Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while...Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while mini-mizing energy consumption.However,enhancing gliding performance is challenging due to the complex system design and limited design experience.To address this challenge,this paper introduces a model-based,multidisciplinary system design optimization method for BWBUGs at the conceptual design stage.First,a model-based,multidisciplinary co-simulation design framework is established to evaluate both system-level and disciplinary indices of BWBUG performance.A data-driven,many-objective multidisciplinary optimization is subsequently employed to explore the design space,yielding 32 Pareto optimal solutions.Finally,a model-based physical system simulation,which represents the design with the largest hyper-volume contribution among the 32 final designs,is established.Its gliding perfor-mance,validated by component behavior,lays the groundwork for constructing the entire system’s digital prototype.In conclusion,this model-based,multidisciplinary design optimization method effectively generates design schemes for innovative underwater vehicles,facilitating the development of digital prototypes.展开更多
Conventional pit excavation engineering methods often struggle to manage the complex deformation patterns associated with asymmetric excavations,resulting in significant safety risks and increased project costs.These ...Conventional pit excavation engineering methods often struggle to manage the complex deformation patterns associated with asymmetric excavations,resulting in significant safety risks and increased project costs.These challenges highlight the need for more precise and efficient design methodologies to ensure structural stability and economic feasibility.This research proposes an innovative automatic optimization inverse design method(AOIDM)that integrates an enhanced genetic algorithm(EGA)with a multiobjective optimization model.By combining advanced computational techniques with engineering principles,this approach improves search efficiency by 30%and enhances deformation control accuracy by 25%.Additionally,the approach exhibits potential for reducing carbon emissions to align with sustainable engineering goals.The effectiveness of this approach was validated through comprehensive data analysis and practical case studies,demonstrating its ability to optimize retaining structure designs under complex asymmetric loading conditions.This research establishes a new standard for precision and efficiency in automated excavation design,with accompanying improvements in safety and cost-effectiveness.Furthermore,it lays the foundation for future geotechnical engineering advancements,offering a robust solution to one of the most challenging aspects of modern excavation projects.展开更多
The application of Low Earth Orbit(LEO)satellite navigation can enhance geometric structure,increase observations and contribute to navigation and positioning.To improve the performance of the navigation constellation...The application of Low Earth Orbit(LEO)satellite navigation can enhance geometric structure,increase observations and contribute to navigation and positioning.To improve the performance of the navigation constellation in China,this study proposes an optimized method of LEO-enhanced navigation constellation for BDS based on Bayesian optimization algorithm.In this paper,four different optimal LEO constellation configurations are designed,and their enhancements to BDS3 navigation performance are analyzed,including Geometric Dilution of Precision(GDOP),the numbers of visible satellites,and the rapid convergence of precision point positioning(PPP).Additionally,the enhancement advantages in China compared to other regions are further discussed.The results demonstrate that regional enhanced constellations with 70,72,80,and 81 satellites at an altitude of 1000 km can significantly improve the navigation performance of the navigation constellation.Globally,the addition of optimized LEO constellations has reduced the hybrid constellation GDOP by 19.0%,18.3%,19.9%,and 20.3%.Similar results can be obtained using the genetic algorithm(GA),but the computational efficiency of Bayesian optimization algorithm is 53.9%higher than that of the genetic algorithm.The number of visible satellites of enhanced constellations in China has increased by more than four on average,which is better than that in other regions.In the PPP experiment,the convergence time of the stations in China and other regions is shortened by 83.0%and 76.2%,respectively,and the navigation performance of hybrid constellations in China is better.展开更多
Plastic forming is one of enabling and fundamental technologies in advanced manufacturing chains. Design optimization is a critical way to improve the performance of the forming system, exploit the advantages of high ...Plastic forming is one of enabling and fundamental technologies in advanced manufacturing chains. Design optimization is a critical way to improve the performance of the forming system, exploit the advantages of high productivity, high product quality, low production cost and short time to market and develop precise, accurate, green, and intelligent(smart) plastic forming technology. However, plastic forming is quite complicated, relating to multi-physics field coupling,multi-factor influence, multi-defect constraint, and triple nonlinear, etc., and the design optimization for plastic forming involves multi-objective, multi-parameter, multi-constraint, nonlinear,high-dimensionality, non-continuity, time-varying, and uncertainty, etc. Therefore, how to achieve accurate and efficient design optimization of products, equipment, tools/dies, and processing as well as materials characterization has always been the research frontier and focus in the field of engineering and manufacturing. In recent years, with the rapid development of computing science, data science and internet of things(Io T), the theories and technologies of design optimization have attracted more and more attention, and developed rapidly in forming process. Accordingly, this paper first introduced the framework of design optimization for plastic forming. Then, focusing on the key problems of design optimization, such as numerical model and optimization algorithm,this paper summarized the research progress on the development and application of the theories and technologies about design optimization in forming process, including deterministic and uncertain optimization. Moreover, the applicability of various modeling methods and optimization algorithms was elaborated in solving the design optimization problems of plastic forming. Finally, considering the development trends of forming technology, this paper discusses some challenges of design optimization that may need to be solved and faced in forming process.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
For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search ...For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search （MOTS） algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and ＂Pareto solution＂, the strategy of ＂searching from many directions＂ and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search （PMOTS） algorithm. Finally, a new hybrid algorithm--HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm （NSGA） is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm.展开更多
Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.Th...Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.This paper aims to present efficient system-level multiobjective optimization methods for the multidisciplinary design optimization of electrical drive systems.From the perspective of quality control,deterministic and robust approaches will be investigated for the development of the optimization models for the proposed methods.Meanwhile,two approximation methods,Kriging model and Taylor expansion are employed to decrease the computation/simulation cost.To illustrate the advantages of the proposed methods,a drive system with a permanent magnet synchronous motor driven by a field oriented control system is investigated.Deterministic and robust Pareto optimal solutions are presented and compared in terms of several steady-state and dynamic performances(like average torque and speed overshoot)of the drive system.The robust multiobjective optimization method can produce optimal Pareto solutions with high manufacturing quality for the drive system.展开更多
Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidisciplinary design optimization ...Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidisciplinary design optimization （MDO） techniques for the design of complex engineering system. An advanced first order second moment method-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing multidisciplinary optimization techniques and the reliability analysis methods. It is seen through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current deterministic optimization process.展开更多
Placement optimization is a crucial phase in chip design,involving the strategic arrangement of cells within a limited region to enhance space utilization and reduce wirelength.Chip design enterprises need to optimize...Placement optimization is a crucial phase in chip design,involving the strategic arrangement of cells within a limited region to enhance space utilization and reduce wirelength.Chip design enterprises need to optimize the placement according to design rules to meet customer demands.While mixed-cell-height circuits are widely used in modern chip design,few studies have simultaneously considered the non-overlapping cells,rails alignment,and minimum implantation area constraints in the placement optimization problems.Hence,this study involves preprocessing the non-linear parts and developing a mixed-integer linear programming model to reduce the cost of legalizing chip placements for businesses.Furthermore,this study designs and implements an exact algorithm based on Benders decomposition,utilizing dual theory to obtain an optimal cut and iteratively solve for the coordinates of cells.Numerical experiments across various scales validate the performance of the algorithm.Through a detailed analysis of the shape of the chip region division,the proportion of different types of cells,the total number of cells and bins,and their impact on the placement,we derive some potentially useful design insights that can benefit chip design enterprises.展开更多
Materials mechanics and structural dynamics provide theoretical support for the structural optimization of amusement facilities.The design code system guides the design process,covering aspects such as strength and fa...Materials mechanics and structural dynamics provide theoretical support for the structural optimization of amusement facilities.The design code system guides the design process,covering aspects such as strength and fatigue life.This paper introduces optimization methods like standardized module interfaces and variable density methods,as well as topics related to finite element simulation,reliability enhancement,innovative practices,and their significance.展开更多
Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structur...Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structure-property relationship in these materials,including forward prediction and inverse design,presents substantial challenges.The inhomogeneous microstructures significantly complicate traditional analytical or simulation-based approaches.Here,we establish a novel framework that integrates the machine learning(ML)-encoded multiscale computational method for forward prediction and Bayesian optimization for inverse design.Unlike prior end-to-end ML methods limited to specific problems,our framework is both load-independent and geometry-independent.This means that a single training session for a constitutive model suffices to tackle various problems directly,eliminating the need for repeated data collection or training.We demonstrate the efficacy and efficiency of this framework using metamaterials with designable elliptical holes or lattice honeycombs microstructures.Leveraging accelerated forward prediction,we can precisely customize the stiffness and shape of metamaterials under diverse loading scenarios,and extend this capability to multi-objective customization seamlessly.Moreover,we achieve topology optimization for stress alleviation at the crack tip,resulting in a significant reduction of Mises stress by up to 41.2%and yielding a theoretical interpretable pattern.This framework offers a general,efficient and precise tool for analyzing the structure-property relationships of novel metamaterials.展开更多
Use of multidisciplinary analysis in reliabilitybased design optimization(RBDO) results in the emergence of the important method of reliability-based multidisciplinary design optimization(RBMDO). To enhance the effici...Use of multidisciplinary analysis in reliabilitybased design optimization(RBDO) results in the emergence of the important method of reliability-based multidisciplinary design optimization(RBMDO). To enhance the efficiency and convergence of the overall solution process,a decoupling algorithm for RBMDO is proposed herein.Firstly, to decouple the multidisciplinary analysis using the individual disciplinary feasible(IDF) approach, the RBMDO is converted into a conventional form of RBDO. Secondly,the incremental shifting vector(ISV) strategy is adopted to decouple the nested optimization of RBDO into a sequential iteration process composed of design optimization and reliability analysis, thereby improving the efficiency significantly. Finally, the proposed RBMDO method is applied to the design of two actual electronic products: an aerial camera and a car pad. For these two applications, two RBMDO models are created, each containing several finite element models(FEMs) and relatively strong coupling between the involved disciplines. The computational results demonstrate the effectiveness of the proposed method.展开更多
Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collabora...Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.展开更多
The innovative Next Generation Subsea Production System(NextGen SPS)concept is a newly proposed petroleum development solution in ultra-deep water areas.The definition of NextGen SPS involves several disciplines,which...The innovative Next Generation Subsea Production System(NextGen SPS)concept is a newly proposed petroleum development solution in ultra-deep water areas.The definition of NextGen SPS involves several disciplines,which makes the design process difficult.In this paper,the definition of NextGen SPS is modeled as an uncertain multidisciplinary design optimization(MDO)problem.The deterministic optimization model is formulated,and three concerning disciplines—cost calculation,hydrodynamic analysis and global performance analysis are presented.Surrogate model technique is applied in the latter two disciplines.Collaborative optimization(CO)architecture is utilized to organize the concerning disciplines.A deterministic CO framework with two disciplinelevel optimizations is proposed firstly.Then the uncertainties of design parameters and surrogate models are incorporated by using interval method,and uncertain CO frameworks with triple loop and double loop optimization structure are established respectively.The optimization results illustrate that,although the deterministic MDO result achieves higher reduction in objective function than the uncertain MDO result,the latter is more reliable than the former.展开更多
The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly comple...The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.展开更多
In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-p...In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.展开更多
To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the ...To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the comprehensive artillery internal ballistic dynamics(AIBD)model,based on propellant combustion,rotation band engraving,projectile axial motion,and rifling wear models,was established and validated.This model was systematically decomposed into subsystems from a system engineering perspective.The study then detailed the MS-MDO methodology,which included Stage I(MDO stage)employing an improved collaborative optimization method for consistent design variables,and Stage II(Performance Optimization)focusing on the independent optimization of local design variables and performance metrics.The methodology was applied to the AIBD problem.Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts,thereby accelerating system-level convergence.Meanwhile,Stage II optimization markedly enhanced overall performance.These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.展开更多
The influence of processing parameters on the precision of parts fabricated by fused deposition modeling （FDM） technology is studied based on a series of performed experiments. Processing parameters of FDM in terms ...The influence of processing parameters on the precision of parts fabricated by fused deposition modeling （FDM） technology is studied based on a series of performed experiments. Processing parameters of FDM in terms of wire-width compensation, extrusion velocity, filing velocity, and layer thickness are chosen as the control fac- tors. Robust design analysis and multi-index fuzzy comprehensive assessment method are used to obtain the opti- mal parameters. Results show that the influencing degrees of these four factors on the precision of as-processed parts are different. The optimizations of individual parameters and their combined effects are of the same impor- tance for a high precision manufacturing.展开更多
The press-fit connector is a typical plug-and-play solderless connection,and it is widely used in signal transmission in fields such as communication and automotive devices.This paper focuses on inverse designing and ...The press-fit connector is a typical plug-and-play solderless connection,and it is widely used in signal transmission in fields such as communication and automotive devices.This paper focuses on inverse designing and optimization of geometric structure,as well as insertion-withdrawal forces of press-fit connector using artificial neural network(ANN)-assisted optimization method.The ANN model is established to approximate the relationship between geometric parameters and insertion-withdrawal forces,of which hyper-parameters of neural network are optimized to improve model performance.Two numerical methods are proposed for inverse designing structural parameters(Model-I)and multi-objective optimization of insertion-withdrawal forces(Model-II)of press-fit connector.In Model-I,a method for inverse designing structure parameters is established,of which an ANN model is coupled with single-objective optimization algorithm.The objective function is established,the inverse problem is solved,and effectiveness is verified.In Model-II,a multi-objective optimization method is proposed,of which an ANN model is coupled with genetic algorithm.The Pareto solution sets of insertion-withdrawal forces are obtained,and results are analyzed.The established ANN-coupled numerical optimization methods are beneficial for improving the design efficiency,and enhancing the connection reliability of the press-fit connector.展开更多
The jacket structure and transition piece comprise the supporting structure of a bottom-fixed offshore wind turbine(OWT)connected to the steel tower,which determines the overall structural dynamic performance of the e...The jacket structure and transition piece comprise the supporting structure of a bottom-fixed offshore wind turbine(OWT)connected to the steel tower,which determines the overall structural dynamic performance of the entire OWT.Ideally,optimal performance can be realized by effectively coordinating two components,notwithstanding their separate design processes.In pursuit of this objective,this paper proposes a concurrent design methodology for the jacket structure and transition piece by exploiting topology optimization(TO).The TO for a three-legged jacket foundation is formulated by minimizing static compliance.In contrast to conventional TO,two separated volume fractions are imposed upon the structural design domain of the jacket structure and transition piece to ensure continuity.A 5 MW(megawatt)OWT supported by a four-legged or three-legged jacket substructure is under investigation.The external loads are derived from various design load cases that are acquired using the commercial software platform DNV Bladed(Det Norske Veritas).Through a comparative analysis of the fundamental frequency and maximum nodal deformation,it was found that the optimized solution demonstrates a reduced weight and superior stiffness.The findings demonstrate the present concurrent design approach using TO can yield significant benefits by reducing the overall design cycle and enhancing the feasibility of the final design.展开更多
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20242194)the National Natural Science Foundation of China(Grant Nos.52175251 and 52205268)+1 种基金the Industry Key Technology Research Fund Project of Northwestern Polytechnical University(Grant No.HYGJXM202318)the National Basic Scientific Research Program(Grant No.JCKY2021206B005).
文摘Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while mini-mizing energy consumption.However,enhancing gliding performance is challenging due to the complex system design and limited design experience.To address this challenge,this paper introduces a model-based,multidisciplinary system design optimization method for BWBUGs at the conceptual design stage.First,a model-based,multidisciplinary co-simulation design framework is established to evaluate both system-level and disciplinary indices of BWBUG performance.A data-driven,many-objective multidisciplinary optimization is subsequently employed to explore the design space,yielding 32 Pareto optimal solutions.Finally,a model-based physical system simulation,which represents the design with the largest hyper-volume contribution among the 32 final designs,is established.Its gliding perfor-mance,validated by component behavior,lays the groundwork for constructing the entire system’s digital prototype.In conclusion,this model-based,multidisciplinary design optimization method effectively generates design schemes for innovative underwater vehicles,facilitating the development of digital prototypes.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3009400)the National Natural Science Foundation of China(Grant Nos.52238009 and 52208344).
文摘Conventional pit excavation engineering methods often struggle to manage the complex deformation patterns associated with asymmetric excavations,resulting in significant safety risks and increased project costs.These challenges highlight the need for more precise and efficient design methodologies to ensure structural stability and economic feasibility.This research proposes an innovative automatic optimization inverse design method(AOIDM)that integrates an enhanced genetic algorithm(EGA)with a multiobjective optimization model.By combining advanced computational techniques with engineering principles,this approach improves search efficiency by 30%and enhances deformation control accuracy by 25%.Additionally,the approach exhibits potential for reducing carbon emissions to align with sustainable engineering goals.The effectiveness of this approach was validated through comprehensive data analysis and practical case studies,demonstrating its ability to optimize retaining structure designs under complex asymmetric loading conditions.This research establishes a new standard for precision and efficiency in automated excavation design,with accompanying improvements in safety and cost-effectiveness.Furthermore,it lays the foundation for future geotechnical engineering advancements,offering a robust solution to one of the most challenging aspects of modern excavation projects.
基金founded by the National Natural Science Foundation of China(42030109)the Startup Foundation for Doctors of Liaoning Province(2021-BS-275)+4 种基金the Scientific Study Project for Institutes of Higher LearningMinistry of EducationLiaoning Province(LJKMZ20220673)the Project supported by the State Key Laboratory of Geodesy and Earths'DynamicsInnovation Academy for Precision Measurement Science and Technology(SKLGED2023-3-2)。
文摘The application of Low Earth Orbit(LEO)satellite navigation can enhance geometric structure,increase observations and contribute to navigation and positioning.To improve the performance of the navigation constellation in China,this study proposes an optimized method of LEO-enhanced navigation constellation for BDS based on Bayesian optimization algorithm.In this paper,four different optimal LEO constellation configurations are designed,and their enhancements to BDS3 navigation performance are analyzed,including Geometric Dilution of Precision(GDOP),the numbers of visible satellites,and the rapid convergence of precision point positioning(PPP).Additionally,the enhancement advantages in China compared to other regions are further discussed.The results demonstrate that regional enhanced constellations with 70,72,80,and 81 satellites at an altitude of 1000 km can significantly improve the navigation performance of the navigation constellation.Globally,the addition of optimized LEO constellations has reduced the hybrid constellation GDOP by 19.0%,18.3%,19.9%,and 20.3%.Similar results can be obtained using the genetic algorithm(GA),but the computational efficiency of Bayesian optimization algorithm is 53.9%higher than that of the genetic algorithm.The number of visible satellites of enhanced constellations in China has increased by more than four on average,which is better than that in other regions.In the PPP experiment,the convergence time of the stations in China and other regions is shortened by 83.0%and 76.2%,respectively,and the navigation performance of hybrid constellations in China is better.
基金the National Natural Science Foundation of China (Nos. 51775441&51835011)the National Science Fund for Excellent Young Scholars (No.51522509)Research Fund of the State Key Laboratory of Solidification Processing (NWPU) of China (KP201608)。
文摘Plastic forming is one of enabling and fundamental technologies in advanced manufacturing chains. Design optimization is a critical way to improve the performance of the forming system, exploit the advantages of high productivity, high product quality, low production cost and short time to market and develop precise, accurate, green, and intelligent(smart) plastic forming technology. However, plastic forming is quite complicated, relating to multi-physics field coupling,multi-factor influence, multi-defect constraint, and triple nonlinear, etc., and the design optimization for plastic forming involves multi-objective, multi-parameter, multi-constraint, nonlinear,high-dimensionality, non-continuity, time-varying, and uncertainty, etc. Therefore, how to achieve accurate and efficient design optimization of products, equipment, tools/dies, and processing as well as materials characterization has always been the research frontier and focus in the field of engineering and manufacturing. In recent years, with the rapid development of computing science, data science and internet of things(Io T), the theories and technologies of design optimization have attracted more and more attention, and developed rapidly in forming process. Accordingly, this paper first introduced the framework of design optimization for plastic forming. Then, focusing on the key problems of design optimization, such as numerical model and optimization algorithm,this paper summarized the research progress on the development and application of the theories and technologies about design optimization in forming process, including deterministic and uncertain optimization. Moreover, the applicability of various modeling methods and optimization algorithms was elaborated in solving the design optimization problems of plastic forming. Finally, considering the development trends of forming technology, this paper discusses some challenges of design optimization that may need to be solved and faced in forming process.
基金financially supported in-part by the National Natural Science Foundation of China(52275011)the Natural Science Foundation of Guangdong Province(2023B1515020080)+3 种基金the Natural Science Foundation of Guangzhou(2024A04J2552)the Fundamental Research Funds for the Central Universities,the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology(CAST)(2021QNRC001)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515011253)the Higher Education Institution Featured Innovation Project of Department of Education of Guangdong Province(GrantNo.2023KTSCX138).
文摘Moles exhibit highly effective capabilities due to their unique body structures and digging techniques,making them ideal models for biomimetic research.However,a major challenge for mole-inspired robots lies in overcoming resistance in granular media when burrowing with forelimbs.In the absence of effective forepaw design strategies,most robotic designs rely on increased power to enhance performance.To address this issue,this paper employs Resistive Force Theory to optimize mole-inspired forepaws,aiming to enhance burrowing efficiency.By analyzing the relationship between geometric parameters and burrowing forces,we propose several forepaw design variations.Through granular resistance assessments,an effective forepaw configuration is identified and further refined using parameters such as longitudinal and transverse curvature.Subsequently,the Particle Swarm Optimization algorithm is applied to determine the optimal forepaw design.In force-loading tests,the optimized forepaw demonstrated a 79.44%reduction in granular lift force and a 22.55%increase in propulsive force compared with the control group.In robotic burrowing experiments,the optimized forepaw achieved the longest burrow displacement(179.528 mm)and the lowest burrowing lift force(0.9355 mm/s),verifying its effectiveness in reducing the lift force and enhancing the propulsive force.
基金National Science Fund for Distinguished Young Scholars (10425208)Programme of Introducing Talents of Discipline to Universities (B07009)
文摘For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search （MOTS） algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and ＂Pareto solution＂, the strategy of ＂searching from many directions＂ and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search （PMOTS） algorithm. Finally, a new hybrid algorithm--HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm （NSGA） is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm.
文摘Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.This paper aims to present efficient system-level multiobjective optimization methods for the multidisciplinary design optimization of electrical drive systems.From the perspective of quality control,deterministic and robust approaches will be investigated for the development of the optimization models for the proposed methods.Meanwhile,two approximation methods,Kriging model and Taylor expansion are employed to decrease the computation/simulation cost.To illustrate the advantages of the proposed methods,a drive system with a permanent magnet synchronous motor driven by a field oriented control system is investigated.Deterministic and robust Pareto optimal solutions are presented and compared in terms of several steady-state and dynamic performances(like average torque and speed overshoot)of the drive system.The robust multiobjective optimization method can produce optimal Pareto solutions with high manufacturing quality for the drive system.
基金National Natural Science Foundation of China (10377015)
文摘Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidisciplinary design optimization （MDO） techniques for the design of complex engineering system. An advanced first order second moment method-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing multidisciplinary optimization techniques and the reliability analysis methods. It is seen through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current deterministic optimization process.
基金supported by the National Natural Science Foundation of China(72025103,72394360,72394362,and 72361137001)the Project of Science and Technology Commission of Shanghai Municipality,China(23JC1402200).
文摘Placement optimization is a crucial phase in chip design,involving the strategic arrangement of cells within a limited region to enhance space utilization and reduce wirelength.Chip design enterprises need to optimize the placement according to design rules to meet customer demands.While mixed-cell-height circuits are widely used in modern chip design,few studies have simultaneously considered the non-overlapping cells,rails alignment,and minimum implantation area constraints in the placement optimization problems.Hence,this study involves preprocessing the non-linear parts and developing a mixed-integer linear programming model to reduce the cost of legalizing chip placements for businesses.Furthermore,this study designs and implements an exact algorithm based on Benders decomposition,utilizing dual theory to obtain an optimal cut and iteratively solve for the coordinates of cells.Numerical experiments across various scales validate the performance of the algorithm.Through a detailed analysis of the shape of the chip region division,the proportion of different types of cells,the total number of cells and bins,and their impact on the placement,we derive some potentially useful design insights that can benefit chip design enterprises.
文摘Materials mechanics and structural dynamics provide theoretical support for the structural optimization of amusement facilities.The design code system guides the design process,covering aspects such as strength and fatigue life.This paper introduces optimization methods like standardized module interfaces and variable density methods,as well as topics related to finite element simulation,reliability enhancement,innovative practices,and their significance.
基金supported by the National Natural Science Foundation of China (Grant Nos.12102021,12372105,12172026,and 12225201)the Fundamental Research Funds for the Central Universities and the Academic Excellence Foundation of BUAA for PhD Students.
文摘Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structure-property relationship in these materials,including forward prediction and inverse design,presents substantial challenges.The inhomogeneous microstructures significantly complicate traditional analytical or simulation-based approaches.Here,we establish a novel framework that integrates the machine learning(ML)-encoded multiscale computational method for forward prediction and Bayesian optimization for inverse design.Unlike prior end-to-end ML methods limited to specific problems,our framework is both load-independent and geometry-independent.This means that a single training session for a constitutive model suffices to tackle various problems directly,eliminating the need for repeated data collection or training.We demonstrate the efficacy and efficiency of this framework using metamaterials with designable elliptical holes or lattice honeycombs microstructures.Leveraging accelerated forward prediction,we can precisely customize the stiffness and shape of metamaterials under diverse loading scenarios,and extend this capability to multi-objective customization seamlessly.Moreover,we achieve topology optimization for stress alleviation at the crack tip,resulting in a significant reduction of Mises stress by up to 41.2%and yielding a theoretical interpretable pattern.This framework offers a general,efficient and precise tool for analyzing the structure-property relationships of novel metamaterials.
基金supported by the Major Program of the National Natural Science Foundation of China (Grant 51490662)the Funds for Distinguished Young Scientists of Hunan Province (Grant 14JJ1016)+1 种基金the State Key Program of the National Science Foundation of China (11232004)the Heavy-duty Tractor Intelligent Manufacturing Technology Research and System Development (Grant 2016YFD0701105)
文摘Use of multidisciplinary analysis in reliabilitybased design optimization(RBDO) results in the emergence of the important method of reliability-based multidisciplinary design optimization(RBMDO). To enhance the efficiency and convergence of the overall solution process,a decoupling algorithm for RBMDO is proposed herein.Firstly, to decouple the multidisciplinary analysis using the individual disciplinary feasible(IDF) approach, the RBMDO is converted into a conventional form of RBDO. Secondly,the incremental shifting vector(ISV) strategy is adopted to decouple the nested optimization of RBDO into a sequential iteration process composed of design optimization and reliability analysis, thereby improving the efficiency significantly. Finally, the proposed RBMDO method is applied to the design of two actual electronic products: an aerial camera and a car pad. For these two applications, two RBMDO models are created, each containing several finite element models(FEMs) and relatively strong coupling between the involved disciplines. The computational results demonstrate the effectiveness of the proposed method.
文摘Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.
基金the National Natural Science Foundation of China(Grant No.51709041).
文摘The innovative Next Generation Subsea Production System(NextGen SPS)concept is a newly proposed petroleum development solution in ultra-deep water areas.The definition of NextGen SPS involves several disciplines,which makes the design process difficult.In this paper,the definition of NextGen SPS is modeled as an uncertain multidisciplinary design optimization(MDO)problem.The deterministic optimization model is formulated,and three concerning disciplines—cost calculation,hydrodynamic analysis and global performance analysis are presented.Surrogate model technique is applied in the latter two disciplines.Collaborative optimization(CO)architecture is utilized to organize the concerning disciplines.A deterministic CO framework with two disciplinelevel optimizations is proposed firstly.Then the uncertainties of design parameters and surrogate models are incorporated by using interval method,and uncertain CO frameworks with triple loop and double loop optimization structure are established respectively.The optimization results illustrate that,although the deterministic MDO result achieves higher reduction in objective function than the uncertain MDO result,the latter is more reliable than the former.
基金financially supported by Guangdong Province Basic and Applied Basic Research Fund Project(Grant No.2022B1515250009)Liaoning Provincial Natural Science Foundation-Doctoral Research Start-up Fund Project(Grant No.2024-BSBA-05)+1 种基金Major Science and Technology Innovation Project in Shandong Province(Grant No.2024CXGC010803)the National Natural Science Foundation of China(Grant Nos.52271269 and 12302147).
文摘The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.
文摘In order to accurately forecast the main engine fuel consumption and reduce the Energy Efficiency Operational Indicator(EEOI)of merchant ships in polar ice areas,the energy transfer relationship between ship-machine-propeller is studied by analyzing the complex force situation during ship navigation and building a MATLAB/Simulink simulation platform based on multi-environmental resistance,propeller efficiency,main engine power,fuel consumption,fuel consumption rate and EEOI calculation module.Considering the environmental factors of wind,wave and ice,the route is divided into sections,the calculation of main engine power,main engine fuel consumption and EEOI for each section is completed,and the speed design is optimized based on the simulation model for each section.Under the requirements of the voyage plan,the optimization results show that the energy efficiency operation index of the whole route is reduced by 3.114%and the fuel consumption is reduced by 9.17 t.
基金supported by the“National Natural Science Foundation of China”(Grant Nos.52105106,52305155)the“Jiangsu Province Natural Science Foundation”(Grant Nos.BK20210342,BK20230904)the“Young Elite Scientists Sponsorship Programby CAST”(Grant No.2023JCJQQT061).
文摘To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the comprehensive artillery internal ballistic dynamics(AIBD)model,based on propellant combustion,rotation band engraving,projectile axial motion,and rifling wear models,was established and validated.This model was systematically decomposed into subsystems from a system engineering perspective.The study then detailed the MS-MDO methodology,which included Stage I(MDO stage)employing an improved collaborative optimization method for consistent design variables,and Stage II(Performance Optimization)focusing on the independent optimization of local design variables and performance metrics.The methodology was applied to the AIBD problem.Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts,thereby accelerating system-level convergence.Meanwhile,Stage II optimization markedly enhanced overall performance.These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.
基金Supported by the Science and Technology Support Key Project of 12th Five-Year of China(2011BAD20B00-4)~~
文摘The influence of processing parameters on the precision of parts fabricated by fused deposition modeling （FDM） technology is studied based on a series of performed experiments. Processing parameters of FDM in terms of wire-width compensation, extrusion velocity, filing velocity, and layer thickness are chosen as the control fac- tors. Robust design analysis and multi-index fuzzy comprehensive assessment method are used to obtain the opti- mal parameters. Results show that the influencing degrees of these four factors on the precision of as-processed parts are different. The optimizations of individual parameters and their combined effects are of the same impor- tance for a high precision manufacturing.
基金supported by the National Natural Science Foundation of China(No.52005378)the opening project fund of Materials Service Safety Assessment Facilities(No.MSAF-2021-107).
文摘The press-fit connector is a typical plug-and-play solderless connection,and it is widely used in signal transmission in fields such as communication and automotive devices.This paper focuses on inverse designing and optimization of geometric structure,as well as insertion-withdrawal forces of press-fit connector using artificial neural network(ANN)-assisted optimization method.The ANN model is established to approximate the relationship between geometric parameters and insertion-withdrawal forces,of which hyper-parameters of neural network are optimized to improve model performance.Two numerical methods are proposed for inverse designing structural parameters(Model-I)and multi-objective optimization of insertion-withdrawal forces(Model-II)of press-fit connector.In Model-I,a method for inverse designing structure parameters is established,of which an ANN model is coupled with single-objective optimization algorithm.The objective function is established,the inverse problem is solved,and effectiveness is verified.In Model-II,a multi-objective optimization method is proposed,of which an ANN model is coupled with genetic algorithm.The Pareto solution sets of insertion-withdrawal forces are obtained,and results are analyzed.The established ANN-coupled numerical optimization methods are beneficial for improving the design efficiency,and enhancing the connection reliability of the press-fit connector.
基金supports were received from the National Key Research and Development Program of China(2024YFE0208600)New Energy Joint Laboratory of China Southern Power Grid Corporation(GDXNY2024KF03)+2 种基金the National Natural Science Foundation of China(Grant No.U24B2090)National Key R&D Program(No.2022YFB4201300)Science and Technology Project of Huaneng Group(HNKJ24-H78).
文摘The jacket structure and transition piece comprise the supporting structure of a bottom-fixed offshore wind turbine(OWT)connected to the steel tower,which determines the overall structural dynamic performance of the entire OWT.Ideally,optimal performance can be realized by effectively coordinating two components,notwithstanding their separate design processes.In pursuit of this objective,this paper proposes a concurrent design methodology for the jacket structure and transition piece by exploiting topology optimization(TO).The TO for a three-legged jacket foundation is formulated by minimizing static compliance.In contrast to conventional TO,two separated volume fractions are imposed upon the structural design domain of the jacket structure and transition piece to ensure continuity.A 5 MW(megawatt)OWT supported by a four-legged or three-legged jacket substructure is under investigation.The external loads are derived from various design load cases that are acquired using the commercial software platform DNV Bladed(Det Norske Veritas).Through a comparative analysis of the fundamental frequency and maximum nodal deformation,it was found that the optimized solution demonstrates a reduced weight and superior stiffness.The findings demonstrate the present concurrent design approach using TO can yield significant benefits by reducing the overall design cycle and enhancing the feasibility of the final design.
