Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is...Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is cumbersome and inefficient.Thus,this work develops a multi-objective optimization method to enhance the torsional resistance of asymmetric base-isolated structures.The primary objective is to simultaneously minimize the interstory rotation of the superstructure,the rotation of the isolation layer,and the interstory displacement of the superstructure without exceeding the isolator displacement limits.A fast non-dominated sorting genetic algorithm(NSGA-Ⅱ)is employed to satisfy this optimization objective.Subsequently,the isolator arrangement,encompassing both positions and categories,is optimized according to this multi-objective optimization method.Additionally,an optimization design platform is developed to streamline the design operation.This platform integrates the input of optimization parameters,the output of optimization results,the finite element analysis,and the multi-objective optimization method proposed herein.Finally,the application of this multi-objective optimization method and its associated platform are demonstrated on two asymmetric base-isolated structures of varying heights and plan configurations.The results indicate that the optimal isolator arrangement derived from the optimization method can further improve the control over the lateral and torsional responses of asymmetric base-isolated structures compared to conventional conceptual design methods.Notably,the interstory rotation of the optimal base-isolated structure is significantly reduced,constituting only approximately 33.7%of that observed in the original base-isolated structure.The proposed platform facilitates the automatic generation of the optimal design scheme for the isolators of asymmetric base-isolated structures,offering valuable insights and guidance for the burgeoning field of intelligent civil engineering design.展开更多
Impinging jet arrays are extensively used in numerous industrial operations,including the cooling of electronics,turbine blades,and other high-heat flux systems because of their superior heat transfer capabilities.Opt...Impinging jet arrays are extensively used in numerous industrial operations,including the cooling of electronics,turbine blades,and other high-heat flux systems because of their superior heat transfer capabilities.Optimizing the design and operating parameters of such systems is essential to enhance cooling efficiency and achieve uniform pressure distribution,which can lead to improved system performance and energy savings.This paper presents two multi-objective optimization methodologies for a turbulent air jet impingement cooling system.The governing equations are resolved employing the commercial computational fluid dynamics(CFD)software ANSYS Fluent v17.The study focuses on four controlling parameters:Reynolds number(Re),swirl number(S),jet-to-jet separation distance(Z/D),and impingement height(H/D).The effects of these parameters on heat transfer and impingement pressure distribution are investigated.Non-dominated Sorting Genetic Algorithm(NSGA-II)and Weighted Sum Method(WSM)are employed to optimize the controlling parameters for maximum cooling performance.The aim is to identify optimal design parameters and system configurations that enhance heat transfer efficiency while achieving a uniform impingement pressure distribution.These findings have practical implications for applications requiring efficient cooling.The optimized design achieved a 12.28%increase in convective heat transfer efficiency with a local Nusselt number of 113.05 compared to 100.69 in the reference design.Enhanced convective cooling and heat flux were observed in the optimized configuration,particularly in areas of direct jet impingement.Additionally,the optimized design maintained lower wall temperatures,demonstrating more effective thermal dissipation.展开更多
Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonli...Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios.This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm Ⅱ(SNSGA).The proposed model optimises the PID controller by minimising key performance metrics:integration time squared error(ITSE),integration time absolute error(ITAE),and rate of change of deviation(J).This approach balances convergence rate,overshoot,and oscillation dynamics effectively.A fuzzy-based method is employed to select the most suitable solution from the Pareto set.The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-Ⅱ and other advanced control methods.In a two-area thermal power system without reheat,the SNSGA significantly reduces settling times for frequency deviations:2.94s for Δf_(1) and 4.98s for Δf_(2),marking improvements of 31.6%and 13.4%over NSGA-Ⅱ,respectively.展开更多
This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is establish...This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.展开更多
In order to address typical problems due to the huge demand of oil for consumption in traditional internal combustion engines,a new more efficient combustion mode is proposed and studied in the framework of Computatio...In order to address typical problems due to the huge demand of oil for consumption in traditional internal combustion engines,a new more efficient combustion mode is proposed and studied in the framework of Computational Fluid Dynamics(CFD).Moreover,a Non-dominated Sorting Genetic Algorithm(NSGA-Ⅱ)is applied to optimize the related parameters,namely,the engine methanol ratio,the fuel injection time,the initial temperature,the Exhaust Gas Re-Circulation(EGR)rate,and the initial pressure.The so-called Conventional Diesel Combustion(CDC),Homogeneous Charge Compression Ignition(HCCI)and the Reactivity Controlled Compression Ignition(RCCI)combustion modes are compared.The results show that RCCI has a higher methanol ratio and an earlier injection timing with moderate EGR rate and higher initial pressure.The initial temperature increases as the methanol ratio increases.In comparison,CDC has the lowest hydrocarbon and CO emissions and the highest combustion efficiency.At different crankshaft rotation angles corresponding to 50%of the combustion amount(CA50),the combustion temperature and boundary layer temperature of HCCI change significantly,while those of RCCI undergo limited variations.At the same CA50,the exergy losses of HCCI and RCCI are lower than that of the CDC.On the basis of these findings,it can be concluded that the methanol/diesel RCCI engine can be used to obtain a clean and efficient combustion process,which should be regarded as a promising combustion mode.展开更多
Electrochemical machining（ECM） is one of the important non-traditional machining processes,which is used for machining of difficult-to-machine materials and intricate profiles.Being a complex process,it is very diff...Electrochemical machining（ECM） is one of the important non-traditional machining processes,which is used for machining of difficult-to-machine materials and intricate profiles.Being a complex process,it is very difficult to determine optimal parameters for improving cutting performance.Metal removal rate and surface roughness are the most important output parameters,which decide the cutting performance.There is no single optimal combination of cutting parameters,as their influences on the metal removal rate and the surface roughness are quite opposite.A multiple regression model was used to represent relationship between input and output variables and a multi-objective optimization method based on a non-dominated sorting genetic algorithm-Ⅱ（NSGA-Ⅱ） was used to optimize ECM process.A non-dominated solution set was obtained.展开更多
To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solve...To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.展开更多
基金National Natural Science Foundation of China under Grant No.52278490。
文摘Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is cumbersome and inefficient.Thus,this work develops a multi-objective optimization method to enhance the torsional resistance of asymmetric base-isolated structures.The primary objective is to simultaneously minimize the interstory rotation of the superstructure,the rotation of the isolation layer,and the interstory displacement of the superstructure without exceeding the isolator displacement limits.A fast non-dominated sorting genetic algorithm(NSGA-Ⅱ)is employed to satisfy this optimization objective.Subsequently,the isolator arrangement,encompassing both positions and categories,is optimized according to this multi-objective optimization method.Additionally,an optimization design platform is developed to streamline the design operation.This platform integrates the input of optimization parameters,the output of optimization results,the finite element analysis,and the multi-objective optimization method proposed herein.Finally,the application of this multi-objective optimization method and its associated platform are demonstrated on two asymmetric base-isolated structures of varying heights and plan configurations.The results indicate that the optimal isolator arrangement derived from the optimization method can further improve the control over the lateral and torsional responses of asymmetric base-isolated structures compared to conventional conceptual design methods.Notably,the interstory rotation of the optimal base-isolated structure is significantly reduced,constituting only approximately 33.7%of that observed in the original base-isolated structure.The proposed platform facilitates the automatic generation of the optimal design scheme for the isolators of asymmetric base-isolated structures,offering valuable insights and guidance for the burgeoning field of intelligent civil engineering design.
文摘Impinging jet arrays are extensively used in numerous industrial operations,including the cooling of electronics,turbine blades,and other high-heat flux systems because of their superior heat transfer capabilities.Optimizing the design and operating parameters of such systems is essential to enhance cooling efficiency and achieve uniform pressure distribution,which can lead to improved system performance and energy savings.This paper presents two multi-objective optimization methodologies for a turbulent air jet impingement cooling system.The governing equations are resolved employing the commercial computational fluid dynamics(CFD)software ANSYS Fluent v17.The study focuses on four controlling parameters:Reynolds number(Re),swirl number(S),jet-to-jet separation distance(Z/D),and impingement height(H/D).The effects of these parameters on heat transfer and impingement pressure distribution are investigated.Non-dominated Sorting Genetic Algorithm(NSGA-II)and Weighted Sum Method(WSM)are employed to optimize the controlling parameters for maximum cooling performance.The aim is to identify optimal design parameters and system configurations that enhance heat transfer efficiency while achieving a uniform impingement pressure distribution.These findings have practical implications for applications requiring efficient cooling.The optimized design achieved a 12.28%increase in convective heat transfer efficiency with a local Nusselt number of 113.05 compared to 100.69 in the reference design.Enhanced convective cooling and heat flux were observed in the optimized configuration,particularly in areas of direct jet impingement.Additionally,the optimized design maintained lower wall temperatures,demonstrating more effective thermal dissipation.
基金supported in part by the Science and Technology Innovation Program of Hunan Province under Grant 2022RC4028in part by the National Natural Science Foundation of China under Grant 62473204+3 种基金in part by the Chunhui Program Collaborative Scientific Research Project under Grant 202202004in part by the Natural Science Foundation of Nanjing University of Posts and Telecommunications under Grants NY221082,NY222144,and NY223075in part by the Huali Program for Excellent Talents in Nanjing University of Posts and Telecommunicationsin part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX24_1215.
文摘Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios.This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm Ⅱ(SNSGA).The proposed model optimises the PID controller by minimising key performance metrics:integration time squared error(ITSE),integration time absolute error(ITAE),and rate of change of deviation(J).This approach balances convergence rate,overshoot,and oscillation dynamics effectively.A fuzzy-based method is employed to select the most suitable solution from the Pareto set.The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-Ⅱ and other advanced control methods.In a two-area thermal power system without reheat,the SNSGA significantly reduces settling times for frequency deviations:2.94s for Δf_(1) and 4.98s for Δf_(2),marking improvements of 31.6%and 13.4%over NSGA-Ⅱ,respectively.
基金Supported by the Natural Science Foundation of Zhejiang Province(No.LQ22F030015).
文摘This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.
文摘In order to address typical problems due to the huge demand of oil for consumption in traditional internal combustion engines,a new more efficient combustion mode is proposed and studied in the framework of Computational Fluid Dynamics(CFD).Moreover,a Non-dominated Sorting Genetic Algorithm(NSGA-Ⅱ)is applied to optimize the related parameters,namely,the engine methanol ratio,the fuel injection time,the initial temperature,the Exhaust Gas Re-Circulation(EGR)rate,and the initial pressure.The so-called Conventional Diesel Combustion(CDC),Homogeneous Charge Compression Ignition(HCCI)and the Reactivity Controlled Compression Ignition(RCCI)combustion modes are compared.The results show that RCCI has a higher methanol ratio and an earlier injection timing with moderate EGR rate and higher initial pressure.The initial temperature increases as the methanol ratio increases.In comparison,CDC has the lowest hydrocarbon and CO emissions and the highest combustion efficiency.At different crankshaft rotation angles corresponding to 50%of the combustion amount(CA50),the combustion temperature and boundary layer temperature of HCCI change significantly,while those of RCCI undergo limited variations.At the same CA50,the exergy losses of HCCI and RCCI are lower than that of the CDC.On the basis of these findings,it can be concluded that the methanol/diesel RCCI engine can be used to obtain a clean and efficient combustion process,which should be regarded as a promising combustion mode.
文摘Electrochemical machining（ECM） is one of the important non-traditional machining processes,which is used for machining of difficult-to-machine materials and intricate profiles.Being a complex process,it is very difficult to determine optimal parameters for improving cutting performance.Metal removal rate and surface roughness are the most important output parameters,which decide the cutting performance.There is no single optimal combination of cutting parameters,as their influences on the metal removal rate and the surface roughness are quite opposite.A multiple regression model was used to represent relationship between input and output variables and a multi-objective optimization method based on a non-dominated sorting genetic algorithm-Ⅱ（NSGA-Ⅱ） was used to optimize ECM process.A non-dominated solution set was obtained.
基金Supported by the National"Thirteenth Five-year Plan"National Key Program(2016YFD0701301)the Heilongjiang Provincial Achievement Transformation Fund Project(NB08B-011)。
文摘To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.
