ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the ...ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.展开更多
The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution l...The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution laws and fracture network characteristics of shale gas infill wells.A mechanism model of CN platform logging data and geomechanical parameters is established to simulate the influence of parent well’s production on the geostress in the infill well area.It is suggested that with the increase of production time,normal fault stress state and horizontal stress deflection will occur.The smaller the parent well spacing and the longer the production time,the earlier the normal fault stress state appears and the larger the range.Based on the model,the fracture network morphology and construction parameters of infill wells are optimized.parentparentparentparent The results indicate that:1:A well spacing of 500 m achieves a Pareto optimum between“full reserve coverage”and“stress barrier”;2:A parent well recovery degree of 30%corresponds to the critical point of stress reversal,where the lateral deflection rate of the infill fracture is less than 8%and the SRV loss is minimized;3:6-cluster intensive completion with twice the liquid intensity increases the fracture complexity index by 1.7 times,enhances well group EUR by 15.4%,and reduces single-well cost by 22%.This research fills the theoretical gap in the collaborative optimization of“multi-parameter,multi-objective and multi-constraint”and provide parameter optimization basis for shale gas infill well development in China and help to improve the development efficiency and economic benefits.展开更多
In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling p...In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling problem is formulated as a combinatorial optimization task with nonlinear objectives and coupled constraints.To solve the non-deterministic polynomial(NP)-hard problem efficiently,a novel learning-enhanced pigeon-inspired optimization(L-PIO)algorithm is proposed.The algorithm integrates a Q-learning mechanism to dynamically regulate control parameters,enabling adaptive exploration–exploitation trade-offs across different optimization phases.Additionally,geometric abstraction techniques are employed to approximate complex reconnaissance regions using maximum inscribed rectangles and spiral path models,allowing for precise cost modeling of UAV paths.The formal objective function is developed to minimize global flight distance and completion time while maximizing reconnaissance priority and task coverage.A series of simulation experiments are conducted under three scenarios:static task allocation,dynamic task emergence,and UAV failure recovery.Comparative analysis with several updated algorithms demonstrates that L-PIO exhibits superior robustness,adaptability,and computational efficiency.The results verify the algorithm's effectiveness in addressing dynamic reconnaissance task planning in real-time multi-UAV applications.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
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
Aqueous zinc-halogen batteries are promising candidates for large-scale energy storage due to their abundant resources,intrinsic safety,and high theoretical capacity.Nevertheless,the uncontrollable zinc dendrite growt...Aqueous zinc-halogen batteries are promising candidates for large-scale energy storage due to their abundant resources,intrinsic safety,and high theoretical capacity.Nevertheless,the uncontrollable zinc dendrite growth and spontaneous shuttle effect of active species have prohibited their practical implementation.Herein,a double-layered protective film based on zinc-ethylenediamine tetramethylene phosphonic acid(ZEA)artificial film and ZnF2-rich solid electrolyte interphase(SEI)layer has been successfully fabricated on the zinc metal anode via electrode/electrolyte synergistic optimization.The ZEA-based artificial film shows strong affinity for the ZnF2-rich SEI layer,therefore effectively suppressing the SEI breakage and facilitating the construction of double-layered protective film on the zinc metal anode.Such double-layered architecture not only modulates Zn2+flux and suppresses the zinc dendrite growth,but also blocks the direct contact between the metal anode and electrolyte,thus mitigating the corrosion from the active species.When employing optimized metal anodes and electrolytes,the as-developed zinc-(dual)halogen batteries present high areal capacity and satisfactory cycling stability.This work provides a new avenue for developing aqueous zinc-(dual)halogen batteries.展开更多
Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian O...Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian Optimization utilizes Gaussian Process (GP) as its core model and employs an adaptive search strategy to efficiently explore and identify optimal combinations of operational parameters within a limited parameter space, thereby enabling rapid optimization of the required parameters to achieve the target droplet size. Traditional methods typically rely on manually selecting a series of operational parameters and conducting multiple simulations to gradually approach the target droplet size. This process is time-consuming and prone to getting trapped in local optima. In contrast, Bayesian Optimization adaptively adjusts its search strategy, significantly reducing computational costs and effectively exploring global optima, thus greatly improving optimization efficiency. Additionally, the study investigates the impact of rectangular rib structures within the T-junction microchannel on droplet generation, revealing how the channel geometry influences droplet formation and size. After determining the target droplet size, we further applied Bayesian Optimization to refine the rib geometry. The integration of Bayesian Optimization with computational fluid dynamics (CFD) offers a promising tool and provides new insights into the optimal design of microfluidic devices.展开更多
Bifunctional oxide-zeolite-based composites(OXZEO)have emerged as promising materials for the direct conversion of syngas to olefins.However,experimental screening and optimization of reaction parameters remain resour...Bifunctional oxide-zeolite-based composites(OXZEO)have emerged as promising materials for the direct conversion of syngas to olefins.However,experimental screening and optimization of reaction parameters remain resource-intensive.To address this challenge,we implemented a three-stage framework integrating machine learning,Bayesian optimization,and experimental validation,utilizing a carefully curated dataset from the literature.Our ensemble-tree model(R^(2)>0.87)identified Zn-Zr and Cu-Mg binary mixed oxides as the most effective OXZEO systems,with their light olefin space-time yields confirmed by physically mixing with HSAPO-34 through experimental validation.Density functional theory calculations further elucidated the activity trends between Zn-Zr and Cu-Mg mixed oxides.Among 16 catalyst and reaction condition descriptors,the oxide/zeolite ratio,reaction temperature,and pressure emerged as the most significant factors.This interpretable,data-driven framework offers a versatile approach that can be applied to other catalytic processes,providing a powerful tool for experiment design and optimization in catalysis.展开更多
Tumor initiation and progression are highly intricate biolog-ical processes,and mutation-driven tumorigenesis is a pri-mary underlying cause.Personalized cancer vaccines have been developed to exploit these specific m...Tumor initiation and progression are highly intricate biolog-ical processes,and mutation-driven tumorigenesis is a pri-mary underlying cause.Personalized cancer vaccines have been developed to exploit these specific mutations,particu-larly in the form of tumor neoantigens,to induce immune responses,particularly the activation of CD8+T cells,which can attack malignant cells.Since tumor mutations result in protein sequence alterations distinct from those in normal tissues,therapies that precisely target these alterations could,in principle,confer effective tumor control while minimizing off-target effects.展开更多
In the wave of urbanization,waterfront landscape optimization is very important,but it is faced with ecological deterioration and other problems.By combing the relevant theories and practices at home and abroad and ma...In the wave of urbanization,waterfront landscape optimization is very important,but it is faced with ecological deterioration and other problems.By combing the relevant theories and practices at home and abroad and making a comparison and summary,the future direction of waterfront research was analyzed.In theory,foreign research has experienced multi-stage development,covering definition classification,design methods,etc.China started late,and is mainly in the exploration stage of learning from foreign experience and combining with local characteristics.The current research and practice have shortcomings such as ignoring users’needs and lacking quantitative evaluation.In the future,the construction of waterfront should focus on the needs of users,use scientific methods to build an evaluation system,integrate multi-disciplines,excavate regional culture,and establish a monitoring mechanism to achieve sustainable and coordinated development of the ecology,society and economy of waterfront.展开更多
Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and ...Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.展开更多
Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-i...Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-inspired materials which have excellent properties not present in conventional composites.To create such materials with desirable mechanical properties,the optimum structural parameters combination must be selected.Moreover,the optimal design of bio-inspired composites needs to take into account the trade-offs between various mechanical properties.In this paper,multi-objective optimization models were developed using structural parameters as design variables and mechanical properties as optimization objectives,including stiffness,strength,toughness,and dynamic damping.Using the NSGA-II optimization algorithm,a set of optimal solutions were solved.Additionally,three different structures in natural nacre were introduced in order to utilize the better structure when design bio-inspired materials.The range of optimal solutions that obtained using results from previous research were examined and explained why this collection of optimal solution ranges is better.Also,optimal solutions were compared with the structural features and mechanical properties of real nacre and artificial biomimetic composites to validate our models.Finally,the optimum design strategies can be obtained for nacre-like composites.Our research methodically proposes an optimization method for achieving load-bearing bio-inspired materials with excellent properties and creates a set of optimal solutions from which designers can select the one that best suits their preferences,allowing the fabricated materials to demonstrate preferred performance.展开更多
Fatigue failure continues to be a significant challenge in designing structural and mechanical components subjected to repeated and complex loading.While earlier studies mainly examined material properties and how str...Fatigue failure continues to be a significant challenge in designing structural and mechanical components subjected to repeated and complex loading.While earlier studies mainly examined material properties and how stress affects lifespan,this review offers the first comprehensive,multiscale comparison of strategies that optimize geometry to improve fatigue performance.This includes everything from microscopic features like the shape of graphite nodules to large-scale design elements such as fillets,notches,and overall structural layouts.We analyze and combine various methods,including topology and shape optimization,the ability of additive manufacturing to finetune internal geometries,and reliability-based design approaches.A key new contribution is our proposal of a standard way to evaluate geometry-focused fatigue design,allowing for consistent comparison and encouraging validation across different fields.Furthermore,we highlight important areas for future research,such as incorporating manufacturing flaws,using multiscale models,and integrating machine learning techniques.This work is the first to provide a broad geometric viewpoint in fatigue engineering,laying the groundwork for future design methods that are driven by data and centered on reliability.展开更多
Objective To optimize the ultrasonic extraction process for benzoic acid as a harmful substance in Paeonia lactiflora Pall.(P.lactiflora Pall.).Methods Methanol and ethanol solutions at different concentration gradien...Objective To optimize the ultrasonic extraction process for benzoic acid as a harmful substance in Paeonia lactiflora Pall.(P.lactiflora Pall.).Methods Methanol and ethanol solutions at different concentration gradients(25,50,75%)were prepared to investigate the effects of extraction solvents on the extraction efficiency of benzoic acid.The influences of ultrasonic frequency(35,50 Hz),ultrasonic power(40,60,80,100 W),ultrasonic time(10,20,30,40,50,60 minutes),and ultrasonic temperature(20,30,40,50℃)on the extraction efficiency were examined.Orthogonal experiments were conducted to analyze the effects of temperature,time,and ultrasonic power on the extraction efficiency and to screen the optimal ultrasonic extraction process.Results Various influencing factors had certain effects on the extraction efficiency of benzoic acid from P.lactiflora Pall.Single-factor analysis revealed that 25%methanol,ultrasonic frequency of 50 Hz,ultrasonic power of 40 W,ultrasonic time of 10minutes,and ultrasonic temperature of 30℃yielded the highest extraction efficiency for benzoic acid.The order of influence of different factors on the extraction efficiency was temperature>time>power.The optimal conditions obtained from orthogonal experiments were:extraction solvent of 25%methanol,ultrasonic frequency of 50 Hz,ultrasonic time of 20 minutes,ultrasonic power of 40 W,and ultrasonic temperature of 30℃.Conclusion Under the conditions of 25%methanol as the extraction solvent,ultrasonic frequency of 50 Hz,ultrasonic time of 20 minutes,ultrasonic power of 40 W,and ultrasonic temperature of 30℃,the extraction efficiency of benzoic acid from P.lactiflora Pall.was the highest.This method offers advantages such as simple operation,small sample size requirement,and low solvent consumption,providing a reliable analytical approach for quality control and safety evaluation of P.lactiflora Pall.展开更多
The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous flui...The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.展开更多
Carbon dioxide-enhanced oil recovery(CO_(2)-EOR)and storage is recognized as an economically feasible technique if used in suitable reservoirs.The type or form and capacity of this CO_(2) sequestration technique is sy...Carbon dioxide-enhanced oil recovery(CO_(2)-EOR)and storage is recognized as an economically feasible technique if used in suitable reservoirs.The type or form and capacity of this CO_(2) sequestration technique is synergistically affected by heat,flow,stress,and chemical reactions.Aimed at addressing the technological issues in applying CO_(2)-EOR and storage in a high water-cut reservoir in Xinjiang,China,this paper proposes a thermo-hydro-mechanical-chemical coupling method during CO_(2) flooding.The potential of CO_(2) sequestration and EOR in the target reservoir is discussed in combination with the surrogate optimization method.This method works better as it considers the evolution of structural trapping,capillary trapping,solubility trapping,and mineral trapping during CO_(2) injection as well as the influence the physical field has on the sequestration capacity for different forms of CO_(2) sequestration.The main mechanisms of CO_(2) sequestration in the high water-cut reservoir is structural trapping,followed by capillary trapping.Solubility trapping and mineral trapping have less contribution to the total sequestration capacity of CO_(2).After optimization,the cumulative oil production was 2.36×10^(6)m^(3),an increase of 0.25×10^(6)m3or 11.9%compared to the pre-optimization value.The CO_(2) sequestration capacity after optimization was 1.39×10^(6)t,which is an increase of 0.23×10^(6)t compared to values obtained before optimization;this effectively increases the area affected by CO_(2) by 24.4%.Of the four trapping mechanisms,capillary trapping and structural trapping showed a high increase of 32.5%and17.28%,respectively,while solubility trapping and mineral trapping only led to an increase of 5.1%and0.43%,respectively.This research could provide theoretical support for fully utilizing the potential of CO_(2)-EOR and sequestration technology.展开更多
The press-fit connector is a typical plug-and-play solderless connection,and it is widely used in signal transmission in fields such as communication and automotive devices.This paper focuses on inverse designing and ...The press-fit connector is a typical plug-and-play solderless connection,and it is widely used in signal transmission in fields such as communication and automotive devices.This paper focuses on inverse designing and optimization of geometric structure,as well as insertion-withdrawal forces of press-fit connector using artificial neural network(ANN)-assisted optimization method.The ANN model is established to approximate the relationship between geometric parameters and insertion-withdrawal forces,of which hyper-parameters of neural network are optimized to improve model performance.Two numerical methods are proposed for inverse designing structural parameters(Model-I)and multi-objective optimization of insertion-withdrawal forces(Model-II)of press-fit connector.In Model-I,a method for inverse designing structure parameters is established,of which an ANN model is coupled with single-objective optimization algorithm.The objective function is established,the inverse problem is solved,and effectiveness is verified.In Model-II,a multi-objective optimization method is proposed,of which an ANN model is coupled with genetic algorithm.The Pareto solution sets of insertion-withdrawal forces are obtained,and results are analyzed.The established ANN-coupled numerical optimization methods are beneficial for improving the design efficiency,and enhancing the connection reliability of the press-fit connector.展开更多
Recent progress in topology optimization(TO)has seen a growing integration of machine learning to accelerate computation.Among these,online learning stands out as a promising strategy for large-scale TO tasks,as it el...Recent progress in topology optimization(TO)has seen a growing integration of machine learning to accelerate computation.Among these,online learning stands out as a promising strategy for large-scale TO tasks,as it eliminates the need for pre-collected training datasets by updating surrogate models dynamically using intermediate optimization data.Stress-constrained lightweight design is an important class of problem with broad engineering relevance.Most existing frameworks use pixel or voxel-based representations and employ the finite element method(FEM)for analysis.The limited continuity across finite elements often compromises the accuracy of stress evaluation.To overcome this limitation,isogeometric analysis is employed as it enables smooth representation of structures and thus more accurate stress computation.However,the complexity of the stress-constrained design problem together with the isogeometric representation results in a large computational cost.This work proposes a multi-grid,single-mesh online learning framework for isogeometric topology optimization(ITO),leveraging the Fourier Neural Operator(FNO)as a surrogate model.Operating entirely within the isogeometric analysis setting,the framework provides smooth geometry representation and precise stress computation,without requiring traditional mesh generation.A localized training approach is employed to enhance scalability,while a multi-grid decomposition scheme incorporates global structural context into local predictions to boost FNO accuracy.By learning the mapping from spatial features to sensitivity fields,the framework enables efficient single-resolution optimization,avoiding the computational burden of two-resolution simulations.The proposed method is validated through 2D stress-constrained design examples,and the effect of key parameters is studied.展开更多
Owing to their excellent biocompatibility and potential for durability enhancement,polymeric heart valves(PHVs)are emerging as a promising alternative to traditional prostheses.Unlike conventional materials,PHVs can b...Owing to their excellent biocompatibility and potential for durability enhancement,polymeric heart valves(PHVs)are emerging as a promising alternative to traditional prostheses.Unlike conventional materials,PHVs can be manufactured under precise design criteria,enabling targeted performance improvements.This study introduces a geometric optimization strategy for enhancing the durability of PHVs.The finite element method(FEM)is combined with a dip-molding technique to develop a novel polymeric aortic valve with improved mechanical properties.The tri-leaflet geometry is parameterized using B-spline curves,and the maximum stress in the valve is reduced from 2.4802 to 1.7773 MPa using a multiobjective optimization algorithm NSGA-II(non-dominated sorting genetic algorithm II).Pre-optimized and optimized valve prototypes were fabricated via dip-molding and evaluated during pulsatile-flow tests and accelerated wear tests.The optimized design meets the ISO 5840 standards,with an effective orifice area of 2.019 cm^(2),a regurgitant fraction of 5.693%,and a transvalvular pressure gradient of 7.576 mmHg.Moreover,the optimized valve maintained its structural integrity and functionality over 14 million cycles of the accelerated wear test,whereas the unoptimized valve failed after two million cycles.These findings confirm that the FEM-based geometric optimization method enhances both the mechanical performance and durability of PHVs.展开更多
基金supported by the National Natural Science Foundation of China under grant number 62066016the Natural Science Foundation of Hunan Province of China under grant number 2024JJ7395+2 种基金International and Regional Science and Technology Cooperation and Exchange Program of the Hunan Association for Science and Technology under grant number 025SKX-KJ-04Hunan Provincial Postgraduate Research Innovation Project under grant numberCX20251611Liye Qin Bamboo Slips Research Special Project of JishouUniversity 25LYY03.
文摘ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants.
文摘The shale gas development in China faces challenges such as complex reservoir conditions and high development costs.Based on the pore pressure and geostress coupling theory,this paper studies the geostress evolution laws and fracture network characteristics of shale gas infill wells.A mechanism model of CN platform logging data and geomechanical parameters is established to simulate the influence of parent well’s production on the geostress in the infill well area.It is suggested that with the increase of production time,normal fault stress state and horizontal stress deflection will occur.The smaller the parent well spacing and the longer the production time,the earlier the normal fault stress state appears and the larger the range.Based on the model,the fracture network morphology and construction parameters of infill wells are optimized.parentparentparentparent The results indicate that:1:A well spacing of 500 m achieves a Pareto optimum between“full reserve coverage”and“stress barrier”;2:A parent well recovery degree of 30%corresponds to the critical point of stress reversal,where the lateral deflection rate of the infill fracture is less than 8%and the SRV loss is minimized;3:6-cluster intensive completion with twice the liquid intensity increases the fracture complexity index by 1.7 times,enhances well group EUR by 15.4%,and reduces single-well cost by 22%.This research fills the theoretical gap in the collaborative optimization of“multi-parameter,multi-objective and multi-constraint”and provide parameter optimization basis for shale gas infill well development in China and help to improve the development efficiency and economic benefits.
基金supported by the National Natural Science Foundation of China(Nos.T2121003,U24B20156)Open Fund of the National Key Laboratory of Helicopter Aeromechanics(No.2024-ZSJ-LB-02-06)。
文摘In dynamic and uncertain reconnaissance missions,effective task assignment and path planning for multiple unmanned aerial vehicles(UAVs)present significant challenges.A stochastic multi-UAV reconnaissance scheduling problem is formulated as a combinatorial optimization task with nonlinear objectives and coupled constraints.To solve the non-deterministic polynomial(NP)-hard problem efficiently,a novel learning-enhanced pigeon-inspired optimization(L-PIO)algorithm is proposed.The algorithm integrates a Q-learning mechanism to dynamically regulate control parameters,enabling adaptive exploration–exploitation trade-offs across different optimization phases.Additionally,geometric abstraction techniques are employed to approximate complex reconnaissance regions using maximum inscribed rectangles and spiral path models,allowing for precise cost modeling of UAV paths.The formal objective function is developed to minimize global flight distance and completion time while maximizing reconnaissance priority and task coverage.A series of simulation experiments are conducted under three scenarios:static task allocation,dynamic task emergence,and UAV failure recovery.Comparative analysis with several updated algorithms demonstrates that L-PIO exhibits superior robustness,adaptability,and computational efficiency.The results verify the algorithm's effectiveness in addressing dynamic reconnaissance task planning in real-time multi-UAV applications.
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.
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support from the National Natural Science Foundation of China(22209089,22178187)Natural Science Foundation of Shandong Province(ZR2022QB048,ZR2021MB006)+2 种基金Excellent Youth Science Foundation of Shandong Province(Overseas)(2023HWYQ-089)the Taishan Scholars Program of Shandong Province(tsqn201909091)Open Research Fund of School of Chemistry and Chemical Engineering,Henan Normal University.
文摘Aqueous zinc-halogen batteries are promising candidates for large-scale energy storage due to their abundant resources,intrinsic safety,and high theoretical capacity.Nevertheless,the uncontrollable zinc dendrite growth and spontaneous shuttle effect of active species have prohibited their practical implementation.Herein,a double-layered protective film based on zinc-ethylenediamine tetramethylene phosphonic acid(ZEA)artificial film and ZnF2-rich solid electrolyte interphase(SEI)layer has been successfully fabricated on the zinc metal anode via electrode/electrolyte synergistic optimization.The ZEA-based artificial film shows strong affinity for the ZnF2-rich SEI layer,therefore effectively suppressing the SEI breakage and facilitating the construction of double-layered protective film on the zinc metal anode.Such double-layered architecture not only modulates Zn2+flux and suppresses the zinc dendrite growth,but also blocks the direct contact between the metal anode and electrolyte,thus mitigating the corrosion from the active species.When employing optimized metal anodes and electrolytes,the as-developed zinc-(dual)halogen batteries present high areal capacity and satisfactory cycling stability.This work provides a new avenue for developing aqueous zinc-(dual)halogen batteries.
基金support from National Key Research and Development Program of China(2023YFC3905400)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA0490102)National Natural Science Foundation of China(22178354,2242100322408374).
文摘Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian Optimization utilizes Gaussian Process (GP) as its core model and employs an adaptive search strategy to efficiently explore and identify optimal combinations of operational parameters within a limited parameter space, thereby enabling rapid optimization of the required parameters to achieve the target droplet size. Traditional methods typically rely on manually selecting a series of operational parameters and conducting multiple simulations to gradually approach the target droplet size. This process is time-consuming and prone to getting trapped in local optima. In contrast, Bayesian Optimization adaptively adjusts its search strategy, significantly reducing computational costs and effectively exploring global optima, thus greatly improving optimization efficiency. Additionally, the study investigates the impact of rectangular rib structures within the T-junction microchannel on droplet generation, revealing how the channel geometry influences droplet formation and size. After determining the target droplet size, we further applied Bayesian Optimization to refine the rib geometry. The integration of Bayesian Optimization with computational fluid dynamics (CFD) offers a promising tool and provides new insights into the optimal design of microfluidic devices.
基金funded by the KRICT Project (KK2512-10) of the Korea Research Institute of Chemical Technology and the Ministry of Trade, Industry and Energy (MOTIE)the Korea Institute for Advancement of Technology (KIAT) through the Virtual Engineering Platform Program (P0022334)+1 种基金supported by the Carbon Neutral Industrial Strategic Technology Development Program (RS-202300261088) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea)Further support was provided by research fund of Chungnam National University。
文摘Bifunctional oxide-zeolite-based composites(OXZEO)have emerged as promising materials for the direct conversion of syngas to olefins.However,experimental screening and optimization of reaction parameters remain resource-intensive.To address this challenge,we implemented a three-stage framework integrating machine learning,Bayesian optimization,and experimental validation,utilizing a carefully curated dataset from the literature.Our ensemble-tree model(R^(2)>0.87)identified Zn-Zr and Cu-Mg binary mixed oxides as the most effective OXZEO systems,with their light olefin space-time yields confirmed by physically mixing with HSAPO-34 through experimental validation.Density functional theory calculations further elucidated the activity trends between Zn-Zr and Cu-Mg mixed oxides.Among 16 catalyst and reaction condition descriptors,the oxide/zeolite ratio,reaction temperature,and pressure emerged as the most significant factors.This interpretable,data-driven framework offers a versatile approach that can be applied to other catalytic processes,providing a powerful tool for experiment design and optimization in catalysis.
基金supported by the National Natural Science Foundation of China(82341042 and 32270993)the PhD program of the Interdisciplinary Research Center,Sun Yat-sen University.
文摘Tumor initiation and progression are highly intricate biolog-ical processes,and mutation-driven tumorigenesis is a pri-mary underlying cause.Personalized cancer vaccines have been developed to exploit these specific mutations,particu-larly in the form of tumor neoantigens,to induce immune responses,particularly the activation of CD8+T cells,which can attack malignant cells.Since tumor mutations result in protein sequence alterations distinct from those in normal tissues,therapies that precisely target these alterations could,in principle,confer effective tumor control while minimizing off-target effects.
文摘In the wave of urbanization,waterfront landscape optimization is very important,but it is faced with ecological deterioration and other problems.By combing the relevant theories and practices at home and abroad and making a comparison and summary,the future direction of waterfront research was analyzed.In theory,foreign research has experienced multi-stage development,covering definition classification,design methods,etc.China started late,and is mainly in the exploration stage of learning from foreign experience and combining with local characteristics.The current research and practice have shortcomings such as ignoring users’needs and lacking quantitative evaluation.In the future,the construction of waterfront should focus on the needs of users,use scientific methods to build an evaluation system,integrate multi-disciplines,excavate regional culture,and establish a monitoring mechanism to achieve sustainable and coordinated development of the ecology,society and economy of waterfront.
基金supported by the National Natural Science Foundation of China(724701189072431011).
文摘Project construction and development are an impor-tant part of future army designs.In today’s world,intelligent war-fare and joint operations have become the dominant develop-ments in warfare,so the construction and development of the army need top-down,top-level design,and comprehensive plan-ning.The traditional project development model is no longer suf-ficient to meet the army’s complex capability requirements.Projects in various fields need to be developed and coordinated to form a joint force and improve the army’s combat effective-ness.At the same time,when a program consists of large-scale project data,the effectiveness of the traditional,precise mathe-matical planning method is greatly reduced because it is time-consuming,costly,and impractical.To solve above problems,this paper proposes a multi-stage program optimization model based on a heterogeneous network and hybrid genetic algo-rithm and verifies the effectiveness and feasibility of the model and algorithm through an example.The results show that the hybrid algorithm proposed in this paper is better than the exist-ing meta-heuristic algorithm.
基金Supported by National Natural Science Foundation of China(Grant Nos.52222505,52321002)Shanghai Municipal Natural Science Foundation o China(Grant No.23ZR1415500)。
文摘Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-inspired materials which have excellent properties not present in conventional composites.To create such materials with desirable mechanical properties,the optimum structural parameters combination must be selected.Moreover,the optimal design of bio-inspired composites needs to take into account the trade-offs between various mechanical properties.In this paper,multi-objective optimization models were developed using structural parameters as design variables and mechanical properties as optimization objectives,including stiffness,strength,toughness,and dynamic damping.Using the NSGA-II optimization algorithm,a set of optimal solutions were solved.Additionally,three different structures in natural nacre were introduced in order to utilize the better structure when design bio-inspired materials.The range of optimal solutions that obtained using results from previous research were examined and explained why this collection of optimal solution ranges is better.Also,optimal solutions were compared with the structural features and mechanical properties of real nacre and artificial biomimetic composites to validate our models.Finally,the optimum design strategies can be obtained for nacre-like composites.Our research methodically proposes an optimization method for achieving load-bearing bio-inspired materials with excellent properties and creates a set of optimal solutions from which designers can select the one that best suits their preferences,allowing the fabricated materials to demonstrate preferred performance.
文摘Fatigue failure continues to be a significant challenge in designing structural and mechanical components subjected to repeated and complex loading.While earlier studies mainly examined material properties and how stress affects lifespan,this review offers the first comprehensive,multiscale comparison of strategies that optimize geometry to improve fatigue performance.This includes everything from microscopic features like the shape of graphite nodules to large-scale design elements such as fillets,notches,and overall structural layouts.We analyze and combine various methods,including topology and shape optimization,the ability of additive manufacturing to finetune internal geometries,and reliability-based design approaches.A key new contribution is our proposal of a standard way to evaluate geometry-focused fatigue design,allowing for consistent comparison and encouraging validation across different fields.Furthermore,we highlight important areas for future research,such as incorporating manufacturing flaws,using multiscale models,and integrating machine learning techniques.This work is the first to provide a broad geometric viewpoint in fatigue engineering,laying the groundwork for future design methods that are driven by data and centered on reliability.
基金supported by the Heilongjiang Provincial Administration of Traditional Chinese Medicine Project(ZHY18-153).
文摘Objective To optimize the ultrasonic extraction process for benzoic acid as a harmful substance in Paeonia lactiflora Pall.(P.lactiflora Pall.).Methods Methanol and ethanol solutions at different concentration gradients(25,50,75%)were prepared to investigate the effects of extraction solvents on the extraction efficiency of benzoic acid.The influences of ultrasonic frequency(35,50 Hz),ultrasonic power(40,60,80,100 W),ultrasonic time(10,20,30,40,50,60 minutes),and ultrasonic temperature(20,30,40,50℃)on the extraction efficiency were examined.Orthogonal experiments were conducted to analyze the effects of temperature,time,and ultrasonic power on the extraction efficiency and to screen the optimal ultrasonic extraction process.Results Various influencing factors had certain effects on the extraction efficiency of benzoic acid from P.lactiflora Pall.Single-factor analysis revealed that 25%methanol,ultrasonic frequency of 50 Hz,ultrasonic power of 40 W,ultrasonic time of 10minutes,and ultrasonic temperature of 30℃yielded the highest extraction efficiency for benzoic acid.The order of influence of different factors on the extraction efficiency was temperature>time>power.The optimal conditions obtained from orthogonal experiments were:extraction solvent of 25%methanol,ultrasonic frequency of 50 Hz,ultrasonic time of 20 minutes,ultrasonic power of 40 W,and ultrasonic temperature of 30℃.Conclusion Under the conditions of 25%methanol as the extraction solvent,ultrasonic frequency of 50 Hz,ultrasonic time of 20 minutes,ultrasonic power of 40 W,and ultrasonic temperature of 30℃,the extraction efficiency of benzoic acid from P.lactiflora Pall.was the highest.This method offers advantages such as simple operation,small sample size requirement,and low solvent consumption,providing a reliable analytical approach for quality control and safety evaluation of P.lactiflora Pall.
基金supported by National Key Research and Development Program of China under Grant 2024YFE0210800National Natural Science Foundation of China under Grant 62495062Beijing Natural Science Foundation under Grant L242017.
文摘The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.
文摘Carbon dioxide-enhanced oil recovery(CO_(2)-EOR)and storage is recognized as an economically feasible technique if used in suitable reservoirs.The type or form and capacity of this CO_(2) sequestration technique is synergistically affected by heat,flow,stress,and chemical reactions.Aimed at addressing the technological issues in applying CO_(2)-EOR and storage in a high water-cut reservoir in Xinjiang,China,this paper proposes a thermo-hydro-mechanical-chemical coupling method during CO_(2) flooding.The potential of CO_(2) sequestration and EOR in the target reservoir is discussed in combination with the surrogate optimization method.This method works better as it considers the evolution of structural trapping,capillary trapping,solubility trapping,and mineral trapping during CO_(2) injection as well as the influence the physical field has on the sequestration capacity for different forms of CO_(2) sequestration.The main mechanisms of CO_(2) sequestration in the high water-cut reservoir is structural trapping,followed by capillary trapping.Solubility trapping and mineral trapping have less contribution to the total sequestration capacity of CO_(2).After optimization,the cumulative oil production was 2.36×10^(6)m^(3),an increase of 0.25×10^(6)m3or 11.9%compared to the pre-optimization value.The CO_(2) sequestration capacity after optimization was 1.39×10^(6)t,which is an increase of 0.23×10^(6)t compared to values obtained before optimization;this effectively increases the area affected by CO_(2) by 24.4%.Of the four trapping mechanisms,capillary trapping and structural trapping showed a high increase of 32.5%and17.28%,respectively,while solubility trapping and mineral trapping only led to an increase of 5.1%and0.43%,respectively.This research could provide theoretical support for fully utilizing the potential of CO_(2)-EOR and sequestration technology.
基金supported by the National Natural Science Foundation of China(No.52005378)the opening project fund of Materials Service Safety Assessment Facilities(No.MSAF-2021-107).
文摘The press-fit connector is a typical plug-and-play solderless connection,and it is widely used in signal transmission in fields such as communication and automotive devices.This paper focuses on inverse designing and optimization of geometric structure,as well as insertion-withdrawal forces of press-fit connector using artificial neural network(ANN)-assisted optimization method.The ANN model is established to approximate the relationship between geometric parameters and insertion-withdrawal forces,of which hyper-parameters of neural network are optimized to improve model performance.Two numerical methods are proposed for inverse designing structural parameters(Model-I)and multi-objective optimization of insertion-withdrawal forces(Model-II)of press-fit connector.In Model-I,a method for inverse designing structure parameters is established,of which an ANN model is coupled with single-objective optimization algorithm.The objective function is established,the inverse problem is solved,and effectiveness is verified.In Model-II,a multi-objective optimization method is proposed,of which an ANN model is coupled with genetic algorithm.The Pareto solution sets of insertion-withdrawal forces are obtained,and results are analyzed.The established ANN-coupled numerical optimization methods are beneficial for improving the design efficiency,and enhancing the connection reliability of the press-fit connector.
基金supported by the Hong Kong Research Grants under Competitive Earmarked Research Grant No.16206320.
文摘Recent progress in topology optimization(TO)has seen a growing integration of machine learning to accelerate computation.Among these,online learning stands out as a promising strategy for large-scale TO tasks,as it eliminates the need for pre-collected training datasets by updating surrogate models dynamically using intermediate optimization data.Stress-constrained lightweight design is an important class of problem with broad engineering relevance.Most existing frameworks use pixel or voxel-based representations and employ the finite element method(FEM)for analysis.The limited continuity across finite elements often compromises the accuracy of stress evaluation.To overcome this limitation,isogeometric analysis is employed as it enables smooth representation of structures and thus more accurate stress computation.However,the complexity of the stress-constrained design problem together with the isogeometric representation results in a large computational cost.This work proposes a multi-grid,single-mesh online learning framework for isogeometric topology optimization(ITO),leveraging the Fourier Neural Operator(FNO)as a surrogate model.Operating entirely within the isogeometric analysis setting,the framework provides smooth geometry representation and precise stress computation,without requiring traditional mesh generation.A localized training approach is employed to enhance scalability,while a multi-grid decomposition scheme incorporates global structural context into local predictions to boost FNO accuracy.By learning the mapping from spatial features to sensitivity fields,the framework enables efficient single-resolution optimization,avoiding the computational burden of two-resolution simulations.The proposed method is validated through 2D stress-constrained design examples,and the effect of key parameters is studied.
基金funded by the National Natural Science Foundation of China(No.82400370)the Interdisciplinary Innovation Team Incubation Project of Children’s Hospital of Fudan University(No.EKYX202416).
文摘Owing to their excellent biocompatibility and potential for durability enhancement,polymeric heart valves(PHVs)are emerging as a promising alternative to traditional prostheses.Unlike conventional materials,PHVs can be manufactured under precise design criteria,enabling targeted performance improvements.This study introduces a geometric optimization strategy for enhancing the durability of PHVs.The finite element method(FEM)is combined with a dip-molding technique to develop a novel polymeric aortic valve with improved mechanical properties.The tri-leaflet geometry is parameterized using B-spline curves,and the maximum stress in the valve is reduced from 2.4802 to 1.7773 MPa using a multiobjective optimization algorithm NSGA-II(non-dominated sorting genetic algorithm II).Pre-optimized and optimized valve prototypes were fabricated via dip-molding and evaluated during pulsatile-flow tests and accelerated wear tests.The optimized design meets the ISO 5840 standards,with an effective orifice area of 2.019 cm^(2),a regurgitant fraction of 5.693%,and a transvalvular pressure gradient of 7.576 mmHg.Moreover,the optimized valve maintained its structural integrity and functionality over 14 million cycles of the accelerated wear test,whereas the unoptimized valve failed after two million cycles.These findings confirm that the FEM-based geometric optimization method enhances both the mechanical performance and durability of PHVs.
