center performance and adjusts replication algorithms in real time to guarantee optimal system efficiency.Experimental evaluations conducted in a simulated cloud environment demonstrate that DMGO significantly outperforms conventional static algorithms,achieving faster data access,lower storage overhead,reduced energy consumption,and improved scalability.The proposed methodology offers a robust and adaptable solution for modern cloud systems,ensuring efficient resource consumption while maintaining high performance.
基金fundings supported by Sichuan Science and Technology Program(2025YFHZ0065).
文摘Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service life,and high reliability.However,in practical design processes,topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions,which traditional methods often struggle accommodate.Therefore,this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization(DHPSO)algorithm.By leveraging the Kriging model as a surrogate,the high cost associated with repeatedly running finite element analysis processes is reduced,addressing the issue of minimizing structural compliance.Meanwhile,the DHPSO algorithm enables a better balance between the population’s developmental and exploratory capabilities,significantly accelerating convergence speed and enhancing global convergence performance.Finally,the proposed method is validated through three different structural examples,demonstrating its superior performance.Observed that the computational that,compared to the traditional Solid Isotropic Material with Penalization(SIMP)method,the proposed approach reduces the upper bound of structural compliance by approximately 30%.Additionally,the optimized results exhibit clear material interfaces without grayscale elements,and the stress concentration factor is reduced by approximately 42%.Consequently,the computational results fromdifferent examples verify the effectiveness and superiority of this study across various fields,achieving the goal of providing more precise optimization results within a shorter timeframe.
基金supported by Projects 12325501,12047503,12247104,and 12322501 of the National Natural Science Foundation of ChinaProject ZDRW-XX-2022-302 of the Chinese Academy of Sciencespartially supported by the Innovation Program for Quantum Science and Technology project 2021ZD0301900。
文摘Combinatorial optimization problems and ground state problems of spin glasses are crucial in various fields of science and technology.However,they often belong to the computational class of NP-hard,presenting significant computational challenges.Traditional algorithms inspired by statistical physics like simulated annealing have been widely adopted.Recently,advancements in Ising machines,such as quantum annealers and coherent Ising machines,offer new paradigms for solving these problems efficiently by embedding them into the analog evolution of nonlinear dynamical systems.However,existing dynamics-based algorithms often suffer from low convergence rates and local minima traps.In this work,we introduce the dual mean-field dynamics into Ising machines.The approach integrates the gradient force and the transverse force into the dynamics of Ising machines in solving combinatorial optimization problems,making it easier for the system to jump out of the local minimums and allowing the dynamics to explore wider in configuration space.We conduct extensive numerical experiments using the Sherrington–Kirkpatrick spin glass up to 10000 spins and the maximum cut problems with the standard G-set benchmarks.The numerical results demonstrate that our dual mean-field dynamics approach enhances the performance of base Ising machines,providing a more effective solution for large-scale combinatorial optimization problems.
基金supported by National Key Research and Development Program of China under Grant 2024YFE0210800National Natural Science Foundation of China under Grant 62495062Beijing Natural Science Foundation under Grant L242017.
文摘The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.
基金supported by the National Nature Science Foundation of China(Nos.62063019)Natural Science Foundation of Gansu Province(22JR5RA241,2023CXZX-465).
文摘In this study,we construct a bi-level optimization model based on the Stackelberg game and propose a robust optimization algorithm for solving the bi-level model,assuming an actual situation with several participants in energy trading.Firstly,the energy trading process is analyzed between each subject based on the establishment of the operation framework of multi-agent participation in energy trading.Secondly,the optimal operation model of each energy trading agent is established to develop a bi-level game model including each energy participant.Finally,a combination algorithm of improved robust optimization over time(ROOT)and CPLEX is proposed to solve the established game model.The experimental results indicate that under different fitness thresholds,the robust optimization results of the proposed algorithm are increased by 56.91%and 68.54%,respectively.The established bi-level game model effectively balances the benefits of different energy trading entities.The proposed algorithm proposed can increase the income of each participant in the game by an average of 8.59%.
基金the Foshan Science and Technology Innovation Team Project(No.FS0AA-KJ919-4402-0060)the National Natural Science Foundation of China(No.62263018)。
文摘To efficiently search out the optimal cam contour,a software integrated optimization method considering cam mechanism’s kinematic and dynamic characteristics was presented,and its effectiveness was demonstrated by a case study of the cam contour optimization for an offset press open-close gripper mechanism.The acceleration curve and the residual vibration model of the follower were separately studied.A symmetric harmonic trapezoidal curve was designed to control the follower’s acceleration,and single-DOF lumped parameter torsional vibration model was proposed to describe the follower’s residual vibration.Accordingly,corresponding motion curve design software and Simulink vibration model of the follower were developed respectively,and they were integrated into an automatic optimization platform with iSIGHT.The multi-objective optimization with objectives of minimizing both the acceleration and the residual vibration of the follower was completed within the platform by using NSGA-II algorithm.An appropriate point with lower acceleration and residual vibration was chosen from Pareto front as an optimal solution of the follower’s motion curve.Based on the follower’s new motion curve,the actual cam contour was generated by inverse kinematic simulation in COSMOSMotion.The offset press that installed our new designed cam exhibited a lower vibration than the previous machine,and the maximum measured acceleration of the offset press at a printing speed of 15000 r/h is reduced by 7.7%.
基金Open Project of Shanghai Key Laboratory of Lightweight Composite,China(No.2232021A4-04)。
文摘Double-shaft-driven needle punching machine is a specialized equipment designed for processing C/C crucible preforms.Its main needle punching module is operated by two sets of reciprocating crank-slider mechanisms.The intense vibration during needle punching not only generates huge noise,but also substantially reduces the quality of the preform.It is imperative to perform a dynamic analysis and optimization of the entire needle punching machine.In this paper,the three-dimensional(3D)model of the entire double-shaft-driven needle punching machine for C/C crucible preforms is established.Based on the modal analysis theory,the modal characteristics of the needle punching machine under various operating conditions are analyzed and its natural frequencies and vibration modes are determined.The harmonic response analysis is then employed to obtain the amplitude of the needle plate at different frequencies,and the structural weak points of the needle punching machine are identified and improved.The feasibility of the optimized scheme is subsequently reevaluated and verified.The results indicate that the first six natural frequencies of the machine increase,and the maximum amplitude of the needle plate decreases by 70.3%.The enhanced dynamic characteristics of the machine significantly improve its performance,enabling more efficient needle punching of C/C crucible preforms.
基金supported by the National Natural Science Foundation of China(Nos.52204136,52474100,and 52204092).
文摘Web pillars enduring complex coupled loads are critical for stability in high-wall mining.This study develops a dynamic failure criterion for web pillars under non-uniform loading using catastrophe theory.Through the analysis of the web pillar-overburden system’s dynamic stress and deformation,a total potential energy function and dynamic failure criterion were established for web pillars.An optimizing method for web pillar parameters was developed in highwall mining.The dynamic criterion established was used to evaluate the dynamic failure and stability of web pillars under static and dynamic loading.Key findings reveal that vertical displacements exhibit exponential-trigonometric variation under static loads and multi-variable power-law behavior under dynamic blasting.Instability risks arise when the roof’s tensile strength-to-stress ratio drops below 1.Using catastrophe theory,the bifurcation setΔ<0 signals sudden instability.The criterion defines failure as when the unstable web pillar section length l1 exceeds the roof’s critical collapse distance l2.Case studies and simulations determine an optimal web pillar width of 4.6 m.This research enhances safety and resource recovery,providing a theoretical framework for advancing highwall mining technology.
基金supported by the National Natural Science Foundation of China(Grant No.62575051)the Aeronautical Science Foundation of China(Grant No.2023M038080001)+1 种基金the Equipment Pre-research Joint Fund of the Ministry of Education(Grant No.8091B042228)the Science and Technology Project of Sichuan Province(Grant Nos.2023NSFSC1964 and 203NSFSC0033).
文摘The design and optimization of nonlinear fiber laser sources,such as soliton self-frequency shift(SSFS)tunable sources and supercontinuum(SC)sources,have traditionally relied on manual tuning and simulations,posing challenges for real-time applications.Machine learning has shown promise in fiber nonlinear propagation characterization,but the optimization and design of nonlinear systems remain relatively unexplored,especially under multitarget optimization conditions.In this paper,we propose a method that combines deep reinforcement learning(DRL)and deep neural network(DNN)to achieve fast synchronization optimization of ultrafast pulse nonlinear propagation in optical fibers under multitarget optimization tasks,with applications demonstrated in complex SSFS and SC generation systems in the mid-infrared band.The results indicate that a set of optimization parameters can be obtained in a few seconds,enabling rapid,automated tuning of pulse parameters in pursuit of diverse optimization objectives.This integration of DRL and DNN models holds transformative potential for the real-time optimization of not only fiber lasers but also a wide variety of complex photonic systems,paving the way for intelligent,adaptive optical system design and operation.
文摘This study discusses a machine learning‑driven methodology for optimizing the aerodynamic performance of both conventional,like common research model(CRM),and non‑conventional,like Bionica box‑wing,aircraft configurations.The approach leverages advanced parameterization techniques,such as class and shape transformation(CST)and Bezier curves,to reduce design complexity while preserving flexibility.Computational fluid dynamics(CFD)simulations are performed to generate a comprehensive dataset,which is used to train an extreme gradient boosting(XGBoost)model for predicting aerodynamic performance.The optimization process,using the non‑dominated sorting genetic algorithm(NSGA‑Ⅱ),results in a 12.3%reduction in drag for the CRM wing and an 18%improvement in the lift‑to‑drag ratio for the Bionica box‑wing.These findings validate the efficacy of machine learning based method in aerodynamic optimization,demonstrating significant efficiency gains across both configurations.
文摘Investors are always willing to receive more data.This has become especially true for the application of modern portfolio theory to the institutional asset allocation process,which requires quantitative estimates of risk and return.When long-term data series are unavailable for analysis,it has become common practice to use recent data only.The danger is that these data may not be representative of future performance.Although longer data series are of poorer quality,are difficult to obtain,and may reflect various political and economic regimes,they often paint a very different picture of emerging market performance.This paper presents an application of a stochastic non-linear optimization model of portfolios including transaction costs in the Brazilian financial market.In order to have that,portfolio theory and optimal control were used as theoretical basis.The first strategy tries to allocate the whole available wealth,not considering the risk associated to portfolio(deterministic result).In this case the investor obtained profits of 7.23%a month,taking into account the three risk aversion levels during the whole planning period.On the contrary,the results from the stochastic algorithm obtain profits of 1.34%a month and 18.06%a year,if the investor has low risk aversion.The profits would be 0.88%a month and 11.02%a year for a medium risk aversion investor.And with high risk aversion,the investor obtains 0.62%a month and 7.68%a year.
基金Supported by National Natural Science Foundation of China(Grant No.2018YFB1703000)State Key Laboratory of Metal Extrusion and Forging Equipment TechnologyChina National Heavy Machinery Research Institute Co.,Ltd.(Grant No.B2408100.W19)。
文摘The solenoid switching valve(SSV)is the key control component of heavy equipment such as continuous casting machines.However,the incompatibility of structural parameters increases the opening and closing time of the SSV.Therefore,this study proposes an optimized design method for an SSV to improve its dynamic performance.First,a multi-physics field-coupling model of the SSV is built,and the effects of different structural parameters on the electromagnetic characteristics are analyzed.After identifying the key influencing parameters,second-order response surface models are established to efficiently predict the opening and closing time.Subsequently,based on the nondominated sorting genetic algorithmⅡ(NSGA-Ⅱ),multi-objective optimization is applied to obtain the Pareto optimal solution of the structural parameters under the double-voltage driving strategy.The structure of the solenoid and valve as well as the dynamic characteristics of the valve are improved.Compared with those before optimization,the optimization results show that the opening and closing time of the optimized SSV are reduced by 24.38%and 51.8%,respectively,and the volume is reduced by 19.7%.The research results and the influence of the solenoid structural parameters on the electromagnetic force provide significant guidance for the design of this type of valve.
基金funded by the“Hundred Outstanding Talents”Support Program of Jining University,a provincial-level key project in the field of natural sciences,grant number 2023ZYRC23Jining Key R&D Program(Soft Science)Project,No.2024JNZC010Shandong Province Key Research and Development Program(Technology-Based Small and Medium-sized Enterprises Innovation Capability Improvement)Project No.2025TSGCCZZB0679.
文摘The rapid advancement of technology and the increasing speed of vehicles have led to a substantial rise in energy consumption and growing concern over environmental pollution.Beyond the promotion of new energy vehicles,reducing aerodynamic drag remains a critical strategy for improving energy efficiency and lowering emissions.This study investigates the influence of key geometric parameters on the aerodynamic drag of vehicles.A parametric vehicle model was developed,and computational fluid dynamics(CFD)simulations were conducted to analyse variations in the drag coefficient(C_(d))and pressure distribution across different design configurations.The results reveal that the optimal aerodynamic performance—characterized by a minimized drag coefficient—is achieved with the following parameter settings:engine hood angle(α)of 15°,windshield angle(β)of 25°,rear window angle(γ)of 40°,rear upwards tail lift angle(θ)of 10°,ground clearance(d)of 100 mm,and side edge angle(s)of 5°.These findings offer valuable guidance for the aerodynamic optimization of vehicle body design and contribute to strategies aimed at energy conservation and emission reduction in the automotive sector.
基金supported by Research Project of Zhuhai City Polytechnic(Grant No.2024KYBS06)Education Research Project of Zhuhai City Polytechnic(Grant No.JY20250404).
文摘The noise generated by high-speed hair dryers significantly affects user experience,with aerodynamic design playing a crucial role in controlling sound emissions.This study investigates the aerodynamic noise characteristics of a commercial high-speed hair dryer through Computational Fluid Dynamics(CFD)analysis.The velocity field,streamline patterns,and vector distribution within the primary flow path and internal cavity were systematically examined.Results indicate that strong interactions between the wake flow generated by the guide vanes and the straight baffle in the rear flow path induce vortex structures near the outlet,which are primarily responsible for highfrequency noise.To address this,the guide vanes and rear flow path geometry were redesigned and optimized for improved acoustic and aerodynamic performance.Underrated operating conditions(28 V,20,000 rpm),the optimized configuration achieves a noise reduction of more than 2.2 dB while increasing outlet wind speed by over 9%.Moreover,the noise suppression effect becomes more pronounced at lower rotational speeds.
基金supported by the National Natural Science Foundation of China(62376290).
文摘This work presents a nonlinear integral-ameliorated model for handling dynamic optimization problems with affine constraints.They pose a challenge as their optimal solutions evolve with time.Traditional iteration-based methods that exactly solve the problem at each time instant,fail to precisely and realtime track the solution due to computational and communication bottlenecks.Our model,through rigorous theoretical analyses,is able to reduce the optimality gap(i.e.,the difference between the model state and optimal solution)to zero in a finite time,and thus,track the solution online.Besides,perturbance is taken into account.We prove that under certain conditions,our model can totally tolerate an important kind of noise that we call“errorrelated noise”.In numerical experiments,compared with six existing methods,our model exhibits superior robustness when contaminated by the error-related noise.The key techniques in the model design involve employing the zeroing neural network to leverage time-derivative information,and introducing an integral term as well as the class C_(L)^(0)functions to enhance convergence and noise resistance.Finally,we establish a model-free control framework for a surgical manipulator with the remote-center-of-motion constraint and compare the performances of the framework based on different models in simulations.The results indicate that our model achieves the best performance among various models employed within the framework.
基金Supported by the National Key Basic Research Program of China("973"Project)(2009CB724401)the China Postdoctoral Science Foundation(20070420208)the Postdoctoral Innovation Foundation of Shandong Province(200702023)~~
文摘Considering the self-excited and forced vibrations in high-speed milling processes, a novel method for dynamic optimization of system stability is used to determine the cutting parameters and structural parameters by increasing the chatter free material removal rate （CF-MRR） and surface finish. The method is hased on the theory of the chatter stability and the semi-bandwidth of the resonant region. The objective function of the method is material removal rate（MRR）,the constraints are chatter stability and surface finish, and the optimizing variables are cutting and structural parameters. The optimization procedure is stated. The method is applied to a milling system and CF-MRR is increased 18.86%. It is shown that the influences of the chatter stability and the resonance are simultaneously considered in the dynamic optimization of the milling system for increasing CF-MRR and the surface finish.
文摘The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.
基金Knowledge-based Ship-design Hyper-integrated Platform(KSHIP) of Ministry of Education and Ministry of Finance,P. R. China(No.200512)
文摘The goal of this effort was to provide a static and dynamic collaborative optimization （CO） model for the design of ship hull structure. The CO model integrated with static, mode and dynamic analyses. In the system-level optimization model, a new objective function was advised, integrating all the subsystem-levels＇ objective functions, so as to eliminate the effects of dimensions and magnitude order. The proposed CO architecture enabled multi-objectives of the system and subsystem-level to be considered at both levels during optimization. A bi-level optimization strategy was advised, using the multi-island genetic algorithm. The proposed model was demonstrated with a deck optimization problem of container ship stern. The analysis progress and results of example show that the CO strategy is not only feasible and reliable, but also well suited for use in actual optimization problems of ship design.
