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
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
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
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
Resilience of air&space defense system of systems(SoSs)is critical to national air defense security.However,the research on it is still scarce.In this study,the resilience of air&space defense SoSs is firstly ...Resilience of air&space defense system of systems(SoSs)is critical to national air defense security.However,the research on it is still scarce.In this study,the resilience of air&space defense SoSs is firstly defined and the kill network theory is established by combining super network and kill chain theory.Two cases of the SoSs are considered:(a)The kill chains are relatively homogenous;(b)The kill chains are relatively heterogenous.Meanwhile,two capability assessment methods,which are based on the number of kill chains and improved self-information quantity,respectively,are proposed.The improved self-information quantity modeled based on nodes and edges can achieve qualitative and quantitative assessment of the combat capability by using linguistic Pythagorean fuzzy sets.Then,a resilient evaluation index consisting of risk response,survivability,and quick recovery is proposed accordingly.Finally,network models for regional air defense and anti-missile SoSs are established respectively,and the resilience measurement results are verified and analyzed under different attack and recovery strategies,and the optimization strategies are also proposed.The proposed theory and method can meet different demands to evaluate combat capability and optimize resilience of various types of air&space defense and similar SoSs.展开更多
Urban agglomerations are spatial entities that promote the development of ‘new urbanization' processes within China. In this context, the concept of ‘multiscale urban agglomeration spaces' encompasses three ...Urban agglomerations are spatial entities that promote the development of ‘new urbanization' processes within China. In this context, the concept of ‘multiscale urban agglomeration spaces' encompasses three linked levels: macroscale urban agglomerations, mesoscale cities, and microscale urban centers. Applying a series of multidisciplinary integrated research methods drawn from geography, urban planning, and architecture, this paper reveals two intensive utilization laws that can be generalized to apply to multiscale urban agglomeration spaces, top-to-bottom ‘positive transmission' linkage and inside-to-outside ‘negative transmission' movement. This paper also proposes optimization transmission theory and policy decision technical pathways that can be applied to these three urban agglomeration spatial scales. Specific technical pathways of transmission include intensive expansion and simulated decision-making in macroscale urban agglomerations, ecology, production, and living space intensive layout and dynamic decision-making in mesoscale cities, and four cores(i.e., ‘single, ring, axis, and pole core') progressive linkage and intensive optimization decision-making in microscale urban centers. The theory and technical pathways proposed in this paper solve the technical problem of optimization and provide intensive methods that can be applied not only at the individual level but also at multiple scales in urban agglomeration spaces. This study also advances a series of comprehensive technical solutions that can be applied to both compact and smart growth cities as well as to urban agglomerations. Solid theoretical support is provided for the optimization of Chinese land development, urbanization, agricultural development, and ecological security.展开更多
Based on density functional theory(DFT)and basic structure models,the chemical reactions on the surface of vanadium-titanium based selective catalytic reduction(SCR)denitrification catalysts were summarized.Reasonable...Based on density functional theory(DFT)and basic structure models,the chemical reactions on the surface of vanadium-titanium based selective catalytic reduction(SCR)denitrification catalysts were summarized.Reasonable structural models(non-periodic and periodic structural models)are the basis of density functional calculations.A periodic structure model was more appropriate to represent the catalyst surface,and its theoretical calculation results were more comparable with the experimental results than a nonperiodic model.It is generally believed that the SCR mechanism where NH3 and NO react to produce N2 and H2 O follows an Eley-Rideal type mechanism.NH2 NO was found to be an important intermediate in the SCR reaction,with multiple production routes.Simultaneously,the effects of H2 O,SO2 and metal on SCR catalysts were also summarized.展开更多
The elliptical cross-section spiral equal-channel extrusion （ECSEE） process is simulated by using Deform-3D finite element software. The ratio m of major-axis to minor-axis length for ellipse-cross-section, the tors...The elliptical cross-section spiral equal-channel extrusion （ECSEE） process is simulated by using Deform-3D finite element software. The ratio m of major-axis to minor-axis length for ellipse-cross-section, the torsion angle u, the round-ellipse cross-section transitional channel L1, the elliptical rotation cross-section transitional channel L2 and the ellipse-round cross-section transitional channel L3 are destined for the extrusion process parameters. The average effective strain eave on cross-section of blank, the deformation uniformity coefficient a and the value of maximum damage dmax are chosen to be the optimize indexes, and the virtual orthogonal experiment of L16 （45） is designed. The correlation degree of the process factors affecting eave, a and dmax is analyzed by the numerical simulation results using the weights and grey association model. The process parameters are optimized by introducing the grey situation decision theory and the ECSEE optimal combination of process parameters is obtained： u of 120 , m of 1.55, L1 of 7 mm, L2 of 10 mm, and L3 of 10 mm. Simulation and experimental results show that the material can be refined with the optimized structural parameters of die. Therefore, the optimization results are satisfactory.展开更多
A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis ca...A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.展开更多
It is essential to consider the effects of incomplete measurement,inaccurate information and inadequate cognition on structural topology optimization.For the multi-material structural topology optimization with non-pr...It is essential to consider the effects of incomplete measurement,inaccurate information and inadequate cognition on structural topology optimization.For the multi-material structural topology optimization with non-probability uncertainty,the multi-material interpolation model is represented by the ordered rational approximation of mat erial properties(ordered RAMP).Combined with structural compliance minimization,the multi-material topology optimization with reliability constraints is established.The corresponding non-probability uncertainties are described by the evidence theory,and the uniformity processing method is introduced to convert the evidence variables into random variables.The first-order reliability method is employed to search the most probable point under the reliability index constraint,and then the random variables are equivalent to the deterministic variables according to the geometric meaning of the reliability index and sensitivity information.Therefore,the non-probabilistic reliability-based multi-material topology optimization is transformed into the conventional deterministic optimization format,followed by the ordered RAMP method to solve the optimization problem.Finally,through numerical examples of 2D and 3D structures,the feasibility and effectiveness of the proposed method are verified to consider the geometrical dimensions and external loading uncertainties.展开更多
Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular...Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.展开更多
Perfect combination of structural size parameters of the hydroforming billets is essential to obtain even wall thicknesses of the car beam. Finite element （ FE ） analysis on hydroforming car beam was carried out, a...Perfect combination of structural size parameters of the hydroforming billets is essential to obtain even wall thicknesses of the car beam. Finite element （ FE ） analysis on hydroforming car beam was carried out, and the results were optimized according to multiple quality objectives by the grey system theory. With bending angle, bending radius and hight difference along the axis direction as variables, orthogonal FE analyses were conducted and the minimum and maximum wall thicknes ses of the billets with different sizes were obtained. Taking the minimum and maximum wall thick nesses as two references, the correlation coefficient between the data for reference and those for comparison by the grey system theory reduced multi objectives to a single quality objective, and the average correlation level of every billet facilitated the optimization of size parameters for hydroform ing car beam. The trial production showed that the optimization approach satisfied the need of hy droforming car beams.展开更多
In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on th...In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on the optimization theory to identifying fire image characteristics. First the optimization of BP neural network adopting Levenberg-Marquardt algorithm with the property of quadratic convergence is discussed, and then a new system of fire image identification is devised. Plenty of experiments and field tests have proved that this system can detect the early-stage fire flame quickly and reliably.展开更多
Fatigue failure is a common failure mode under the action of cyclic loads in engineering applications,which often occurs with no obvious signal.The maximum structural stress is far below the allowable stress when the ...Fatigue failure is a common failure mode under the action of cyclic loads in engineering applications,which often occurs with no obvious signal.The maximum structural stress is far below the allowable stress when the structures are damaged.Aiming at the lightweight structure,fatigue topology optimization design is investigated to avoid the occurrence of fatigue failure in the structural conceptual design beforehand.Firstly,the fatigue life is expressed by topology variables and the fatigue life filter function.The continuum fatigue optimization model is established with the independent continuous mapping(ICM)method.Secondly,fatigue life constraints are transformed to distortion energy constraints explicitly by taking advantage of the distortion energy theory.Thirdly,the optimization formulation is solved by the dual sequence quadratic programming(DSQP).And the design scheme of lightweight structure considering the fatigue characteristics is obtained.Finally,numerical examples illustrate the practicality and effectiveness of the fatigue optimization method.This method further expands the theoretical application of the ICM method and provides a novel approach for the fatigue optimization problem.展开更多
Network virtualization(NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks(VNs) with different protocols to coexist on a shared substrate network(SN). One ...Network virtualization(NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks(VNs) with different protocols to coexist on a shared substrate network(SN). One main challenge in NV is virtual network embedding(VNE). VNE is a NPhard problem. Previous VNE algorithms in the literature are mostly heuristic, while the remaining algorithms are exact. Heuristic algorithms aim to find a feasible embedding of each VN, not optimal or sub-optimal, in polynomial time. Though presenting the optimal or sub-optimal embedding per VN, exact algorithms are too time-consuming in smallscaled networks, not to mention moderately sized networks. To make a trade-off between the heuristic and the exact, this paper presents an effective algorithm, labeled as VNE-RSOT(Restrictive Selection and Optimization Theory), to solve the VNE problem. The VNERSOT can embed virtual nodes and links per VN simultaneously. The restrictive selection contributes to selecting candidate substrate nodes and paths and largely cuts down on the number of integer variables, used in the following optimization theory approach. The VNE-RSOT fights to minimize substrate resource consumption and accommodates more VNs. To highlight the efficiency of VNERSOT, a simulation against typical and stateof-art heuristic algorithms and a pure exact algorithm is made. Numerical results reveal that virtual network request(VNR) acceptance ratio of VNE-RSOT is, at least, 10% higher than the best-behaved heuristic. Other metrics, such as the execution time, are also plotted to emphasize and highlight the efficiency of VNE-RSOT.展开更多
In 2014, 50 years following the introduction of density functional theory (DFT), a rigorous understanding of it was published [AIP Advances, 4, 127,104 (2014)]. This understanding includes two features that complete t...In 2014, 50 years following the introduction of density functional theory (DFT), a rigorous understanding of it was published [AIP Advances, 4, 127,104 (2014)]. This understanding includes two features that complete the theory in practice, inasmuch as they are necessary for its correct application in electronic structure calculations;this understanding elucidates what appears to have been the crucial misunderstanding for 50 years, namely, the confusion between a stationary solution, attainable with most basis sets, following self-consistent iterations, with the ground state solution. The latter is obtained by a calculation that employs the well-defined optimal basis set for the system. The aim of this work is to review the above understanding and to extend it to the relativistic generalization of density functional theory by Rajagopal and Callaway [Phys. Rev. B7, 1912 (1973)]. This extension straightforwardly follows similar steps taken in the non-relativistic case, with the four-component current density, in the former, replacing the electronic charge density, in the latter. This new understanding, which completes relativistic DFT in practice, is expected to be needed for the study of heavy atoms and of materials (from molecules to solids) containing them—as is the case for some high temperature superconductors.展开更多
A criterion restraining mehod adopted to the dynamic optimization for materials heat- ing process has been developed,by regarding it as controlled object of a regulator in control theory and combining the optimal crit...A criterion restraining mehod adopted to the dynamic optimization for materials heat- ing process has been developed,by regarding it as controlled object of a regulator in control theory and combining the optimal criterion of it.The method may be available to optimize fuel supply along furnace length and to provide a theoretical basis for the optimization of fuel supply in time-varying.For soaking pit,the calculation shows that in case of optimization by criterion restraining method,the thermal efficiency is 3.1 higher than the modelling prediction result.In comparison with heat flux-decomposing method,the thermal efficiency increased from 43.1 to 43.8%,and the energy consumption was reduced by 7.28% relative to the practical production data.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.
基金financially supported in-part by the National Natural Science Foundation of China(52275011)the Natural Science Foundation of Guangdong Province(2023B1515020080)+3 种基金the Natural Science Foundation of Guangzhou(2024A04J2552)the Fundamental Research Funds for the Central Universities,the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(CAST)(2021QNRC001)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515011253)the Higher Education Institution Featured Innovation Project of Department of Education of Guangdong Province(GrantNo.2023KTSCX138).
文摘Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic research.However,a major challenge for mole-inspired robots lies in overcoming resistance in granular media when burrowing with forelimbs.In the absence of effective forepaw design strategies,most robotic designs rely on increased power to enhance performance.To address this issue,this paper employs Resistive Force Theory to optimize mole-inspired forepaws,aiming to enhance burrowing efficiency.By analyzing the relationship between geometric parameters and burrowing forces,we propose several forepaw design variations.Through granular resistance assessments,an effective forepaw configuration is identified and further refined using parameters such as longitudinal and transverse curvature.Subsequently,the Particle Swarm Optimization algorithm is applied to determine the optimal forepaw design.In force-loading tests,the optimized forepaw demonstrated a 79.44%reduction in granular lift force and a 22.55%increase in propulsive force compared with the control group.In robotic burrowing experiments,the optimized forepaw achieved the longest burrow displacement(179.528 mm)and the lowest burrowing lift force(0.9355 mm/s),verifying its effectiveness in reducing the lift force and enhancing the propulsive force.
文摘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 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)
基金supported by National Natural Science Foundation of China,grant numbers 72001214National Social Science Foundation of China,Young Talent Fund of University Association for Science and Technology in Shaanxi,China,No.20190108Natural Science Foundation of Shaanxi Province,grant number 2020JQ-484.
文摘Resilience of air&space defense system of systems(SoSs)is critical to national air defense security.However,the research on it is still scarce.In this study,the resilience of air&space defense SoSs is firstly defined and the kill network theory is established by combining super network and kill chain theory.Two cases of the SoSs are considered:(a)The kill chains are relatively homogenous;(b)The kill chains are relatively heterogenous.Meanwhile,two capability assessment methods,which are based on the number of kill chains and improved self-information quantity,respectively,are proposed.The improved self-information quantity modeled based on nodes and edges can achieve qualitative and quantitative assessment of the combat capability by using linguistic Pythagorean fuzzy sets.Then,a resilient evaluation index consisting of risk response,survivability,and quick recovery is proposed accordingly.Finally,network models for regional air defense and anti-missile SoSs are established respectively,and the resilience measurement results are verified and analyzed under different attack and recovery strategies,and the optimization strategies are also proposed.The proposed theory and method can meet different demands to evaluate combat capability and optimize resilience of various types of air&space defense and similar SoSs.
基金Under the auspices of Major Program of the National Natural Science Foundation of China ‘Coupled mechanisms and interactive coercing effects between urbanization and eco-environment in mega-urban agglomerations’(No.41590842)
文摘Urban agglomerations are spatial entities that promote the development of ‘new urbanization' processes within China. In this context, the concept of ‘multiscale urban agglomeration spaces' encompasses three linked levels: macroscale urban agglomerations, mesoscale cities, and microscale urban centers. Applying a series of multidisciplinary integrated research methods drawn from geography, urban planning, and architecture, this paper reveals two intensive utilization laws that can be generalized to apply to multiscale urban agglomeration spaces, top-to-bottom ‘positive transmission' linkage and inside-to-outside ‘negative transmission' movement. This paper also proposes optimization transmission theory and policy decision technical pathways that can be applied to these three urban agglomeration spatial scales. Specific technical pathways of transmission include intensive expansion and simulated decision-making in macroscale urban agglomerations, ecology, production, and living space intensive layout and dynamic decision-making in mesoscale cities, and four cores(i.e., ‘single, ring, axis, and pole core') progressive linkage and intensive optimization decision-making in microscale urban centers. The theory and technical pathways proposed in this paper solve the technical problem of optimization and provide intensive methods that can be applied not only at the individual level but also at multiple scales in urban agglomeration spaces. This study also advances a series of comprehensive technical solutions that can be applied to both compact and smart growth cities as well as to urban agglomerations. Solid theoretical support is provided for the optimization of Chinese land development, urbanization, agricultural development, and ecological security.
基金supported by the National Key Research&Development(R&D)Program of China(No.2017YFC0210500)the National Natural Science Foundation of China(No.51938014)
文摘Based on density functional theory(DFT)and basic structure models,the chemical reactions on the surface of vanadium-titanium based selective catalytic reduction(SCR)denitrification catalysts were summarized.Reasonable structural models(non-periodic and periodic structural models)are the basis of density functional calculations.A periodic structure model was more appropriate to represent the catalyst surface,and its theoretical calculation results were more comparable with the experimental results than a nonperiodic model.It is generally believed that the SCR mechanism where NH3 and NO react to produce N2 and H2 O follows an Eley-Rideal type mechanism.NH2 NO was found to be an important intermediate in the SCR reaction,with multiple production routes.Simultaneously,the effects of H2 O,SO2 and metal on SCR catalysts were also summarized.
基金co-supported by National Natural Science Foundation of China (No. 51275414)Aeronautical Science Foundation of China (No. 2011ZE53059)+1 种基金National Defense Basic Research Program (No. 51318040105)Graduate Starting Seed Fund of Northwestern Polytechnical University(No. Z2011006)
文摘The elliptical cross-section spiral equal-channel extrusion （ECSEE） process is simulated by using Deform-3D finite element software. The ratio m of major-axis to minor-axis length for ellipse-cross-section, the torsion angle u, the round-ellipse cross-section transitional channel L1, the elliptical rotation cross-section transitional channel L2 and the ellipse-round cross-section transitional channel L3 are destined for the extrusion process parameters. The average effective strain eave on cross-section of blank, the deformation uniformity coefficient a and the value of maximum damage dmax are chosen to be the optimize indexes, and the virtual orthogonal experiment of L16 （45） is designed. The correlation degree of the process factors affecting eave, a and dmax is analyzed by the numerical simulation results using the weights and grey association model. The process parameters are optimized by introducing the grey situation decision theory and the ECSEE optimal combination of process parameters is obtained： u of 120 , m of 1.55, L1 of 7 mm, L2 of 10 mm, and L3 of 10 mm. Simulation and experimental results show that the material can be refined with the optimized structural parameters of die. Therefore, the optimization results are satisfactory.
基金This work was supported by the National Natural Science Foundation of China(Grant No.42050104)the Science Foundation of SINOPEC Group(Grant No.P20030).
文摘A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.
基金supported by the Natural Science Foundation of China(Grant No.51705268)Shandong Provincial Natural Science Foundation,China(Grant No.ZR2016EEB20)China Postdoctoral Science Foundation Funded Project(Grant No.2017M612191)。
文摘It is essential to consider the effects of incomplete measurement,inaccurate information and inadequate cognition on structural topology optimization.For the multi-material structural topology optimization with non-probability uncertainty,the multi-material interpolation model is represented by the ordered rational approximation of mat erial properties(ordered RAMP).Combined with structural compliance minimization,the multi-material topology optimization with reliability constraints is established.The corresponding non-probability uncertainties are described by the evidence theory,and the uniformity processing method is introduced to convert the evidence variables into random variables.The first-order reliability method is employed to search the most probable point under the reliability index constraint,and then the random variables are equivalent to the deterministic variables according to the geometric meaning of the reliability index and sensitivity information.Therefore,the non-probabilistic reliability-based multi-material topology optimization is transformed into the conventional deterministic optimization format,followed by the ordered RAMP method to solve the optimization problem.Finally,through numerical examples of 2D and 3D structures,the feasibility and effectiveness of the proposed method are verified to consider the geometrical dimensions and external loading uncertainties.
基金Project(61174140)supported by the National Natural Science Foundation of ChinaProject(13JJA002)supported by Hunan Provincial Natural Science Foundation,ChinaProject(20110161110035)supported by the Doctoral Fund of Ministry of Education of China
文摘Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.
基金Supported by the National Key Technology R&D Program of the 11th Five-Year Plan of China(2006BAF04B05)the Natural Science Foundation of Shanxi Province(2010021024-2)
文摘Perfect combination of structural size parameters of the hydroforming billets is essential to obtain even wall thicknesses of the car beam. Finite element （ FE ） analysis on hydroforming car beam was carried out, and the results were optimized according to multiple quality objectives by the grey system theory. With bending angle, bending radius and hight difference along the axis direction as variables, orthogonal FE analyses were conducted and the minimum and maximum wall thicknes ses of the billets with different sizes were obtained. Taking the minimum and maximum wall thick nesses as two references, the correlation coefficient between the data for reference and those for comparison by the grey system theory reduced multi objectives to a single quality objective, and the average correlation level of every billet facilitated the optimization of size parameters for hydroform ing car beam. The trial production showed that the optimization approach satisfied the need of hy droforming car beams.
文摘In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on the optimization theory to identifying fire image characteristics. First the optimization of BP neural network adopting Levenberg-Marquardt algorithm with the property of quadratic convergence is discussed, and then a new system of fire image identification is devised. Plenty of experiments and field tests have proved that this system can detect the early-stage fire flame quickly and reliably.
基金This work was supported by the National Natural Science Foundation of China(11872080)Beijing Natural Science Foundation(3192005).
文摘Fatigue failure is a common failure mode under the action of cyclic loads in engineering applications,which often occurs with no obvious signal.The maximum structural stress is far below the allowable stress when the structures are damaged.Aiming at the lightweight structure,fatigue topology optimization design is investigated to avoid the occurrence of fatigue failure in the structural conceptual design beforehand.Firstly,the fatigue life is expressed by topology variables and the fatigue life filter function.The continuum fatigue optimization model is established with the independent continuous mapping(ICM)method.Secondly,fatigue life constraints are transformed to distortion energy constraints explicitly by taking advantage of the distortion energy theory.Thirdly,the optimization formulation is solved by the dual sequence quadratic programming(DSQP).And the design scheme of lightweight structure considering the fatigue characteristics is obtained.Finally,numerical examples illustrate the practicality and effectiveness of the fatigue optimization method.This method further expands the theoretical application of the ICM method and provides a novel approach for the fatigue optimization problem.
基金supported by the National Basic Research Program of China (973 Program) under Grant 2013CB329104the National Natural Science Foundation of China under Grant 61372124 and 61427801the Key Projects of Natural Science Foundation of Jiangsu University under Grant 11KJA510001
文摘Network virtualization(NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks(VNs) with different protocols to coexist on a shared substrate network(SN). One main challenge in NV is virtual network embedding(VNE). VNE is a NPhard problem. Previous VNE algorithms in the literature are mostly heuristic, while the remaining algorithms are exact. Heuristic algorithms aim to find a feasible embedding of each VN, not optimal or sub-optimal, in polynomial time. Though presenting the optimal or sub-optimal embedding per VN, exact algorithms are too time-consuming in smallscaled networks, not to mention moderately sized networks. To make a trade-off between the heuristic and the exact, this paper presents an effective algorithm, labeled as VNE-RSOT(Restrictive Selection and Optimization Theory), to solve the VNE problem. The VNERSOT can embed virtual nodes and links per VN simultaneously. The restrictive selection contributes to selecting candidate substrate nodes and paths and largely cuts down on the number of integer variables, used in the following optimization theory approach. The VNE-RSOT fights to minimize substrate resource consumption and accommodates more VNs. To highlight the efficiency of VNERSOT, a simulation against typical and stateof-art heuristic algorithms and a pure exact algorithm is made. Numerical results reveal that virtual network request(VNR) acceptance ratio of VNE-RSOT is, at least, 10% higher than the best-behaved heuristic. Other metrics, such as the execution time, are also plotted to emphasize and highlight the efficiency of VNE-RSOT.
文摘In 2014, 50 years following the introduction of density functional theory (DFT), a rigorous understanding of it was published [AIP Advances, 4, 127,104 (2014)]. This understanding includes two features that complete the theory in practice, inasmuch as they are necessary for its correct application in electronic structure calculations;this understanding elucidates what appears to have been the crucial misunderstanding for 50 years, namely, the confusion between a stationary solution, attainable with most basis sets, following self-consistent iterations, with the ground state solution. The latter is obtained by a calculation that employs the well-defined optimal basis set for the system. The aim of this work is to review the above understanding and to extend it to the relativistic generalization of density functional theory by Rajagopal and Callaway [Phys. Rev. B7, 1912 (1973)]. This extension straightforwardly follows similar steps taken in the non-relativistic case, with the four-component current density, in the former, replacing the electronic charge density, in the latter. This new understanding, which completes relativistic DFT in practice, is expected to be needed for the study of heavy atoms and of materials (from molecules to solids) containing them—as is the case for some high temperature superconductors.
文摘A criterion restraining mehod adopted to the dynamic optimization for materials heat- ing process has been developed,by regarding it as controlled object of a regulator in control theory and combining the optimal criterion of it.The method may be available to optimize fuel supply along furnace length and to provide a theoretical basis for the optimization of fuel supply in time-varying.For soaking pit,the calculation shows that in case of optimization by criterion restraining method,the thermal efficiency is 3.1 higher than the modelling prediction result.In comparison with heat flux-decomposing method,the thermal efficiency increased from 43.1 to 43.8%,and the energy consumption was reduced by 7.28% relative to the practical production data.
