The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous flui...The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance.展开更多
In 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
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 integration of additive manufacturing and topology optimization makes it possible to fabricate complex configurations,especially for microscale structures,which can guarantee the realization of high-performance st...The integration of additive manufacturing and topology optimization makes it possible to fabricate complex configurations,especially for microscale structures,which can guarantee the realization of high-performance structural designs.However,topology results often contain microstructures(several multicellular scales)similar to the characteristic length of local macrostructures,leading to errors in structural performance analysis based on classical theories.Therefore,it is necessary to consider the size effect in topology optimization.In this paper,we establish a novel topology optimization model utilizing the integral nonlocal theory to account for the size effect.The approach consists of an integral constitutive model that incorporates a kernel function,enabling the description of stress at a specific point in relation to strain in a distant field.Topology optimization structures based on nonlocal theory are presented for some benchmark examples,and the results are compared with those based on classical medium theory.The material layout exhibits significant differences between the two approaches,highlighting the necessity of topology optimization based on nonlocal theory and the effectiveness of the proposed method.展开更多
The structural, electronic, and optical properties of Cu2Zn1−xBaxSn1−ySiyS4 compounds have been calculated using GGA-PBE function within the framework of Density Functional Theory (DFT). In the present work, lattice p...The structural, electronic, and optical properties of Cu2Zn1−xBaxSn1−ySiyS4 compounds have been calculated using GGA-PBE function within the framework of Density Functional Theory (DFT). In the present work, lattice parameters remained the same, that is tetragonal crystal structure for 0% and 100% doping concentration. The electronic band gap of Cu2Zn1−xBaxSn1−ySiyS4 compounds has been gradually increased for continuous increment of doping concentration where the highest electronic band gap is 1.117 eV for Cu2BaSiS4 structure. Moreover, the band gap changes from direct to indirect band gap with the increase of doping concentration in the parent compound. The absorption coefficient has been found to be high (> 104 cm−1) in UV-region for all the doping concentration which makes the studied compound as a potential candidate of absorber layer in the UV detector. The theoretical study of the effect of double doping in the CZTS compound is very interesting for improving the quality of it and it would be a reference for the theoretical and experimental researchers.展开更多
Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequ...Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.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
A corrosion discoloration model for copper-nickel alloys in Cl^(−)environments was established using CIE-Lab,UV-VIS absorption spectroscopy,X-ray diffraction,and X-ray photoelectron spectroscopy.The corrosion discolor...A corrosion discoloration model for copper-nickel alloys in Cl^(−)environments was established using CIE-Lab,UV-VIS absorption spectroscopy,X-ray diffraction,and X-ray photoelectron spectroscopy.The corrosion discoloration process and the corresponding main corrosion products can be summarized as follows:silver-white(Cu+Ni)→green(NiO)→reddishbrown(NiO+Cu_(2)O)→black(NiO+Cu_(2)O+CuO).Density functional theory was employed to explain the corrosion process of copper-nickel alloys and the detrimental effect of Cl^(−).The results indicate that adsorbates preferentially bind to nickel,leading to the preferential formation of NiO,which imparts a green appearance to the surface.Furthermore,the difficulty in forming nickel cation vacancies and the higher diffusion barrier for nickel inhibit the migration of species within the oxide layer.Notably,nickel also suppresses carrier migration within the oxide layer,reducing the charge transfer rate.In contrast,the promotion of corrosion by Cl^(−)is primarily attributed to the reduction in surface work function and the formation energy of cation vacancies.展开更多
Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urge...Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urgency of demand at disaster-affected sites. Firstly, urgency cost, economic cost, and transportation distance cost were identified as key objectives. The study applied fuzzy theory integration to construct a triangular fuzzy multi-objective site selection decision model. Next, the defuzzification theory transformed the fuzzy decision model into a precise one. Subsequently, an improved Chaotic Quantum Multi-Objective Harris Hawks Optimization (CQ-MOHHO) algorithm was proposed to solve the model. The CQ-MOHHO algorithm was shown to rapidly produce high-quality Pareto front solutions and identify optimal site selection schemes for emergency resource distribution centers through case studies. This outcome verified the feasibility and efficacy of the site selection decision model and the CQ-MOHHO algorithm. To further assess CQ-MOHHO’s performance, Zitzler-Deb-Thiele (ZDT) test functions, commonly used in multi-objective optimization, were employed. Comparisons with Multi-Objective Harris Hawks Optimization (MOHHO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Grey Wolf Optimizer (MOGWO) using Generational Distance (GD), Hypervolume (HV), and Inverted Generational Distance (IGD) metrics showed that CQ-MOHHO achieved superior global search ability, faster convergence, and higher solution quality. The CQ-MOHHO algorithm efficiently achieved a balance between multiple objectives, providing decision-makers with satisfactory solutions and a valuable reference for researching and applying emergency site selection problems.展开更多
As primary load-bearing components extensively utilized in engineering applications,beam structures necessitate the design of their microstructural configurations to achieve lightweight objectives while satisfying div...As primary load-bearing components extensively utilized in engineering applications,beam structures necessitate the design of their microstructural configurations to achieve lightweight objectives while satisfying diverse mechanical performance requirements.Combining topology optimization with fully coupled homogenization beam theory,we provide a highly efficient design tool to access desirable periodic microstructures for beams.The present optimization framework comprehensively takes into account for key deformation modes,including tension,bending,torsion,and shear deformation,all within a unified formulation.Several numerical results prove that our method can be used to handle kinds of microstructure design for beam-like structures,e.g.,extreme tension(compression)-torsion stiffness,maximization of minimum critical buckling load,and minimization of structural compliance.When optimizing microstructures for macroscopic performance,we emphasize investigating the influence of shear stiffness on the optimized results.The novel chiral beam-like structures are fabricated and tested.The experimental results indicate that the optimized tension(compression)-torsion structure has excellent buffer characteristics,as compared with the traditional square tube.This proposed optimization framework can be further extended to other physical problems of Timoshenko beams.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
Organic semiconductor materials have demonstrated extensive potential in the field of gas sensors due to the advantages including designable chemical structure,tunable physical and chemical properties.Through density ...Organic semiconductor materials have demonstrated extensive potential in the field of gas sensors due to the advantages including designable chemical structure,tunable physical and chemical properties.Through density functional theory(DFT)calculations,researchers can investigate gas sensing mechanisms,optimize,and predict the electronic structures and response characteristics of these materials,and thereby identify candidate materials with promising gas sensing applications for targeted design.This review concentrates on three primary applications of DFT technology in the realm of organic semiconductor-based gas sensors:(1)Investigating the sensing mechanisms by analyzing the interactions between gas molecules and sensing materials through DFT,(2)simulating the dynamic responses of gas molecules,which involves the behavior on the sensing interface using DFT combined with other computational methods to explore adsorption and diffusion processes,and(3)exploring and designing sensitive materials by employing DFT for screening and predicting chemical structures,thereby developing new sensing materials with exceptional performance.Furthermore,this review examines current research outcomes and anticipates the extensive application prospects of DFT technology in the domain of organic semiconductor-based gas sensors.These efforts are expected to provide valuable insights for further indepth exploration of DFT applications in sensor technology,thereby fostering significant advancements and innovations in the field.展开更多
This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networ...This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networks.Unlike conventional Deep Reinforcement Learning(DRL)-based approaches for CW size adjustment,which often suffer from overestimation bias and limited exploration diversity,leading to suboptimal throughput and collision performance.Our framework integrates the Gumbel distribution and extreme value theory to systematically enhance action selection under varying network conditions.First,SETL adopts a DDQN architecture(SETL-DDQN)to improve Q-value estimation accuracy and enhance training stability.Second,we incorporate a Gumbel distribution-driven exploration mechanism,forming SETL-DDQN(Gumbel),which employs the extreme value theory to promote diverse action selection,replacing the conventional-greedy exploration that undergoes early convergence to suboptimal solutions.Both models are evaluated through extensive simulations in static and time-varying IEEE 802.11 network scenarios.The results demonstrate that our approach consistently achieves higher throughput,lower collision rates,and improved adaptability,even under abrupt fluctuations in traffic load and network conditions.In particular,the Gumbel-based mechanism enhances the balance between exploration and exploitation,facilitating faster adaptation to varying congestion levels.These findings position Gumbel-enhanced DRL as an effective and robust solution for CW optimization in wireless networks,offering notable gains in efficiency and reliability over existing methods.展开更多
A systematic and effective optimization is proposed for the design of a three-dimensional （3-D） vehicle suspension model with eight degrees of freedom （DOF）, including vertical seat motion, vehicle suspension, pit...A systematic and effective optimization is proposed for the design of a three-dimensional （3-D） vehicle suspension model with eight degrees of freedom （DOF）, including vertical seat motion, vehicle suspension, pitching and rolling motions, and vertical wheel motions using the evolutionary game theory. A new design of the passive suspension is aided by game theory to attain the best compromise between ride quality and suspension deflections. Extensive simulations are performed on three type road surface models A, B, C pavement grades based on the guidelines provided by ISO-2631 with the Matlab/Simulink environment. The preliminary results show that, when the passive suspension is optimized via the proposed approach, a substantial improvement in the vertical ride quality is obtained while keeping the suspension deflections within their allowable clearance when the vehicle moves at a constant velocity v=20 m/s, and the comfort performance of a suspension seat can be enhanced by 20%-30%.展开更多
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the financial support to this work by the National Natural Science Foundation of China(Grant Nos.12272076 and 11802164)the 111 Project(B14013).
文摘The integration of additive manufacturing and topology optimization makes it possible to fabricate complex configurations,especially for microscale structures,which can guarantee the realization of high-performance structural designs.However,topology results often contain microstructures(several multicellular scales)similar to the characteristic length of local macrostructures,leading to errors in structural performance analysis based on classical theories.Therefore,it is necessary to consider the size effect in topology optimization.In this paper,we establish a novel topology optimization model utilizing the integral nonlocal theory to account for the size effect.The approach consists of an integral constitutive model that incorporates a kernel function,enabling the description of stress at a specific point in relation to strain in a distant field.Topology optimization structures based on nonlocal theory are presented for some benchmark examples,and the results are compared with those based on classical medium theory.The material layout exhibits significant differences between the two approaches,highlighting the necessity of topology optimization based on nonlocal theory and the effectiveness of the proposed method.
文摘The structural, electronic, and optical properties of Cu2Zn1−xBaxSn1−ySiyS4 compounds have been calculated using GGA-PBE function within the framework of Density Functional Theory (DFT). In the present work, lattice parameters remained the same, that is tetragonal crystal structure for 0% and 100% doping concentration. The electronic band gap of Cu2Zn1−xBaxSn1−ySiyS4 compounds has been gradually increased for continuous increment of doping concentration where the highest electronic band gap is 1.117 eV for Cu2BaSiS4 structure. Moreover, the band gap changes from direct to indirect band gap with the increase of doping concentration in the parent compound. The absorption coefficient has been found to be high (> 104 cm−1) in UV-region for all the doping concentration which makes the studied compound as a potential candidate of absorber layer in the UV detector. The theoretical study of the effect of double doping in the CZTS compound is very interesting for improving the quality of it and it would be a reference for the theoretical and experimental researchers.
文摘Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.
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supported by the Key Program of the National Natural Science Foundation of China(Grant No.51131007)the National Key Research and Development Program of China(Grant No.2021YFC2803102).
文摘A corrosion discoloration model for copper-nickel alloys in Cl^(−)environments was established using CIE-Lab,UV-VIS absorption spectroscopy,X-ray diffraction,and X-ray photoelectron spectroscopy.The corrosion discoloration process and the corresponding main corrosion products can be summarized as follows:silver-white(Cu+Ni)→green(NiO)→reddishbrown(NiO+Cu_(2)O)→black(NiO+Cu_(2)O+CuO).Density functional theory was employed to explain the corrosion process of copper-nickel alloys and the detrimental effect of Cl^(−).The results indicate that adsorbates preferentially bind to nickel,leading to the preferential formation of NiO,which imparts a green appearance to the surface.Furthermore,the difficulty in forming nickel cation vacancies and the higher diffusion barrier for nickel inhibit the migration of species within the oxide layer.Notably,nickel also suppresses carrier migration within the oxide layer,reducing the charge transfer rate.In contrast,the promotion of corrosion by Cl^(−)is primarily attributed to the reduction in surface work function and the formation energy of cation vacancies.
文摘Addressing the complex issue of emergency resource distribution center site selection in uncertain environments, this study was conducted to comprehensively consider factors such as uncertainty parameters and the urgency of demand at disaster-affected sites. Firstly, urgency cost, economic cost, and transportation distance cost were identified as key objectives. The study applied fuzzy theory integration to construct a triangular fuzzy multi-objective site selection decision model. Next, the defuzzification theory transformed the fuzzy decision model into a precise one. Subsequently, an improved Chaotic Quantum Multi-Objective Harris Hawks Optimization (CQ-MOHHO) algorithm was proposed to solve the model. The CQ-MOHHO algorithm was shown to rapidly produce high-quality Pareto front solutions and identify optimal site selection schemes for emergency resource distribution centers through case studies. This outcome verified the feasibility and efficacy of the site selection decision model and the CQ-MOHHO algorithm. To further assess CQ-MOHHO’s performance, Zitzler-Deb-Thiele (ZDT) test functions, commonly used in multi-objective optimization, were employed. Comparisons with Multi-Objective Harris Hawks Optimization (MOHHO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Grey Wolf Optimizer (MOGWO) using Generational Distance (GD), Hypervolume (HV), and Inverted Generational Distance (IGD) metrics showed that CQ-MOHHO achieved superior global search ability, faster convergence, and higher solution quality. The CQ-MOHHO algorithm efficiently achieved a balance between multiple objectives, providing decision-makers with satisfactory solutions and a valuable reference for researching and applying emergency site selection problems.
基金supported by the National Natural Science Foundation of China(grant number 11902015)the Open Fund of Deceleration and Landing Laboratory of the Fifth Academy of Aerospace Science and Technology Group(grant number EDL19092138)the Ministry of Education Chunhui Plan(HZKY20220014).
文摘As primary load-bearing components extensively utilized in engineering applications,beam structures necessitate the design of their microstructural configurations to achieve lightweight objectives while satisfying diverse mechanical performance requirements.Combining topology optimization with fully coupled homogenization beam theory,we provide a highly efficient design tool to access desirable periodic microstructures for beams.The present optimization framework comprehensively takes into account for key deformation modes,including tension,bending,torsion,and shear deformation,all within a unified formulation.Several numerical results prove that our method can be used to handle kinds of microstructure design for beam-like structures,e.g.,extreme tension(compression)-torsion stiffness,maximization of minimum critical buckling load,and minimization of structural compliance.When optimizing microstructures for macroscopic performance,we emphasize investigating the influence of shear stiffness on the optimized results.The novel chiral beam-like structures are fabricated and tested.The experimental results indicate that the optimized tension(compression)-torsion structure has excellent buffer characteristics,as compared with the traditional square tube.This proposed optimization framework can be further extended to other physical problems of Timoshenko beams.
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 National Natural Science Foundation of China(Nos.92263109 and 61904188)the Shanghai Rising-Star Program(No.22QA1410400)。
文摘Organic semiconductor materials have demonstrated extensive potential in the field of gas sensors due to the advantages including designable chemical structure,tunable physical and chemical properties.Through density functional theory(DFT)calculations,researchers can investigate gas sensing mechanisms,optimize,and predict the electronic structures and response characteristics of these materials,and thereby identify candidate materials with promising gas sensing applications for targeted design.This review concentrates on three primary applications of DFT technology in the realm of organic semiconductor-based gas sensors:(1)Investigating the sensing mechanisms by analyzing the interactions between gas molecules and sensing materials through DFT,(2)simulating the dynamic responses of gas molecules,which involves the behavior on the sensing interface using DFT combined with other computational methods to explore adsorption and diffusion processes,and(3)exploring and designing sensitive materials by employing DFT for screening and predicting chemical structures,thereby developing new sensing materials with exceptional performance.Furthermore,this review examines current research outcomes and anticipates the extensive application prospects of DFT technology in the domain of organic semiconductor-based gas sensors.These efforts are expected to provide valuable insights for further indepth exploration of DFT applications in sensor technology,thereby fostering significant advancements and innovations in the field.
文摘This study introduces the Smart Exponential-Threshold-Linear with Double Deep Q-learning Network(SETL-DDQN)and an extended Gumbel distribution method,designed to optimize the Contention Window(CW)in IEEE 802.11 networks.Unlike conventional Deep Reinforcement Learning(DRL)-based approaches for CW size adjustment,which often suffer from overestimation bias and limited exploration diversity,leading to suboptimal throughput and collision performance.Our framework integrates the Gumbel distribution and extreme value theory to systematically enhance action selection under varying network conditions.First,SETL adopts a DDQN architecture(SETL-DDQN)to improve Q-value estimation accuracy and enhance training stability.Second,we incorporate a Gumbel distribution-driven exploration mechanism,forming SETL-DDQN(Gumbel),which employs the extreme value theory to promote diverse action selection,replacing the conventional-greedy exploration that undergoes early convergence to suboptimal solutions.Both models are evaluated through extensive simulations in static and time-varying IEEE 802.11 network scenarios.The results demonstrate that our approach consistently achieves higher throughput,lower collision rates,and improved adaptability,even under abrupt fluctuations in traffic load and network conditions.In particular,the Gumbel-based mechanism enhances the balance between exploration and exploitation,facilitating faster adaptation to varying congestion levels.These findings position Gumbel-enhanced DRL as an effective and robust solution for CW optimization in wireless networks,offering notable gains in efficiency and reliability over existing methods.
基金Supported by Program for New Century Excellent Talents in University (070003)the Natural Science Foundation of Anhui Province (070414154)~~
文摘A systematic and effective optimization is proposed for the design of a three-dimensional （3-D） vehicle suspension model with eight degrees of freedom （DOF）, including vertical seat motion, vehicle suspension, pitching and rolling motions, and vertical wheel motions using the evolutionary game theory. A new design of the passive suspension is aided by game theory to attain the best compromise between ride quality and suspension deflections. Extensive simulations are performed on three type road surface models A, B, C pavement grades based on the guidelines provided by ISO-2631 with the Matlab/Simulink environment. The preliminary results show that, when the passive suspension is optimized via the proposed approach, a substantial improvement in the vertical ride quality is obtained while keeping the suspension deflections within their allowable clearance when the vehicle moves at a constant velocity v=20 m/s, and the comfort performance of a suspension seat can be enhanced by 20%-30%.
基金supported by National Natural Science Foundation of China(61425008,61333004,61273054)Top-Notch Young Talents Program of China,and Aeronautical Foundation of China(2013585104)
