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
Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian O...Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian Optimization utilizes Gaussian Process (GP) as its core model and employs an adaptive search strategy to efficiently explore and identify optimal combinations of operational parameters within a limited parameter space, thereby enabling rapid optimization of the required parameters to achieve the target droplet size. Traditional methods typically rely on manually selecting a series of operational parameters and conducting multiple simulations to gradually approach the target droplet size. This process is time-consuming and prone to getting trapped in local optima. In contrast, Bayesian Optimization adaptively adjusts its search strategy, significantly reducing computational costs and effectively exploring global optima, thus greatly improving optimization efficiency. Additionally, the study investigates the impact of rectangular rib structures within the T-junction microchannel on droplet generation, revealing how the channel geometry influences droplet formation and size. After determining the target droplet size, we further applied Bayesian Optimization to refine the rib geometry. The integration of Bayesian Optimization with computational fluid dynamics (CFD) offers a promising tool and provides new insights into the optimal design of microfluidic devices.展开更多
Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated opt...Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approach–Taking a small-radius curve of a high-speed railway as the research object,field measurements were conducted to obtain track parameters and wheel–rail profiles.A coupled vehicle-track dynamics model was established.Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.Findings–Key service parameters affecting wheel–rail creepage were identified,including the matching relationship between curve geometry and vehicle speed and rail profile parameters.The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis,leading to the establishment of parameter optimization criteria.Originality/value–This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves.A response surface-based parameter-creepage relationship model was established,and a multi-parameter coordinated optimization strategy was proposed.The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.展开更多
The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this ...The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this process still encounters numerous challenges in attaining optimal economic and environmental performance. Therefore, an ensemble learning (EL) framework is proposed for the BCP process in this study to optimize the synergistic benefits while minimizing negative environmental impacts. Six different ensemble learning models are developed to investigate the impact of input features, such as biomass characteristics, coal characteristics, and pyrolysis conditions on the product profit and CO_(2) emissions of the BCP processes. The Optuna method is further employed to automatically optimize the hyperparameters of BCP process models for enhancing their predictive accuracy and robustness. The results indicate that the categorical boosting (CAB) model of the BCP process has demonstrated exceptional performance in accurately predicting its product profit and CO_(2) emission (R2>0.92) after undergoing five-fold cross-validation. To enhance the interpretability of this preferred model, the Shapley additive explanations and partial dependence plot analyses are conducted to evaluate the impact and importance of biomass characteristics, coal characteristics, and pyrolysis conditions on the product profitability and CO_(2) emissions of the BCP processes. Finally, the preferred model coupled with a reference vector guided evolutionary algorithm is carried to identify the optimal conditions for maximizing the product profit of BCP process products while minimizing CO_(2) emissions. It indicates the optimal BCP process can achieve high product profits (5290.85 CNY·t−1) and low CO_(2) emissions (7.45 kg·t^(−1)).展开更多
The optimization of polymer structures aims to determine an optimal sequence or topology that achieves a given target property or structural performance.This inverse design problem involves searching within a vast com...The optimization of polymer structures aims to determine an optimal sequence or topology that achieves a given target property or structural performance.This inverse design problem involves searching within a vast combinatorial phase space defined by components,se-quences,and topologies,and is often computationally intractable due to its NP-hard nature.At the core of this challenge lies the need to evalu-ate complex correlations among structural variables,a classical problem in both statistical physics and combinatorial optimization.To address this,we adopt a mean-field approach that decouples direct variable-variable interactions into effective interactions between each variable and an auxiliary field.The simulated bifurcation(SB)algorithm is employed as a mean-field-based optimization framework.It constructs a Hamiltonian dynamical system by introducing generalized momentum fields,enabling efficient decoupling and dynamic evolution of strongly coupled struc-tural variables.Using the sequence optimization of a linear copolymer adsorbing on a solid surface as a case study,we demonstrate the applica-bility of the SB algorithm to high-dimensional,non-differentiable combinatorial optimization problems.Our results show that SB can efficiently discover polymer sequences with excellent adsorption performance within a reasonable computational time.Furthermore,it exhibits robust con-vergence and high parallel scalability across large design spaces.The approach developed in this work offers a new computational pathway for polymer structure optimization.It also lays a theoretical foundation for future extensions to topological design problems,such as optimizing the number and placement of side chains,as well as the co-optimization of sequence and topology.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
Web pillars enduring complex coupled loads are critical for stability in high-wall mining.This study develops a dynamic failure criterion for web pillars under non-uniform loading using catastrophe theory.Through the ...Web pillars enduring complex coupled loads are critical for stability in high-wall mining.This study develops a dynamic failure criterion for web pillars under non-uniform loading using catastrophe theory.Through the analysis of the web pillar-overburden system’s dynamic stress and deformation,a total potential energy function and dynamic failure criterion were established for web pillars.An optimizing method for web pillar parameters was developed in highwall mining.The dynamic criterion established was used to evaluate the dynamic failure and stability of web pillars under static and dynamic loading.Key findings reveal that vertical displacements exhibit exponential-trigonometric variation under static loads and multi-variable power-law behavior under dynamic blasting.Instability risks arise when the roof’s tensile strength-to-stress ratio drops below 1.Using catastrophe theory,the bifurcation setΔ<0 signals sudden instability.The criterion defines failure as when the unstable web pillar section length l1 exceeds the roof’s critical collapse distance l2.Case studies and simulations determine an optimal web pillar width of 4.6 m.This research enhances safety and resource recovery,providing a theoretical framework for advancing highwall mining technology.展开更多
An efficient,diversified,and low-dimensional airfoil parameterization method is critical to airfoil aerodynamic optimization design.This paper proposes a supersonic airfoil parameterization method based on a bijective...An efficient,diversified,and low-dimensional airfoil parameterization method is critical to airfoil aerodynamic optimization design.This paper proposes a supersonic airfoil parameterization method based on a bijective cycle generative adversarial network(Bicycle-GAN),whose performance is compared with that of the conditional variational autoencoder(cVAE)based parameterization method in terms of parsimony,flawlessness,intuitiveness,and physicality.In all four aspects,the Bicycle-GAN-based parameterization method is superior to the cVAEbased parameterization method.Combined with multifidelity Gaussian process regression(MFGPR)surrogate model and a Bayesian optimization algorithm,a Bicycle-GAN-based optimization framework is established for the aerodynamic performance optimization of airfoils immersed in supersonic flow,which is compared with the cVAE-based optimization method in terms of optimized efficiency and effectiveness.The MFGPR surrogate model is established using low-fidelity aerodynamic data obtained from supersonic thin-airfoil theory and high-fidelity aerodynamic data obtained from steady CFD simulation.For both supersonic conditions,the CFD simulation costs are reduced by>20%compared with those of the cVAE-based optimization,and better optimization results are obtained through the Bicycle-GAN model.The optimization results for this supersonic flow point to a sharper leading edge,a smaller camber and thickness with a flatter lower surface,and a maximum thickness at 50%chord length.The advantages of the Bicycle-GAN and MFGPR models are comprehensively demonstrated in terms of airfoil generation characteristics,surrogate model prediction accuracy and optimization efficiency.展开更多
Inflatable deployable structures inspired by origami have significant applications in space missions such as solar arrays and antennas.In this paper,a generalized Miura-ori tubular cell(GMTC)is presented as the basic ...Inflatable deployable structures inspired by origami have significant applications in space missions such as solar arrays and antennas.In this paper,a generalized Miura-ori tubular cell(GMTC)is presented as the basic cell to design a family of inflatable origami tubular structures with the targeted configuration.First,the classification of rigid foldable degree-4 vertices is studied thoroughly.Since the proposed GMTC is comprised of forming units(FU)and linking units(LU),types of FUs and LUs are investigated based on the classification of degree-4 vertices,respectively.The rigid foldability of the GMTC is presented by studying the kinematics of the FUs and LUs.Volume of the GMTC is analyzed to investigate multistable configurations of the basic cell.The variations in volume of the GMTC offer great potential for developing the inflatable tubular structure.Design method and parametric optimization of the tubular structure with targeted configuration are proposed.The feasibility of the approach is validated by the approximation of four different cases,namely parabolic,semicircular,trapezoidal,and straight-arc hybrid tubular structures.展开更多
The recent development in Lucknow shows that the amount of built mass may increase significantly soon,which may affect outdoor thermal comfort.This study aims to achieve a better alternative to the geometrical configu...The recent development in Lucknow shows that the amount of built mass may increase significantly soon,which may affect outdoor thermal comfort.This study aims to achieve a better alternative to the geometrical configuration of vertical surfaces that helps improve the outdoor thermal comfort level.The study primarily deals with the exploration of built forms by altering the planar forms,heights,and orientations to arrive at a better composition of vertical surfaces.144 typologies were finally generated,which were then simulated in ENVI-met.The results show that,with the I-shaped typology it is difficult to reduce solar access,whereas in terms of ventilation,the typology performed better than L-shaped and C-shaped typologies.For this reason,the hours of solar access,as well as wind speed,should be seen together while developing the built-form typology.Urban neighborhoods can be designed with streets and open spaces oriented primarily to northeast-southwest and northwest-southeast directions which allow the open spaces to be thermally more comfortable than the rest of the orientation.This research highlights the importance of varying building heights to enhance thermal comfort.The findings provide valuable insights for composite climate cities like lucknow and can serve as a framework for future design strategies aimed at mitigating outdoor thermal discomfort.It is therefore important for planners,urban designers,and architects to design considering the minimal impact of the upcoming development on the thermal comfort level.展开更多
The performance and efficiency of hydraulic excavators heavily depend on the design and optimization of their working devices.The working device,which consists of the boom,arm,and bucket,plays a crucial role in determ...The performance and efficiency of hydraulic excavators heavily depend on the design and optimization of their working devices.The working device,which consists of the boom,arm,and bucket,plays a crucial role in determining the machine's digging capacity,stability,and overall operational efficiency.This paper presents a comprehensive study on the dynamics simulation and optimization of hydraulic excavator working devices.The paper outlines the fundamental principles of dynamic modeling,incorporating multi-body dynamics and hydraulic system analysis.It further explores various simulation techniques to evaluate the performance of the working device under varying operational conditions,including load and hydraulic system effects.The study also addresses performance optimization,focusing on multi-objective optimization methods that balance multiple factors such as energy efficiency,speed,and load capacity.Additionally,the paper discusses key factors influencing performance,such as mechanical design,material properties,and operational conditions.The results of the dynamic simulations and optimization analyses demonstrate potential improvements in operational efficiency and system stability,providing a valuable framework for the design and enhancement of hydraulic excavator working devices.展开更多
Additive Manufacturing(AM)has significantly impacted the development of high-performance materials and structures,offering new possibilities for industries ranging from aerospace to biomedicine.This special issue feat...Additive Manufacturing(AM)has significantly impacted the development of high-performance materials and structures,offering new possibilities for industries ranging from aerospace to biomedicine.This special issue features pioneering research that integrates AI-driven methods with AM,enabling the design and fabrication of complex,optimized structures with enhanced properties.展开更多
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
An optimizing method for designing the wireless power receiving coil(RC)is proposed in this paper to address issues such as insufficient and fluctuating power supply in the near-infrared capsule robot.An elec-tromagne...An optimizing method for designing the wireless power receiving coil(RC)is proposed in this paper to address issues such as insufficient and fluctuating power supply in the near-infrared capsule robot.An elec-tromagnetic and circuit analysis is conducted to establish the magnetic induction intensity and equivalent circuit models for the wireless power transmission system.Combining these models involves using the number of layers in each dimension as the optimization variable.Constraints are imposed based on the normalized standard deviation of the receiving-end load power and spatial dimensions.At the same time,the optimization objective aims to maximize the average power of the receiving-end load.This process leads to formulating an optimization model for the RC.Finally,three-dimensional RCs with three different sets of parameters are wound,and the receiving-end load power of these coils is experimentally tested under various drive currents.The experimental values of the receiving-end load power exhibit a consistent trend with theoretical values,with experimental values consistently lower than theoretical values.The optimized coil parameters are determined by conducting comparative exper-iments,with a theoretical value of 4.6%for the normalized standard deviation of the receiving-end load power and an average experimental value of 9.6%.The study addressed the power supply issue of near-infrared capsule robots,which is important for early diagnosing and treating gastrointestinal diseases.展开更多
The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount ...The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.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
Aiming at the problems in current cam profile optimization processes, such as simple dynamics models, limited geometric accuracy and low design automatization level, a new dynamic optimization mode is put forward. Bas...Aiming at the problems in current cam profile optimization processes, such as simple dynamics models, limited geometric accuracy and low design automatization level, a new dynamic optimization mode is put forward. Based on the parameterization modeling technique of MSC. ADAMS platform, the different steps in current mode are reorganized, thus obtaining an upgraded mode called the ＂parameterized-prototype-based cam profile dynamic optimization mode＂. A parameterized prototype（PP） of valve mechanism is constructed in the course of dynamic optimization for cam profiles. Practically, by utilizing PP and considering the flexibility of the parts in valve mechanism, geometric accuracy and design automatization are improved.展开更多
The well number and the cavity length of 1.55μm wavelength In 1-x-y Ga y Al x As MQW DFB lasers are optimized using a simple model.A low threshold,maximum operating temperature of 550～560K,and high relaxat...The well number and the cavity length of 1.55μm wavelength In 1-x-y Ga y Al x As MQW DFB lasers are optimized using a simple model.A low threshold,maximum operating temperature of 550～560K,and high relaxation oscillation frequency of over 30GHz MQW DFB laser is presented.展开更多
A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. Th...A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. The Kriging approximate model with updating is introduced to reduce the computational cost of MDF caused by the complex structure. The computational efficiency is remarkably improved as the lack of iterative process during reliability analysis. Special attention is paid to a turbine blade design optimization by adopting the proposed method. Results show that the method is much more efficient than the commonly used double-loop based RBMDO method. It is feasible and efficient to apply the method to the engineering design.展开更多
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support from National Key Research and Development Program of China(2023YFC3905400)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA0490102)National Natural Science Foundation of China(22178354,2242100322408374).
文摘Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian Optimization utilizes Gaussian Process (GP) as its core model and employs an adaptive search strategy to efficiently explore and identify optimal combinations of operational parameters within a limited parameter space, thereby enabling rapid optimization of the required parameters to achieve the target droplet size. Traditional methods typically rely on manually selecting a series of operational parameters and conducting multiple simulations to gradually approach the target droplet size. This process is time-consuming and prone to getting trapped in local optima. In contrast, Bayesian Optimization adaptively adjusts its search strategy, significantly reducing computational costs and effectively exploring global optima, thus greatly improving optimization efficiency. Additionally, the study investigates the impact of rectangular rib structures within the T-junction microchannel on droplet generation, revealing how the channel geometry influences droplet formation and size. After determining the target droplet size, we further applied Bayesian Optimization to refine the rib geometry. The integration of Bayesian Optimization with computational fluid dynamics (CFD) offers a promising tool and provides new insights into the optimal design of microfluidic devices.
基金sponsored by the National Natural Science Foundation of China(Grant No.52405443)the Technology Research and Development Plan of China Railway(Grant No.N2023G063)the Fund of China Academy of Railway Sciences Corporation Limited(Grant No.2023YJ054).
文摘Purpose–To investigate the influence of vehicle operation speed,curve geometry parameters and rail profile parameters on wheel–rail creepage in high-speed railway curves and propose a multi-parameter coordinated optimization strategy to reduce wheel–rail contact fatigue damage.Design/methodology/approach–Taking a small-radius curve of a high-speed railway as the research object,field measurements were conducted to obtain track parameters and wheel–rail profiles.A coupled vehicle-track dynamics model was established.Multiple numerical experiments were designed using the Latin Hypercube Sampling method to extract wheel-rail creepage indicators and construct a parameter-creepage response surface model.Findings–Key service parameters affecting wheel–rail creepage were identified,including the matching relationship between curve geometry and vehicle speed and rail profile parameters.The influence patterns of various parameters on wheel–rail creepage were revealed through response surface analysis,leading to the establishment of parameter optimization criteria.Originality/value–This study presents the systematic investigation of wheel–rail creepage characteristics under multi-parameter coupling in high-speed railway curves.A response surface-based parameter-creepage relationship model was established,and a multi-parameter coordinated optimization strategy was proposed.The research findings provide theoretical guidance for controlling wheel–rail contact fatigue damage and optimizing wheel–rail profiles in high-speed railway curves.
基金support from the National Natural Science Foundation of China(22108052).
文摘The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this process still encounters numerous challenges in attaining optimal economic and environmental performance. Therefore, an ensemble learning (EL) framework is proposed for the BCP process in this study to optimize the synergistic benefits while minimizing negative environmental impacts. Six different ensemble learning models are developed to investigate the impact of input features, such as biomass characteristics, coal characteristics, and pyrolysis conditions on the product profit and CO_(2) emissions of the BCP processes. The Optuna method is further employed to automatically optimize the hyperparameters of BCP process models for enhancing their predictive accuracy and robustness. The results indicate that the categorical boosting (CAB) model of the BCP process has demonstrated exceptional performance in accurately predicting its product profit and CO_(2) emission (R2>0.92) after undergoing five-fold cross-validation. To enhance the interpretability of this preferred model, the Shapley additive explanations and partial dependence plot analyses are conducted to evaluate the impact and importance of biomass characteristics, coal characteristics, and pyrolysis conditions on the product profitability and CO_(2) emissions of the BCP processes. Finally, the preferred model coupled with a reference vector guided evolutionary algorithm is carried to identify the optimal conditions for maximizing the product profit of BCP process products while minimizing CO_(2) emissions. It indicates the optimal BCP process can achieve high product profits (5290.85 CNY·t−1) and low CO_(2) emissions (7.45 kg·t^(−1)).
基金supported by the Fundamental Research Funds for the Central Universities(No.2024JBZX029)Shijiazhuang High Level Science and Technology Innovation and Entrepreneurship Talent Project(No.08202307)the National Natural Science Foundation of China(NSFC)(No.22173004).
文摘The optimization of polymer structures aims to determine an optimal sequence or topology that achieves a given target property or structural performance.This inverse design problem involves searching within a vast combinatorial phase space defined by components,se-quences,and topologies,and is often computationally intractable due to its NP-hard nature.At the core of this challenge lies the need to evalu-ate complex correlations among structural variables,a classical problem in both statistical physics and combinatorial optimization.To address this,we adopt a mean-field approach that decouples direct variable-variable interactions into effective interactions between each variable and an auxiliary field.The simulated bifurcation(SB)algorithm is employed as a mean-field-based optimization framework.It constructs a Hamiltonian dynamical system by introducing generalized momentum fields,enabling efficient decoupling and dynamic evolution of strongly coupled struc-tural variables.Using the sequence optimization of a linear copolymer adsorbing on a solid surface as a case study,we demonstrate the applica-bility of the SB algorithm to high-dimensional,non-differentiable combinatorial optimization problems.Our results show that SB can efficiently discover polymer sequences with excellent adsorption performance within a reasonable computational time.Furthermore,it exhibits robust con-vergence and high parallel scalability across large design spaces.The approach developed in this work offers a new computational pathway for polymer structure optimization.It also lays a theoretical foundation for future extensions to topological design problems,such as optimizing the number and placement of side chains,as well as the co-optimization of sequence and topology.
基金supported by the National Natural Science Foundation of China(No.52005378)the opening project fund of Materials Service Safety Assessment Facilities(No.MSAF-2021-107).
文摘The press-fit connector is a typical plug-and-play solderless connection,and it is widely used in signal transmission in fields such as communication and automotive devices.This paper focuses on inverse designing and optimization of geometric structure,as well as insertion-withdrawal forces of press-fit connector using artificial neural network(ANN)-assisted optimization method.The ANN model is established to approximate the relationship between geometric parameters and insertion-withdrawal forces,of which hyper-parameters of neural network are optimized to improve model performance.Two numerical methods are proposed for inverse designing structural parameters(Model-I)and multi-objective optimization of insertion-withdrawal forces(Model-II)of press-fit connector.In Model-I,a method for inverse designing structure parameters is established,of which an ANN model is coupled with single-objective optimization algorithm.The objective function is established,the inverse problem is solved,and effectiveness is verified.In Model-II,a multi-objective optimization method is proposed,of which an ANN model is coupled with genetic algorithm.The Pareto solution sets of insertion-withdrawal forces are obtained,and results are analyzed.The established ANN-coupled numerical optimization methods are beneficial for improving the design efficiency,and enhancing the connection reliability of the press-fit connector.
基金supported by the National Natural Science Foundation of China(Nos.52204136,52474100,and 52204092).
文摘Web pillars enduring complex coupled loads are critical for stability in high-wall mining.This study develops a dynamic failure criterion for web pillars under non-uniform loading using catastrophe theory.Through the analysis of the web pillar-overburden system’s dynamic stress and deformation,a total potential energy function and dynamic failure criterion were established for web pillars.An optimizing method for web pillar parameters was developed in highwall mining.The dynamic criterion established was used to evaluate the dynamic failure and stability of web pillars under static and dynamic loading.Key findings reveal that vertical displacements exhibit exponential-trigonometric variation under static loads and multi-variable power-law behavior under dynamic blasting.Instability risks arise when the roof’s tensile strength-to-stress ratio drops below 1.Using catastrophe theory,the bifurcation setΔ<0 signals sudden instability.The criterion defines failure as when the unstable web pillar section length l1 exceeds the roof’s critical collapse distance l2.Case studies and simulations determine an optimal web pillar width of 4.6 m.This research enhances safety and resource recovery,providing a theoretical framework for advancing highwall mining technology.
基金supported by the National Natural Science Founda-tion of China(Grant No 12302226)the China Postdoctoral Science Foundation(Grant No BX20230453).
文摘An efficient,diversified,and low-dimensional airfoil parameterization method is critical to airfoil aerodynamic optimization design.This paper proposes a supersonic airfoil parameterization method based on a bijective cycle generative adversarial network(Bicycle-GAN),whose performance is compared with that of the conditional variational autoencoder(cVAE)based parameterization method in terms of parsimony,flawlessness,intuitiveness,and physicality.In all four aspects,the Bicycle-GAN-based parameterization method is superior to the cVAEbased parameterization method.Combined with multifidelity Gaussian process regression(MFGPR)surrogate model and a Bayesian optimization algorithm,a Bicycle-GAN-based optimization framework is established for the aerodynamic performance optimization of airfoils immersed in supersonic flow,which is compared with the cVAE-based optimization method in terms of optimized efficiency and effectiveness.The MFGPR surrogate model is established using low-fidelity aerodynamic data obtained from supersonic thin-airfoil theory and high-fidelity aerodynamic data obtained from steady CFD simulation.For both supersonic conditions,the CFD simulation costs are reduced by>20%compared with those of the cVAE-based optimization,and better optimization results are obtained through the Bicycle-GAN model.The optimization results for this supersonic flow point to a sharper leading edge,a smaller camber and thickness with a flatter lower surface,and a maximum thickness at 50%chord length.The advantages of the Bicycle-GAN and MFGPR models are comprehensively demonstrated in terms of airfoil generation characteristics,surrogate model prediction accuracy and optimization efficiency.
基金supported by the National Natural Science Foundation of China(No.52222501,52075016,52192632)the Fundamental Research Funds for the Central Universities(Grant No.YWF-23-L-904).
文摘Inflatable deployable structures inspired by origami have significant applications in space missions such as solar arrays and antennas.In this paper,a generalized Miura-ori tubular cell(GMTC)is presented as the basic cell to design a family of inflatable origami tubular structures with the targeted configuration.First,the classification of rigid foldable degree-4 vertices is studied thoroughly.Since the proposed GMTC is comprised of forming units(FU)and linking units(LU),types of FUs and LUs are investigated based on the classification of degree-4 vertices,respectively.The rigid foldability of the GMTC is presented by studying the kinematics of the FUs and LUs.Volume of the GMTC is analyzed to investigate multistable configurations of the basic cell.The variations in volume of the GMTC offer great potential for developing the inflatable tubular structure.Design method and parametric optimization of the tubular structure with targeted configuration are proposed.The feasibility of the approach is validated by the approximation of four different cases,namely parabolic,semicircular,trapezoidal,and straight-arc hybrid tubular structures.
文摘The recent development in Lucknow shows that the amount of built mass may increase significantly soon,which may affect outdoor thermal comfort.This study aims to achieve a better alternative to the geometrical configuration of vertical surfaces that helps improve the outdoor thermal comfort level.The study primarily deals with the exploration of built forms by altering the planar forms,heights,and orientations to arrive at a better composition of vertical surfaces.144 typologies were finally generated,which were then simulated in ENVI-met.The results show that,with the I-shaped typology it is difficult to reduce solar access,whereas in terms of ventilation,the typology performed better than L-shaped and C-shaped typologies.For this reason,the hours of solar access,as well as wind speed,should be seen together while developing the built-form typology.Urban neighborhoods can be designed with streets and open spaces oriented primarily to northeast-southwest and northwest-southeast directions which allow the open spaces to be thermally more comfortable than the rest of the orientation.This research highlights the importance of varying building heights to enhance thermal comfort.The findings provide valuable insights for composite climate cities like lucknow and can serve as a framework for future design strategies aimed at mitigating outdoor thermal discomfort.It is therefore important for planners,urban designers,and architects to design considering the minimal impact of the upcoming development on the thermal comfort level.
文摘The performance and efficiency of hydraulic excavators heavily depend on the design and optimization of their working devices.The working device,which consists of the boom,arm,and bucket,plays a crucial role in determining the machine's digging capacity,stability,and overall operational efficiency.This paper presents a comprehensive study on the dynamics simulation and optimization of hydraulic excavator working devices.The paper outlines the fundamental principles of dynamic modeling,incorporating multi-body dynamics and hydraulic system analysis.It further explores various simulation techniques to evaluate the performance of the working device under varying operational conditions,including load and hydraulic system effects.The study also addresses performance optimization,focusing on multi-objective optimization methods that balance multiple factors such as energy efficiency,speed,and load capacity.Additionally,the paper discusses key factors influencing performance,such as mechanical design,material properties,and operational conditions.The results of the dynamic simulations and optimization analyses demonstrate potential improvements in operational efficiency and system stability,providing a valuable framework for the design and enhancement of hydraulic excavator working devices.
文摘Additive Manufacturing(AM)has significantly impacted the development of high-performance materials and structures,offering new possibilities for industries ranging from aerospace to biomedicine.This special issue features pioneering research that integrates AI-driven methods with AM,enabling the design and fabrication of complex,optimized structures with enhanced properties.
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the Project of the Science and Technology Commission of Shanghai Municipality(No.20142201300)the National Facility for Translational Medicine(Shanghai)Open Project Foundation(No.TMSK-2021-302)the China Postdoctoral Science Foundation(No.2023M732267)。
文摘An optimizing method for designing the wireless power receiving coil(RC)is proposed in this paper to address issues such as insufficient and fluctuating power supply in the near-infrared capsule robot.An elec-tromagnetic and circuit analysis is conducted to establish the magnetic induction intensity and equivalent circuit models for the wireless power transmission system.Combining these models involves using the number of layers in each dimension as the optimization variable.Constraints are imposed based on the normalized standard deviation of the receiving-end load power and spatial dimensions.At the same time,the optimization objective aims to maximize the average power of the receiving-end load.This process leads to formulating an optimization model for the RC.Finally,three-dimensional RCs with three different sets of parameters are wound,and the receiving-end load power of these coils is experimentally tested under various drive currents.The experimental values of the receiving-end load power exhibit a consistent trend with theoretical values,with experimental values consistently lower than theoretical values.The optimized coil parameters are determined by conducting comparative exper-iments,with a theoretical value of 4.6%for the normalized standard deviation of the receiving-end load power and an average experimental value of 9.6%.The study addressed the power supply issue of near-infrared capsule robots,which is important for early diagnosing and treating gastrointestinal diseases.
基金supported by the National Key R&D Program of China(No.2021YFB1715000)the National Natural Science Foundation of China(No.52375073)。
文摘The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.
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.
文摘Aiming at the problems in current cam profile optimization processes, such as simple dynamics models, limited geometric accuracy and low design automatization level, a new dynamic optimization mode is put forward. Based on the parameterization modeling technique of MSC. ADAMS platform, the different steps in current mode are reorganized, thus obtaining an upgraded mode called the ＂parameterized-prototype-based cam profile dynamic optimization mode＂. A parameterized prototype（PP） of valve mechanism is constructed in the course of dynamic optimization for cam profiles. Practically, by utilizing PP and considering the flexibility of the parts in valve mechanism, geometric accuracy and design automatization are improved.
文摘The well number and the cavity length of 1.55μm wavelength In 1-x-y Ga y Al x As MQW DFB lasers are optimized using a simple model.A low threshold,maximum operating temperature of 550～560K,and high relaxation oscillation frequency of over 30GHz MQW DFB laser is presented.
基金Supported by the National High Technology Research and Development Program of China("863" Program) (2009AA04Z418, 2007AA04Z404)the National "111" Project(B07050)~~
文摘A new reliability-based multidisciplinary design optimization （RBMDO） framework is proposed by combining the single-loop-based reliability analysis （SLBRA） method with multidisciplinary feasible （MDF） method. The Kriging approximate model with updating is introduced to reduce the computational cost of MDF caused by the complex structure. The computational efficiency is remarkably improved as the lack of iterative process during reliability analysis. Special attention is paid to a turbine blade design optimization by adopting the proposed method. Results show that the method is much more efficient than the commonly used double-loop based RBMDO method. It is feasible and efficient to apply the method to the engineering design.
