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
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
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
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
Surrogate-Based Optimization(SBO) is becoming increasingly popular since it can remarkably reduce the computational cost for design optimizations based on high-fidelity and expensive numerical analyses. However, for c...Surrogate-Based Optimization(SBO) is becoming increasingly popular since it can remarkably reduce the computational cost for design optimizations based on high-fidelity and expensive numerical analyses. However, for complicated optimization problems with a large design space, many design variables, and strong nonlinearity, SBO converges slowly and shows imperfection in local exploitation. This paper proposes a trust region method within the framework of an SBO process based on the Kriging model. In each refinement cycle, new samples are selected by a certain design of experiment method within a variable design space, which is sequentially updated by the trust region method. A multi-dimensional trust-region radius is proposed to improve the adaptability of the developed methodology. Further, the scale factor and the limit factor of the trust region are studied to evaluate their effects on the optimization process. Thereafter, different SBO methods using error-based exploration, prediction-based exploitation, refinement based on the expected improvement function, a hybrid refinement strategy, and the developed trust-regionbased refinement are utilized in four analytical tests. Further, the developed optimization methodology is employed in the drag minimization of an RAE2822 airfoil. Results indicate that it has better robustness and local exploitation capability in comparison with those of other SBO展开更多
This paper proposes a hybrid architecture based on Multi-disciplinary Design Optimization(MDO) with the Variable Complexity Modeling(VCM) method, to solve the problem of general design optimization for a stratosphere ...This paper proposes a hybrid architecture based on Multi-disciplinary Design Optimization(MDO) with the Variable Complexity Modeling(VCM) method, to solve the problem of general design optimization for a stratosphere airship. Firstly, MDO based on the Concurrent SubSpace Optimization(CSSO) strategy is improved for handling the subsystem coupling problem in stratosphere airship design which contains aerodynamics, structure, and energy. Secondly, the VCM method based on the surrogate model is presented for reducing the computational complexity in high-fidelity modeling without loss of accuracy. Moreover, the global-to-local optimization strategy is added to the architecture to enhance the process. Finally, the result gives a prominent stratosphere airship general solution that validates the feasibility and efficiency of the optimization architecture. Besides, a sensitivity analysis is conducted to outline the critical impact upon stratosphere airship design.展开更多
This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational mach...This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational machine learning models are applied in reservoir development optimization.Traditional regression-based models often struggle in complex scenarios,but the proposed relational and regression-based composite differential evolution(RRCODE)method combines a Gaussian naive Bayes relational model with a radial basis function network regression model.This integration effectively captures complex relationships in the optimization process,improving both accuracy and convergence speed.Experimental tests on a multi-layer multi-channel reservoir model,the Egg reservoir model,and a real-field reservoir model(the S reservoir)demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery.Moreover,the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead.These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations,offering more efficient and economically viable solutions for oilfield development.展开更多
Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to...Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to mitigate these losses by redirecting wakes away from downstream turbines.However,the effectiveness of yaw-based AWC is highly dependent on the accuracy of the underlying wake prediction models,which often require site-specific adjustments to reflect local atmospheric conditions and turbine characteristics.This paper presents an integrated,data-driven framework tomaximize wind farmpower output.Themethodology consists of three key stages.First,a practical simulation-assisted matching method is developed to estimate the True North Alignment(TNA)of each turbine using historical Supervisory Control and Data Acquisition(SCADA)data,resolving a common source of operational uncertainty.Second,key wake expansion parameters of the Floris engineering wake model are calibrated using site-specific SCADA power data,tailoring the model to the JibeiWind Farm in China.Finally,using this calibrated model,the derivative-free solver NOMAD is employed to determine the optimal yaw angle settings for an 11-turbine cluster under various wind conditions.Simulation studies,based on real operational scenarios,demonstrate the effectiveness of the proposed framework.The optimized yaw control strategies achieved total power output gains of up to 5.4%compared to the baseline zero-yaw operation under specific wake-inducing conditions.Crucially,the analysis reveals that using the site-specific calibrated model for optimization yields substantially better results than using a model with generic parameters,providing an additional power gain of up to 1.43%in tested scenarios.These findings underscore the critical importance of TNA estimation and site-specific model calibration for developing effective AWC strategies.The proposed integrated approach provides a robust and practical workflow for designing and pre-validating yaw control settings,offering a valuable tool for enhancing the economic performance of wind farms.展开更多
The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable deve...The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable development,oilfield reconstruction was usually conducted in discrete rather than continuous space.Motivated by economic and sustainability goals,a 3-phase heuristic model for oilfield reconstruction was developed to mine potential locations in continuous space.In phase 1,considering the process characteristics of the oil and gas gathering system,potential locations were mined in continuous space.In phase 2,incorporating comprehensive reconstruction measures,a reconstruction model was established in discrete space.In phase 3,the topology was further adjusted in continuous space.Subsequently,the model was transformed into a single-objective mixed integer linear programming model using the augmented ε-constraint method.Numerical experiments revealed that the small number of potential locations could effectively reduce the reconstruction cost,and the quality of potential locations mined in phase 1 surpassed those generated in random or grid form.Case studies showed that cost and carbon emissions for a new block were reduced by up to 10.45% and 7.21 %,respectively.These reductions were because the potential locations mined in 1P reduced the number of metering stations,and 3P adjusted the locations of metering stations in continuous space to shorten the pipeline length.For an old oilfield,the load and connection ratios of the old metering station increased to 89.7% and 94.9%,respectively,enhancing operation efficiency.Meanwhile,recycling facilitated the diversification of reconstruction measures and yielded a profit of 582,573 ¥,constituting 5.56% of the total cost.This study adopted comprehensive reconstruction measures and tapped into potential reductions in cost and carbon emissions for oilfield reconstruction,offering valuable insights for future oilfield design and construction.展开更多
This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic(FRP)fishing vessels to address critical limitations of conventional designs,including excessive weight,mate...This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic(FRP)fishing vessels to address critical limitations of conventional designs,including excessive weight,material inefficiency,and performance redundancy.By integrating surrogate modeling techniques with a multi-objective genetic algorithm(MOGA),we have developed a systematic approach that encompasses parametric modeling,finite element analysis under extreme operational conditions,and multi-fidelity performance evaluation.Through a 10-t electric winch case study,the methodology’s effectiveness is demonstrated via parametric characterization of structural integrity,stiffness behavior,and mass distribution.The comparative analysis identified optimal surrogate models for predicting key performance metrics,which enabled the construction of a robust multi-objective optimization model.The MOGA-derived Pareto solutions produced a design configuration achieving 7.86%mass reduction,2.01%safety factor improvement,and 23.97%deformation mitigation.Verification analysis confirmed the optimization scheme’s reliability in balancing conflicting design requirements.This research establishes a generalized framework for marine deck machinery modernization,particularly addressing the structural compatibility challenges in FRP vessel retrofitting.The proposed methodology demonstrates significant potential for facilitating sustainable upgrades of fishing vessel equipment through systematic performance optimization.展开更多
Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challen...Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challenge to building a VF model is devising an adaptive model updating strategy that jointly selects additional low-fidelity(LF)and/or HF samples.The additional samples must enhance the model accuracy while maximizing the computational efficiency.We propose ISMA-VFEEI,a global optimization framework that integrates an Improved Slime-Mould Algorithm(ISMA)and a Variable-Fidelity Expected Extension Improvement(VFEEI)learning function to construct a VF surrogate model efficiently.First,A cost-aware VFEEI function guides the adaptive LF/HF sampling by explicitly incorporating evaluation cost and existing sample proximity.Second,ISMA is employed to solve the resulting non-convex optimization problem and identify global optimal infill points for model enhancement.The efficacy of ISMA-VFEEI is demonstrated through six numerical benchmarks and one real-world engineering case study.The engineering case study of a high-speed railway Electric Multiple Unit(EMU),the optimization objective of a sanding device attained a minimum value of 1.546 using only 20 HF evaluations,outperforming all the compared methods.展开更多
To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the mod...To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the model predictive static programming method is developed by incorporating neighboring terms and trust region,enabling rapid generation of precise optimal solutions.Next,an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution,while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution.To validate the feasibility and accuracy of the proposed method,a near-space vehicle example is analyzed and simulated during its glide phase.The simulation results demonstrate that the proposed method aligns with theoretical derivations and outperforms existing methods in terms of convergence speed and accuracy.Therefore,the proposed method offers significant practical value for solving the fast trajectory optimization problem in near-space vehicle applications.展开更多
With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learnin...With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learning methods.Therefore,in the process of reforming and developing higher education,it is essential to take digital technology empowering the optimization of the education industry as a breakthrough,focusing on five key areas:the construction of smart classrooms,the digital integration of teaching resources,the development of personalized learning support systems,the reform of online-offline hybrid teaching,and the intelligentization of educational management.This paper also examines the experiences,challenges,and shortcomings of typical universities in using digital technology to improve teaching quality,optimize resource allocation,and innovate teaching management models.Finally,corresponding countermeasures and suggestions are proposed to facilitate the smooth implementation of digital transformation in higher education institutions.展开更多
Accurate quantification of carbon and water fluxes dynamics in arid and semi-arid ecosystems is a critical scientific challenge for regional carbon neutrality assessments and sustainable water resource management.In t...Accurate quantification of carbon and water fluxes dynamics in arid and semi-arid ecosystems is a critical scientific challenge for regional carbon neutrality assessments and sustainable water resource management.In this study,we developed a multi-flux global sensitivity discriminant index(D_(sen))by integrating the Biome-BGCMuSo model with eddy covariance flux observations.This index was combined with a Bayesian optimization algorithm to conduct parameter optimization.The results demonstrated that:(1)Sensitivity analysis identified 13 highly sensitive parameters affecting carbon and water fluxes.Among these,the canopy light extinction coefficient(k)and the fraction of leaf N in Rubisco(FLNR)exhibited significantly higher sensitivity to carbon fluxes(GPP,NEE,Reco;D_(sen)>10%)compared to water flux(ET).This highlights the strong dependence of carbon cycle simulations on vegetation physiological parameters.(2)The Bayesian optimization framework efficiently converged 30 parameter spaces within 50 iterations,markedly improving carbon fluxes simulation accuracy.The Kling-Gupta efficiency(KGE)values for Gross Primary Production(GPP),Net Ecosystem Exchange(NEE),and Total Respiration(Reco)increased by 44.94%,69.23%and 123%,respectively.The optimization prioritized highly sensitive parameters,underscoring the necessity of parameter sensitivity stratification.(3)The optimized model effectively reproduced carbon sink characteristics in mountain meadows during the growing season(cumulative NEE=-375 g C/m^(2)).It revealed synergistic carbon-water fluxes interactions governed by coupled photosynthesis-stomatal pathways and identified substrate supply limitations on heterotrophic respiration.This study proposes a novel multi-flux sensitivity index and an efficient optimization framework,elucidating the coupling mechanisms between vegetation physiological regulation(k,FLNR)and environmental stressors(VPD,SWD)in carbonwater cycles.The methodology offers a practical approach for arid ecosystem model optimization and provides theoretical insights for grassland management through canopy structure regulation and water-use efficiency enhancement.展开更多
In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However...In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.展开更多
A new 18-lump kinetic model for naphtha catalytic reforming reactions is discussed. By developing this model as a user module, a whole industrial continuous catalytic reforming process is simulated on Aspen plus plat-...A new 18-lump kinetic model for naphtha catalytic reforming reactions is discussed. By developing this model as a user module, a whole industrial continuous catalytic reforming process is simulated on Aspen plus plat-form. The technique utilizes the strong databases, complete sets of modules, and flexible simulation tools of the Aspen plus system and retains the characteristics of the proposed kinetic model. The calculated results are in fair agreement with the actual operating data. Based on the model of the whole reforming process, the process is opti-mized and the optimization results are tested in the actual industrial unit for about two months. The test shows that the process profit increases about 1000yuan·h-1 averagely, which is close to the calculated result.展开更多
This paper describes parallel simulation of the memory/computation-intensive acoustic wave equation with CPU template buffer optimization. Considering the 8-core CPU shared storage platform as an example,we obtain a o...This paper describes parallel simulation of the memory/computation-intensive acoustic wave equation with CPU template buffer optimization. Considering the 8-core CPU shared storage platform as an example,we obtain a one-time speed-up ratio of 6.7× compared with the serial program by using a coarse-grained OpenMP parallel scheme. Then,data is vectorized on the template buffer using the single instruction-multiple data(SIMD) technique to further exploit the computing potential of the CPUs. We apply an 8-channel parallel vector to simulate seismic wavefields with the 256-bit advanced vector extensions(AVX) instruction set. This increases the computing bandwidth,thus eliminating a significant volume of the computing instructions and obtaining a secondary speed-up ratio of 3–7×. In addition,we use 32-byte data alignment,shortest data direction vectorization,and loop tiling optimization algorithm to achieve faster program execution. Finally,we analyze the factors affecting the secondary speed-up of AVX through three-dimensional modeling experiments with the salt model.The results indicate that the memory,cache,and register can better cooperate with each other and the speed-up is increased by optimizing the AVX algorithm.展开更多
The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain deg...The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.展开更多
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.
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 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co-supported by the National Natural Science Foundation of China (No. 11502209)the Free Research Projects of the Central University Funding of China (No. 3102015ZY007)
文摘Surrogate-Based Optimization(SBO) is becoming increasingly popular since it can remarkably reduce the computational cost for design optimizations based on high-fidelity and expensive numerical analyses. However, for complicated optimization problems with a large design space, many design variables, and strong nonlinearity, SBO converges slowly and shows imperfection in local exploitation. This paper proposes a trust region method within the framework of an SBO process based on the Kriging model. In each refinement cycle, new samples are selected by a certain design of experiment method within a variable design space, which is sequentially updated by the trust region method. A multi-dimensional trust-region radius is proposed to improve the adaptability of the developed methodology. Further, the scale factor and the limit factor of the trust region are studied to evaluate their effects on the optimization process. Thereafter, different SBO methods using error-based exploration, prediction-based exploitation, refinement based on the expected improvement function, a hybrid refinement strategy, and the developed trust-regionbased refinement are utilized in four analytical tests. Further, the developed optimization methodology is employed in the drag minimization of an RAE2822 airfoil. Results indicate that it has better robustness and local exploitation capability in comparison with those of other SBO
基金supported in part by the National Key R&D Program of China(No.2016YFB1200100)
文摘This paper proposes a hybrid architecture based on Multi-disciplinary Design Optimization(MDO) with the Variable Complexity Modeling(VCM) method, to solve the problem of general design optimization for a stratosphere airship. Firstly, MDO based on the Concurrent SubSpace Optimization(CSSO) strategy is improved for handling the subsystem coupling problem in stratosphere airship design which contains aerodynamics, structure, and energy. Secondly, the VCM method based on the surrogate model is presented for reducing the computational complexity in high-fidelity modeling without loss of accuracy. Moreover, the global-to-local optimization strategy is added to the architecture to enhance the process. Finally, the result gives a prominent stratosphere airship general solution that validates the feasibility and efficiency of the optimization architecture. Besides, a sensitivity analysis is conducted to outline the critical impact upon stratosphere airship design.
基金supported by the National Natural Science Foundation of China under Grant 52325402,52274057,and 52074340the National Key R&D Program of China under Grant 2023YFB4104200+2 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028China Scholarship Council under Grant 202306450108.
文摘This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational machine learning models are applied in reservoir development optimization.Traditional regression-based models often struggle in complex scenarios,but the proposed relational and regression-based composite differential evolution(RRCODE)method combines a Gaussian naive Bayes relational model with a radial basis function network regression model.This integration effectively captures complex relationships in the optimization process,improving both accuracy and convergence speed.Experimental tests on a multi-layer multi-channel reservoir model,the Egg reservoir model,and a real-field reservoir model(the S reservoir)demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery.Moreover,the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead.These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations,offering more efficient and economically viable solutions for oilfield development.
基金the Science and Technology Project of China South Power Grid Co., Ltd. under Grant No. 036000KK52222044 (GDKJXM20222430).
文摘Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to mitigate these losses by redirecting wakes away from downstream turbines.However,the effectiveness of yaw-based AWC is highly dependent on the accuracy of the underlying wake prediction models,which often require site-specific adjustments to reflect local atmospheric conditions and turbine characteristics.This paper presents an integrated,data-driven framework tomaximize wind farmpower output.Themethodology consists of three key stages.First,a practical simulation-assisted matching method is developed to estimate the True North Alignment(TNA)of each turbine using historical Supervisory Control and Data Acquisition(SCADA)data,resolving a common source of operational uncertainty.Second,key wake expansion parameters of the Floris engineering wake model are calibrated using site-specific SCADA power data,tailoring the model to the JibeiWind Farm in China.Finally,using this calibrated model,the derivative-free solver NOMAD is employed to determine the optimal yaw angle settings for an 11-turbine cluster under various wind conditions.Simulation studies,based on real operational scenarios,demonstrate the effectiveness of the proposed framework.The optimized yaw control strategies achieved total power output gains of up to 5.4%compared to the baseline zero-yaw operation under specific wake-inducing conditions.Crucially,the analysis reveals that using the site-specific calibrated model for optimization yields substantially better results than using a model with generic parameters,providing an additional power gain of up to 1.43%in tested scenarios.These findings underscore the critical importance of TNA estimation and site-specific model calibration for developing effective AWC strategies.The proposed integrated approach provides a robust and practical workflow for designing and pre-validating yaw control settings,offering a valuable tool for enhancing the economic performance of wind farms.
基金supported by the National Natural Science Foundation of China (Grant No.52174065)the National Natural Science Foundation of China (Grant No.52304071)+1 种基金China University of Petroleum,Beijing (Grant No.ZX20220040)MOE Key Laboratory of Petroleum Engineering (China University of Petroleum,No.2462024PTJS002)。
文摘The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable development,oilfield reconstruction was usually conducted in discrete rather than continuous space.Motivated by economic and sustainability goals,a 3-phase heuristic model for oilfield reconstruction was developed to mine potential locations in continuous space.In phase 1,considering the process characteristics of the oil and gas gathering system,potential locations were mined in continuous space.In phase 2,incorporating comprehensive reconstruction measures,a reconstruction model was established in discrete space.In phase 3,the topology was further adjusted in continuous space.Subsequently,the model was transformed into a single-objective mixed integer linear programming model using the augmented ε-constraint method.Numerical experiments revealed that the small number of potential locations could effectively reduce the reconstruction cost,and the quality of potential locations mined in phase 1 surpassed those generated in random or grid form.Case studies showed that cost and carbon emissions for a new block were reduced by up to 10.45% and 7.21 %,respectively.These reductions were because the potential locations mined in 1P reduced the number of metering stations,and 3P adjusted the locations of metering stations in continuous space to shorten the pipeline length.For an old oilfield,the load and connection ratios of the old metering station increased to 89.7% and 94.9%,respectively,enhancing operation efficiency.Meanwhile,recycling facilitated the diversification of reconstruction measures and yielded a profit of 582,573 ¥,constituting 5.56% of the total cost.This study adopted comprehensive reconstruction measures and tapped into potential reductions in cost and carbon emissions for oilfield reconstruction,offering valuable insights for future oilfield design and construction.
基金supported by the Basic Public Welfare Research Program of Zhejiang Province(No.LGN22E050005).
文摘This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic(FRP)fishing vessels to address critical limitations of conventional designs,including excessive weight,material inefficiency,and performance redundancy.By integrating surrogate modeling techniques with a multi-objective genetic algorithm(MOGA),we have developed a systematic approach that encompasses parametric modeling,finite element analysis under extreme operational conditions,and multi-fidelity performance evaluation.Through a 10-t electric winch case study,the methodology’s effectiveness is demonstrated via parametric characterization of structural integrity,stiffness behavior,and mass distribution.The comparative analysis identified optimal surrogate models for predicting key performance metrics,which enabled the construction of a robust multi-objective optimization model.The MOGA-derived Pareto solutions produced a design configuration achieving 7.86%mass reduction,2.01%safety factor improvement,and 23.97%deformation mitigation.Verification analysis confirmed the optimization scheme’s reliability in balancing conflicting design requirements.This research establishes a generalized framework for marine deck machinery modernization,particularly addressing the structural compatibility challenges in FRP vessel retrofitting.The proposed methodology demonstrates significant potential for facilitating sustainable upgrades of fishing vessel equipment through systematic performance optimization.
基金funded by National Natural Science Foundation of China(grant No.52405255)Special Program of Huzhou(grant No.2023GZ05)+1 种基金Projects of Huzhou Science and Technology Correspondent(grant No.2023KT76)Guangdong Basic and Applied Basic Research Foundation(grant No.2025A1515010487)。
文摘Variable-fidelity(VF)surrogate models have received increasing attention in engineering design optimization as they can approximate expensive high-fidelity(HF)simulations with reduced computational power.A key challenge to building a VF model is devising an adaptive model updating strategy that jointly selects additional low-fidelity(LF)and/or HF samples.The additional samples must enhance the model accuracy while maximizing the computational efficiency.We propose ISMA-VFEEI,a global optimization framework that integrates an Improved Slime-Mould Algorithm(ISMA)and a Variable-Fidelity Expected Extension Improvement(VFEEI)learning function to construct a VF surrogate model efficiently.First,A cost-aware VFEEI function guides the adaptive LF/HF sampling by explicitly incorporating evaluation cost and existing sample proximity.Second,ISMA is employed to solve the resulting non-convex optimization problem and identify global optimal infill points for model enhancement.The efficacy of ISMA-VFEEI is demonstrated through six numerical benchmarks and one real-world engineering case study.The engineering case study of a high-speed railway Electric Multiple Unit(EMU),the optimization objective of a sanding device attained a minimum value of 1.546 using only 20 HF evaluations,outperforming all the compared methods.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(No.52425212)National Key Research and Development Program of China(No.2021YFA0717100)National Natural Science Foundation of China(Nos.12072270,U2013206,and 52442214).
文摘To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the model predictive static programming method is developed by incorporating neighboring terms and trust region,enabling rapid generation of precise optimal solutions.Next,an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution,while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution.To validate the feasibility and accuracy of the proposed method,a near-space vehicle example is analyzed and simulated during its glide phase.The simulation results demonstrate that the proposed method aligns with theoretical derivations and outperforms existing methods in terms of convergence speed and accuracy.Therefore,the proposed method offers significant practical value for solving the fast trajectory optimization problem in near-space vehicle applications.
文摘With the advancement of digital technology,new technologies such as artificial intelligence,big data,and cloud computing have gradually permeated higher education,leading to fundamental changes in teaching and learning methods.Therefore,in the process of reforming and developing higher education,it is essential to take digital technology empowering the optimization of the education industry as a breakthrough,focusing on five key areas:the construction of smart classrooms,the digital integration of teaching resources,the development of personalized learning support systems,the reform of online-offline hybrid teaching,and the intelligentization of educational management.This paper also examines the experiences,challenges,and shortcomings of typical universities in using digital technology to improve teaching quality,optimize resource allocation,and innovate teaching management models.Finally,corresponding countermeasures and suggestions are proposed to facilitate the smooth implementation of digital transformation in higher education institutions.
基金jointly funded by the National Natural Science Foundation of China(Grant No.42161024)the Central Financial Forestry and Grassland Science and Technology Extension Demonstration Project(2025)(Grant No.Xin[2025]TG 09)。
文摘Accurate quantification of carbon and water fluxes dynamics in arid and semi-arid ecosystems is a critical scientific challenge for regional carbon neutrality assessments and sustainable water resource management.In this study,we developed a multi-flux global sensitivity discriminant index(D_(sen))by integrating the Biome-BGCMuSo model with eddy covariance flux observations.This index was combined with a Bayesian optimization algorithm to conduct parameter optimization.The results demonstrated that:(1)Sensitivity analysis identified 13 highly sensitive parameters affecting carbon and water fluxes.Among these,the canopy light extinction coefficient(k)and the fraction of leaf N in Rubisco(FLNR)exhibited significantly higher sensitivity to carbon fluxes(GPP,NEE,Reco;D_(sen)>10%)compared to water flux(ET).This highlights the strong dependence of carbon cycle simulations on vegetation physiological parameters.(2)The Bayesian optimization framework efficiently converged 30 parameter spaces within 50 iterations,markedly improving carbon fluxes simulation accuracy.The Kling-Gupta efficiency(KGE)values for Gross Primary Production(GPP),Net Ecosystem Exchange(NEE),and Total Respiration(Reco)increased by 44.94%,69.23%and 123%,respectively.The optimization prioritized highly sensitive parameters,underscoring the necessity of parameter sensitivity stratification.(3)The optimized model effectively reproduced carbon sink characteristics in mountain meadows during the growing season(cumulative NEE=-375 g C/m^(2)).It revealed synergistic carbon-water fluxes interactions governed by coupled photosynthesis-stomatal pathways and identified substrate supply limitations on heterotrophic respiration.This study proposes a novel multi-flux sensitivity index and an efficient optimization framework,elucidating the coupling mechanisms between vegetation physiological regulation(k,FLNR)and environmental stressors(VPD,SWD)in carbonwater cycles.The methodology offers a practical approach for arid ecosystem model optimization and provides theoretical insights for grassland management through canopy structure regulation and water-use efficiency enhancement.
基金funding support from the National Natural Science Foundation of China(No.52204065,No.ZX20230398)supported by a grant from the Human Resources Development Program(No.20216110100070)of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)。
文摘In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.
基金Supported by the National Natural Science Foundation of China (No.60421002).
文摘A new 18-lump kinetic model for naphtha catalytic reforming reactions is discussed. By developing this model as a user module, a whole industrial continuous catalytic reforming process is simulated on Aspen plus plat-form. The technique utilizes the strong databases, complete sets of modules, and flexible simulation tools of the Aspen plus system and retains the characteristics of the proposed kinetic model. The calculated results are in fair agreement with the actual operating data. Based on the model of the whole reforming process, the process is opti-mized and the optimization results are tested in the actual industrial unit for about two months. The test shows that the process profit increases about 1000yuan·h-1 averagely, which is close to the calculated result.
基金funded by the National Natural Science Foundation of China (No. 41274140)the National Science and Technology Major Projects of China (No. 2017ZX05035003-001)
文摘This paper describes parallel simulation of the memory/computation-intensive acoustic wave equation with CPU template buffer optimization. Considering the 8-core CPU shared storage platform as an example,we obtain a one-time speed-up ratio of 6.7× compared with the serial program by using a coarse-grained OpenMP parallel scheme. Then,data is vectorized on the template buffer using the single instruction-multiple data(SIMD) technique to further exploit the computing potential of the CPUs. We apply an 8-channel parallel vector to simulate seismic wavefields with the 256-bit advanced vector extensions(AVX) instruction set. This increases the computing bandwidth,thus eliminating a significant volume of the computing instructions and obtaining a secondary speed-up ratio of 3–7×. In addition,we use 32-byte data alignment,shortest data direction vectorization,and loop tiling optimization algorithm to achieve faster program execution. Finally,we analyze the factors affecting the secondary speed-up of AVX through three-dimensional modeling experiments with the salt model.The results indicate that the memory,cache,and register can better cooperate with each other and the speed-up is increased by optimizing the AVX algorithm.
基金Financial support was provided by the State Grid Sichuan Electric Power Company Science and Technology Project“Key Research on Development Path Planning and Key Operation Technologies of New Rural Electrification Construction”under Grant No.52199623000G.
文摘The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.
