Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences.Metaheuristic algorithms,in particular,have proven highly effective in complex optimization scenarios characte...Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences.Metaheuristic algorithms,in particular,have proven highly effective in complex optimization scenarios characterized by high dimensionality and intricate variable relationships.The Mountain Gazelle Optimizer(MGO)is notably effective but struggles to balance local search refinement and global space exploration,often leading to premature convergence and entrapment in local optima.This paper presents the Improved MGO(IMGO),which integrates three synergistic enhancements:dynamic chaos mapping using piecewise chaotic sequences to boost explo-ration diversity;Opposition-Based Learning(OBL)with adaptive,diversity-driven activation to speed up convergence;and structural refinements to the position update mechanisms to enhance exploitation.The IMGO underwent a comprehensive evaluation using 52 standardised benchmark functions and seven engineering optimization problems.Benchmark evaluations showed that IMGO achieved the highest rank in best solution quality for 31 functions,the highest rank in mean performance for 18 functions,and the highest rank in worst-case performance for 14 functions among 11 competing algorithms.Statistical validation using Wilcoxon signed-rank tests confirmed that IMGO outperformed individual competitors across 16 to 50 functions,depending on the algorithm.At the same time,Friedman ranking analysis placed IMGO with an average rank of 4.15,compared to the baseline MGO’s 4.38,establishing the best overall performance.The evaluation of engineering problems revealed consistent improvements,including an optimal cost of 1.6896 for the welded beam design vs.MGO’s 1.7249,a minimum cost of 5885.33 for the pressure vessel design vs.MGO’s 6300,and a minimum weight of 2964.52 kg for the speed reducer design vs.MGO’s 2990.00 kg.Ablation studies identified OBL as the strongest individual contributor,whereas complete integration achieved superior performance through synergistic interactions among components.Computational complexity analysis established an O(T×N×5×f(P))time complexity,representing a 1.25×increase in fitness evaluation relative to the baseline MGO,validating the favorable accuracy-efficiency trade-offs for practical optimization applications.展开更多
Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified...Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications.展开更多
Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced...Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced by the injector critically influences the performance of the entire accelerator-based scientific research apparatus.The injectors of such facilities usually use photocathode and thermionic-cathode electron guns.Although the photocathode injector can produce electron beams of excellent quality,its associated laser system is massive and intricate.The thermionic-cathode electron gun,especially the gridded electron gun injector,has a simple structure capable of generating numerous electron beams.However,its emittance is typically high.In this study,methods to reduce beam emittance are explored through a comprehensive analysis of various grid structures and preliminary design results,examining the evolution of beam phase space at different grid positions.An optimization method for reducing the emittance of a gridded thermionic-cathode electron gun is proposed through theoretical derivation,electromagnetic-field simulation,and beam-dynamics simulation.A 50%reduction in emittance was achieved for a 50 keV,1.7 A electron gun,laying the foundation for the subsequent design of a high-current,low-emittance injector.展开更多
Based on the Fluent numerical simulation method,this study systematically analyzed the structural parameters of the spiral tube heat exchanger and the influence of the external baffle on its heat transfer performance....Based on the Fluent numerical simulation method,this study systematically analyzed the structural parameters of the spiral tube heat exchanger and the influence of the external baffle on its heat transfer performance.The results show that when the equivalent diameter of the spiral tube increased from 16.68 to 21.23 mm,its surface heat transfer coefficient decreased from 22,040 to 17,230 W/m^(2)⋅K,and the outlet air temperature dropped from 822.3 to 807.3 K.However,the pressure loss decreased from 2.692 to 0.958 kPa.which reveals the contradiction between the heat transfer efficiency and the flow resistance.By adding a baffle to enhance the turbulent disturbance,the wall heat flux density is increased by 21.17%,the surface heat transfer coefficient is increased by 12.1%,and the outlet temperature is optimized,which verifies the significant improvement of the heat transfer performance by the countercurrent design.Comprehensive research shows that the collaborative optimization of spiral tube equivalent diameter parameters and baffle flow control is the key to improve the comprehensive performance of heat exchanger.Theresearch results provide a theoretical basis for energy-saving design of industrial heat exchangers.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
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f...Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.展开更多
High-concentration photovoltaic(HCPV)systems present significant thermal management challenges due to the intense heat fluxes generated under concentrated solar irradiation,especially in arid environments.Effective he...High-concentration photovoltaic(HCPV)systems present significant thermal management challenges due to the intense heat fluxes generated under concentrated solar irradiation,especially in arid environments.Effective heat dissipation is critical to prevent performance degradation and structural failure.This study investigates the thermal performance and design optimization of an enhanced HCPV module,integrating numerical,analytical,and experimental methods.A coupled optical-thermal-electrical model was developed to simulate ray tracing,heat transfer,and temperature-dependent electrical behaviour,with predictions validated under real-world desert conditions.Compared to a baseline commercial module operating at 106℃,the optimized design achieved a peak temperature reduction of 16℃,lowering the cell temperature to 90℃under a concentration ratio of 961×and direct normal irradiance(DNI)of 950 W/m^(2).The total thermal resistance was reduced from 0.25 to 0.15 K/W(a 40%improvement),and the electrical efficiency increased from 37.5%to 38.6%,representing a relative gain of approximately 3.1%.The system consistently maintained a fill factor exceeding 78%,underscoring stable performance under high thermal load.These findings demonstrate that targeted thermal design,informed by integrated modeling,is essential for unlocking the reliability and efficiency of high-flux solar energy systems.展开更多
Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longe...Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.展开更多
With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electro...With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electronic instruments.Therefore,the design and preparation of electromagnetic absorbing composites represent an efficient approach to mitigate the current hazards of electromagnetic radiation.However,traditional electromagnetic absorbers are difficult to satisfy the demands of actual utilization in the face of new challenges,and emerging absorbents have garnered increasing attention due to their structure and performance-based advantages.In this review,several emerging composites of Mxene-based,biochar-based,chiral,and heat-resisting are discussed in detail,including their synthetic strategy,structural superiority and regulation method,and final optimization of electromagnetic absorption ca-pacity.These insights provide a comprehensive reference for the future development of new-generation electromagnetic-wave absorption composites.Moreover,the potential development directions of these emerging absorbers have been proposed as well.展开更多
This study investigates the potential of Prosopis cineraria Leaves Powder(PCLP)as a biosorbent for removing lead(Pb)and zinc(Zn)from aqueous solutions,optimizing the process using Response Surface Methodology(RSM).Pro...This study investigates the potential of Prosopis cineraria Leaves Powder(PCLP)as a biosorbent for removing lead(Pb)and zinc(Zn)from aqueous solutions,optimizing the process using Response Surface Methodology(RSM).Prosopis cineraria,commonly known as Khejri,is a drought-resistant tree with significant promise in environmental applications.The research employed a Central Composite Design(CCD)to examine the independent and combined effects of key process variables,including initial metal ion concentration,contact time,pH,and PCLP dosage.RSM was used to develop mathematical models that explain the relationship between these factors and the efficiency of metal removal,allowing the determination of optimal operating conditions.The experimental results indicated that the Langmuir isotherm model was the most appropriate for describing the biosorption of both metals,suggesting favorable adsorption characteristics.Additionally,the D-R isotherm confirmed that chemisorption was the primary mechanism involved in the biosorption process.For lead removal,the optimal conditions were found to be 312.23 K temperature,pH 4.72,58.5 mg L-1 initial concentration,and 0.27 g biosorbent dosage,achieving an 83.77%removal efficiency.For zinc,the optimal conditions were 312.4 K,pH 5.86,53.07 mg L-1 initial concentration,and the same biosorbent dosage,resulting in a 75.86%removal efficiency.These findings highlight PCLP’s potential as an effective,eco-friendly biosorbent for sustainable heavy metal removal in water treatment.展开更多
Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic research.However,a major challenge for mole-inspired robots lies in overco...Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic research.However,a major challenge for mole-inspired robots lies in overcoming resistance in granular media when burrowing with forelimbs.In the absence of effective forepaw design strategies,most robotic designs rely on increased power to enhance performance.To address this issue,this paper employs Resistive Force Theory to optimize mole-inspired forepaws,aiming to enhance burrowing efficiency.By analyzing the relationship between geometric parameters and burrowing forces,we propose several forepaw design variations.Through granular resistance assessments,an effective forepaw configuration is identified and further refined using parameters such as longitudinal and transverse curvature.Subsequently,the Particle Swarm Optimization algorithm is applied to determine the optimal forepaw design.In force-loading tests,the optimized forepaw demonstrated a 79.44%reduction in granular lift force and a 22.55%increase in propulsive force compared with the control group.In robotic burrowing experiments,the optimized forepaw achieved the longest burrow displacement(179.528 mm)and the lowest burrowing lift force(0.9355 mm/s),verifying its effectiveness in reducing the lift force and enhancing the propulsive force.展开更多
Modern aircraft tend to use fuel thermal management systems to cool onboard heat sources.However,the design of heat transfer architectures for fuel thermal management systems relies on the experience of the engineers ...Modern aircraft tend to use fuel thermal management systems to cool onboard heat sources.However,the design of heat transfer architectures for fuel thermal management systems relies on the experience of the engineers and lacks theoretical guidance.This paper proposes a concise graph representation method based on graph theory for fuel thermal management systems,which can represent all possible connections between subsystems.A generalized optimization algorithm is proposed for fuel thermal management system architecture to minimize the heat sink.This algorithm can autonomously arrange subsystems with heat production differences and efficiently utilize the architecture of the fuel heat sink.At the same time,two evaluation indices are proposed from the perspective of subsystems.These indices intuitively and clearly show that the reason for the high efficiency of heat sink utilization is the balanced and moderate cooling of each subsystem and verify the rationality of the architecture optimization method.A set of simulations are also conducted,which demonstrate that the fuel tank temperature has no effect on the performance of the architecture.This paper provides a reference for the architectural design of aircraft fuel thermal management systems.The metrics used in this paper can also be utilized to evaluate the existing architecture.展开更多
With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ...With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.展开更多
To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy...To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.展开更多
The Report of the 20th National Congress of the Communist Party of China explicitly emphasized the promotion of educational digitalization.The rapid development of new media in the era of network information has not o...The Report of the 20th National Congress of the Communist Party of China explicitly emphasized the promotion of educational digitalization.The rapid development of new media in the era of network information has not only broadened the horizons of college students but also profoundly transformed the content and methods of ideological and political education.As the frontline of ideological work,colleges and universities in Xinjiang are guided by the Party’s strategy for governing Xinjiang in the new era to advance network ideological and political education.This is of great significance in guiding students to develop correct network literacy and promoting ideological and political education to keep pace with the times.Through methods such as text analysis,questionnaire surveys,and interviews,this paper outlines the concept,characteristics,and value of network ideological and political education in colleges and universities in Xinjiang,analyzes its current development status and existing issues,and proposes optimization paths such as adhering to correct political guidance,highlighting regional characteristics,innovating educational methods,and strengthening subject construction.These efforts aim to fulfill the fundamental task of“cultivating talents with moral integrity”and serve the overall goal of social stability and long-term peace in Xinjiang.展开更多
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 beam pumping unit(BPU)remains the most stable and reliable equipment for crude oil lifting.Despite its simple four-link mechanism,the structural design of the BPU presents a constrained single-objective optimizati...The beam pumping unit(BPU)remains the most stable and reliable equipment for crude oil lifting.Despite its simple four-link mechanism,the structural design of the BPU presents a constrained single-objective optimization problem.Currently,a comprehensive framework for the structural design and optimization of compound balanced BPUs is lacking.Therefore,this study proposes a novel structural design scheme for BPUs,aiming to meet the practical needs of designers and operators by sequentially optimizing both the dynamic characteristics and balance properties of the BPUs.A dynamic model of compound balanced BPU was established based on D'Alembert's principle.The constraints for structural dimensions were formulated based on the actual operational requirements and design experience with BPUs.To optimize the structure,three algorithms were employed:the particle swarm optimization(PSO)algorithm,the genetic algorithm(GA),and the gray wolf optimization(GWO)algorithm.Each newly generated individuals are regulated by constraints to ensure the rationality of the outcomes.Furthermore,the integration of three algorithms ensures the increased likelihood of attaining the global optimal solution.The polished rod acceleration of the optimized structure is significantly reduced,and the dynamic characteristics of the up and down strokes are essentially symmetrical.Additionally,these three algorithms are also applied to the balance optimization of BPUs based on the measured dynamometer card.The calculation results demonstrate that the GWO-based optimization method exhibits excellent robustness in terms of structural optimization by enhancing the operational smoothness of the BPU,as well as in balance optimization by achieving energy conservation.By applying the optimization scheme proposed in this paper,the CYJW7-3-23HF type of BPU was designed,achieving a maximum polished rod acceleration of±0.675 m/s^(2) when operating at a stroke of 6 min^(−1).When deployed in two wells,the root-mean-square(RMS)torque was minimized,reaching values of 7.539 kN·m and 12.921 kN·m,respectively.The proposed design method not only contributes to the personalized customization but also improves the design efficiency of compound balanced BPUs.展开更多
In this work,an oscillating-body wave energy converter(OBWEC)with a hydraulic power take-off(PTO)system named“Dolphin 1”is designed,in which the hydraulic PTO system is equivalent to a transfer station and plays a c...In this work,an oscillating-body wave energy converter(OBWEC)with a hydraulic power take-off(PTO)system named“Dolphin 1”is designed,in which the hydraulic PTO system is equivalent to a transfer station and plays a crucial role in ensuring the stability of the electrical energy output and the efficiency of the overall system.A corresponding mathematical model for the hydraulic PTO system has been established,the factors that influence its performance have been studied,and an algorithm for solving the optimal working pressure has been derived in this paper.Moreover,a PID control method to enable the hydraulic PTO system to automatically achieve optimal performance under different wave conditions has been designed.The results indicate that,compared with single-chamber hydraulic cylinders,double-chamber hydraulic cylinders have a wider application range and greater performance;the accumulator can stabilize the output power of the hydraulic PTO system and slightly increase it;excessively large or small hydraulic motor displacement hinders system performance;and each wave condition corresponds to a unique optimal working pressure for the hydraulic PTO system.In addition,the relationship between the optimal working pressure P_(m)and the pressure P_(h)of the wave force acting on the piston satisfies P_(m)^(2)=∫_(t_(1))^(t_(2))P_(h)^(2)dt/(t_(2)-t_(1)).Furthermore,adjusting the hydraulic motor displacement automatically via a PID controller ensures that the actual working pressure of the hydraulic PTO system consistently reaches or approaches its theoretically optimal value under various wave conditions,which is a very effective control method for enhancing the performance of the hydraulic PTO system.展开更多
In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization al...In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization algorithms used in dispersion curve inversion are highly dependent on the initial model and are prone to being trapped in local optima,while classical global optimization algorithms often suffer from slow convergence and low solution accuracy.To address these issues,this study introduces the Osprey Optimization Algorithm(OOA),known for its strong global search and local exploitation capabilities,into the inversion of dispersion curves to enhance inversion performance.In noiseless theoretical models,the OOA demonstrates excellent inversion accuracy and stability,accurately recovering model parameters.Even in noisy models,OOA maintains robust performance,achieving high inversion precision under high-noise conditions.In multimode dispersion curve tests,OOA effectively handles higher modes due to its efficient global and local search capabilities,and the inversion results show high consistency with theoretical values.Field data from the Wyoming region in the United States and a landfill site in Italy further verify the practical applicability of the OOA.Comprehensive test results indicate that the OOA outperforms the Particle Swarm Optimization(PSO)algorithm,providing a highly accurate and reliable inversion strategy for dispersion curve inversion.展开更多
Phase-matching quantum-key distribution(PM-QKD)has achieved significant results in various practical applications.However,real-time communication requires dynamic adjustment and optimization of key parameters during c...Phase-matching quantum-key distribution(PM-QKD)has achieved significant results in various practical applications.However,real-time communication requires dynamic adjustment and optimization of key parameters during communication.In this letter,we predict the PM-QKD parameters using nature-inspired algorithms(NIAs).The results are obtained from an exhaustive traversal algorithm(ETA),which serves as a benchmark.We mainly study the parameter optimization effects of the two NIAs:ant colony optimization(ACO)and the genetic algorithm(GA).The configuration of the inherent parameters of these algorithms in the decoy-state PM-QKD is also discussed.The simulation results indicate that the parameters obtained by the ACO exhibit superior convergence and stability,whereas the GA results are relatively scattered.Nevertheless,more than 97%of the key rates predicted by both algorithms are highly consistent with the optimal key rate.Moreover,the relative error of the key rates remained below 10%.Furthermore,NIAs maintain power consumption below 8 W and require three orders of magnitude less computing time than ETA.展开更多
文摘Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences.Metaheuristic algorithms,in particular,have proven highly effective in complex optimization scenarios characterized by high dimensionality and intricate variable relationships.The Mountain Gazelle Optimizer(MGO)is notably effective but struggles to balance local search refinement and global space exploration,often leading to premature convergence and entrapment in local optima.This paper presents the Improved MGO(IMGO),which integrates three synergistic enhancements:dynamic chaos mapping using piecewise chaotic sequences to boost explo-ration diversity;Opposition-Based Learning(OBL)with adaptive,diversity-driven activation to speed up convergence;and structural refinements to the position update mechanisms to enhance exploitation.The IMGO underwent a comprehensive evaluation using 52 standardised benchmark functions and seven engineering optimization problems.Benchmark evaluations showed that IMGO achieved the highest rank in best solution quality for 31 functions,the highest rank in mean performance for 18 functions,and the highest rank in worst-case performance for 14 functions among 11 competing algorithms.Statistical validation using Wilcoxon signed-rank tests confirmed that IMGO outperformed individual competitors across 16 to 50 functions,depending on the algorithm.At the same time,Friedman ranking analysis placed IMGO with an average rank of 4.15,compared to the baseline MGO’s 4.38,establishing the best overall performance.The evaluation of engineering problems revealed consistent improvements,including an optimal cost of 1.6896 for the welded beam design vs.MGO’s 1.7249,a minimum cost of 5885.33 for the pressure vessel design vs.MGO’s 6300,and a minimum weight of 2964.52 kg for the speed reducer design vs.MGO’s 2990.00 kg.Ablation studies identified OBL as the strongest individual contributor,whereas complete integration achieved superior performance through synergistic interactions among components.Computational complexity analysis established an O(T×N×5×f(P))time complexity,representing a 1.25×increase in fitness evaluation relative to the baseline MGO,validating the favorable accuracy-efficiency trade-offs for practical optimization applications.
文摘Optimizing convolutional neural networks(CNNs)for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy.This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection.Unlike conventional single-objective approaches,the proposed method formulates a global multi-objective fitness function that integrates accuracy,precision,recall,and model size(speed/model complexity penalty)with adjustable weights.This design enables both single-objective and weightedsum multi-objective optimization,allowing adaptive selection of optimal CNN configurations for diverse deployment requirements.Two representativemetaheuristic algorithms,GeneticAlgorithm(GA)and Particle Swarm Optimization(PSO),are employed to optimize CNNhyperparameters and structure.At each generation/iteration,the best configuration is selected as themost balanced solution across optimization objectives,i.e.,the one achieving themaximum value of the global objective function.Experimental validation on two benchmark datasets,Edge-IIoT and CIC-IoT2023,demonstrates that the proposed GA-and PSO-based models significantly enhance detection accuracy(94.8%–98.3%)and generalization compared with manually tuned CNN configurations,while maintaining compact architectures.The results confirm that the multi-objective framework effectively balances predictive performance and computational efficiency.This work establishes a generalizable and adaptive optimization strategy for deep learning-based IoT attack detection and provides a foundation for future hybrid metaheuristic extensions in broader IoT security applications.
基金supported by the Hundred-person Program of Chinese Academy of Sciences and the National Natural Science Foundation of China(No.11905074).
文摘Electron beam injectors are pivotal components of large-scale scientific instruments,such as synchrotron radiation sources,free-electron lasers,and electron-positron colliders.The quality of the electron beam produced by the injector critically influences the performance of the entire accelerator-based scientific research apparatus.The injectors of such facilities usually use photocathode and thermionic-cathode electron guns.Although the photocathode injector can produce electron beams of excellent quality,its associated laser system is massive and intricate.The thermionic-cathode electron gun,especially the gridded electron gun injector,has a simple structure capable of generating numerous electron beams.However,its emittance is typically high.In this study,methods to reduce beam emittance are explored through a comprehensive analysis of various grid structures and preliminary design results,examining the evolution of beam phase space at different grid positions.An optimization method for reducing the emittance of a gridded thermionic-cathode electron gun is proposed through theoretical derivation,electromagnetic-field simulation,and beam-dynamics simulation.A 50%reduction in emittance was achieved for a 50 keV,1.7 A electron gun,laying the foundation for the subsequent design of a high-current,low-emittance injector.
基金supported by the Jing-Jin-Ji Regional Integrated Environmental Improvement-National Science and Technology Major Project(No.2024ZD1200400).
文摘Based on the Fluent numerical simulation method,this study systematically analyzed the structural parameters of the spiral tube heat exchanger and the influence of the external baffle on its heat transfer performance.The results show that when the equivalent diameter of the spiral tube increased from 16.68 to 21.23 mm,its surface heat transfer coefficient decreased from 22,040 to 17,230 W/m^(2)⋅K,and the outlet air temperature dropped from 822.3 to 807.3 K.However,the pressure loss decreased from 2.692 to 0.958 kPa.which reveals the contradiction between the heat transfer efficiency and the flow resistance.By adding a baffle to enhance the turbulent disturbance,the wall heat flux density is increased by 21.17%,the surface heat transfer coefficient is increased by 12.1%,and the outlet temperature is optimized,which verifies the significant improvement of the heat transfer performance by the countercurrent design.Comprehensive research shows that the collaborative optimization of spiral tube equivalent diameter parameters and baffle flow control is the key to improve the comprehensive performance of heat exchanger.Theresearch results provide a theoretical basis for energy-saving design of industrial heat exchangers.
基金supported by the National Natural Science Foundation of China(61503408)。
文摘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National Natural Science Foundation of China,No.42301470,No.52270185,No.42171389Capacity Building Program of Local Colleges and Universities in Shanghai,No.21010503300。
文摘Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers.
基金funded by King Abdullah City for Atomic and Renewable Energy(KACARE),grant number“PC-2020-1”.
文摘High-concentration photovoltaic(HCPV)systems present significant thermal management challenges due to the intense heat fluxes generated under concentrated solar irradiation,especially in arid environments.Effective heat dissipation is critical to prevent performance degradation and structural failure.This study investigates the thermal performance and design optimization of an enhanced HCPV module,integrating numerical,analytical,and experimental methods.A coupled optical-thermal-electrical model was developed to simulate ray tracing,heat transfer,and temperature-dependent electrical behaviour,with predictions validated under real-world desert conditions.Compared to a baseline commercial module operating at 106℃,the optimized design achieved a peak temperature reduction of 16℃,lowering the cell temperature to 90℃under a concentration ratio of 961×and direct normal irradiance(DNI)of 950 W/m^(2).The total thermal resistance was reduced from 0.25 to 0.15 K/W(a 40%improvement),and the electrical efficiency increased from 37.5%to 38.6%,representing a relative gain of approximately 3.1%.The system consistently maintained a fill factor exceeding 78%,underscoring stable performance under high thermal load.These findings demonstrate that targeted thermal design,informed by integrated modeling,is essential for unlocking the reliability and efficiency of high-flux solar energy systems.
基金supported by a Horizontal Project on the Development of a Hybrid Energy Storage Simulation Model for Wind Power Based on an RT-LAB Simulation System(PH2023000190)the Inner Mongolia Natural Science Foundation Project and the Optimization of Exergy Efficiency of a Hybrid Energy Storage System with Crossover Control for Wind Power(2023JQ04).
文摘Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.
基金supported by the Surface Project of Local De-velopment in Science and Technology Guided by Central Govern-ment(No.2021ZYD0041)the National Natural Science Founda-tion of China(Nos.52377026 and 52301192)+3 种基金the Natural Science Foundation of Shandong Province(No.ZR2019YQ24)the Taishan Scholars and Young Experts Program of Shandong Province(No.tsqn202103057)the Special Financial of Shandong Province(Struc-tural Design of High-efficiency Electromagnetic Wave-absorbing Composite Materials and Construction of Shandong Provincial Tal-ent Teams)the“Sanqin Scholars”Innovation Teams Project of Shaanxi Province(Clean Energy Materials and High-Performance Devices Innovation Team of Shaanxi Dongling Smelting Co.,Ltd.).
文摘With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electronic instruments.Therefore,the design and preparation of electromagnetic absorbing composites represent an efficient approach to mitigate the current hazards of electromagnetic radiation.However,traditional electromagnetic absorbers are difficult to satisfy the demands of actual utilization in the face of new challenges,and emerging absorbents have garnered increasing attention due to their structure and performance-based advantages.In this review,several emerging composites of Mxene-based,biochar-based,chiral,and heat-resisting are discussed in detail,including their synthetic strategy,structural superiority and regulation method,and final optimization of electromagnetic absorption ca-pacity.These insights provide a comprehensive reference for the future development of new-generation electromagnetic-wave absorption composites.Moreover,the potential development directions of these emerging absorbers have been proposed as well.
文摘This study investigates the potential of Prosopis cineraria Leaves Powder(PCLP)as a biosorbent for removing lead(Pb)and zinc(Zn)from aqueous solutions,optimizing the process using Response Surface Methodology(RSM).Prosopis cineraria,commonly known as Khejri,is a drought-resistant tree with significant promise in environmental applications.The research employed a Central Composite Design(CCD)to examine the independent and combined effects of key process variables,including initial metal ion concentration,contact time,pH,and PCLP dosage.RSM was used to develop mathematical models that explain the relationship between these factors and the efficiency of metal removal,allowing the determination of optimal operating conditions.The experimental results indicated that the Langmuir isotherm model was the most appropriate for describing the biosorption of both metals,suggesting favorable adsorption characteristics.Additionally,the D-R isotherm confirmed that chemisorption was the primary mechanism involved in the biosorption process.For lead removal,the optimal conditions were found to be 312.23 K temperature,pH 4.72,58.5 mg L-1 initial concentration,and 0.27 g biosorbent dosage,achieving an 83.77%removal efficiency.For zinc,the optimal conditions were 312.4 K,pH 5.86,53.07 mg L-1 initial concentration,and the same biosorbent dosage,resulting in a 75.86%removal efficiency.These findings highlight PCLP’s potential as an effective,eco-friendly biosorbent for sustainable heavy metal removal in water treatment.
基金financially supported in-part by the National Natural Science Foundation of China(52275011)the Natural Science Foundation of Guangdong Province(2023B1515020080)+3 种基金the Natural Science Foundation of Guangzhou(2024A04J2552)the Fundamental Research Funds for the Central Universities,the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(CAST)(2021QNRC001)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515011253)the Higher Education Institution Featured Innovation Project of Department of Education of Guangdong Province(GrantNo.2023KTSCX138).
文摘Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic research.However,a major challenge for mole-inspired robots lies in overcoming resistance in granular media when burrowing with forelimbs.In the absence of effective forepaw design strategies,most robotic designs rely on increased power to enhance performance.To address this issue,this paper employs Resistive Force Theory to optimize mole-inspired forepaws,aiming to enhance burrowing efficiency.By analyzing the relationship between geometric parameters and burrowing forces,we propose several forepaw design variations.Through granular resistance assessments,an effective forepaw configuration is identified and further refined using parameters such as longitudinal and transverse curvature.Subsequently,the Particle Swarm Optimization algorithm is applied to determine the optimal forepaw design.In force-loading tests,the optimized forepaw demonstrated a 79.44%reduction in granular lift force and a 22.55%increase in propulsive force compared with the control group.In robotic burrowing experiments,the optimized forepaw achieved the longest burrow displacement(179.528 mm)and the lowest burrowing lift force(0.9355 mm/s),verifying its effectiveness in reducing the lift force and enhancing the propulsive force.
文摘Modern aircraft tend to use fuel thermal management systems to cool onboard heat sources.However,the design of heat transfer architectures for fuel thermal management systems relies on the experience of the engineers and lacks theoretical guidance.This paper proposes a concise graph representation method based on graph theory for fuel thermal management systems,which can represent all possible connections between subsystems.A generalized optimization algorithm is proposed for fuel thermal management system architecture to minimize the heat sink.This algorithm can autonomously arrange subsystems with heat production differences and efficiently utilize the architecture of the fuel heat sink.At the same time,two evaluation indices are proposed from the perspective of subsystems.These indices intuitively and clearly show that the reason for the high efficiency of heat sink utilization is the balanced and moderate cooling of each subsystem and verify the rationality of the architecture optimization method.A set of simulations are also conducted,which demonstrate that the fuel tank temperature has no effect on the performance of the architecture.This paper provides a reference for the architectural design of aircraft fuel thermal management systems.The metrics used in this paper can also be utilized to evaluate the existing architecture.
基金funded by Jilin Province Science and Technology Development Plan Project,grant number 20220203163SF.
文摘With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.
基金Supported by State Grid Zhejiang Electric Power Co.,Ltd.Science and Technology Project Funding(No.B311DS230005).
文摘To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.
基金Social Science Fund Project of the Xinjiang Uygur Autonomous Region“Research on the Construction of Network Ideological Discourse Power in Colleges and Universities in Xinjiang”(2023BKS010)。
文摘The Report of the 20th National Congress of the Communist Party of China explicitly emphasized the promotion of educational digitalization.The rapid development of new media in the era of network information has not only broadened the horizons of college students but also profoundly transformed the content and methods of ideological and political education.As the frontline of ideological work,colleges and universities in Xinjiang are guided by the Party’s strategy for governing Xinjiang in the new era to advance network ideological and political education.This is of great significance in guiding students to develop correct network literacy and promoting ideological and political education to keep pace with the times.Through methods such as text analysis,questionnaire surveys,and interviews,this paper outlines the concept,characteristics,and value of network ideological and political education in colleges and universities in Xinjiang,analyzes its current development status and existing issues,and proposes optimization paths such as adhering to correct political guidance,highlighting regional characteristics,innovating educational methods,and strengthening subject construction.These efforts aim to fulfill the fundamental task of“cultivating talents with moral integrity”and serve the overall goal of social stability and long-term peace in Xinjiang.
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.
基金supported by the Key Laboratory of Petroleum and Natural Gas Equipment,Ministry of Education(No.OGE202303-08)Engineering Technology Research Center for Industrial Internet of Things and Intelligent Sensing,Hubei Province(No.KXZ 202203).
文摘The beam pumping unit(BPU)remains the most stable and reliable equipment for crude oil lifting.Despite its simple four-link mechanism,the structural design of the BPU presents a constrained single-objective optimization problem.Currently,a comprehensive framework for the structural design and optimization of compound balanced BPUs is lacking.Therefore,this study proposes a novel structural design scheme for BPUs,aiming to meet the practical needs of designers and operators by sequentially optimizing both the dynamic characteristics and balance properties of the BPUs.A dynamic model of compound balanced BPU was established based on D'Alembert's principle.The constraints for structural dimensions were formulated based on the actual operational requirements and design experience with BPUs.To optimize the structure,three algorithms were employed:the particle swarm optimization(PSO)algorithm,the genetic algorithm(GA),and the gray wolf optimization(GWO)algorithm.Each newly generated individuals are regulated by constraints to ensure the rationality of the outcomes.Furthermore,the integration of three algorithms ensures the increased likelihood of attaining the global optimal solution.The polished rod acceleration of the optimized structure is significantly reduced,and the dynamic characteristics of the up and down strokes are essentially symmetrical.Additionally,these three algorithms are also applied to the balance optimization of BPUs based on the measured dynamometer card.The calculation results demonstrate that the GWO-based optimization method exhibits excellent robustness in terms of structural optimization by enhancing the operational smoothness of the BPU,as well as in balance optimization by achieving energy conservation.By applying the optimization scheme proposed in this paper,the CYJW7-3-23HF type of BPU was designed,achieving a maximum polished rod acceleration of±0.675 m/s^(2) when operating at a stroke of 6 min^(−1).When deployed in two wells,the root-mean-square(RMS)torque was minimized,reaching values of 7.539 kN·m and 12.921 kN·m,respectively.The proposed design method not only contributes to the personalized customization but also improves the design efficiency of compound balanced BPUs.
基金financially supported by the National Natural Science Foundation of China(Grant Nos.52071094 and 51979065).
文摘In this work,an oscillating-body wave energy converter(OBWEC)with a hydraulic power take-off(PTO)system named“Dolphin 1”is designed,in which the hydraulic PTO system is equivalent to a transfer station and plays a crucial role in ensuring the stability of the electrical energy output and the efficiency of the overall system.A corresponding mathematical model for the hydraulic PTO system has been established,the factors that influence its performance have been studied,and an algorithm for solving the optimal working pressure has been derived in this paper.Moreover,a PID control method to enable the hydraulic PTO system to automatically achieve optimal performance under different wave conditions has been designed.The results indicate that,compared with single-chamber hydraulic cylinders,double-chamber hydraulic cylinders have a wider application range and greater performance;the accumulator can stabilize the output power of the hydraulic PTO system and slightly increase it;excessively large or small hydraulic motor displacement hinders system performance;and each wave condition corresponds to a unique optimal working pressure for the hydraulic PTO system.In addition,the relationship between the optimal working pressure P_(m)and the pressure P_(h)of the wave force acting on the piston satisfies P_(m)^(2)=∫_(t_(1))^(t_(2))P_(h)^(2)dt/(t_(2)-t_(1)).Furthermore,adjusting the hydraulic motor displacement automatically via a PID controller ensures that the actual working pressure of the hydraulic PTO system consistently reaches or approaches its theoretically optimal value under various wave conditions,which is a very effective control method for enhancing the performance of the hydraulic PTO system.
基金sponsored by China Geological Survey Project(DD20243193 and DD20230206508).
文摘In Rayleigh wave exploration,the inversion of dispersion curves is a crucial step for obtaining subsurface stratigraphic information,characterized by its multi-parameter and multi-extremum nature.Local optimization algorithms used in dispersion curve inversion are highly dependent on the initial model and are prone to being trapped in local optima,while classical global optimization algorithms often suffer from slow convergence and low solution accuracy.To address these issues,this study introduces the Osprey Optimization Algorithm(OOA),known for its strong global search and local exploitation capabilities,into the inversion of dispersion curves to enhance inversion performance.In noiseless theoretical models,the OOA demonstrates excellent inversion accuracy and stability,accurately recovering model parameters.Even in noisy models,OOA maintains robust performance,achieving high inversion precision under high-noise conditions.In multimode dispersion curve tests,OOA effectively handles higher modes due to its efficient global and local search capabilities,and the inversion results show high consistency with theoretical values.Field data from the Wyoming region in the United States and a landfill site in Italy further verify the practical applicability of the OOA.Comprehensive test results indicate that the OOA outperforms the Particle Swarm Optimization(PSO)algorithm,providing a highly accurate and reliable inversion strategy for dispersion curve inversion.
基金supported by the State Key Laboratory of Information Photonics and Optical Communications(Beijing University of Posts and Telecommunications)No.IPOC2021ZT10BUPT Excellent Ph.D.Students Foundation(Grant No.CX2023207)the BUPT innovation and entrepreneurship support program No.2024-YC-A188。
文摘Phase-matching quantum-key distribution(PM-QKD)has achieved significant results in various practical applications.However,real-time communication requires dynamic adjustment and optimization of key parameters during communication.In this letter,we predict the PM-QKD parameters using nature-inspired algorithms(NIAs).The results are obtained from an exhaustive traversal algorithm(ETA),which serves as a benchmark.We mainly study the parameter optimization effects of the two NIAs:ant colony optimization(ACO)and the genetic algorithm(GA).The configuration of the inherent parameters of these algorithms in the decoy-state PM-QKD is also discussed.The simulation results indicate that the parameters obtained by the ACO exhibit superior convergence and stability,whereas the GA results are relatively scattered.Nevertheless,more than 97%of the key rates predicted by both algorithms are highly consistent with the optimal key rate.Moreover,the relative error of the key rates remained below 10%.Furthermore,NIAs maintain power consumption below 8 W and require three orders of magnitude less computing time than ETA.
