Data center industries have been facing huge energy challenges due to escalating power consumption and associated carbon emissions.In the context of carbon neutrality,the integration of data centers with renewable ene...Data center industries have been facing huge energy challenges due to escalating power consumption and associated carbon emissions.In the context of carbon neutrality,the integration of data centers with renewable energy has become a prevailing trend.To advance the renewable energy integration in data centers,it is imperative to thoroughly explore the data centers’operational flexibility.Computing workloads and refrigeration systems are recognized as two promising flexible resources for power regulationwithin data centermicro-grids.This paper identifies and categorizes delay-tolerant computing workloads into three types(long-running non-interruptible,long-running interruptible,and short-running)and develops mathematical time-shifting models for each.Additionally,this paper examines the thermal dynamics of the computer room and derives a time-varying temperature model coupled to refrigeration power.Building on these models,this paper proposes a two-stage,multi-time scale optimization scheduling framework that jointly coordinates computing workloads time-shift in day-ahead scheduling and refrigeration power control in intra-day dispatch to mitigate renewable variability.A case study demonstrates that the framework effectively enhances the renewable-energy utilization,improves the operational economy of the data center microgrid,and mitigates the impact of renewable power uncertainty.The results highlight the potential of coordinated computing workloads and thermal system flexibility to support greener,more cost-effective data center operation.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
Build-up panels for the commercial aircraft fuselage subjected to the axial compression load are studied by both experimental and theoretical methods.An integral panel is designed with the same overall size and weight...Build-up panels for the commercial aircraft fuselage subjected to the axial compression load are studied by both experimental and theoretical methods.An integral panel is designed with the same overall size and weight as the build-up structure,and finite element models（FEMs）of these two panels are established.Experimental results of build-up panels agree well with the FEM results with the nonliearity and the large deformation,so FEMs are validated.FEM calculation results of these two panels indicate that the failure mode of the integral panel is different from that of the build-up panel,and the failure load increases by 18.4% up to post-buckling.Furthermore,the integral structure is optimized by using the multi-island genetic algorithm and the sequential quadratic programming.Compared with the initial design,the optimal mass is reduced by 8.7% and the strength is unchanged.展开更多
The stress minimization multi-material topology optimization(MMTO)approach has recently attracted significant attention because of its applications in aerospace and mechanical engineering.Nonetheless,the stress minimi...The stress minimization multi-material topology optimization(MMTO)approach has recently attracted significant attention because of its applications in aerospace and mechanical engineering.Nonetheless,the stress minimization MMTO approach may result in stress surpassing the material's tolerance limit,potentially culminating in failure.This research proposes a novel way for imposing stress constraints on each material to regulate their respective stress levels.The fundamental concept is that each material possesses its own interpolation function for the stress model.The maximum von Mises stress for each material can be established with the definition of an upper limit,ensuring that the materials will perform safely and effectively.This aids topological structures in resisting failure and augmenting strength.A multi-physics system including thermoelastic and self-weight loads is concurrently examined alongside stress limitations.The global stress constraint utilizes the p-norm function,and the adjoint method is used to derive sensitivity.This work employs a three-field strategy utilizing density filtering and Heaviside projection functions to mitigate the artificial stress in low density.The technique is assessed through two-dimensional(2D)and three-dimensional(3D)examples,illustrating the influence of stress limits on the compliance minimization under heat and self-weight loads.The optimized results indicate a substantial decrease in the stress levels accompanied by a minor gain in compliance,while maintaining the stress within the specified range for all materials.展开更多
Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental planning.However,SSL prediction is highly complex due to its nonlinear an...Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental planning.However,SSL prediction is highly complex due to its nonlinear and dynamic nature,making traditional empirical models inadequate.This study proposes a novel hybrid approach,integrating the Adaptive Neuro-Fuzzy Inference System(ANFIS)with the Gradient-Based Optimizer(GBO),to enhance SSL forecasting accuracy.The research compares the performance of ANFIS-GBO with three alternative models:standard ANFIS,ANFIS with Particle Swarm Optimization(ANFIS-PSO),and ANFIS with Grey Wolf Optimization(ANFIS-GWO).Historical SSL and streamflow data from the Bailong River Basin,China,are used to train and validate the models.The input selection process is optimized using the Multivariate Adaptive Regression Splines(MARS)method.Model performance is evaluated using statistical metrics such as Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),Nash Sutcliffe Efficiency(NSE),and Determination Coefficient(R^(2)).Additionally,visual assessments,including scatter plots,Taylor diagrams,and violin plots,provide further insights into model reliability.The results indicate that including historical SSL data improves predictive accuracy,with ANFIS-GBO outperforming the other models.ANFIS-GBO achieves the lowest RMSE and MAE and the highest NSE and R^(2),demonstrating its superior learning ability and adaptability.The findings highlight the effectiveness of nature-inspired optimization algorithms in enhancing sediment load forecasting and contribute to the advancement of AI-based hydrological modeling.Future research should explore the integration of additional environmental and climatic variables to enhance predictive capabilities further.展开更多
The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique cha...The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique challenges when inertial loads become dominant:non-monotonous behavior of the objective function,possible unconstrained characterization of the optimal solution,and parasitic effects.Herein,an improved Guide-Weight approach is introduced,which effectively addresses the structural topology optimization problem when subjected to inertial loads.Smooth and fast convergence of the compliance is achieved by the approach,while also maintaining the effectiveness of the volume constraints.The rational approximation of material properties model and smooth design are utilized to guarantee clear boundaries of the final structure,facilitating its seamless integration into manufacturing processes.The framework provided by the alternating active-phase algorithm is employed to decompose the multi-material topological problem under inertial loading into a set of sub-problems.The optimization of multi-material under inertial loads is accomplished through the effective resolution of these sub-problems using the improved Guide-Weight method.The effectiveness of the proposed approach is demonstrated through numerical examples involving two-phase and multi-phase materials.展开更多
The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is n...The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.展开更多
Web pillars enduring complex coupled loads are critical for stability in high-wall mining.This study develops a dynamic failure criterion for web pillars under non-uniform loading using catastrophe theory.Through the ...Web pillars enduring complex coupled loads are critical for stability in high-wall mining.This study develops a dynamic failure criterion for web pillars under non-uniform loading using catastrophe theory.Through the analysis of the web pillar-overburden system’s dynamic stress and deformation,a total potential energy function and dynamic failure criterion were established for web pillars.An optimizing method for web pillar parameters was developed in highwall mining.The dynamic criterion established was used to evaluate the dynamic failure and stability of web pillars under static and dynamic loading.Key findings reveal that vertical displacements exhibit exponential-trigonometric variation under static loads and multi-variable power-law behavior under dynamic blasting.Instability risks arise when the roof’s tensile strength-to-stress ratio drops below 1.Using catastrophe theory,the bifurcation setΔ<0 signals sudden instability.The criterion defines failure as when the unstable web pillar section length l1 exceeds the roof’s critical collapse distance l2.Case studies and simulations determine an optimal web pillar width of 4.6 m.This research enhances safety and resource recovery,providing a theoretical framework for advancing highwall mining technology.展开更多
Traditional demand response(DR)programs for energy-intensive industries(EIIs)primarily rely on electricity price signals and often overlook carbon emission factors,limiting their effectiveness in supporting lowcarbon ...Traditional demand response(DR)programs for energy-intensive industries(EIIs)primarily rely on electricity price signals and often overlook carbon emission factors,limiting their effectiveness in supporting lowcarbon transitions.To address this challenge,this paper proposes an electricity–carbon integratedDR strategy based on a bi-level collaborative optimization framework that coordinates the interaction between the grid and EIIs.At the upper level,the grid operatorminimizes generation and curtailment costs by optimizing unit commitment while determining real-time electricity prices and dynamic carbon emission factors.At the lower level,EIIs respond to these dual signals by minimizing their combined electricity and carbon trading costs,considering their participation in medium-and long-term electricity markets,day-ahead spot markets,and carbon emissions trading schemes.The model accounts for direct and indirect carbon emissions,distributed photovoltaic(PV)generation,and battery energy storage systems.This interaction is structured as a Stackelberg game,where the grid acts as the leader and EIIs as followers,enabling dynamic feedback between pricing signals and load response.Simulation studies on an improved IEEE 30-bus system,with a cement plant as a representative user form EIIs,show that the proposed strategy reduces user-side carbon emissions by 7.95% and grid-side generation cost by 4.66%,though the user’s energy cost increases by 7.80% due to carbon trading.Theresults confirmthat the joint guidance of electricity and carbon prices effectively reshapes user load profiles,encourages peak shaving,and improves PV utilization.This coordinated approach not only achieves emission reduction and cost efficiency but also offers a theoretical and practical foundation for integrating carbon pricing into demand-side energy management in future low-carbon power systems.展开更多
Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehi...Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes.However,due to demand fluctuations from changing product orders and unforeseen railway scheduling delays,manually adjusted allocation and loading may lead to excessive loading and unloading distances and times,ultimately increasing transportation costs for enterprises.To address these issues,this paper proposes a data-driven two-stage robust optimization(TSRO)framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism(GSTAC-AM),which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading.Specifically,GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers.Then,a robust counterpart model is formulated to ensure computational tractability.In addition,a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes.Finally,the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.展开更多
The purpose of this paper is to present a novel topology optimization approach to control precisely the output loads under static loads and harmonic excitations.We introduce the Artificial Bar Element(ABE)at the desig...The purpose of this paper is to present a novel topology optimization approach to control precisely the output loads under static loads and harmonic excitations.We introduce the Artificial Bar Element(ABE)at the designated output positions,where the output loads are equivalently measured and constrained with the nodal displacements of ABE.Optimization model is then formulated considering the output load constraints as well as the minimization of strain energy and dynamic displacement responses respectively under the static and dynamic conditions.The influences of the ABEs stiffness,different material usages of the design domain,widths of the output loads constraint intervals and variation ratios of output loads are discussed in detail.The proposed method is verified with several numerical examples with clear and reasonable load transfer paths.展开更多
Fuel reload pattern optimization is essential for attaining maximum fuel burnup for minimization of generation cost while minimizing power peaking factor(PPF).The aim of this work is to carry out detailed assessment o...Fuel reload pattern optimization is essential for attaining maximum fuel burnup for minimization of generation cost while minimizing power peaking factor(PPF).The aim of this work is to carry out detailed assessment of particle swarm optimization(PSO) in the context of fuel reload pattern search. With astronomically large number of possible loading patterns, the main constraints are limiting local power peaking factor, fixed number of assemblies,fixed fuel enrichment, and burnable poison rods. In this work, initial loading pattern of fixed batches of fuel assemblies is optimized by using particle swarm optimization technique employing novel feature of varying inertial weights with the objective function to obtain both flat power profile and cycle k_(eff)>1. For neutronics calculation, PSU-LEOPARD-generated assembly depletiondependent group-constant-based ADD files are used. The assembly data description file generated by PSU-LEOPARD is used as input cross-section library to MCRAC code, which computes normalized power profile of all fuel assemblies of PWR nuclear reactor core. The standard PSO with varying inertial weights is then employed to avoid trapping in local minima. A series of experiments havebeen conducted to obtain near-optimal converged fuelloading pattern of 300 MWe PWR Chashma reactor. The optimized loading pattern is found in good agreement with results found in literature. Hybrid scheme of PSO with simulated annealing has also been implemented and resulted in faster convergence.展开更多
For the topology optimization of structures with design-dependent pressure,an intuitive way is to directly describe the loading boundary of the structure,and then update the load on it.However,boundary recognition is ...For the topology optimization of structures with design-dependent pressure,an intuitive way is to directly describe the loading boundary of the structure,and then update the load on it.However,boundary recognition is usually cumbersome and inaccurate.Furthermore,the pressure is always loaded either outside or inside the structure,instead of both.Hence,the inner enclosed and outer open spaces should be distinguished to recognize the loading surfaces.To handle the above issues,a thermal-solid–fluid method for topology optimization with design-dependent pressure load is proposed in this paper.In this method,the specific void phase is defined to be an incompressible hydrostatic fluid,through which the pressure load can be transferred without any needs for special loading surface recognition.The nonlinear-virtual thermal method(N-VTM)is used to distinguish the enclosed and open voids by the temperature difference between the enclosed(with higher temperature)and open(with lower temperature)voids,where the solid areas are treated as the thermal insulation material,and other areas are filled with the self-heating highly thermally conductive material.The mixed displacement–pressure formulation is used to model this solid–fluid problem.The method is easily implemented in the standard density approach and its effectiveness is verified and illustrated by several typical examples at the end of the paper.展开更多
Avoiding the folding defect and improving the die filling capability in the transitional region are desired in isothermal local loading forming of a large-scale Ti-alloy rib-web component（LTRC）. To achieve a high-pr...Avoiding the folding defect and improving the die filling capability in the transitional region are desired in isothermal local loading forming of a large-scale Ti-alloy rib-web component（LTRC）. To achieve a high-precision LTRC, the folding evolution and die filling process in the transitional region were investigated by 3 D finite element simulation and experiment using an equal-thickness billet（ETB）. It is found that the initial volume distribution in the second-loading region can greatly affect the amount of material transferred into the first-loading region during the second-loading step, and thus lead to the folding defect. Besides, an improper initial volume distribution results in non-concurrent die filling in the cavities of ribs after the second-loading step, and then causes die underfilling. To this end, an unequal-thickness billet（UTB） was employed with the initial volume distribution optimized by the response surface method（RSM）. For a certain eigenstructure, the critical value of the percentage of transferred material determined by the ETB was taken as a constraint condition for avoiding the folding defect in the UTB optimization process,and the die underfilling rate was considered as the optimization objective. Then, based on the RSM models of the percentage of transferred material and the die underfilling rate, non-folding parameter combinations and optimum die filling were achieved. Lastly, an optimized UTB was obtained and verified by the simulation and experiment.展开更多
This paper presents an extended topology optimization approach considering joint load constraints with geo-metrical nonlinearity in design of assembled structures.The geometrical nonlinearity is firstly included to re...This paper presents an extended topology optimization approach considering joint load constraints with geo-metrical nonlinearity in design of assembled structures.The geometrical nonlinearity is firstly included to reflect the structural response and the joint load distribution under large deformation.To avoid a failure of fastener joints,topology optimization is then carried out to minimize the structural end compliance in the equilibrium state while controlling joint loads intensities over fasteners.During nonlinear analysis and optimization,a novel implementation of adjoint vector sensitivity analysis along with super element condensation is introduced to address numerical instability issues.The degrees of freedom of weak regions are condensed so that their influences are excluded from the iterative Newton-Raphson(NR)solution.Numerical examples are presented to validate the efficiency and robustness of the proposed method.The effects of joint load constraints and geometrical nonlinearity are highlighted by comparing numerical optimization results.展开更多
Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance...Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.展开更多
In power communication networks,it is a challenge to decrease the risk of different services efficiently to improve operation reliability.One of the important factor in reflecting communication risk is service route d...In power communication networks,it is a challenge to decrease the risk of different services efficiently to improve operation reliability.One of the important factor in reflecting communication risk is service route distribution.However,existing routing algorithms do not take into account the degree of importance of services,thereby leading to load unbalancing and increasing the risks of services and networks.A routing optimization mechanism based on load balancing for power communication networks is proposed to address the abovementioned problems.First,the mechanism constructs an evaluation model to evaluate the service and network risk degree using combination of devices,service load,and service characteristics.Second,service weights are determined with modified relative entropy TOPSIS method,and a balanced service routing determination algorithm is proposed.Results of simulations on practical network topology show that the mechanism can optimize the network risk degree and load balancing degree efficiently.展开更多
This study aims to provide the pilot with optimal control time histories for stabilization of a helicopter after releasing the slung load in aerial delivery missions. A model with 21 degrees of freedom(21-DOF) has bee...This study aims to provide the pilot with optimal control time histories for stabilization of a helicopter after releasing the slung load in aerial delivery missions. A model with 21 degrees of freedom(21-DOF) has been developed and validated for a helicopter slung load system. The control history is generated with detailed procedure based on trajectory optimization. Effects of the objective function formulation on the results are discussed and rules are obtained to assist in the objective function determination. We conclude that the pilot should first decrease and then increase the collective control and adjust the longitudinal control to stabilize the helicopter after the in-hover slung load release. The obtained control history is reasonable and helpful for safety and efficiency improvement. Effects of path constraints and the Flight Control System(FCS) are studied. More stringent path constraints will lead to longer time spent and more controls. Stronger stiffness and weaker damping from the FCS will cause milder control histories but sharper on-axis state histories.展开更多
In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic alg...In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The blade is stacked by the center of gravity in radial direction with five sections. For each blade section, independent suction and pressure sides are constructed from the camber line using Bezier curves. Three-dimensional flow analysis is carried out to verify the performance of the new blade. It is found that the new blade has improved the blade performance by 0.5%. Consequently, it is verified that the new blade is effective to improve the turbine internal efficiency and to lower the turbine weight and manufacturing cost by reducing the blade number by about 15%.展开更多
A topology optimization formulation is developed to find the stiffest structure with desirable material distribution subjected to seismic loads. Finite element models of the structures are generated and the optimality...A topology optimization formulation is developed to find the stiffest structure with desirable material distribution subjected to seismic loads. Finite element models of the structures are generated and the optimality criteria method is modified using a simple penalty approach and introducing fictitious strain energy to simultaneously consider both material volume and displacement constraints. Different types of shear walls with/without opening are investigated. Additionally, the effects of shear wall-frame interaction for single and coupled shear walls are studied. Gravity and seismic loads are applied to the shear walls so that the definitions provide a practical approach for locating the critical parts of these structures. The results suggest new viewpoints for architectural and structural engineering for placement of openings.展开更多
基金supported by Science and Technology Standard Project of Guangdong Electric Power Design Institute(ER11301W,ER11811W).
文摘Data center industries have been facing huge energy challenges due to escalating power consumption and associated carbon emissions.In the context of carbon neutrality,the integration of data centers with renewable energy has become a prevailing trend.To advance the renewable energy integration in data centers,it is imperative to thoroughly explore the data centers’operational flexibility.Computing workloads and refrigeration systems are recognized as two promising flexible resources for power regulationwithin data centermicro-grids.This paper identifies and categorizes delay-tolerant computing workloads into three types(long-running non-interruptible,long-running interruptible,and short-running)and develops mathematical time-shifting models for each.Additionally,this paper examines the thermal dynamics of the computer room and derives a time-varying temperature model coupled to refrigeration power.Building on these models,this paper proposes a two-stage,multi-time scale optimization scheduling framework that jointly coordinates computing workloads time-shift in day-ahead scheduling and refrigeration power control in intra-day dispatch to mitigate renewable variability.A case study demonstrates that the framework effectively enhances the renewable-energy utilization,improves the operational economy of the data center microgrid,and mitigates the impact of renewable power uncertainty.The results highlight the potential of coordinated computing workloads and thermal system flexibility to support greener,more cost-effective data center operation.
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.
文摘Build-up panels for the commercial aircraft fuselage subjected to the axial compression load are studied by both experimental and theoretical methods.An integral panel is designed with the same overall size and weight as the build-up structure,and finite element models（FEMs）of these two panels are established.Experimental results of build-up panels agree well with the FEM results with the nonliearity and the large deformation,so FEMs are validated.FEM calculation results of these two panels indicate that the failure mode of the integral panel is different from that of the build-up panel,and the failure load increases by 18.4% up to post-buckling.Furthermore,the integral structure is optimized by using the multi-island genetic algorithm and the sequential quadratic programming.Compared with the initial design,the optimal mass is reduced by 8.7% and the strength is unchanged.
基金Project supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2025-02303676)。
文摘The stress minimization multi-material topology optimization(MMTO)approach has recently attracted significant attention because of its applications in aerospace and mechanical engineering.Nonetheless,the stress minimization MMTO approach may result in stress surpassing the material's tolerance limit,potentially culminating in failure.This research proposes a novel way for imposing stress constraints on each material to regulate their respective stress levels.The fundamental concept is that each material possesses its own interpolation function for the stress model.The maximum von Mises stress for each material can be established with the definition of an upper limit,ensuring that the materials will perform safely and effectively.This aids topological structures in resisting failure and augmenting strength.A multi-physics system including thermoelastic and self-weight loads is concurrently examined alongside stress limitations.The global stress constraint utilizes the p-norm function,and the adjoint method is used to derive sensitivity.This work employs a three-field strategy utilizing density filtering and Heaviside projection functions to mitigate the artificial stress in low density.The technique is assessed through two-dimensional(2D)and three-dimensional(3D)examples,illustrating the influence of stress limits on the compliance minimization under heat and self-weight loads.The optimized results indicate a substantial decrease in the stress levels accompanied by a minor gain in compliance,while maintaining the stress within the specified range for all materials.
基金supported by the National Natural Science Foundation of China(52350410465)the General Projects of Guangdong Natural Science Research Projects(2023A1515011520).
文摘Accurate daily suspended sediment load(SSL)prediction is essential for sustainable water resource management,sediment control,and environmental planning.However,SSL prediction is highly complex due to its nonlinear and dynamic nature,making traditional empirical models inadequate.This study proposes a novel hybrid approach,integrating the Adaptive Neuro-Fuzzy Inference System(ANFIS)with the Gradient-Based Optimizer(GBO),to enhance SSL forecasting accuracy.The research compares the performance of ANFIS-GBO with three alternative models:standard ANFIS,ANFIS with Particle Swarm Optimization(ANFIS-PSO),and ANFIS with Grey Wolf Optimization(ANFIS-GWO).Historical SSL and streamflow data from the Bailong River Basin,China,are used to train and validate the models.The input selection process is optimized using the Multivariate Adaptive Regression Splines(MARS)method.Model performance is evaluated using statistical metrics such as Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),Nash Sutcliffe Efficiency(NSE),and Determination Coefficient(R^(2)).Additionally,visual assessments,including scatter plots,Taylor diagrams,and violin plots,provide further insights into model reliability.The results indicate that including historical SSL data improves predictive accuracy,with ANFIS-GBO outperforming the other models.ANFIS-GBO achieves the lowest RMSE and MAE and the highest NSE and R^(2),demonstrating its superior learning ability and adaptability.The findings highlight the effectiveness of nature-inspired optimization algorithms in enhancing sediment load forecasting and contribute to the advancement of AI-based hydrological modeling.Future research should explore the integration of additional environmental and climatic variables to enhance predictive capabilities further.
基金supported by the National Natural Science Foundation of China(Grant No.52172356)the Hunan Provincial Natural Science Foundation of China(Grant No.2022JJ10012).
文摘The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique challenges when inertial loads become dominant:non-monotonous behavior of the objective function,possible unconstrained characterization of the optimal solution,and parasitic effects.Herein,an improved Guide-Weight approach is introduced,which effectively addresses the structural topology optimization problem when subjected to inertial loads.Smooth and fast convergence of the compliance is achieved by the approach,while also maintaining the effectiveness of the volume constraints.The rational approximation of material properties model and smooth design are utilized to guarantee clear boundaries of the final structure,facilitating its seamless integration into manufacturing processes.The framework provided by the alternating active-phase algorithm is employed to decompose the multi-material topological problem under inertial loading into a set of sub-problems.The optimization of multi-material under inertial loads is accomplished through the effective resolution of these sub-problems using the improved Guide-Weight method.The effectiveness of the proposed approach is demonstrated through numerical examples involving two-phase and multi-phase materials.
文摘The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap.
基金supported by the National Natural Science Foundation of China(Nos.52204136,52474100,and 52204092).
文摘Web pillars enduring complex coupled loads are critical for stability in high-wall mining.This study develops a dynamic failure criterion for web pillars under non-uniform loading using catastrophe theory.Through the analysis of the web pillar-overburden system’s dynamic stress and deformation,a total potential energy function and dynamic failure criterion were established for web pillars.An optimizing method for web pillar parameters was developed in highwall mining.The dynamic criterion established was used to evaluate the dynamic failure and stability of web pillars under static and dynamic loading.Key findings reveal that vertical displacements exhibit exponential-trigonometric variation under static loads and multi-variable power-law behavior under dynamic blasting.Instability risks arise when the roof’s tensile strength-to-stress ratio drops below 1.Using catastrophe theory,the bifurcation setΔ<0 signals sudden instability.The criterion defines failure as when the unstable web pillar section length l1 exceeds the roof’s critical collapse distance l2.Case studies and simulations determine an optimal web pillar width of 4.6 m.This research enhances safety and resource recovery,providing a theoretical framework for advancing highwall mining technology.
基金supported by the Science and Technology Project of Yunnan Power Grid Co.,Ltd.under Grant No.YNKJXM20222410.
文摘Traditional demand response(DR)programs for energy-intensive industries(EIIs)primarily rely on electricity price signals and often overlook carbon emission factors,limiting their effectiveness in supporting lowcarbon transitions.To address this challenge,this paper proposes an electricity–carbon integratedDR strategy based on a bi-level collaborative optimization framework that coordinates the interaction between the grid and EIIs.At the upper level,the grid operatorminimizes generation and curtailment costs by optimizing unit commitment while determining real-time electricity prices and dynamic carbon emission factors.At the lower level,EIIs respond to these dual signals by minimizing their combined electricity and carbon trading costs,considering their participation in medium-and long-term electricity markets,day-ahead spot markets,and carbon emissions trading schemes.The model accounts for direct and indirect carbon emissions,distributed photovoltaic(PV)generation,and battery energy storage systems.This interaction is structured as a Stackelberg game,where the grid acts as the leader and EIIs as followers,enabling dynamic feedback between pricing signals and load response.Simulation studies on an improved IEEE 30-bus system,with a cement plant as a representative user form EIIs,show that the proposed strategy reduces user-side carbon emissions by 7.95% and grid-side generation cost by 4.66%,though the user’s energy cost increases by 7.80% due to carbon trading.Theresults confirmthat the joint guidance of electricity and carbon prices effectively reshapes user load profiles,encourages peak shaving,and improves PV utilization.This coordinated approach not only achieves emission reduction and cost efficiency but also offers a theoretical and practical foundation for integrating carbon pricing into demand-side energy management in future low-carbon power systems.
基金supported in part by the National Natural Science Foundation of China(NSFC)(92267205)the Natural Science Foundation of Hunan Province(2025JJ10007,2025JJ60423)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(ICT2024 B66).
文摘Most enterprises rely on railway transportation to deliver their products to customers,particularly in the salt lake chemical industry.Notably,allocating products to freight spaces and their assembly on transport vehicles are critical pre-transportation processes.However,due to demand fluctuations from changing product orders and unforeseen railway scheduling delays,manually adjusted allocation and loading may lead to excessive loading and unloading distances and times,ultimately increasing transportation costs for enterprises.To address these issues,this paper proposes a data-driven two-stage robust optimization(TSRO)framework embedding with the gated stacked temporal autoencoder clustering based on the attention mechanism(GSTAC-AM),which aims to overcome demand uncertainty and enhance the efficiency of freight allocation and loading.Specifically,GSTAC-AM is developed to help predict the deviation level of demand uncertainty and mitigate the impact of potential outliers.Then,a robust counterpart model is formulated to ensure computational tractability.In addition,a multi-stage hybrid heuristic algorithm is designed to handle the large scale and complexity inherent in the freight space allocation and loading processes.Finally,the effectiveness and applicability of the proposed framework are validated through a real case study conducted in a large salt lake chemical enterprise.
基金supported by National Key Research and Development Program(No.2017YFB1102800)NSFC for Excellent Young Scholars(No.11722219)Key Project of NSFC(Nos.51790171,5171101743)
文摘The purpose of this paper is to present a novel topology optimization approach to control precisely the output loads under static loads and harmonic excitations.We introduce the Artificial Bar Element(ABE)at the designated output positions,where the output loads are equivalently measured and constrained with the nodal displacements of ABE.Optimization model is then formulated considering the output load constraints as well as the minimization of strain energy and dynamic displacement responses respectively under the static and dynamic conditions.The influences of the ABEs stiffness,different material usages of the design domain,widths of the output loads constraint intervals and variation ratios of output loads are discussed in detail.The proposed method is verified with several numerical examples with clear and reasonable load transfer paths.
文摘Fuel reload pattern optimization is essential for attaining maximum fuel burnup for minimization of generation cost while minimizing power peaking factor(PPF).The aim of this work is to carry out detailed assessment of particle swarm optimization(PSO) in the context of fuel reload pattern search. With astronomically large number of possible loading patterns, the main constraints are limiting local power peaking factor, fixed number of assemblies,fixed fuel enrichment, and burnable poison rods. In this work, initial loading pattern of fixed batches of fuel assemblies is optimized by using particle swarm optimization technique employing novel feature of varying inertial weights with the objective function to obtain both flat power profile and cycle k_(eff)>1. For neutronics calculation, PSU-LEOPARD-generated assembly depletiondependent group-constant-based ADD files are used. The assembly data description file generated by PSU-LEOPARD is used as input cross-section library to MCRAC code, which computes normalized power profile of all fuel assemblies of PWR nuclear reactor core. The standard PSO with varying inertial weights is then employed to avoid trapping in local minima. A series of experiments havebeen conducted to obtain near-optimal converged fuelloading pattern of 300 MWe PWR Chashma reactor. The optimized loading pattern is found in good agreement with results found in literature. Hybrid scheme of PSO with simulated annealing has also been implemented and resulted in faster convergence.
基金support to this work by the National Natural Science Foundation of China (Grant Nos.U1808215 and 11821202)the 111 Project (B14013)the Fundamental Research Funds for the Central Universities of China (DUT21GF101).
文摘For the topology optimization of structures with design-dependent pressure,an intuitive way is to directly describe the loading boundary of the structure,and then update the load on it.However,boundary recognition is usually cumbersome and inaccurate.Furthermore,the pressure is always loaded either outside or inside the structure,instead of both.Hence,the inner enclosed and outer open spaces should be distinguished to recognize the loading surfaces.To handle the above issues,a thermal-solid–fluid method for topology optimization with design-dependent pressure load is proposed in this paper.In this method,the specific void phase is defined to be an incompressible hydrostatic fluid,through which the pressure load can be transferred without any needs for special loading surface recognition.The nonlinear-virtual thermal method(N-VTM)is used to distinguish the enclosed and open voids by the temperature difference between the enclosed(with higher temperature)and open(with lower temperature)voids,where the solid areas are treated as the thermal insulation material,and other areas are filled with the self-heating highly thermally conductive material.The mixed displacement–pressure formulation is used to model this solid–fluid problem.The method is easily implemented in the standard density approach and its effectiveness is verified and illustrated by several typical examples at the end of the paper.
基金supports of the National Natural Science Foundation of China (No. 51575449)Research Fund of the State Key Laboratory of Solidification Processing (NWPU) of China (No. 104-QP2014)+1 种基金the 111 Project (No. B08040)the Fundamental Research Funds for the Central Universities (3102015AX004)
文摘Avoiding the folding defect and improving the die filling capability in the transitional region are desired in isothermal local loading forming of a large-scale Ti-alloy rib-web component（LTRC）. To achieve a high-precision LTRC, the folding evolution and die filling process in the transitional region were investigated by 3 D finite element simulation and experiment using an equal-thickness billet（ETB）. It is found that the initial volume distribution in the second-loading region can greatly affect the amount of material transferred into the first-loading region during the second-loading step, and thus lead to the folding defect. Besides, an improper initial volume distribution results in non-concurrent die filling in the cavities of ribs after the second-loading step, and then causes die underfilling. To this end, an unequal-thickness billet（UTB） was employed with the initial volume distribution optimized by the response surface method（RSM）. For a certain eigenstructure, the critical value of the percentage of transferred material determined by the ETB was taken as a constraint condition for avoiding the folding defect in the UTB optimization process,and the die underfilling rate was considered as the optimization objective. Then, based on the RSM models of the percentage of transferred material and the die underfilling rate, non-folding parameter combinations and optimum die filling were achieved. Lastly, an optimized UTB was obtained and verified by the simulation and experiment.
基金co-supported by National Key Research and Development Program(No.2017YFB1102800)NSFC for Excellent Young Scholars(No.11722219)Key Project of NSFC(Nos.51790171,5171101743,51735005,11620101002,and 11432011).
文摘This paper presents an extended topology optimization approach considering joint load constraints with geo-metrical nonlinearity in design of assembled structures.The geometrical nonlinearity is firstly included to reflect the structural response and the joint load distribution under large deformation.To avoid a failure of fastener joints,topology optimization is then carried out to minimize the structural end compliance in the equilibrium state while controlling joint loads intensities over fasteners.During nonlinear analysis and optimization,a novel implementation of adjoint vector sensitivity analysis along with super element condensation is introduced to address numerical instability issues.The degrees of freedom of weak regions are condensed so that their influences are excluded from the iterative Newton-Raphson(NR)solution.Numerical examples are presented to validate the efficiency and robustness of the proposed method.The effects of joint load constraints and geometrical nonlinearity are highlighted by comparing numerical optimization results.
基金Item Sponsored by National Natural Science Foundation of China(50974039)
文摘Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization.
基金supported by the State Grid project which names the simulation and service quality evaluation technology research of power communication network(No.XX71-14-046)
文摘In power communication networks,it is a challenge to decrease the risk of different services efficiently to improve operation reliability.One of the important factor in reflecting communication risk is service route distribution.However,existing routing algorithms do not take into account the degree of importance of services,thereby leading to load unbalancing and increasing the risks of services and networks.A routing optimization mechanism based on load balancing for power communication networks is proposed to address the abovementioned problems.First,the mechanism constructs an evaluation model to evaluate the service and network risk degree using combination of devices,service load,and service characteristics.Second,service weights are determined with modified relative entropy TOPSIS method,and a balanced service routing determination algorithm is proposed.Results of simulations on practical network topology show that the mechanism can optimize the network risk degree and load balancing degree efficiently.
基金supported by the National Natural Science Foundation of China (Nos. 11672128)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘This study aims to provide the pilot with optimal control time histories for stabilization of a helicopter after releasing the slung load in aerial delivery missions. A model with 21 degrees of freedom(21-DOF) has been developed and validated for a helicopter slung load system. The control history is generated with detailed procedure based on trajectory optimization. Effects of the objective function formulation on the results are discussed and rules are obtained to assist in the objective function determination. We conclude that the pilot should first decrease and then increase the collective control and adjust the longitudinal control to stabilize the helicopter after the in-hover slung load release. The obtained control history is reasonable and helpful for safety and efficiency improvement. Effects of path constraints and the Flight Control System(FCS) are studied. More stringent path constraints will lead to longer time spent and more controls. Stronger stiffness and weaker damping from the FCS will cause milder control histories but sharper on-axis state histories.
文摘In order to improve turbine internal efficiency and lower manufacturing cost, a new highly loaded rotating blade has been developed. The 3D optimization design method based on artificial neural network and genetic algorithm is adopted to construct the blade shape. The blade is stacked by the center of gravity in radial direction with five sections. For each blade section, independent suction and pressure sides are constructed from the camber line using Bezier curves. Three-dimensional flow analysis is carried out to verify the performance of the new blade. It is found that the new blade has improved the blade performance by 0.5%. Consequently, it is verified that the new blade is effective to improve the turbine internal efficiency and to lower the turbine weight and manufacturing cost by reducing the blade number by about 15%.
文摘A topology optimization formulation is developed to find the stiffest structure with desirable material distribution subjected to seismic loads. Finite element models of the structures are generated and the optimality criteria method is modified using a simple penalty approach and introducing fictitious strain energy to simultaneously consider both material volume and displacement constraints. Different types of shear walls with/without opening are investigated. Additionally, the effects of shear wall-frame interaction for single and coupled shear walls are studied. Gravity and seismic loads are applied to the shear walls so that the definitions provide a practical approach for locating the critical parts of these structures. The results suggest new viewpoints for architectural and structural engineering for placement of openings.
