This paper considers dealing with path constraints in the framework of the improved control vector iteration (CVI) approach. Two available ways for enforcing equality path constraints are presented, which can be dir...This paper considers dealing with path constraints in the framework of the improved control vector iteration (CVI) approach. Two available ways for enforcing equality path constraints are presented, which can be directly incorporated into the improved CVI approach. Inequality path constraints are much more difficult to deal with, even for small scale problems, because the time intervals where the inequality path constraints are active are unknown in advance. To overcome the challenge, the ll penalty function and a novel smoothing technique are in-troduced, leading to a new effective approach. Moreover, on the basis of the relevant theorems, a numerical algo-rithm is proposed for nonlinear dynamic optimization problems with inequality path constraints. Results obtained from the classic batch reaCtor operation problem are in agreement with the literature reoorts, and the comoutational efficiency is also high.展开更多
Three-way control combiner valves(TCCVs)are critical components used in nuclear power plants to regulate the concentration of boron acid for neutron absorption and reactor safety.However,current TCCV designs often suf...Three-way control combiner valves(TCCVs)are critical components used in nuclear power plants to regulate the concentration of boron acid for neutron absorption and reactor safety.However,current TCCV designs often suffer from suboptimal control performance and high flow resistance,leading to control deviations and reduced operational efficiency.In this paper,a numerical model based on the standard K–ωturbulence model is established and validated against experimental data to analyze the flow characteristics and local flow resistance of a TCCV.A parametric design method for the throttling windows is proposed,establishing relationships between shape parameters and performance indexes,including control performance and flow resistance.The adaptive non-dominated sorting genetic algorithm(ANSGA-II)is used to optimize the shape parameters of the throttling windows.The optimization results show an improvement in the performance indexes of the TCCV,with the adjustable operating range increasing by 31.0%and the maximum local resistance decreasing by 18.3%.We also introduce the concepts of effective and controllable domains to characterize the inlet backflow phenomena and regulation dead zones,which are crucial for ensuring the reliability and effectiveness of control valves.These findings provide insights for enhancing the design and performance of TCCVs in nuclear power plants.展开更多
Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The app...Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The applications span across non-volatile memory,neuromorphic computing,hardware security,and beyond,prompting memristors to become a versatile solution for next-generation computing and data storage systems.Despite enormous potential of memristors,the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability,device reproducibility,and array scalability.This review systematically explores recent advancements in high-performance memristor technologies,focusing on performance enhancement strategies through material engineering,structural design,pulse protocol optimization,and algorithm control.We provide an in-depth analysis of key performance metrics tailored to specific applications,including non-volatile memory,neuromorphic computing,and hardware security.Furthermore,we propose a co-design framework that integrates device-level optimizations with operational-level improvements,aiming to bridge the gap between theoretical models and practical implementations.展开更多
Conformal truss-like lattice structures face significant manufacturability challenges in additive manufac-turing due to overhang angle limitations.To address this problem,we propose a novel angle-constrained optimizat...Conformal truss-like lattice structures face significant manufacturability challenges in additive manufac-turing due to overhang angle limitations.To address this problem,we propose a novel angle-constrained optimization method grounded in the global adjustment of nodal coordinates.First,a build direction is selected to minimize the number of violating struts.Then,an angular-constraint matrix is assembled from strut direction vectors,and analytical sensitivities with respect to nodal coordinates are derived to enable efficient constrained optimization under nonlinear angular inequality constraints.Numerical studies on two complex curved-surface lattices demonstrate that all overhang violations are eliminated while only minor changes are induced in global stiffness and strength.In particular,the maximum displacement of an ergonomic insole varies by only 2.87%after optimization.The results confirm the method’s versatility and engineering robustness,providing a practical approach for additive manufacturing-oriented lattice structure design.展开更多
An efficient and innovative method is presented for the stress-related structural topology optimization(TO)in coupled mechanical-pressure systems by leveraging flexible polygonal meshes.With a polytopal composite fini...An efficient and innovative method is presented for the stress-related structural topology optimization(TO)in coupled mechanical-pressure systems by leveraging flexible polygonal meshes.With a polytopal composite finite element approach,the volumetric locking in nearly incompressible materials is reduced.A fluid-flow-based model is built,in which a design-dependent pressure variable is introduced to capture the loading conditions within the system.The P-norm approach consolidates the stress metrics into a global measure,while the clustered regional scaling and adaptive techniques enhance the solutions for stress-limited cases.The primary contributions of this work include a novel framework for addressing the stress challenges in coupled mechanical-pressure systems via flow-based modeling,the adaptability to both compressible and nearly incompressible materials,and the compatibility with diverse mesh types,including triangular,quadrilateral,and polygonal elements.The numerical examples demonstrate,for the first time,optimized topologies for nearly incompressible materials under stress constraints in coupled mechanical-pressure environments,emphasizing the unique strength of this approach.展开更多
Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers of...Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers offer advantages such as reduced material usage,lower refrigerant charge,and compact structure.However,they also face challenges,including increased refrigerant pressure drop and smaller heat transfer area inside the tubes.This paper combines the advantages and disadvantages of both small and large-diameter tubes and proposes a combined-diameter heat exchanger,consisting of large and small diameters,for use in the indoor units of split-type air conditioners.There are relatively few studies in this area.In this paper,A theoretical and numerical computation method is employed to establish a theoretical-numerical calculation model,and its reliability is verified through experiments.Using this model,the optimal combined diameters and flow path design for a combined-diameter heat exchanger using R32 as the working fluid are derived.The results show that the heat transfer performance of all combined diameter configurations improves by 2.79%to 8.26%compared to the baseline design,with the coefficient of performance(COP)increasing from 4.15 to 4.27~4.5.These designs can save copper material,but at the cost of an increase in pressure drop by 66.86%to 131.84%.The scheme IIIH,using R32,is the optimal combined-diameter and flow path configuration that balances both heat transfer performance and economic cost.展开更多
Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for ...Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for network coverage optimization.ARO-PSO successfully combines the stochastic characteristics of ARO and the global characteristics of PSO.Firstly,to optimize the quality of the initial population,Sine chaos mapping is introduced to initialize the population;secondly,to better balance the exploration and exploitation,adaptive settings are made;finally,combined with the characteristics of the ARO energy factor,a population decreasing strategy is introduced to further accelerate the convergence speed of the algorithm.Experimental and analytical comparisons are made with ARO and PSO and 6 other excellent optimizers on 13 benchmark functions.The results show that ARO-PSO largely outperforms the original algorithm.Finally,ARO-PSO is applied to WSN coverage optimization experiments in 2D and 3D environments,and the proposed algorithm exhibits higher network coverage and improves the monitoring quality of the network compared to standard ARO and PSO and other state-of-the-art algorithms.The experimental results fully demonstrate the superiority of the ARO-PSO-based WSN node deployment optimization method.展开更多
Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic e...Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe.展开更多
In the conceptual design phase of the satellite thermal management system,components layout optimization and structural topology optimization of satellite panel can meet global and local thermal management requirement...In the conceptual design phase of the satellite thermal management system,components layout optimization and structural topology optimization of satellite panel can meet global and local thermal management requirements,respectively.However,achieving non-interfering coupling between these two optimization processes remains a challenge.An integrated layout-structure design method based on thermal metamaterials is proposed,which comprises two design stages.In the first stage,components layout optimization is conducted to maximize temperature uniformity within the satellite module,yielding a globally optimized layout with balanced thermal characteristics.In the second stage,topology optimization guided by the design principle of thermal metamaterials is implemented in critical local panel regions to satisfy differentiated heat transfer requirements of components with diverse functional and thermal sensitivity properties.The key innovation lies in utilizing thermal metamaterials as a mediator to synergistically couple global components layout optimization with local structural topology optimization,which enables customized local heat flux manipulation without interfering with the globally optimized temperature field derived from the layout optimization.The method introduces neither additional mass nor special materials,offering advantages of low cost,high reliability,and strong versatility.It provides a new solution paradigm for the design of passive thermal management systems in satellites.展开更多
Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike tradi...Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike traditional many-objective optimization methods,which typically attempt comprehensive coverage of the Pareto front,F4M optimization emphasizes finding a small representative solution set to efficiently address highdimensional objective spaces.Motivated by the computational complexity and practical relevance of F4M optimization,this paper proposes a new evolutionary algorithm explicitly tailored for efficiently solving F4M optimization problems.Inspired by Smetric selection evolutionary multi-objective optimization algorithm(SMS-EMOA),our proposed approach employs a(μ+1)-evolution strategy guided by the objective of F4M optimization.Furthermore,to facilitate rigorous performance assessment,we propose a novel benchmark test suite specifically designed for F4M optimization by leveraging the similarity betw een the R2indicator and F4M formulations.Our test suite is highly flexible,allowing any existing multi-objective optimization problem to be transformed into a corresponding F4M instance via scalarization using the weighted Tchebycheff function.Comprehensive experimental evaluations on benchmarks demonstrate the superior performance of our algorithm compared to existing state-of-the-art algorithms,especially on instances involving a large number of objectives.The source code of the proposed algorithm will be released publicly.Source code is available at http://gffzz188fe103f8f1460asqu99ouuwwk5x6opf.ffgz.tsg.suse.edu.cn/MOL-SZU/SoM-EMOA.展开更多
Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightw...Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightweight of two distinct material characteristics.MFHTWTs can achieve energy absorption through the coupling of material plastic deformation and fracture,demonstrating significant engineering value in passive safety.This review provides a comprehensive examination of the crashworthiness topology optimization of MFHTWTs,aiming to demonstrate that a deeply integrated approach combining topology and parameter opti-mization can realize an optimal design method for MFHTWTs,thereby maximizing the functional utilization of limited material.Firstly,the review highlights the crashworthiness topology optimization methods(CTOMs)based on thin-walled structures.With a particular focus on metal,the review discusses both the practical ap-plicability and limitations of CTOMs under crash conditions.Additionally,based on the methodology of the equivalent static load method(ESLM),the review emphasizes that topology optimization methods considering continuous fiber paths and multi-material interface connections are also applicable to the crashworthiness op-timization of MFHTWTs.Furthermore,to couple structural parameters and configuration characteristics,in-tegrated topology optimization methods,including parameter optimization,are proposed to provide a valuable reference for the global optimization of MFHTWTs.Thus,these methods can establish the mapping relationship between key parameters and the structural energy absorption capacity.展开更多
Multiple unmanned aerial vehicles(UAVs)play a vital role in monitoring and data collection in wide area environments with harsh conditions.In most scenarios,issues such as real-time data retrieval and real-time UAV po...Multiple unmanned aerial vehicles(UAVs)play a vital role in monitoring and data collection in wide area environments with harsh conditions.In most scenarios,issues such as real-time data retrieval and real-time UAV positioning are often disregarded,essentially neglecting the communication constraints.In this paper,we comprehensively address both the coverage of the target area and the data transmission capabilities of the flying ad hoc network(FANET).The data throughput of the network is therefore maximized by optimizing the network topology and UAV trajectories.The resultant optimization problem is effectively solved by the proposed reinforcement learning-based trajectory planning(RL-TP)algorithm and the convex-based topology optimization(C-TOP)algorithm sequentially.The RL-TP optimizes the UAV paths while considering the constraints of FANET.The C-TOP maximizes the data throughput of the network while simultaneously constraining the neighbors and transmit powers of the UAVs,which is shown to be a convex problem that can be efficiently solved in polynomial time.Simulations and field experimental results show that the proposed optimization strategy can effectively plan the UAV trajectories and significantly improve the data throughput of the FANET over the adaptive local minimum spanning tree(A-LMST)and cyclic pruning-assisted power optimization(CPAPO)methods.展开更多
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu...Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:http://gffzz188fe103f8f1460asqu99ouuwwk5x6opf.ffgz.tsg.suse.edu.cn/jinhao-ok1/Topo-for-DCTWS.git.展开更多
Deep reinforcement learning(DRL)has demonstrated exceptional capabilities in combinatorial optimization,which automatically devises policies for solution construction and optimizer refinement.DRL is particularly adept...Deep reinforcement learning(DRL)has demonstrated exceptional capabilities in combinatorial optimization,which automatically devises policies for solution construction and optimizer refinement.DRL is particularly adept in generating training samples by itself,thereby providing the flexibility to solve a variety of combinatorial optimization problems without supervision.While DRL takes actions according to states extracted from problem-specific information,it cannot be directly applied to black-box continuous optimization lacking explicit information.To address this issue,this paper proposes a search space independent operator based DRL method for black-box continuous optimization.It conceptualizes the optimization process driven by search space independent operators as a Markov decision process,wherein actions are defined as operators and states are extracted from solutions generated by operators.In contrast to other DRLassisted metaheuristics,the proposed method does not rely on any existing metaheuristic.Instead,it innovates by creating totally new operators,able to surpass the performance boundaries of existing metaheuristics.Compared with state-of-the-art metaheuristics and DRL methods,the proposed method shows significantly faster convergence speed on challenging continuous optimization problems.展开更多
When the Mars vehicles encounter a storm or other failure situations,the ability to abort and rise to a predetermined orbit in the powered descent flight phase is critical.Unlike the abort guidance mission of lunar ex...When the Mars vehicles encounter a storm or other failure situations,the ability to abort and rise to a predetermined orbit in the powered descent flight phase is critical.Unlike the abort guidance mission of lunar exploration,Mars has an atmosphere and stronger gravity,leading to high requirements on the efficiency,autonomy,reliability,and robustness of the guidance system.To this end,this paper proposes a safety abort guidance algorithm to stop the descent of the Mars vehicle with fast responses in the initial stage of abort.Then,to meet the requirements of rendezvous and docking with the orbiter,the constraints of terminal orbit parameters and orbit insertion time and position in the perifocal coordinate system are explicitly defined.To achieve a higher terminal orbit,a“powered-coast-powered”trajectory onboard optimization problem is established,which allows the trajectory optimization problem to be handled by convex optimization rapidly and accurately.Moreover,a switching strategy between the online trajectory optimization and terminal iterative guidance is detailed to ensure the accuracy of terminal orbit insertion.Finally,numerical experiments with Monte Carlo tests under various conditions are conducted to verify the efficiency,robustness,and onboard application performance of the proposed method.展开更多
This paper presents a tunable elastic metamaterial structure with periodically arranged resonators.The resonator consists of two beams and a moveable lumped mass block.The dynamical model of the proposed structure is ...This paper presents a tunable elastic metamaterial structure with periodically arranged resonators.The resonator consists of two beams and a moveable lumped mass block.The dynamical model of the proposed structure is established in frequency domain.The frequency responses are obtained by applying the spectral element method and finite element method.The influences of the structural and material parameters on the wave properties are explored.It is shown that the movement of the mass blocks has great relationships with the bandgaps,such as frequency shift and broadening effects.The parameter optimization is given by using genetic algorithms in order to improve the obtained results.The optimal positions of the mass blocks are addressed and ultra-wide bandgaps are revealed.An experimental platform is designed and performed to validate the bandgap characteristics.展开更多
Axial piston pumps are widely used to supply the fluid power,but their significant vibration has become a growing concern.To address this issue,this paper presents a novel dynamic model for the axial piston pump and m...Axial piston pumps are widely used to supply the fluid power,but their significant vibration has become a growing concern.To address this issue,this paper presents a novel dynamic model for the axial piston pump and mitigates both flow fluctuation and mechanical vibration powers through a multi-objective optimization method.A Lumped-Parameter(LP)model is first introduced to describe dynamic behaviors of the entire pump assembly,and the Newmark-βmethod is adopted to calculate flow fluctuation and mechanical vibration powers.Experimental validation is conducted to ensure the accuracy of the proposed model.Using this validated model,a multiobjective optimization algorithm is employed to optimize the structural parameters of three representative valve plate types,aiming to simultaneously reduce flow fluctuation and mechanical vibrations.The optimization results demonstrate a significant reduction in the pump vibration power,as well as improvements in cavitation and pressure overshoot conditions.Among these optimized designs,the valve plate with hole-shaped damping grooves shows the lowest vibration power,while the valve plate with the triangular damping grooves achieves the lowest maximum piston chamber pressure.This study offers a promising approach for designing quieter axial piston pumps,which promotes the fluid power technology.展开更多
Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods.To advance the field’s focus on algorithmic performance,this study introduces the T...Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods.To advance the field’s focus on algorithmic performance,this study introduces the Theory Evolution Optimization(TEO)–an efficient metaheuristic inspired by the evolution of scientific theory.TEO simulates the competitive,accumulative,and replacement processes among scientific hypotheses,mirroring the evolution from a hypothesis to an established scientific theory.The performance of TEO is validated through extensive experimental simulations and benchmarked against 28 popular algorithms,including highly competitive champions such as EBOwithCMAR,LSHADE_cnEpSi,and LSHADE.Pairwise comparisons between TEO and the latest algorithms are conducted using the Wilcoxon signed-rank test,with multiple comparisons managed by the Friedman test.Initially,TEO is tested on the classical IEEE CEC2017 and the latest IEEE CEC2022 benchmark functions.TEO successfully addresses four prominent engineering design problems in constrained continuous space for practical applications.Additionally,a binary TEO(BTEO)variant is introduced and applied to feature selection tasks in discrete space.Experimental results consistently demonstrate that TEO proposes highly competitive outcomes in optimization problems.The source codes for this research are accessible to the public at http://gffzze767f4cc5ce545d8squ99ouuwwk5x6opf.ffgz.tsg.suse.edu.cn/TEO.html.展开更多
This paper addresses the challenge of mass uncertainty during the powered descent phase of a Mars lander and proposes a robust powered descent guidance algorithm that accounts for uncertainties in mass and fuel consum...This paper addresses the challenge of mass uncertainty during the powered descent phase of a Mars lander and proposes a robust powered descent guidance algorithm that accounts for uncertainties in mass and fuel consumption.First,the traditional trajectory optimization method based on convex optimization is improved by developing a fast and accurate solution approach using sequential convex optimization.Second,the effects of mass uncertainty on position are modeled and analyzed,with corresponding computational methods provided for different scenarios.Third,the worst-case scenario under mass uncertainty is analyzed through both geometric and theoretical approaches,and a modified glide-slope constraint method is proposed to ensure safe landing even in adverse conditions.Moreover,a closed-loop receding horizon based guidance is developed to further mitigate the effects of mass uncertainty and improve terminal landing accuracy.Finally,the proposed improved convex optimization algorithm and robust trajectory optimization algorithm are validated through simulation cases and compared with a probabilistic approach.The simulations further test various initial positions,velocities,and glide-slope angles,demonstrating that the solutions are both accurate and robust.展开更多
The synergistic mechanism of multiple process parameters on the solidification structure of niobium containing austenitic stainless steel during continuous casting is complex,which seriously affects the quality of con...The synergistic mechanism of multiple process parameters on the solidification structure of niobium containing austenitic stainless steel during continuous casting is complex,which seriously affects the quality of continuous casting billets and seamless pipes.In order to optimize the quality of continuous casting billet,a finite element model of solidification and heat transfer in continuous casting process was established for the secondary cooling process of continuous casting billet.The control variable method was used to explore the influence of casting speed and superheat on the solidification process.At the same time,an orthogonal scheme was designed to study the coupling effect of multiple process parameters on the heat transfer and solidification state of continuous casting billets,and optimized process parameters were selected.The optimization results of process parameters were verified through production experiments,and it is found that the enrichment of coarse niobium compounds directly causes the initiation and propagation of inner wall cracks during the large deformation hot piercing of S30432 seamless tubes.Process parameter optimization,especially the synergistic effect of the decrease of superheat and increase of specific water flow promotes the grain refinement and expension of equiaxed crystal zone,thereby mitigating the segregation of Nb elements and improving the distribution of niobium compounds.展开更多
基金Supported by the National Natural Science Foundation of China(U1162130)the National High Technology Research and Development Program of China(2006AA05Z226)Outstanding Youth Science Foundation of Zhejiang Province(R4100133)
摘要This paper considers dealing with path constraints in the framework of the improved control vector iteration (CVI) approach. Two available ways for enforcing equality path constraints are presented, which can be directly incorporated into the improved CVI approach. Inequality path constraints are much more difficult to deal with, even for small scale problems, because the time intervals where the inequality path constraints are active are unknown in advance. To overcome the challenge, the ll penalty function and a novel smoothing technique are in-troduced, leading to a new effective approach. Moreover, on the basis of the relevant theorems, a numerical algo-rithm is proposed for nonlinear dynamic optimization problems with inequality path constraints. Results obtained from the classic batch reaCtor operation problem are in agreement with the literature reoorts, and the comoutational efficiency is also high.
基金supported by the National Natural Science Foundation of China(No.52422506).
摘要Three-way control combiner valves(TCCVs)are critical components used in nuclear power plants to regulate the concentration of boron acid for neutron absorption and reactor safety.However,current TCCV designs often suffer from suboptimal control performance and high flow resistance,leading to control deviations and reduced operational efficiency.In this paper,a numerical model based on the standard K–ωturbulence model is established and validated against experimental data to analyze the flow characteristics and local flow resistance of a TCCV.A parametric design method for the throttling windows is proposed,establishing relationships between shape parameters and performance indexes,including control performance and flow resistance.The adaptive non-dominated sorting genetic algorithm(ANSGA-II)is used to optimize the shape parameters of the throttling windows.The optimization results show an improvement in the performance indexes of the TCCV,with the adjustable operating range increasing by 31.0%and the maximum local resistance decreasing by 18.3%.We also introduce the concepts of effective and controllable domains to characterize the inlet backflow phenomena and regulation dead zones,which are crucial for ensuring the reliability and effectiveness of control valves.These findings provide insights for enhancing the design and performance of TCCVs in nuclear power plants.
基金supported by the National Key R&D Project from the Minister of Science and Technology(2024YFA1211500)the National Natural Science Foundation of China(Grant Nos.62304130,62405158 and 62574123)+1 种基金the Shanghai youth science and technology star project(24QA2702800)Shanghai Key Laboratory of Chips and Systems for Intelligent Connected Vehicle。
摘要Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The applications span across non-volatile memory,neuromorphic computing,hardware security,and beyond,prompting memristors to become a versatile solution for next-generation computing and data storage systems.Despite enormous potential of memristors,the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability,device reproducibility,and array scalability.This review systematically explores recent advancements in high-performance memristor technologies,focusing on performance enhancement strategies through material engineering,structural design,pulse protocol optimization,and algorithm control.We provide an in-depth analysis of key performance metrics tailored to specific applications,including non-volatile memory,neuromorphic computing,and hardware security.Furthermore,we propose a co-design framework that integrates device-level optimizations with operational-level improvements,aiming to bridge the gap between theoretical models and practical implementations.
基金supported by the National Natural Science Foundation of China(Grant Nos.12432005 and 12472116)the Fundamental Research Funds for the Central Universities(DUTZD25240).
摘要Conformal truss-like lattice structures face significant manufacturability challenges in additive manufac-turing due to overhang angle limitations.To address this problem,we propose a novel angle-constrained optimization method grounded in the global adjustment of nodal coordinates.First,a build direction is selected to minimize the number of violating struts.Then,an angular-constraint matrix is assembled from strut direction vectors,and analytical sensitivities with respect to nodal coordinates are derived to enable efficient constrained optimization under nonlinear angular inequality constraints.Numerical studies on two complex curved-surface lattices demonstrate that all overhang violations are eliminated while only minor changes are induced in global stiffness and strength.In particular,the maximum displacement of an ergonomic insole varies by only 2.87%after optimization.The results confirm the method’s versatility and engineering robustness,providing a practical approach for additive manufacturing-oriented lattice structure design.
基金National Research Foundation of Korea(No.2025-02303676)。
摘要An efficient and innovative method is presented for the stress-related structural topology optimization(TO)in coupled mechanical-pressure systems by leveraging flexible polygonal meshes.With a polytopal composite finite element approach,the volumetric locking in nearly incompressible materials is reduced.A fluid-flow-based model is built,in which a design-dependent pressure variable is introduced to capture the loading conditions within the system.The P-norm approach consolidates the stress metrics into a global measure,while the clustered regional scaling and adaptive techniques enhance the solutions for stress-limited cases.The primary contributions of this work include a novel framework for addressing the stress challenges in coupled mechanical-pressure systems via flow-based modeling,the adaptability to both compressible and nearly incompressible materials,and the compatibility with diverse mesh types,including triangular,quadrilateral,and polygonal elements.The numerical examples demonstrate,for the first time,optimized topologies for nearly incompressible materials under stress constraints in coupled mechanical-pressure environments,emphasizing the unique strength of this approach.
基金supported by Supported by the Scientific Research Foundation for High-Level Talents of Zhoukou Normal University(ZKNUC2024018).
摘要Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers offer advantages such as reduced material usage,lower refrigerant charge,and compact structure.However,they also face challenges,including increased refrigerant pressure drop and smaller heat transfer area inside the tubes.This paper combines the advantages and disadvantages of both small and large-diameter tubes and proposes a combined-diameter heat exchanger,consisting of large and small diameters,for use in the indoor units of split-type air conditioners.There are relatively few studies in this area.In this paper,A theoretical and numerical computation method is employed to establish a theoretical-numerical calculation model,and its reliability is verified through experiments.Using this model,the optimal combined diameters and flow path design for a combined-diameter heat exchanger using R32 as the working fluid are derived.The results show that the heat transfer performance of all combined diameter configurations improves by 2.79%to 8.26%compared to the baseline design,with the coefficient of performance(COP)increasing from 4.15 to 4.27~4.5.These designs can save copper material,but at the cost of an increase in pressure drop by 66.86%to 131.84%.The scheme IIIH,using R32,is the optimal combined-diameter and flow path configuration that balances both heat transfer performance and economic cost.
基金the National Key Research and Development Program of China(No.2022ZD0119001)。
摘要Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for network coverage optimization.ARO-PSO successfully combines the stochastic characteristics of ARO and the global characteristics of PSO.Firstly,to optimize the quality of the initial population,Sine chaos mapping is introduced to initialize the population;secondly,to better balance the exploration and exploitation,adaptive settings are made;finally,combined with the characteristics of the ARO energy factor,a population decreasing strategy is introduced to further accelerate the convergence speed of the algorithm.Experimental and analytical comparisons are made with ARO and PSO and 6 other excellent optimizers on 13 benchmark functions.The results show that ARO-PSO largely outperforms the original algorithm.Finally,ARO-PSO is applied to WSN coverage optimization experiments in 2D and 3D environments,and the proposed algorithm exhibits higher network coverage and improves the monitoring quality of the network compared to standard ARO and PSO and other state-of-the-art algorithms.The experimental results fully demonstrate the superiority of the ARO-PSO-based WSN node deployment optimization method.
基金supported by the National Natural Science Foundation of China(61503408)。
摘要Frequency diverse array multiple-input multiple-output(FDA-MIMO)radar has gained considerable research attention due to its ability to effectively counter active repeater deception jamming in complex electromagnetic environments.The effectiveness of interference suppression by FDA-MIMO is limited by the inherent range-angle coupling issue in the FDA beampattern.Existing literature primarily focuses on control methods for FDA-MIMO radar beam direction under the assumption of static beampatterns,with insufficient exploration of techniques for managing nonstationary beam directions.To address this gap,this paper initially introduces the FDA-MIMO signal model and the calculation formula for the FDA-MIMO array output using the minimum variance distortionless response(MVDR)beamformer.Building on this,the problem of determining the optimal frequency offset for the FDA is rephrased as a convex optimization problem,which is then resolved using the cuckoo search(CS)algorithm.Simulations confirm the effectiveness of the proposed approach,showing that the frequency offsets obtained through the CS algorithm can create a dot-shaped beam direction at the target location while effectively suppressing interference signals within the mainlobe.
基金funded by State Key Laboratory of MicroSpacecraft Rapid Design and Intelligent Cluster,China(No.MS01240104)the Youth Program of the Self-Innovation Science Fund,China(No.ZK2023-41)from the National University of Defense Technology(NUDT)China and the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX20240155)。
摘要In the conceptual design phase of the satellite thermal management system,components layout optimization and structural topology optimization of satellite panel can meet global and local thermal management requirements,respectively.However,achieving non-interfering coupling between these two optimization processes remains a challenge.An integrated layout-structure design method based on thermal metamaterials is proposed,which comprises two design stages.In the first stage,components layout optimization is conducted to maximize temperature uniformity within the satellite module,yielding a globally optimized layout with balanced thermal characteristics.In the second stage,topology optimization guided by the design principle of thermal metamaterials is implemented in critical local panel regions to satisfy differentiated heat transfer requirements of components with diverse functional and thermal sensitivity properties.The key innovation lies in utilizing thermal metamaterials as a mediator to synergistically couple global components layout optimization with local structural topology optimization,which enables customized local heat flux manipulation without interfering with the globally optimized temperature field derived from the layout optimization.The method introduces neither additional mass nor special materials,offering advantages of low cost,high reliability,and strong versatility.It provides a new solution paradigm for the design of passive thermal management systems in satellites.
基金supported by the National Natural Science Foundation of China(62472292,62471310,62376115)Guangdong Basic and Applied Basic Research Foundation(2025A1515011638)the Research Grants Council of the Hong Kong Special Administrative Region,China(GRF Project No.CityU11215622)。
摘要Few-for-many(F4M)optimization,recently introduced as a novel paradigm in multi-objective optimization,aims to find a small set of solutions that effectively handle a large number of conflicting objectives.Unlike traditional many-objective optimization methods,which typically attempt comprehensive coverage of the Pareto front,F4M optimization emphasizes finding a small representative solution set to efficiently address highdimensional objective spaces.Motivated by the computational complexity and practical relevance of F4M optimization,this paper proposes a new evolutionary algorithm explicitly tailored for efficiently solving F4M optimization problems.Inspired by Smetric selection evolutionary multi-objective optimization algorithm(SMS-EMOA),our proposed approach employs a(μ+1)-evolution strategy guided by the objective of F4M optimization.Furthermore,to facilitate rigorous performance assessment,we propose a novel benchmark test suite specifically designed for F4M optimization by leveraging the similarity betw een the R2indicator and F4M formulations.Our test suite is highly flexible,allowing any existing multi-objective optimization problem to be transformed into a corresponding F4M instance via scalarization using the weighted Tchebycheff function.Comprehensive experimental evaluations on benchmarks demonstrate the superior performance of our algorithm compared to existing state-of-the-art algorithms,especially on instances involving a large number of objectives.The source code of the proposed algorithm will be released publicly.Source code is available at http://gffzz188fe103f8f1460asqu99ouuwwk5x6opf.ffgz.tsg.suse.edu.cn/MOL-SZU/SoM-EMOA.
基金Supported by National Natural Science Foundation of China(Grant Nos.52202431,52172353).
摘要Based on the demands for crashworthiness and lightweight in the passive safety of transportation vehicles,metal-fiber reinforced polymer(FRP)hybrid thin-walled tubes(MFHTWTs)integrate the toughness,strength and lightweight of two distinct material characteristics.MFHTWTs can achieve energy absorption through the coupling of material plastic deformation and fracture,demonstrating significant engineering value in passive safety.This review provides a comprehensive examination of the crashworthiness topology optimization of MFHTWTs,aiming to demonstrate that a deeply integrated approach combining topology and parameter opti-mization can realize an optimal design method for MFHTWTs,thereby maximizing the functional utilization of limited material.Firstly,the review highlights the crashworthiness topology optimization methods(CTOMs)based on thin-walled structures.With a particular focus on metal,the review discusses both the practical ap-plicability and limitations of CTOMs under crash conditions.Additionally,based on the methodology of the equivalent static load method(ESLM),the review emphasizes that topology optimization methods considering continuous fiber paths and multi-material interface connections are also applicable to the crashworthiness op-timization of MFHTWTs.Furthermore,to couple structural parameters and configuration characteristics,in-tegrated topology optimization methods,including parameter optimization,are proposed to provide a valuable reference for the global optimization of MFHTWTs.Thus,these methods can establish the mapping relationship between key parameters and the structural energy absorption capacity.
基金supported by the National Natural Science Foundation of China(62373201,61973173)the Technology Researchand Development Program of Tianjin(20YFZCSY00830,18ZXZNGX00340)。
摘要Multiple unmanned aerial vehicles(UAVs)play a vital role in monitoring and data collection in wide area environments with harsh conditions.In most scenarios,issues such as real-time data retrieval and real-time UAV positioning are often disregarded,essentially neglecting the communication constraints.In this paper,we comprehensively address both the coverage of the target area and the data transmission capabilities of the flying ad hoc network(FANET).The data throughput of the network is therefore maximized by optimizing the network topology and UAV trajectories.The resultant optimization problem is effectively solved by the proposed reinforcement learning-based trajectory planning(RL-TP)algorithm and the convex-based topology optimization(C-TOP)algorithm sequentially.The RL-TP optimizes the UAV paths while considering the constraints of FANET.The C-TOP maximizes the data throughput of the network while simultaneously constraining the neighbors and transmit powers of the UAVs,which is shown to be a convex problem that can be efficiently solved in polynomial time.Simulations and field experimental results show that the proposed optimization strategy can effectively plan the UAV trajectories and significantly improve the data throughput of the FANET over the adaptive local minimum spanning tree(A-LMST)and cyclic pruning-assisted power optimization(CPAPO)methods.
基金supported by the National Natural Science Foundation of China(No.12202295)the International(Regional)Cooperation and Exchange Projects of the National Natural Science Foundation of China(No.W2421002)+2 种基金the Sichuan Science and Technology Program(No.2025ZNSFSC0845)Zhejiang Provincial Natural Science Foundation of China(No.ZCLZ24A0201)the Fundamental Research Funds for the Provincial Universities of Zhejiang(No.GK249909299001-004)。
摘要Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:http://gffzz188fe103f8f1460asqu99ouuwwk5x6opf.ffgz.tsg.suse.edu.cn/jinhao-ok1/Topo-for-DCTWS.git.
基金supported in part by the National Natural Science Foundation of China(62136008,62276001,U21A20512,W2441019)the Anhui Provincial Natural Science Foundation(2308085J03)the Excellent Youth Foundation of Anhui Provincial Colleges(2022AH030013)。
摘要Deep reinforcement learning(DRL)has demonstrated exceptional capabilities in combinatorial optimization,which automatically devises policies for solution construction and optimizer refinement.DRL is particularly adept in generating training samples by itself,thereby providing the flexibility to solve a variety of combinatorial optimization problems without supervision.While DRL takes actions according to states extracted from problem-specific information,it cannot be directly applied to black-box continuous optimization lacking explicit information.To address this issue,this paper proposes a search space independent operator based DRL method for black-box continuous optimization.It conceptualizes the optimization process driven by search space independent operators as a Markov decision process,wherein actions are defined as operators and states are extracted from solutions generated by operators.In contrast to other DRLassisted metaheuristics,the proposed method does not rely on any existing metaheuristic.Instead,it innovates by creating totally new operators,able to surpass the performance boundaries of existing metaheuristics.Compared with state-of-the-art metaheuristics and DRL methods,the proposed method shows significantly faster convergence speed on challenging continuous optimization problems.
基金supported by the State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster of China(Grant No.MS01240114)the Research Start-up Funds of Hangzhou International Innovation Institute of Beihang University of China(Grant No.2024KQ089)+1 种基金the National Natural Science Foundation of China(Grant Nos.12502404,62573024)the Foundation of Science and Technology on Space Intelligent Control Laboratory of China(Grant No.HTKJ2024-KL502030)。
摘要When the Mars vehicles encounter a storm or other failure situations,the ability to abort and rise to a predetermined orbit in the powered descent flight phase is critical.Unlike the abort guidance mission of lunar exploration,Mars has an atmosphere and stronger gravity,leading to high requirements on the efficiency,autonomy,reliability,and robustness of the guidance system.To this end,this paper proposes a safety abort guidance algorithm to stop the descent of the Mars vehicle with fast responses in the initial stage of abort.Then,to meet the requirements of rendezvous and docking with the orbiter,the constraints of terminal orbit parameters and orbit insertion time and position in the perifocal coordinate system are explicitly defined.To achieve a higher terminal orbit,a“powered-coast-powered”trajectory onboard optimization problem is established,which allows the trajectory optimization problem to be handled by convex optimization rapidly and accurately.Moreover,a switching strategy between the online trajectory optimization and terminal iterative guidance is detailed to ensure the accuracy of terminal orbit insertion.Finally,numerical experiments with Monte Carlo tests under various conditions are conducted to verify the efficiency,robustness,and onboard application performance of the proposed method.
基金supported by the National Natural Science Foundation of China(Grant Nos.12172119(PI:Prof.Xiaochen Mao)11872169(PI:Prof.Xiaochen Mao)+1 种基金12102175(PI:Dr.Youheng Dong),11925205(PI:Prof.Lifeng Wang))the Natural Science Foundation of Jiangsu Province of China(Grant No.BK20191295(PI:Prof.Xiaochen Mao)).
摘要This paper presents a tunable elastic metamaterial structure with periodically arranged resonators.The resonator consists of two beams and a moveable lumped mass block.The dynamical model of the proposed structure is established in frequency domain.The frequency responses are obtained by applying the spectral element method and finite element method.The influences of the structural and material parameters on the wave properties are explored.It is shown that the movement of the mass blocks has great relationships with the bandgaps,such as frequency shift and broadening effects.The parameter optimization is given by using genetic algorithms in order to improve the obtained results.The optimal positions of the mass blocks are addressed and ultra-wide bandgaps are revealed.An experimental platform is designed and performed to validate the bandgap characteristics.
基金supported by the National Natural Science Foundation of China(Nos.U24B2049,52175062)the Natural Science Foundation of Fujian Province,China(No.2024J09013)。
摘要Axial piston pumps are widely used to supply the fluid power,but their significant vibration has become a growing concern.To address this issue,this paper presents a novel dynamic model for the axial piston pump and mitigates both flow fluctuation and mechanical vibration powers through a multi-objective optimization method.A Lumped-Parameter(LP)model is first introduced to describe dynamic behaviors of the entire pump assembly,and the Newmark-βmethod is adopted to calculate flow fluctuation and mechanical vibration powers.Experimental validation is conducted to ensure the accuracy of the proposed model.Using this validated model,a multiobjective optimization algorithm is employed to optimize the structural parameters of three representative valve plate types,aiming to simultaneously reduce flow fluctuation and mechanical vibrations.The optimization results demonstrate a significant reduction in the pump vibration power,as well as improvements in cavitation and pressure overshoot conditions.Among these optimized designs,the valve plate with hole-shaped damping grooves shows the lowest vibration power,while the valve plate with the triangular damping grooves achieves the lowest maximum piston chamber pressure.This study offers a promising approach for designing quieter axial piston pumps,which promotes the fluid power technology.
基金supported by the National Natural Science Foundation of China(62571374)“Pioneering Leadership+X”Research and Development Plan of Zhejiang Provincial Department of Science and Technology(2024C03237)the Natural Science Foundation of Hangzhou(2024SZRYBH180010).
摘要Metaheuristic algorithms have emerged as indispensable tools for solving NP-hard optimization problems that defy traditional methods.To advance the field’s focus on algorithmic performance,this study introduces the Theory Evolution Optimization(TEO)–an efficient metaheuristic inspired by the evolution of scientific theory.TEO simulates the competitive,accumulative,and replacement processes among scientific hypotheses,mirroring the evolution from a hypothesis to an established scientific theory.The performance of TEO is validated through extensive experimental simulations and benchmarked against 28 popular algorithms,including highly competitive champions such as EBOwithCMAR,LSHADE_cnEpSi,and LSHADE.Pairwise comparisons between TEO and the latest algorithms are conducted using the Wilcoxon signed-rank test,with multiple comparisons managed by the Friedman test.Initially,TEO is tested on the classical IEEE CEC2017 and the latest IEEE CEC2022 benchmark functions.TEO successfully addresses four prominent engineering design problems in constrained continuous space for practical applications.Additionally,a binary TEO(BTEO)variant is introduced and applied to feature selection tasks in discrete space.Experimental results consistently demonstrate that TEO proposes highly competitive outcomes in optimization problems.The source codes for this research are accessible to the public at http://gffzze767f4cc5ce545d8squ99ouuwwk5x6opf.ffgz.tsg.suse.edu.cn/TEO.html.
基金co-supported by the National Natural Science Foundation of China(No.U23B6001)the Natural Science Foundation of Heilongjiang Province,China(No.LH2022F023)the Fundamental Research Funds for the Central Universities,China(No.HIT.OCEF.2023009)。
摘要This paper addresses the challenge of mass uncertainty during the powered descent phase of a Mars lander and proposes a robust powered descent guidance algorithm that accounts for uncertainties in mass and fuel consumption.First,the traditional trajectory optimization method based on convex optimization is improved by developing a fast and accurate solution approach using sequential convex optimization.Second,the effects of mass uncertainty on position are modeled and analyzed,with corresponding computational methods provided for different scenarios.Third,the worst-case scenario under mass uncertainty is analyzed through both geometric and theoretical approaches,and a modified glide-slope constraint method is proposed to ensure safe landing even in adverse conditions.Moreover,a closed-loop receding horizon based guidance is developed to further mitigate the effects of mass uncertainty and improve terminal landing accuracy.Finally,the proposed improved convex optimization algorithm and robust trajectory optimization algorithm are validated through simulation cases and compared with a probabilistic approach.The simulations further test various initial positions,velocities,and glide-slope angles,demonstrating that the solutions are both accurate and robust.
基金supported by the National Natural Science Foundation of China(Nos.U25A20282,U23A20628,52375394,52305429)the Major Project of Science and Technology in Shanxi(Nos.202501050201012,202301050201004)。
摘要The synergistic mechanism of multiple process parameters on the solidification structure of niobium containing austenitic stainless steel during continuous casting is complex,which seriously affects the quality of continuous casting billets and seamless pipes.In order to optimize the quality of continuous casting billet,a finite element model of solidification and heat transfer in continuous casting process was established for the secondary cooling process of continuous casting billet.The control variable method was used to explore the influence of casting speed and superheat on the solidification process.At the same time,an orthogonal scheme was designed to study the coupling effect of multiple process parameters on the heat transfer and solidification state of continuous casting billets,and optimized process parameters were selected.The optimization results of process parameters were verified through production experiments,and it is found that the enrichment of coarse niobium compounds directly causes the initiation and propagation of inner wall cracks during the large deformation hot piercing of S30432 seamless tubes.Process parameter optimization,especially the synergistic effect of the decrease of superheat and increase of specific water flow promotes the grain refinement and expension of equiaxed crystal zone,thereby mitigating the segregation of Nb elements and improving the distribution of niobium compounds.