This research focuses on using BIM modeling optimization to control construction-period risks in the pre-construction stage of industrial factory buildings.It analyzes common risk factors and limitations of traditiona...This research focuses on using BIM modeling optimization to control construction-period risks in the pre-construction stage of industrial factory buildings.It analyzes common risk factors and limitations of traditional approaches.BIM-based methods like collision detection,4D simulation,multi-dimensional data integration,etc.,can effectively mitigate risks.Stakeholder collaboration,digital twin testing,and lean BIM integration is also crucial.Case studies show BIM can reduce risks by 32-41%,with a three phase roadmap provided.展开更多
Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help...Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ...Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.展开更多
Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring...Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring their functional integrity and performance.To achieve sustainable manufacturing in FDM,it is necessary to optimize the print quality and time efficiency concurrently.However,owing to the complex interactions of printing parameters,achieving a balanced optimization of both remains challenging.This study examines four key factors affecting dimensional accuracy and print time:printing speed,layer thickness,nozzle temperature,and bed temperature.Fifty parameter sets were generated using enhanced Latin hypercube sampling.A whale optimization algorithm(WOA)-enhanced support vector regression(SVR)model was developed to predict dimen-sional errors and print time effectively,with non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)utilized for multi-objective optimization.The technique for Order Preference by Similarity to Ideal Solution(TOPSIS)was applied to select a balanced solution from the Pareto front.In experimental validation,the parts printed using the optimized parameters exhibited excellent dimensional accuracy and printing efficiency.This study comprehensively considered optimizing the printing time and size to meet quality requirements while achieving higher printing efficiency and aiding in the realization of sustainable manufacturing in the field of AM.In addition,the printing of a specific prosthetic component was used as a case study,highlighting the high demands on both dimensional precision and printing efficiency.The optimized process parameters required significantly less printing time,while satisfying the dimensional accuracy requirements.This study provides valuable insights for achieving sustainable AM using FDM.展开更多
Improving the specific,technical,economic,and environmental characteristics of piston engines(ICE)operating on alternative gaseous fuels is a pressing task for the energy and mechanical engineering industries.The aim ...Improving the specific,technical,economic,and environmental characteristics of piston engines(ICE)operating on alternative gaseous fuels is a pressing task for the energy and mechanical engineering industries.The aim of the study was to optimize the parameters of the ICE working cycle after replacing the base fuel(propane-butane blend)with syngas from wood sawdust to improve its technical and economic performance based on mathematical modeling.The modeling results were verified through experimental studies(differences for key parameters did not exceed 4.0%).The object of the study was an electric generator based on a single-cylinder spark ignition engine with a power of 1 kW.The article describes the main approaches to creating a mathematical model of the engine working cycle,a test bench for modeling verification,physicochemical properties of the base fuel(propane-butane blend),and laboratory syngas.It was shown that replacing the fuel from a propane-butane blend to laboratory syngas caused a decrease in engine efficiency to 33%(the efficiency of the base ICE was 0.179 vs.the efficiency of 0.119 for the converted ICE for the 0.59 kW power mode).Engine efficiency was chosen as the key criterion for optimizing the working cycle.As a result of optimization,the efficiency of the converted syngas engine was 6.1%higher than that of the base engine running on the propane-butane blend,and the power drop did not exceed 8.0%.Thus,careful fine-tuning of the working cycle parameters allows increasing the technical and economic characteristics of the syngas engine to the level of ICEs running on traditional types of fuel.展开更多
High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of ...High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.展开更多
This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task...This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task allocation for vigilance roles and the coverage planning of the perception ranges.Firstly,vigilance behavioral patterns and processes in animal populations within natural habitats are investigated.Inspired by these biological vigilance behaviors,an efficient vigilance task allocation model for MAS is proposed.Secondly,the subsequent optimization of task layouts can achieve efficient surveillance coverage with fewer agents,minimizing resource consumption.Thirdly,an improved particle swarm optimization(IPSO)algorithm is proposed,which incorporates fitness-driven adaptive inertia weight dynamics.According to simulation analysis and comparative studies,optimal parameter configurations for genetic algorithm(GA)and IPSO are determined.Finally,the results indicate the proposed IPSO's superior performance to both GA and standard particle swarm optimization(PSO)in vigilance task allocation optimization,with satisfying advantages in computational efficiency and solution quality.展开更多
Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing...Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing attention on specific properties of adopted numerical optimization approaches.We collect and discuss the methods based on nonlinear programming,semidefinite programming,mixed-integer programming,mathematical programming with complementarity constraints,difference-of-convex programming,optimization methods using surrogate models and machine learning techniques,and metaheuristics.As a closely related topic,we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling.We conclude the paper by drawing several remarks through this review.展开更多
The roller is one of the fundamental elements of ore belt conveyor systems since it supports,guides,and directs material on the belt.This component comprises a body(the external tube)that rotates around a fixed shaft ...The roller is one of the fundamental elements of ore belt conveyor systems since it supports,guides,and directs material on the belt.This component comprises a body(the external tube)that rotates around a fixed shaft supported by easels.The external tube and shaft of rollers used in ore conveyor belts are mostly made of steel,resulting in high mass,hindering maintenance and replacement.Aiming to achieve mass reduction,we conducted a structural optimization of a roller with a polymeric external tube(hereafter referred to as a polymeric roller),seeking the optimal values for two design parameters:the inner diameter of the external tube and the shaft diameter.The optimization was constrained by admissible values for maximum stress,maximum deflection and misalignment angle between the shaft and bearings.A finite element model was built in Ansys Workbench to obtain the structural response of the system.The roller considered is composed of an external tube made of high-density polyethylene(HDPE),bearing seats of polyamide 6(PA6),and a steel shaft.To characterize the polymeric materials(HDPE and PA6),stress relaxation tests were conducted,and the data on shear modulus variation over time were inserted into the model to calculate Prony series terms to account for viscoelastic effects.The roller optimization was performed using surrogate modeling based on radial basis functions,with the Globalized Bounded Nelder-Mead(GBNM)algorithm as the optimizer.Two optimization cases were conducted.In the first case,concerning the roller’s initial material settings,the designs found violated the constraints and could not reduce mass.In the second case,by using PA6 in both bearing seats and the tube,a design configuration was found that respected all constraints and reduced the roller mass by 15.5%,equivalent to 5.15 kg.This study is among the first to integrate experimentally obtained viscoelastic data into the surrogate-based optimization of polymeric rollers,combining methodological innovation with industrial relevance.展开更多
Trans-medium flight vehicles can combine high aerial maneuverability and underwater concealment ability,which have attracted much attention recently.As the most crucial procedure,the trajectory design generally determ...Trans-medium flight vehicles can combine high aerial maneuverability and underwater concealment ability,which have attracted much attention recently.As the most crucial procedure,the trajectory design generally determines the trans-medium flight vehicle performance.To quantitatively analyze the flight vehicle performance,an entire aerial-aquatic trajectory model is developed in this paper.Different from modeling a trajectory purely for the water entry process,the constructed entire trajectory model has integrated aerial,water entry,and underwater trajectories together,which can consider the influence of the connected trajectories.As for the aerial and underwater trajectories,explicit dynamic models are established to obtain the trajectory parameters.Due to the complicated fluid force during high-velocity water entry,a computational fluid dynamics model is investigated to analyze this phase.The compu-tational domain size is adaptively refined according to the final aerial trajectory state,where the redundant computational domain is removed.An entire trajectory optimization problem is then formulated to maximize the total flight range via tuning the joint states of different trajectories.Simultaneously,several constraints,i.e.,the max impact load,trajectory height,etc.,are involved in the optimization problem.Rather than directly optimizing by a heuristic algorithm,a multi-surrogate cooperative sampling-based optimization method is proposed to alleviate the computational complexity of the entire trajectory optimization problem.In this method,various surrogates coopera-tively generate infill sample points,thereby preventing the poor approximation.After optimization,the total flight range can be improved by 20%,while all the constraints are satisfied.The result demonstrates the effectiveness and practicability of the developed model and optimization framework.展开更多
In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictiv...In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictive maintenance(PdM)strategy based on Remaining Useful Life(RUL)estimation.First,a RUL prediction model is established using the Transformer architecture,which enables the effective processing of sequential degradation data.By employing the historical degradation data of PV modules,the proposed model provides accurate forecasts of the remaining useful life,thereby supplying essential inputs for maintenance decision-making.Subsequently,the RUL information obtained from the prediction process is integrated into the optimization of maintenance policies.An opposition-based learning Harris Hawks Optimization(OHHO)algorithm is introduced to jointly optimize two critical parameters:the maintenance threshold L,which specifies the degradation level at which maintenance should be performed,and the recovery factor r,which reflects the extent to which the system performance is restored after maintenance.The objective of this joint optimization is to minimize the overall operation and maintenance cost while maintaining system availability.Finally,simulation experiments are conducted to evaluate the performance of the proposed PdM strategy.The results indicate that,compared with conventional corrective maintenance(CM)and periodic maintenance(PM)strategies,the RUL-driven PdM approach achieves a reduction in the average cost rate by approximately 20.7%and 17.9%,respectively,thereby demonstrating its potential effectiveness for practical PV maintenance applications.展开更多
Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.W...Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
Refill friction stir spot welding process is difficultly optimized by accurate modeling because of the high-order functional relationship between welding parameters and joint strength.A database of the welding process...Refill friction stir spot welding process is difficultly optimized by accurate modeling because of the high-order functional relationship between welding parameters and joint strength.A database of the welding process was first established with 6061-T6 aluminum alloy and DP780 galvanized steel as base materials.This dataset was then optimized using a backpropagation neural network.Analyses and mining of the experimental data confirmed the multidimensional mapping relationship between welding parameters and joint strength.Subsequently,intelligent optimization of the welding process and prediction of joint strength were achieved.At the predicted welding parameter(plunging rotation speedω1=1733 r/min,refilling rotation speedω_(2)=1266 r/min,plunging depth p=1.9 mm,and welding speed v=0.5 mm/s),the tensile shear fracture load of the joint reached a maximum value of 10,172 N,while the experimental result was 9980 N,with an error of 1.92%.Furthermore,the correlation of welding parameters-microstructure-joint strength was established.展开更多
Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural ...Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.展开更多
In the construction and maintenance for large space equipment,it is essential to ensure the control accuracy and improve the dexterity of the space manipulator.In this paper,a FiniteTime Convergence Kinematic Control(...In the construction and maintenance for large space equipment,it is essential to ensure the control accuracy and improve the dexterity of the space manipulator.In this paper,a FiniteTime Convergence Kinematic Control(FTCKC)added with Acceleration Level Dexterity Optimization(ALDO)scheme is proposed to solve the kinematic uncertainty and dexterity optimization problems of redundant space manipulators.Concretely,distinguishing from the asymptotic convergence property of traditional adaptive Jacobian methods,the FTCKC scheme is adopted to construct the equality constraint to address the model uncertainty problem,and its error can converge within a finite time.Subsequently,the dexterity index is reconstructed at acceleration level by a multi-level target handling method.Then,the equality constraint,optimization task,and limit constraints are reformulated as a quadratic programming problem.Moreover,a Recurrent Neural Network(RNN)is engineered for the constructed FTCKC-ALDO scheme.Finally,the superiority of the FTCKC-ALDO-RNN scheme is verified by experiments.展开更多
In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a ...In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a composition solution under their budget constraints.Existing studies typically evaluate satisfaction solely based on energy transmission capacity,while overlooking critical factors such as price and trustworthiness of the provider,leading to a mismatch between optimization outcomes and user needs.To address this gap,we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios,systematically incorporating service price,transmission capacity,and trustworthiness into the satisfaction assessment framework.Furthermore,we propose a Budget-Aware Preference Adjustment Model that predicts users’baseline preference weights from historical data and dynamically adjusts them according to budget levels,thereby reflecting user preferences more realistically under varying budget constraints.In addition,to tackle the composition optimization problem,we develop a ReflectiveEvolutionary Large Language Model—Guided Ant Colony Optimization algorithm,which leverages the reflective evolution capability of large language models to iteratively generate and refine heuristic information that guides the search process.Experimental results demonstrate that the proposed framework effectively integrates personalized preferences with budget sensitivity,accurately predicts users’preferences,and significantly enhances their satisfaction under complex constraints.展开更多
In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op...In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.展开更多
Process of dynamic recrystallization(DRX)plays a crucial role in altering the microstructure and enhancing the mechanical characteristics of CrNiMoVW steel.However,its initiation mechanism,deformation conditions,and p...Process of dynamic recrystallization(DRX)plays a crucial role in altering the microstructure and enhancing the mechanical characteristics of CrNiMoVW steel.However,its initiation mechanism,deformation conditions,and predictive models remain insufficiently understood,requiring further research to optimize the processing technology.In the present study,hot compression experiments were carried out on 30CrNiMoVW steel under deformation conditions with temperatures ranging from 950 to 1,250℃and strain rates from 0.001 to 1 s~(-1),during which true stress-strain curves were obtained.Based on friction and temperature corrections applied to these curves,a constitutive equation for 30CrNiMoVW steel was established,and its accuracy was verified through fitting analysis.Simultaneously,the study identified limitations in the initial volume fraction model,prompting the development of a modified recrystallization volume fraction model that was validated via correlation analysis between experimental data and model predictions.Furthermore,building upon the modified recrystallization volume fraction model,a novel recrystallization rate model was developed,and three characteristic strain points were determined.These points segmented the rate curve into three stages:a slow initiation stage(0,ε1),a rapid growth stage(1,ε3),and a slow equilibrium stage(e3,0.9).Notably,the value ofε3 was considered the most economical,ensuring the formation of fine and uniform grains during production while optimizing the process,reducing energy consumption and costs,and enhancing overall material performance.Finally,based on the physical constitutive relationships and kinetic models,a multiscale simulation approach combining the finite element method(FEM)and cellular automata(CA)was employed to predict the microstructural evolution of 30CrNiMoVW steel.The simulation results demonstrate that the FEM&CA approach can accurately reproduce the dynamic recrystallization behavior and microstructural evolution observed experimentally.This work provides critical guidance for the development of forging processes for 30CrNiMoVW steel.展开更多
文摘This research focuses on using BIM modeling optimization to control construction-period risks in the pre-construction stage of industrial factory buildings.It analyzes common risk factors and limitations of traditional approaches.BIM-based methods like collision detection,4D simulation,multi-dimensional data integration,etc.,can effectively mitigate risks.Stakeholder collaboration,digital twin testing,and lean BIM integration is also crucial.Case studies show BIM can reduce risks by 32-41%,with a three phase roadmap provided.
基金supported by National Key Research and Development Program of China (2023YFB3307800)National Natural Science Foundation of China (Key Program: 62136003, 62373155)+1 种基金Major Science and Technology Project of Xinjiang (No. 2022A01006-4)the Fundamental Research Funds for the Central Universities。
文摘Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
文摘Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
文摘Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.
基金supporteded by Natural Science Foundation of Shanghai(Grant No.22ZR1463900)State Key Laboratory of Mechanical System and Vibration(Grant No.MSV202318)the Fundamental Research Funds for the Central Universities(Grant No.22120220649).
文摘Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring their functional integrity and performance.To achieve sustainable manufacturing in FDM,it is necessary to optimize the print quality and time efficiency concurrently.However,owing to the complex interactions of printing parameters,achieving a balanced optimization of both remains challenging.This study examines four key factors affecting dimensional accuracy and print time:printing speed,layer thickness,nozzle temperature,and bed temperature.Fifty parameter sets were generated using enhanced Latin hypercube sampling.A whale optimization algorithm(WOA)-enhanced support vector regression(SVR)model was developed to predict dimen-sional errors and print time effectively,with non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)utilized for multi-objective optimization.The technique for Order Preference by Similarity to Ideal Solution(TOPSIS)was applied to select a balanced solution from the Pareto front.In experimental validation,the parts printed using the optimized parameters exhibited excellent dimensional accuracy and printing efficiency.This study comprehensively considered optimizing the printing time and size to meet quality requirements while achieving higher printing efficiency and aiding in the realization of sustainable manufacturing in the field of AM.In addition,the printing of a specific prosthetic component was used as a case study,highlighting the high demands on both dimensional precision and printing efficiency.The optimized process parameters required significantly less printing time,while satisfying the dimensional accuracy requirements.This study provides valuable insights for achieving sustainable AM using FDM.
基金the Ministry of Science and Higher Education of the Russian Federation(Ural Federal University Program of Development within the Priority-2030 Program)is gratefully acknowledged.
文摘Improving the specific,technical,economic,and environmental characteristics of piston engines(ICE)operating on alternative gaseous fuels is a pressing task for the energy and mechanical engineering industries.The aim of the study was to optimize the parameters of the ICE working cycle after replacing the base fuel(propane-butane blend)with syngas from wood sawdust to improve its technical and economic performance based on mathematical modeling.The modeling results were verified through experimental studies(differences for key parameters did not exceed 4.0%).The object of the study was an electric generator based on a single-cylinder spark ignition engine with a power of 1 kW.The article describes the main approaches to creating a mathematical model of the engine working cycle,a test bench for modeling verification,physicochemical properties of the base fuel(propane-butane blend),and laboratory syngas.It was shown that replacing the fuel from a propane-butane blend to laboratory syngas caused a decrease in engine efficiency to 33%(the efficiency of the base ICE was 0.179 vs.the efficiency of 0.119 for the converted ICE for the 0.59 kW power mode).Engine efficiency was chosen as the key criterion for optimizing the working cycle.As a result of optimization,the efficiency of the converted syngas engine was 6.1%higher than that of the base engine running on the propane-butane blend,and the power drop did not exceed 8.0%.Thus,careful fine-tuning of the working cycle parameters allows increasing the technical and economic characteristics of the syngas engine to the level of ICEs running on traditional types of fuel.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2020-NR049579).
文摘High-dimensional data causes difficulties in machine learning due to high time consumption and large memory requirements.In particular,in amulti-label environment,higher complexity is required asmuch as the number of labels.Moreover,an optimization problem that fully considers all dependencies between features and labels is difficult to solve.In this study,we propose a novel regression-basedmulti-label feature selectionmethod that integrates mutual information to better exploit the underlying data structure.By incorporating mutual information into the regression formulation,the model captures not only linear relationships but also complex non-linear dependencies.The proposed objective function simultaneously considers three types of relationships:(1)feature redundancy,(2)featurelabel relevance,and(3)inter-label dependency.These three quantities are computed usingmutual information,allowing the proposed formulation to capture nonlinear dependencies among variables.These three types of relationships are key factors in multi-label feature selection,and our method expresses them within a unified formulation,enabling efficient optimization while simultaneously accounting for all of them.To efficiently solve the proposed optimization problem under non-negativity constraints,we develop a gradient-based optimization algorithm with fast convergence.Theexperimental results on sevenmulti-label datasets show that the proposed method outperforms existingmulti-label feature selection techniques.
基金The National Natural Science Foundation of China(62203015,62233001,62273351)The Beijing Natural Science Foundation(4242038)。
文摘This paper considers the swarm vigilance problem for multi-agent systems(MAS),where multiple agents are deployed within a rectangular region for perception-based vigilance.There are two main challenges,namely the task allocation for vigilance roles and the coverage planning of the perception ranges.Firstly,vigilance behavioral patterns and processes in animal populations within natural habitats are investigated.Inspired by these biological vigilance behaviors,an efficient vigilance task allocation model for MAS is proposed.Secondly,the subsequent optimization of task layouts can achieve efficient surveillance coverage with fewer agents,minimizing resource consumption.Thirdly,an improved particle swarm optimization(IPSO)algorithm is proposed,which incorporates fitness-driven adaptive inertia weight dynamics.According to simulation analysis and comparative studies,optimal parameter configurations for genetic algorithm(GA)and IPSO are determined.Finally,the results indicate the proposed IPSO's superior performance to both GA and standard particle swarm optimization(PSO)in vigilance task allocation optimization,with satisfying advantages in computational efficiency and solution quality.
文摘Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing attention on specific properties of adopted numerical optimization approaches.We collect and discuss the methods based on nonlinear programming,semidefinite programming,mixed-integer programming,mathematical programming with complementarity constraints,difference-of-convex programming,optimization methods using surrogate models and machine learning techniques,and metaheuristics.As a closely related topic,we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling.We conclude the paper by drawing several remarks through this review.
基金funded by Vale S.A.company(www.vale.com)and the Institute of Technology Vale(ITV—www.itv.org),grant number SAP 4600048682.
文摘The roller is one of the fundamental elements of ore belt conveyor systems since it supports,guides,and directs material on the belt.This component comprises a body(the external tube)that rotates around a fixed shaft supported by easels.The external tube and shaft of rollers used in ore conveyor belts are mostly made of steel,resulting in high mass,hindering maintenance and replacement.Aiming to achieve mass reduction,we conducted a structural optimization of a roller with a polymeric external tube(hereafter referred to as a polymeric roller),seeking the optimal values for two design parameters:the inner diameter of the external tube and the shaft diameter.The optimization was constrained by admissible values for maximum stress,maximum deflection and misalignment angle between the shaft and bearings.A finite element model was built in Ansys Workbench to obtain the structural response of the system.The roller considered is composed of an external tube made of high-density polyethylene(HDPE),bearing seats of polyamide 6(PA6),and a steel shaft.To characterize the polymeric materials(HDPE and PA6),stress relaxation tests were conducted,and the data on shear modulus variation over time were inserted into the model to calculate Prony series terms to account for viscoelastic effects.The roller optimization was performed using surrogate modeling based on radial basis functions,with the Globalized Bounded Nelder-Mead(GBNM)algorithm as the optimizer.Two optimization cases were conducted.In the first case,concerning the roller’s initial material settings,the designs found violated the constraints and could not reduce mass.In the second case,by using PA6 in both bearing seats and the tube,a design configuration was found that respected all constraints and reduced the roller mass by 15.5%,equivalent to 5.15 kg.This study is among the first to integrate experimentally obtained viscoelastic data into the surrogate-based optimization of polymeric rollers,combining methodological innovation with industrial relevance.
基金supported by the National Natural Science Foundation of China(Grant Nos.52425211,52272360,and 52472394)Chongqing Natural Science Foundation(CSTB2023NSCQ-MSX0300)。
文摘Trans-medium flight vehicles can combine high aerial maneuverability and underwater concealment ability,which have attracted much attention recently.As the most crucial procedure,the trajectory design generally determines the trans-medium flight vehicle performance.To quantitatively analyze the flight vehicle performance,an entire aerial-aquatic trajectory model is developed in this paper.Different from modeling a trajectory purely for the water entry process,the constructed entire trajectory model has integrated aerial,water entry,and underwater trajectories together,which can consider the influence of the connected trajectories.As for the aerial and underwater trajectories,explicit dynamic models are established to obtain the trajectory parameters.Due to the complicated fluid force during high-velocity water entry,a computational fluid dynamics model is investigated to analyze this phase.The compu-tational domain size is adaptively refined according to the final aerial trajectory state,where the redundant computational domain is removed.An entire trajectory optimization problem is then formulated to maximize the total flight range via tuning the joint states of different trajectories.Simultaneously,several constraints,i.e.,the max impact load,trajectory height,etc.,are involved in the optimization problem.Rather than directly optimizing by a heuristic algorithm,a multi-surrogate cooperative sampling-based optimization method is proposed to alleviate the computational complexity of the entire trajectory optimization problem.In this method,various surrogates coopera-tively generate infill sample points,thereby preventing the poor approximation.After optimization,the total flight range can be improved by 20%,while all the constraints are satisfied.The result demonstrates the effectiveness and practicability of the developed model and optimization framework.
基金supported by the National Natural Science Foundation of China(No.51767017)the Key Research and Development Program of Gansu Province(No.25YFGA032)the Industry Support and Guidance Project for Higher Education Institutions of Gansu Province(No.2022CYZC-22).
文摘In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic(PV)modules,this study proposes a predictive maintenance(PdM)strategy based on Remaining Useful Life(RUL)estimation.First,a RUL prediction model is established using the Transformer architecture,which enables the effective processing of sequential degradation data.By employing the historical degradation data of PV modules,the proposed model provides accurate forecasts of the remaining useful life,thereby supplying essential inputs for maintenance decision-making.Subsequently,the RUL information obtained from the prediction process is integrated into the optimization of maintenance policies.An opposition-based learning Harris Hawks Optimization(OHHO)algorithm is introduced to jointly optimize two critical parameters:the maintenance threshold L,which specifies the degradation level at which maintenance should be performed,and the recovery factor r,which reflects the extent to which the system performance is restored after maintenance.The objective of this joint optimization is to minimize the overall operation and maintenance cost while maintaining system availability.Finally,simulation experiments are conducted to evaluate the performance of the proposed PdM strategy.The results indicate that,compared with conventional corrective maintenance(CM)and periodic maintenance(PM)strategies,the RUL-driven PdM approach achieves a reduction in the average cost rate by approximately 20.7%and 17.9%,respectively,thereby demonstrating its potential effectiveness for practical PV maintenance applications.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R442)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Accurate parameter extraction of photovoltaic(PV)models plays a critical role in enabling precise performance prediction,optimal system sizing,and effective operational control under diverse environmental conditions.While a wide range of metaheuristic optimisation techniques have been applied to this problem,many existing methods are hindered by slow convergence rates,susceptibility to premature stagnation,and reduced accuracy when applied to complex multi-diode PV configurations.These limitations can lead to suboptimal modelling,reducing the efficiency of PV system design and operation.In this work,we propose an enhanced hybrid optimisation approach,the modified Spider Wasp Optimization(mSWO)with Opposition-Based Learning algorithm,which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization(SWO)metaheuristic with the diversityenhancing mechanism of Opposition-Based Learning(OBL).The hybridisation is designed to dynamically expand the search space coverage,avoid premature convergence,and improve both convergence speed and precision in highdimensional optimisation tasks.The mSWO algorithm is applied to three well-established PV configurations:the single diode model(SDM),the double diode model(DDM),and the triple diode model(TDM).Real experimental current-voltage(I-V)datasets from a commercial PV module under standard test conditions(STC)are used for evaluation.Comparative analysis is conducted against eighteen advanced metaheuristic algorithms,including BSDE,RLGBO,GWOCS,MFO,EO,TSA,and SCA.Performance metrics include minimum,mean,and maximum root mean square error(RMSE),standard deviation(SD),and convergence behaviour over 30 independent runs.The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models,achieving the lowest RMSE values of 0.000986022(SDM),0.000982884(DDM),and 0.000982529(TDM),with minimal SD values,indicating remarkable repeatability.Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods,with the performance gap widening as model complexity increases.These findings demonstrate that mSWO provides a scalable,computationally efficient,and highly reliable framework for PV parameter extraction.Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems,including performance monitoring,fault detection,and intelligent control,thereby contributing to the optimisation of next-generation solar energy solutions.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金the financial supports provided by the National Key Research and Development Program of China(2023YFE0201500)the National Natural Science Foundation of China(52375315)+2 种基金the Key Talent Plan Project of Guangdong Province(2023TQ07C702)the Research and Development Program in Key Areas of Dongguan(20201200300122)the GDAS’Project of Science and Technology Development(2022GDASZH-2022010203).
文摘Refill friction stir spot welding process is difficultly optimized by accurate modeling because of the high-order functional relationship between welding parameters and joint strength.A database of the welding process was first established with 6061-T6 aluminum alloy and DP780 galvanized steel as base materials.This dataset was then optimized using a backpropagation neural network.Analyses and mining of the experimental data confirmed the multidimensional mapping relationship between welding parameters and joint strength.Subsequently,intelligent optimization of the welding process and prediction of joint strength were achieved.At the predicted welding parameter(plunging rotation speedω1=1733 r/min,refilling rotation speedω_(2)=1266 r/min,plunging depth p=1.9 mm,and welding speed v=0.5 mm/s),the tensile shear fracture load of the joint reached a maximum value of 10,172 N,while the experimental result was 9980 N,with an error of 1.92%.Furthermore,the correlation of welding parameters-microstructure-joint strength was established.
基金supported by the National Key Research and Development Program of China(2023YFF0906502)the Postgraduate Research and Innovation Project of Hunan Province under Grant(CX20240473).
文摘Due to the digital transformation tendency among cultural institutions and the substantial influence of the social media platform,the demands of visual communication keep increasing for promoting traditional cultural artifacts online.As an effective medium,posters serve to attract public attention and facilitate broader engagement with cultural artifacts.However,existing poster generation methods mainly rely on fixed templates and manual design,which limits their scalability and adaptability to the diverse visual and semantic features of the artifacts.Therefore,we propose CAPGen,an automated aesthetic Cultural Artifacts Poster Generation framework built on a Multimodal Large Language Model(MLLM)with integrated iterative optimization.During our research,we collaborated with designers to define principles of graphic design for cultural artifact posters,to guide the MLLM in generating layout parameters.Later,we generated these parameters into posters.Finally,we refined the posters using an MLLM integrated with a multi-round iterative optimization mechanism.Qualitative results show that CAPGen consistently outperforms baseline methods in both visual quality and aesthetic performance.Furthermore,ablation studies indicate that the prompt,iterative optimization mechanism,and design principles significantly enhance the effectiveness of poster generation.
基金supported by the National Natural Science Foundation of China(Nos.92148203 and T2388101)。
文摘In the construction and maintenance for large space equipment,it is essential to ensure the control accuracy and improve the dexterity of the space manipulator.In this paper,a FiniteTime Convergence Kinematic Control(FTCKC)added with Acceleration Level Dexterity Optimization(ALDO)scheme is proposed to solve the kinematic uncertainty and dexterity optimization problems of redundant space manipulators.Concretely,distinguishing from the asymptotic convergence property of traditional adaptive Jacobian methods,the FTCKC scheme is adopted to construct the equality constraint to address the model uncertainty problem,and its error can converge within a finite time.Subsequently,the dexterity index is reconstructed at acceleration level by a multi-level target handling method.Then,the equality constraint,optimization task,and limit constraints are reformulated as a quadratic programming problem.Moreover,a Recurrent Neural Network(RNN)is engineered for the constructed FTCKC-ALDO scheme.Finally,the superiority of the FTCKC-ALDO-RNN scheme is verified by experiments.
基金supported by the National Natural Science Foundation of China under Grant 62472264the Natural Science Distinguished Youth Foundation of Shandong Province under Grant ZR2025QA13。
文摘In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a composition solution under their budget constraints.Existing studies typically evaluate satisfaction solely based on energy transmission capacity,while overlooking critical factors such as price and trustworthiness of the provider,leading to a mismatch between optimization outcomes and user needs.To address this gap,we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios,systematically incorporating service price,transmission capacity,and trustworthiness into the satisfaction assessment framework.Furthermore,we propose a Budget-Aware Preference Adjustment Model that predicts users’baseline preference weights from historical data and dynamically adjusts them according to budget levels,thereby reflecting user preferences more realistically under varying budget constraints.In addition,to tackle the composition optimization problem,we develop a ReflectiveEvolutionary Large Language Model—Guided Ant Colony Optimization algorithm,which leverages the reflective evolution capability of large language models to iteratively generate and refine heuristic information that guides the search process.Experimental results demonstrate that the proposed framework effectively integrates personalized preferences with budget sensitivity,accurately predicts users’preferences,and significantly enhances their satisfaction under complex constraints.
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
基金supported by the National Natural Science Foundation of China(52071012)the National Natural Science Foundation of China(Grant No.52101119)+5 种基金the Open Foundation of State Key Laboratory for Advanced Metals and Materials(2022-Z01)the Open Research Fund of National Key Laboratory of Advanced Casting Technologies(CAT2023-004)the Key Research and Development Program of Shandong Province(2022JMRH0209)Hebei Province Innovation Capability Enhancement Plan Project(No.244A7607D)the Beijing Municipal Natural Science Foundation(No.2214072)Young Elite Scientist Sponsorship Program by CAST(No.2021QNRC001)。
文摘Process of dynamic recrystallization(DRX)plays a crucial role in altering the microstructure and enhancing the mechanical characteristics of CrNiMoVW steel.However,its initiation mechanism,deformation conditions,and predictive models remain insufficiently understood,requiring further research to optimize the processing technology.In the present study,hot compression experiments were carried out on 30CrNiMoVW steel under deformation conditions with temperatures ranging from 950 to 1,250℃and strain rates from 0.001 to 1 s~(-1),during which true stress-strain curves were obtained.Based on friction and temperature corrections applied to these curves,a constitutive equation for 30CrNiMoVW steel was established,and its accuracy was verified through fitting analysis.Simultaneously,the study identified limitations in the initial volume fraction model,prompting the development of a modified recrystallization volume fraction model that was validated via correlation analysis between experimental data and model predictions.Furthermore,building upon the modified recrystallization volume fraction model,a novel recrystallization rate model was developed,and three characteristic strain points were determined.These points segmented the rate curve into three stages:a slow initiation stage(0,ε1),a rapid growth stage(1,ε3),and a slow equilibrium stage(e3,0.9).Notably,the value ofε3 was considered the most economical,ensuring the formation of fine and uniform grains during production while optimizing the process,reducing energy consumption and costs,and enhancing overall material performance.Finally,based on the physical constitutive relationships and kinetic models,a multiscale simulation approach combining the finite element method(FEM)and cellular automata(CA)was employed to predict the microstructural evolution of 30CrNiMoVW steel.The simulation results demonstrate that the FEM&CA approach can accurately reproduce the dynamic recrystallization behavior and microstructural evolution observed experimentally.This work provides critical guidance for the development of forging processes for 30CrNiMoVW steel.
