This paper presents a structured methodology for local network design engineering (SMLNDE). A complex and fuzzy project for local network design can be decomposed into a set of simple and particular activities using t...This paper presents a structured methodology for local network design engineering (SMLNDE). A complex and fuzzy project for local network design can be decomposed into a set of simple and particular activities using the SMLNDE. The SMLNDE allows rigorous requirements definition and permits the exhaustive consideration of the large number of factors influencing local network design engineering. The complete and clear design documentations and an optimal design can also be provided by the methodology. The SMLNDE has been implemented using the structured analysis and design technique. The study shows that the SMLNDE is an effective design methodology for the large and complex local networks.展开更多
The Runge-Kutta optimiser(RUN)algorithm,renowned for its powerful optimisation capabilities,faces challenges in dealing with increasing complexity in real-world problems.Specifically,it shows deficiencies in terms of ...The Runge-Kutta optimiser(RUN)algorithm,renowned for its powerful optimisation capabilities,faces challenges in dealing with increasing complexity in real-world problems.Specifically,it shows deficiencies in terms of limited local exploration capabilities and less precise solutions.Therefore,this research aims to integrate the topological search(TS)mechanism with the gradient search rule(GSR)into the framework of RUN,introducing an enhanced algorithm called TGRUN to improve the performance of the original algorithm.The TS mechanism employs a circular topological scheme to conduct a thorough exploration of solution regions surrounding each solution,enabling a careful examination of valuable solution areas and enhancing the algorithm’s effectiveness in local exploration.To prevent the algorithm from becoming trapped in local optima,the GSR also integrates gradient descent principles to direct the algorithm in a wider investigation of the global solution space.This study conducted a serious of experiments on the IEEE CEC2017 comprehensive benchmark function to assess the enhanced effectiveness of TGRUN.Additionally,the evaluation includes real-world engineering design and feature selection problems serving as an additional test for assessing the optimisation capabilities of the algorithm.The validation outcomes indicate a significant improvement in the optimisation capabilities and solution accuracy of TGRUN.展开更多
Owning various crystal structures and high theoretical capacity,metal tellurides are emerging as promising electrode materials for high-performance metal-ion batteries(MBs).Since metal telluride-based MBs are quite ne...Owning various crystal structures and high theoretical capacity,metal tellurides are emerging as promising electrode materials for high-performance metal-ion batteries(MBs).Since metal telluride-based MBs are quite new,fundamental issues raise regarding the energy storage mechanism and other aspects affecting electrochemical performance.Severe volume expansion,low intrinsic conductivity and slow ion diffusion kinetics jeopardize the performance of metal tellurides,so that rational design and engineering are crucial to circumvent these disadvantages.Herein,this review provides an in-depth discussion of recent investigations and progresses of metal tellurides,beginning with a critical discussion on the energy storage mechanisms of metal tellurides in various MBs.In the following,recent design and engineering strategies of metal tellurides,including morphology engineering,compositing,defect engineering and heterostructure construction,for high-performance MBs are summarized.The primary focus is to present a comprehensive understanding of the structural evolution based on the mechanism and corresponding effects of dimension control,composition,electron configuration and structural complexity on the electrochemical performance.In closing,outlooks and prospects for future development of metal tellurides are proposed.This work also highlights the promising directions of design and engineering strategies of metal tellurides with high performance and low cost.展开更多
Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,...Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,it also has certain drawbacks for the exploration stage and the egg hatch process,resulting in slow convergence speed and inferior solution quality.To address the above issues,a novel multi-strategy improved SO(MISO)with the assistance of population crowding analysis is proposed in this article.In the algorithm,a novel multi-strategy operator is designed for the exploration stage,which not only focuses on using the information of better performing individuals to improve the quality of solution,but also focuses on maintaining population diversity.To boost the efficiency of the egg hatch process,the multi-strategy egg hatch process is proposed to regenerate individuals according to the results of the population crowding analysis.In addition,a local search method is employed to further enhance the convergence speed and the local search capability.MISO is first compared with three sets of algorithms in the CEC2020 benchmark functions,including SO with its two recently discussed variants,ten advanced MAs,and six powerful CEC competition algorithms.The performance of MISO is then verified on five practical engineering design problems.The experimental results show that MISO provides a promising performance for the above optimization cases in terms of convergence speed and solution quality.展开更多
Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as ...Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.展开更多
This paper focuses on the research of the main transformer selection and layout scheme for new energy step-up substations.From the perspective of engineering design,it analyzes the principles of main transformer selec...This paper focuses on the research of the main transformer selection and layout scheme for new energy step-up substations.From the perspective of engineering design,it analyzes the principles of main transformer selection,key parameters,and their matching with the characteristics of new energy.It also explores the layout methods and optimization strategies.Combined with typical case studies,optimization suggestions are proposed for the design of main transformers in new energy step-up substations.The research shows that rational main transformer selection and scientific layout schemes can better adapt to the characteristics of new energy projects while effectively improving land use efficiency and economic viability.This study can provide technical experience support for the design of new energy projects.展开更多
To support and serve engineering design, creative design based on knowledge management is proposed. The key knowledge factors of creative design are analyzed and discussed, and knowledge extraction tools are utilized ...To support and serve engineering design, creative design based on knowledge management is proposed. The key knowledge factors of creative design are analyzed and discussed, and knowledge extraction tools are utilized to distill the important knowledge to serve for knowledge resource of creative design. The implementation of creative design mode is described and executed, which can promote the intelligent asset of the enterprise and shorten the period of creative design. With this study, design afflatus and conceptual design can be achieved expediently and effectively.展开更多
In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization （MOEO）, to solve the constrained multiobjective optimization problems （MOPs）. Th...In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization （MOEO）, to solve the constrained multiobjective optimization problems （MOPs）. The proposed approach is validated by three constrained benchmark problems and successfully applied to handling three multiobjective engineering design problems reported in literature. Simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i.e., NSGA-11, SPEA2 and PAES. Thus MOEO can be considered a good alternative to solve constrained multiobjective optimization problems.展开更多
This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but...This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.展开更多
Ultra-high strength alloys with good ductility are ideal materials for lightweight structural application in various industries. However, improving the strength of alloys frequently results in a reduction in ductility...Ultra-high strength alloys with good ductility are ideal materials for lightweight structural application in various industries. However, improving the strength of alloys frequently results in a reduction in ductility, which is known as the strength-ductility trade-off in metallic materials. Current alloy design strategies for improving the ductility of ultra-high strength alloys mainly focus on the selection of alloy composition （atomic length scale） or manipulating ultra-fine and nano-grained microstructure （grain length scale）. The intermediate length scale between atomic and grain scales is the dislocation length scale. A new alloy design concept based on such dislocation length scale, namely dislocation engineering, is illustrated in the present work. This dislocation engineering concept has been successfully substantiated by the design and fabrication of a deformed and partitioned （D＆P） steel with a yield strength of 2,2 GPa and an uniform elongation of 16%. In this D＆P steel, high dislocation density can not only increase strength but also improve ductility. High dislocation density is mainly responsible for the improved yield strength through dislocation forest hardening, whilst the improved ductility is achieved by the glide of intensive mobile dislocations and well-controlled transformation-induced plasticity （TRIP） effect, both of which are governed by the high dislocation density resulting from warm rolling and martensitic transformation during cold rolling. In addition, the present work proposes for the first time to apply such dislocation engineering concept to the quenching and partitioning （Q＆P） steel by incorporating a warm rolling process prior to the quenching step, with an aim to improve simultaneously the strength and ductility of the Q＆P steel. It is believed that dislocation engineering provides a new promising alloy design strategy for producing novel strong and ductile alloys.展开更多
The Efficient Global Optimization(EGO)algorithm has been widely used in the numerical design optimization of engineering systems.However,the need for an uncertainty estimator limits the selection of a surrogate model....The Efficient Global Optimization(EGO)algorithm has been widely used in the numerical design optimization of engineering systems.However,the need for an uncertainty estimator limits the selection of a surrogate model.In this paper,a Sequential Ensemble Optimization(SEO)algorithm based on the ensemble model is proposed.In the proposed algorithm,there is no limitation on the selection of an individual surrogate model.Specifically,the SEO is built based on the EGO by extending the EGO algorithm so that it can be used in combination with the ensemble model.Also,a new uncertainty estimator for any surrogate model named the General Uncertainty Estimator(GUE)is proposed.The performance of the proposed SEO algorithm is verified by the simulations using ten well-known mathematical functions with varying dimensions.The results show that the proposed SEO algorithm performs better than the traditional EGO algorithm in terms of both the final optimization results and the convergence rate.Further,the proposed algorithm is applied to the global optimization control for turbo-fan engine acceleration schedule design.展开更多
Recently, some results have been acquired with the Monte- Carlo statistical experiments in the design of ocean en gineering. The results show that Monte-Carlo statistical experiments can be widely used in estimating t...Recently, some results have been acquired with the Monte- Carlo statistical experiments in the design of ocean en gineering. The results show that Monte-Carlo statistical experiments can be widely used in estimating the parameters of wave statistical distributions, checking the probability model of the long- term wave extreme value distribution under a typhoon condition and calculating the failure probability of the ocean platforms.展开更多
The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements.As a representative,Slime mould algorithm(SMA)is widely used because of its superior initial perform...The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements.As a representative,Slime mould algorithm(SMA)is widely used because of its superior initial performance.Therefore,this paper focuses on the improvement of the SMA and the mitigation of its stagnation problems.For this aim,the structure of SMA is adjusted to develop the efficiency of the original method.As a stochastic optimizer,SMA mainly stimulates the behavior of slime mold in nature.For the harmony of the exploration and exploitation of SMA,the paper proposed an enhanced algorithm of SMA called ECSMA,in which two mechanisms are embedded into the structure:elite strategy,and chaotic stochastic strategy.The details of the original SMA and the two introduced strategies are given in this paper.Then,the advantages of the improved SMA through mechanism comparison,balance-diversity analysis,and contrasts with other counterparts are validated.The experimental results demonstrate that both mechanisms have a significant enhancing effect on SMA.Also,SMA is applied to four structural design issues of the welded beam design problem,PV design problem,I-beam design problem,and cantilever beam design problem with excellent results.展开更多
A modeling method is proposed, which combines texture mapping, LOD and geometric modeling. The theory and the application of NURBS in geometric modeling are presented. The methods of NURBS commonly used in the visuali...A modeling method is proposed, which combines texture mapping, LOD and geometric modeling. The theory and the application of NURBS in geometric modeling are presented. The methods of NURBS commonly used in the visualization in engineering design are discussed. Some examples are presented.展开更多
Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate struct...Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carded out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.展开更多
HT7U is a large fusion experimental device. It will be built in the Institute of Plasma Physics of Chinese Academy of Sciences. The mission of HT-7U is to develop the scientific basis for a continuously operating toka...HT7U is a large fusion experimental device. It will be built in the Institute of Plasma Physics of Chinese Academy of Sciences. The mission of HT-7U is to develop the scientific basis for a continuously operating tokama-k fusion reactor. This paper describes only a toroidal field (TF) superconducting magnet system of HT7U. In this paper, design criteria of conductor and stability analysis, coil winding and support structure design of magnet system, mechanical calculation and stress analysis, heat load evaluation are given.展开更多
Substantially lightweight brake discs with high wear resistance are highly desirable in the automotive industry.This paper presents an investigation of the precision-engineering design and development of automotive br...Substantially lightweight brake discs with high wear resistance are highly desirable in the automotive industry.This paper presents an investigation of the precision-engineering design and development of automotive brake discs using nonhomogeneous Al/SiC metal-matrixcomposite materials.The design and development are based on modeling and analysis following stringent precision-engineering principles,i.e.,brake-disc systems that operate repeatably and stably over time as enabled by precision-engineering design.The design and development are further supported by tribological experimental testing and finite-element simulations.The results show the industrial feasibility of the innovative design approach and the application merits of using advanced metal-matrix-composite materials for next-generation automotive and electric vehicles.展开更多
Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems...Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.展开更多
China Petroleum Design & Engineering Company (CPDEC) is a new corporation established in the year 2001. The company puts forward its development objectives for becoming a first-class national and international des...China Petroleum Design & Engineering Company (CPDEC) is a new corporation established in the year 2001. The company puts forward its development objectives for becoming a first-class national and international desigh firm within 5 to 10 years. In this paper, the theories and techniques related to the business environment and strategy are used to make an analysis of the companys development objectives. According to the analysis of the environment and internal conditions of CPDEC, we suggest adoping the strategic measures for reaching the development objectives on the basis of the SWOT (the acronym of Strengths, Weaknesses, Opportunities and Threats) analysis method. Some conclusions and recommendations on the companys situation and strategies are given.展开更多
文摘This paper presents a structured methodology for local network design engineering (SMLNDE). A complex and fuzzy project for local network design can be decomposed into a set of simple and particular activities using the SMLNDE. The SMLNDE allows rigorous requirements definition and permits the exhaustive consideration of the large number of factors influencing local network design engineering. The complete and clear design documentations and an optimal design can also be provided by the methodology. The SMLNDE has been implemented using the structured analysis and design technique. The study shows that the SMLNDE is an effective design methodology for the large and complex local networks.
基金Natural Science Foundation of Zhejiang Province,Grant/Award Numbers:LTGS23E070001,LZ22F020005,LTGY24C060004National Natural Science Foundation of China,Grant/Award Numbers:62076185,62301367,62273263。
文摘The Runge-Kutta optimiser(RUN)algorithm,renowned for its powerful optimisation capabilities,faces challenges in dealing with increasing complexity in real-world problems.Specifically,it shows deficiencies in terms of limited local exploration capabilities and less precise solutions.Therefore,this research aims to integrate the topological search(TS)mechanism with the gradient search rule(GSR)into the framework of RUN,introducing an enhanced algorithm called TGRUN to improve the performance of the original algorithm.The TS mechanism employs a circular topological scheme to conduct a thorough exploration of solution regions surrounding each solution,enabling a careful examination of valuable solution areas and enhancing the algorithm’s effectiveness in local exploration.To prevent the algorithm from becoming trapped in local optima,the GSR also integrates gradient descent principles to direct the algorithm in a wider investigation of the global solution space.This study conducted a serious of experiments on the IEEE CEC2017 comprehensive benchmark function to assess the enhanced effectiveness of TGRUN.Additionally,the evaluation includes real-world engineering design and feature selection problems serving as an additional test for assessing the optimisation capabilities of the algorithm.The validation outcomes indicate a significant improvement in the optimisation capabilities and solution accuracy of TGRUN.
基金supported by the International Collaboration Program of Jilin Provincial Department of Science and Technology,China(20230402051GH)the National Natural Science Foundation of China(51932003,51902050)+2 种基金the Open Project Program of Key Laboratory of Preparation and Application of Environmental friendly Materials(Jilin Normal University)of Ministry of China(2021006)the Fundamental Research Funds for the Central Universities JLU“Double-First Class”Discipline for Materials Science&Engineering。
文摘Owning various crystal structures and high theoretical capacity,metal tellurides are emerging as promising electrode materials for high-performance metal-ion batteries(MBs).Since metal telluride-based MBs are quite new,fundamental issues raise regarding the energy storage mechanism and other aspects affecting electrochemical performance.Severe volume expansion,low intrinsic conductivity and slow ion diffusion kinetics jeopardize the performance of metal tellurides,so that rational design and engineering are crucial to circumvent these disadvantages.Herein,this review provides an in-depth discussion of recent investigations and progresses of metal tellurides,beginning with a critical discussion on the energy storage mechanisms of metal tellurides in various MBs.In the following,recent design and engineering strategies of metal tellurides,including morphology engineering,compositing,defect engineering and heterostructure construction,for high-performance MBs are summarized.The primary focus is to present a comprehensive understanding of the structural evolution based on the mechanism and corresponding effects of dimension control,composition,electron configuration and structural complexity on the electrochemical performance.In closing,outlooks and prospects for future development of metal tellurides are proposed.This work also highlights the promising directions of design and engineering strategies of metal tellurides with high performance and low cost.
基金supported by Grant(42271391 and 62006214)from National Natural Science Foundation of Chinaby Grant(8091B022148)from Joint Funds of Equipment Pre-Research and Ministry of Education of China+1 种基金by Grant(2023BIB015)from Special Project of Hubei Key Research and Development Programby Grant(KLIGIP-2021B03)from Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing.
文摘Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,it also has certain drawbacks for the exploration stage and the egg hatch process,resulting in slow convergence speed and inferior solution quality.To address the above issues,a novel multi-strategy improved SO(MISO)with the assistance of population crowding analysis is proposed in this article.In the algorithm,a novel multi-strategy operator is designed for the exploration stage,which not only focuses on using the information of better performing individuals to improve the quality of solution,but also focuses on maintaining population diversity.To boost the efficiency of the egg hatch process,the multi-strategy egg hatch process is proposed to regenerate individuals according to the results of the population crowding analysis.In addition,a local search method is employed to further enhance the convergence speed and the local search capability.MISO is first compared with three sets of algorithms in the CEC2020 benchmark functions,including SO with its two recently discussed variants,ten advanced MAs,and six powerful CEC competition algorithms.The performance of MISO is then verified on five practical engineering design problems.The experimental results show that MISO provides a promising performance for the above optimization cases in terms of convergence speed and solution quality.
文摘Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.
文摘This paper focuses on the research of the main transformer selection and layout scheme for new energy step-up substations.From the perspective of engineering design,it analyzes the principles of main transformer selection,key parameters,and their matching with the characteristics of new energy.It also explores the layout methods and optimization strategies.Combined with typical case studies,optimization suggestions are proposed for the design of main transformers in new energy step-up substations.The research shows that rational main transformer selection and scientific layout schemes can better adapt to the characteristics of new energy projects while effectively improving land use efficiency and economic viability.This study can provide technical experience support for the design of new energy projects.
基金This project is supported by National Basic Research Program of China(973Program,No.2003CB317005)Shuguang Program of Shanghai City Educational Conunittee China(No.05SG15).
文摘To support and serve engineering design, creative design based on knowledge management is proposed. The key knowledge factors of creative design are analyzed and discussed, and knowledge extraction tools are utilized to distill the important knowledge to serve for knowledge resource of creative design. The implementation of creative design mode is described and executed, which can promote the intelligent asset of the enterprise and shorten the period of creative design. With this study, design afflatus and conceptual design can be achieved expediently and effectively.
基金Project (No.60574063) the National Natural Science Foundation of China
文摘In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization （MOEO）, to solve the constrained multiobjective optimization problems （MOPs）. The proposed approach is validated by three constrained benchmark problems and successfully applied to handling three multiobjective engineering design problems reported in literature. Simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i.e., NSGA-11, SPEA2 and PAES. Thus MOEO can be considered a good alternative to solve constrained multiobjective optimization problems.
文摘This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.
基金the support from Research Grants Council of Hong Kong (Grants No. 17203014, HKU712713E and 17255016)the National Natural Science Foundation of China (Grant No. U1560204)
文摘Ultra-high strength alloys with good ductility are ideal materials for lightweight structural application in various industries. However, improving the strength of alloys frequently results in a reduction in ductility, which is known as the strength-ductility trade-off in metallic materials. Current alloy design strategies for improving the ductility of ultra-high strength alloys mainly focus on the selection of alloy composition （atomic length scale） or manipulating ultra-fine and nano-grained microstructure （grain length scale）. The intermediate length scale between atomic and grain scales is the dislocation length scale. A new alloy design concept based on such dislocation length scale, namely dislocation engineering, is illustrated in the present work. This dislocation engineering concept has been successfully substantiated by the design and fabrication of a deformed and partitioned （D＆P） steel with a yield strength of 2,2 GPa and an uniform elongation of 16%. In this D＆P steel, high dislocation density can not only increase strength but also improve ductility. High dislocation density is mainly responsible for the improved yield strength through dislocation forest hardening, whilst the improved ductility is achieved by the glide of intensive mobile dislocations and well-controlled transformation-induced plasticity （TRIP） effect, both of which are governed by the high dislocation density resulting from warm rolling and martensitic transformation during cold rolling. In addition, the present work proposes for the first time to apply such dislocation engineering concept to the quenching and partitioning （Q＆P） steel by incorporating a warm rolling process prior to the quenching step, with an aim to improve simultaneously the strength and ductility of the Q＆P steel. It is believed that dislocation engineering provides a new promising alloy design strategy for producing novel strong and ductile alloys.
基金the financial support of the National Natural Science Foundation of China(Nos.52076180,51876176 and 51906204)National Science and Technology Major Project,China(No.2017-I0001-0001)。
文摘The Efficient Global Optimization(EGO)algorithm has been widely used in the numerical design optimization of engineering systems.However,the need for an uncertainty estimator limits the selection of a surrogate model.In this paper,a Sequential Ensemble Optimization(SEO)algorithm based on the ensemble model is proposed.In the proposed algorithm,there is no limitation on the selection of an individual surrogate model.Specifically,the SEO is built based on the EGO by extending the EGO algorithm so that it can be used in combination with the ensemble model.Also,a new uncertainty estimator for any surrogate model named the General Uncertainty Estimator(GUE)is proposed.The performance of the proposed SEO algorithm is verified by the simulations using ten well-known mathematical functions with varying dimensions.The results show that the proposed SEO algorithm performs better than the traditional EGO algorithm in terms of both the final optimization results and the convergence rate.Further,the proposed algorithm is applied to the global optimization control for turbo-fan engine acceleration schedule design.
文摘Recently, some results have been acquired with the Monte- Carlo statistical experiments in the design of ocean en gineering. The results show that Monte-Carlo statistical experiments can be widely used in estimating the parameters of wave statistical distributions, checking the probability model of the long- term wave extreme value distribution under a typhoon condition and calculating the failure probability of the ocean platforms.
基金supported in part by the National Natural Science Foundation of China(J2124006,62076185)。
文摘The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements.As a representative,Slime mould algorithm(SMA)is widely used because of its superior initial performance.Therefore,this paper focuses on the improvement of the SMA and the mitigation of its stagnation problems.For this aim,the structure of SMA is adjusted to develop the efficiency of the original method.As a stochastic optimizer,SMA mainly stimulates the behavior of slime mold in nature.For the harmony of the exploration and exploitation of SMA,the paper proposed an enhanced algorithm of SMA called ECSMA,in which two mechanisms are embedded into the structure:elite strategy,and chaotic stochastic strategy.The details of the original SMA and the two introduced strategies are given in this paper.Then,the advantages of the improved SMA through mechanism comparison,balance-diversity analysis,and contrasts with other counterparts are validated.The experimental results demonstrate that both mechanisms have a significant enhancing effect on SMA.Also,SMA is applied to four structural design issues of the welded beam design problem,PV design problem,I-beam design problem,and cantilever beam design problem with excellent results.
文摘A modeling method is proposed, which combines texture mapping, LOD and geometric modeling. The theory and the application of NURBS in geometric modeling are presented. The methods of NURBS commonly used in the visualization in engineering design are discussed. Some examples are presented.
基金Supported by National Hi-tech Research and Development Program of China(863 Program,Grant No.2015AA042101)
文摘Complex engineered systems are often difficult to analyze and design due to the tangled interdependencies among their subsystems and components. Conventional design methods often need exact modeling or accurate structure decomposition, which limits their practical application. The rapid expansion of data makes utilizing data to guide and improve system design indispensable in practical engineering. In this paper, a data driven uncertainty evaluation approach is proposed to support the design of complex engineered systems. The core of the approach is a data-mining based uncertainty evaluation method that predicts the uncertainty level of a specific system design by means of analyzing association relations along different system attributes and synthesizing the information entropy of the covered attribute areas, and a quantitative measure of system uncertainty can be obtained accordingly. Monte Carlo simulation is introduced to get the uncertainty extrema, and the possible data distributions under different situations is discussed in detail The uncertainty values can be normalized using the simulation results and the values can be used to evaluate different system designs. A prototype system is established, and two case studies have been carded out. The case of an inverted pendulum system validates the effectiveness of the proposed method, and the case of an oil sump design shows the practicability when two or more design plans need to be compared. This research can be used to evaluate the uncertainty of complex engineered systems completely relying on data, and is ideally suited for plan selection and performance analysis in system design.
文摘HT7U is a large fusion experimental device. It will be built in the Institute of Plasma Physics of Chinese Academy of Sciences. The mission of HT-7U is to develop the scientific basis for a continuously operating tokama-k fusion reactor. This paper describes only a toroidal field (TF) superconducting magnet system of HT7U. In this paper, design criteria of conductor and stability analysis, coil winding and support structure design of magnet system, mechanical calculation and stress analysis, heat load evaluation are given.
文摘Substantially lightweight brake discs with high wear resistance are highly desirable in the automotive industry.This paper presents an investigation of the precision-engineering design and development of automotive brake discs using nonhomogeneous Al/SiC metal-matrixcomposite materials.The design and development are based on modeling and analysis following stringent precision-engineering principles,i.e.,brake-disc systems that operate repeatably and stably over time as enabled by precision-engineering design.The design and development are further supported by tribological experimental testing and finite-element simulations.The results show the industrial feasibility of the innovative design approach and the application merits of using advanced metal-matrix-composite materials for next-generation automotive and electric vehicles.
基金funded by Firat University Scientific Research Projects Management Unit for the scientific research project of Feyza AltunbeyÖzbay,numbered MF.23.49.
文摘Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.
文摘China Petroleum Design & Engineering Company (CPDEC) is a new corporation established in the year 2001. The company puts forward its development objectives for becoming a first-class national and international desigh firm within 5 to 10 years. In this paper, the theories and techniques related to the business environment and strategy are used to make an analysis of the companys development objectives. According to the analysis of the environment and internal conditions of CPDEC, we suggest adoping the strategic measures for reaching the development objectives on the basis of the SWOT (the acronym of Strengths, Weaknesses, Opportunities and Threats) analysis method. Some conclusions and recommendations on the companys situation and strategies are given.
