Increasing attention has been paid to the efficiency improvement of the induction traction system of high-speed trains due to the high demand for energy saving. In emergency self-propelled mode, however, the dc-link v...Increasing attention has been paid to the efficiency improvement of the induction traction system of high-speed trains due to the high demand for energy saving. In emergency self-propelled mode, however, the dc-link voltage and the traction power of the motor are significantly reduced, resulting in decreased traction efficiency due to the low load and low speed operations. Aiming to tackle this problem, a novel efficiency improved control method is introduced to the emergency mode of high-speed train traction system in this paper. In the proposed method, a total loss model of induction motor considering the behaviors of both iron and copper loss is established. An improved iterative algorithm with decreased computational burden is then introduced, resulting in a fast solving of the optimal flux reference for loss minimization at each control period. In addition, considering the parameter variation problem due to the low load and low speed operations, a parameter estimation method is integrated to improve the controller's robustness. The effectiveness of the proposed method on efficiency improvement at low voltage and low load conditions is demonstrated by simulated and experimental results.展开更多
Linear induction motors are superior to rotary induction motors in direct drive systems because they can generate direct forward thrust force independent of mechanical transmission.However,due to the large air gap and...Linear induction motors are superior to rotary induction motors in direct drive systems because they can generate direct forward thrust force independent of mechanical transmission.However,due to the large air gap and cut-open magnetic circuit,their efficiency and power factor are quite low,which limit their application in high power drive systems.To attempt this challenge,this work presents a system-level optimization method for a single-sided linear induction motor drive system.Not only the motor but also the control system is included in the analysis.A system-level optimization method is employed to gain optimal steady-state and dynamic performances.To validate the effectiveness of the proposed optimization method,experimental results on a linear induction motor drive are presented and discussed.展开更多
It’s systematically analyzed that energy efficiency optimization technology has been applied in the field of steel industry. The fundamental principal of energy optimization technology is reasonably matching the qual...It’s systematically analyzed that energy efficiency optimization technology has been applied in the field of steel industry. The fundamental principal of energy optimization technology is reasonably matching the quality and price of energy as well as energy-dominated systematic energy efficiency management system. Specific measures of energy optimization have been put forward, which include taking high efficiency utilized technology such as energy saving from the original, the production process and recycling of waste heat and waste energy etc., integrating and configuring energy in an optimized way of high efficiency and excellent quality, fully realizing the function of different energy in order to optimize the utilization sequence of energy, and improving the energy medium system by themselves. Finally it is clearly pointed out that the steel industry should pay more consideration about the great deal of energy system which they have used now and an ideal energy evaluation methodology and standard should be built as soon as possible if they want to take full usage of the real role and function of energy in all aspects.展开更多
With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid elec...With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.展开更多
On-machine measurement(OMM)stands out as a pivotal technology in complex curved surface adaptive machining.However,the complex structure inherent in workpieces poses a significant challenge as the stylus orientation f...On-machine measurement(OMM)stands out as a pivotal technology in complex curved surface adaptive machining.However,the complex structure inherent in workpieces poses a significant challenge as the stylus orientation frequently shifts during the measurement process.Consequently,a substantial amount of time is allocated to calibrating pre-travel error and probe movement.Furthermore,the frequent movement of machine tools also increases the influence of machine errors.To enhance both accuracy and efficiency,an optimization strategy for the OMM process is proposed.Based on the kinematic chain of the machine tools,the relationship between the angle combination of rotary axes,the stylus orientation,and the calibration position of pre-travel error is disclosed.Additionally,an OMM efficiency optimization model for complex curved surfaces is developed.This model is solved to produce the optimal efficiency angle combinations for each to-be-measured point.Within each angle combination,the effects of positioning errors on measurement results are addressed by coordinate system offset and measurement result compensation method.Finally,the experiments on an impeller are used to demonstrate the practical utility of the proposed method.展开更多
Optimizing chemical reaction parameters is an expensive optimization problem. Each experiment takes a long time and the raw materials are expensive. High-throughput methods combined with the parallel Efficient Global ...Optimizing chemical reaction parameters is an expensive optimization problem. Each experiment takes a long time and the raw materials are expensive. High-throughput methods combined with the parallel Efficient Global Optimization algorithm can effectively improve the efficiency of the search for optimal chemical reaction parameters. In this paper, we propose a multi-objective populated expectation improvement criterion for providing multiple near-optimal solutions in high-throughput chemical reaction optimization. An l-NSGA2, employing the Pseudo-power transformation method, is utilized to maximize the expected improvement acquisition function, resulting in a Pareto solution set comprising multiple designs. The approximation of the cost function can be calculated by the ensemble Gaussian process model, which greatly reduces the cost of the exact Gaussian process model. The proposed optimization method was tested on a SNAr benchmark problem. The results show that compared with the previous high-throughput experimental methods, our method can reduce the number of experiments by almost half. At the same time, it theoretically enhances temporal and spatial yields while minimizing by-product formation, potentially guiding real chemical reaction optimization.展开更多
In this paper,an improved discharging circuit was proposed to quicken the decay of the current in the drive coil in a reluctance accelerator when the armature reaches the center of the coil.The aim of this is to preve...In this paper,an improved discharging circuit was proposed to quicken the decay of the current in the drive coil in a reluctance accelerator when the armature reaches the center of the coil.The aim of this is to prevent the suck-back effect caused by the residual current in drive coil.The method is adding a reverse charging branch with a small capacitor in the traditional pulsed discharging circuit.The results under the traditional circuit and the improved circuit were compared in a simulation.The experiment then verified the simulations and they had good agreement.Simulation and experiment both demonstrated the improved circuit can effectively prevent the suck-back effect and increase the efficiency.At the voltage of 800 V,an efficiency increase of 36.34% was obtained.展开更多
A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal...A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.展开更多
This study delves into the intricate relationship between iron(Fe)content in kaolinite and its impact on the adsorption behavior of sodium oleate.The effects of different iron concentrations on adsorption energy,hydro...This study delves into the intricate relationship between iron(Fe)content in kaolinite and its impact on the adsorption behavior of sodium oleate.The effects of different iron concentrations on adsorption energy,hydrogen bond kinetics and adsorption efficiency were studied through simulation and experimental verification.The results show that the presence of iron in the kaolinite structure significantly improves the adsorption capacity of sodium oleate.Kaolinite samples with high iron content have better adsorption properties,lower adsorption energy levels and shorter and stronger hydrogen bonds than pure kaolinite.The optimal concentration of oleic acid ions for achieving maximum adsorption efficiency was identified as 1.2 mmol/L across different kaolinite samples.At this concentration,the adsorption rates and capacities reach their peak,with Fe-enriched kaolinite samples exhibiting notably higher flotation recovery rates.This optimal concentration represents a balance between sufficient oleic acid ion availability for surface interactions and the prevention of self-aggregation phenomena that could hinder adsorption.This study offers promising avenues for optimizing the flotation process in mineral processing applications.展开更多
By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite co...By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [-1,1].Some measures in the optimization algorithm,such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline,were taken to ensure its speediness and veracity in seeking the optimization process.The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision.Furthermore,the average truncated generations,the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally.It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.展开更多
Axial flux permanent magnet synchronous motors(AFPMSMs)have been widely used in wind-power generation,electric vehicles,aircraft,and other renewable-energy applications owing to their high power density,operating effi...Axial flux permanent magnet synchronous motors(AFPMSMs)have been widely used in wind-power generation,electric vehicles,aircraft,and other renewable-energy applications owing to their high power density,operating efficiency,and integrability.To facilitate comprehensive research on AFPMSM,this article reviews the developments in the research on the design and control optimization of AFPMSMs.First,the basic topologies of AFPMSMs are introduced and classified.Second,the key points of the design optimization of core and coreless AFPMSMs are summarized from the aspects of parameter design,structure design,and material optimization.Third,because efficiency improvement is an issue that needs to be addressed when AFPMSMs are applied to electric or other vehicles,the development status of efficiency-optimization control strategies is reviewed.Moreover,control strategies proposed to suppress torque ripple caused by the small inductance of disc coreless permanent magnet synchronous motors(DCPMSMs)are summarized.An overview of the rotor-synchronization control strategies for disc contra-rotating permanent magnet synchronous motors(CRPMSMs)is presented.Finally,the current difficulties and development trends revealed in this review are discussed.展开更多
With the rapid development of wireless technologies,wireless access networks have entered their Fifth-Generation(5G)system phase.The heterogeneous and complex nature of a 5G system,with its numerous technological scen...With the rapid development of wireless technologies,wireless access networks have entered their Fifth-Generation(5G)system phase.The heterogeneous and complex nature of a 5G system,with its numerous technological scenarios,poses significant challenges to wireless resource management,making radio resource optimization an important aspect of Device-to-Device(D2D)communication in such systems.Cellular D2D communication can improve spectrum efficiency,increase system capacity,and reduce base station communication burdens by sharing authorized cell resources;however,can also cause serious interference.Therefore,research focusing on reducing this interference by optimizing the configuration of shared cellular resources has also grown in importance.This paper proposes a novel algorithm to address the problems of co-channel interference and energy efficiency optimization in a long-term evolution network.The proposed algorithm uses the fuzzy clustering method,which employs minimum outage probability to divide D2D users into several groups in order to improve system throughput and reduce interference between users.An efficient power control algorithm based on game theory is also proposed to optimize user transmission power within each group and thereby improve user energy efficiency.Simulation results show that these proposed algorithms can effectively improve system throughput,reduce co-channel interference,and enhance energy efficiency.展开更多
Two new techniques for efficiency-optimization control(EOC) of induction motor drives were proposed. The first method combined Loss Model and "golden section technique", which was faster than the available m...Two new techniques for efficiency-optimization control(EOC) of induction motor drives were proposed. The first method combined Loss Model and "golden section technique", which was faster than the available methods. Secondly, the low-frequency ripple torque due to decrease of rotor flux was compensated in a feedforward manner. If load torque or speed command changed, the efficiency search algorithm would be abandoned and the rated flux would be established to get the best transient response. The close agreement between the simulation and the experimental results confirmed the validity and usefulness of the proposed techniques.展开更多
The Efficient Global Optimization(EGO)algorithm has been widely used in the numerical design optimization of engineering systems.However,the need for an uncertainty estimator limits the selection of a surrogate model....The Efficient Global Optimization(EGO)algorithm has been widely used in the numerical design optimization of engineering systems.However,the need for an uncertainty estimator limits the selection of a surrogate model.In this paper,a Sequential Ensemble Optimization(SEO)algorithm based on the ensemble model is proposed.In the proposed algorithm,there is no limitation on the selection of an individual surrogate model.Specifically,the SEO is built based on the EGO by extending the EGO algorithm so that it can be used in combination with the ensemble model.Also,a new uncertainty estimator for any surrogate model named the General Uncertainty Estimator(GUE)is proposed.The performance of the proposed SEO algorithm is verified by the simulations using ten well-known mathematical functions with varying dimensions.The results show that the proposed SEO algorithm performs better than the traditional EGO algorithm in terms of both the final optimization results and the convergence rate.Further,the proposed algorithm is applied to the global optimization control for turbo-fan engine acceleration schedule design.展开更多
Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.T...Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.展开更多
Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy...Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy, of numerical integrations in solving FE numerical substructure in RTHSs. First, sparse matrix storage schemes are adopted to decrease the computational time of FE numerical substructure. In this way, the task execution time（TET） decreases such that the scale of the numerical substructure model increases. Subsequently, several commonly used explicit numerical integration algorithms, including the central difference method（CDM）, the Newmark explicit method, the Chang method and the Gui-λ method, are comprehensively compared to evaluate their computational time in solving FE numerical substructure. CDM is better than the other explicit integration algorithms when the damping matrix is diagonal, while the Gui-λ（λ = 4） method is advantageous when the damping matrix is non-diagonal. Finally, the effect of time delay on the computational accuracy of RTHSs is investigated by simulating structure-foundation systems. Simulation results show that the influences of time delay on the displacement response become obvious with the mass ratio increasing, and delay compensation methods may reduce the relative error of the displacement peak value to less than 5% even under the large time-step and large time delay.展开更多
The larger the difference between the willingness scale of tobacco family farmers and the optimal scale of efficiency,the greater the degree of irrationality,and the higher the decision making risk.With the aid of DEA...The larger the difference between the willingness scale of tobacco family farmers and the optimal scale of efficiency,the greater the degree of irrationality,and the higher the decision making risk.With the aid of DEA model,this study calculated the optimal scale of efficiency of Guiyang tobacco family farms.Using the ratio of willingness scale and efficiency optimal scale,it measured the degree of irrationality of family farmers.In addition,with the help of multiple linear regression model,it explained the irrational decision making mechanism of family farmers.Finally,it made a portrait of farmers who tend to make irrational decisions,to find specific farmers and guide them in their production and operation,reduce the risk of planting scale decision making and stabilize the sustainable development of the tobacco industry.展开更多
Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biolo...Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculate the mutual information, energy cost, and energy efficiency of an array of these bistable units. We find that the optimal number of units could maximize this array＇s energy efficiency in encoding pulse inputs, which depends on the fixed energy cost. We conclude that demand for energy efficiency in biological systems may strongly influence the size of these systems under the pressure of natural selection.展开更多
This paper proposes an innovative simultaneous optimization approach for single and multi-component mass exchanger network synthesis (MENS). A retrofitted stage-wise superstructure and a parallelized random walk algor...This paper proposes an innovative simultaneous optimization approach for single and multi-component mass exchanger network synthesis (MENS). A retrofitted stage-wise superstructure and a parallelized random walk algorithm with compulsive evolution (RWCE) are adopted. An iterative calculation method is designed to satisfy the requirements of multi-component mass transfer, with a relaxation for the outlet composition of the lean streams. The parametric analysis shows that the relaxation coefficient plays a major role in driving the convergence of the method. To improve the robustness of the established model, an adaptive relaxation coefficient strategy is implemented for multi-component MENS problems. In a divergence situation, the outlet concentration of the lean stream can be adjusted automatically by a random relaxation coefficient. Finally, three industrial MENS examples are considered in this work, whose total annual cost (TAC) are reduced by 7179, 2212, and 551 $·year^(-1). The corresponding optimization times are obtained to be 336, 125, and 145 s. The results indicate improvements in the economy and time, demonstrating that the parallelized RWCE can yield an optimal TAC and optimization efficiency compared to previous results. Overall, the adaptive relaxation coefficient strategy enhances the convergence for multi-component MENS problems.展开更多
基金supported in part by the Science Foundation of the Chinese Academy of Railway Sciences under Grant Number:2023QT001。
文摘Increasing attention has been paid to the efficiency improvement of the induction traction system of high-speed trains due to the high demand for energy saving. In emergency self-propelled mode, however, the dc-link voltage and the traction power of the motor are significantly reduced, resulting in decreased traction efficiency due to the low load and low speed operations. Aiming to tackle this problem, a novel efficiency improved control method is introduced to the emergency mode of high-speed train traction system in this paper. In the proposed method, a total loss model of induction motor considering the behaviors of both iron and copper loss is established. An improved iterative algorithm with decreased computational burden is then introduced, resulting in a fast solving of the optimal flux reference for loss minimization at each control period. In addition, considering the parameter variation problem due to the low load and low speed operations, a parameter estimation method is integrated to improve the controller's robustness. The effectiveness of the proposed method on efficiency improvement at low voltage and low load conditions is demonstrated by simulated and experimental results.
文摘Linear induction motors are superior to rotary induction motors in direct drive systems because they can generate direct forward thrust force independent of mechanical transmission.However,due to the large air gap and cut-open magnetic circuit,their efficiency and power factor are quite low,which limit their application in high power drive systems.To attempt this challenge,this work presents a system-level optimization method for a single-sided linear induction motor drive system.Not only the motor but also the control system is included in the analysis.A system-level optimization method is employed to gain optimal steady-state and dynamic performances.To validate the effectiveness of the proposed optimization method,experimental results on a linear induction motor drive are presented and discussed.
文摘It’s systematically analyzed that energy efficiency optimization technology has been applied in the field of steel industry. The fundamental principal of energy optimization technology is reasonably matching the quality and price of energy as well as energy-dominated systematic energy efficiency management system. Specific measures of energy optimization have been put forward, which include taking high efficiency utilized technology such as energy saving from the original, the production process and recycling of waste heat and waste energy etc., integrating and configuring energy in an optimized way of high efficiency and excellent quality, fully realizing the function of different energy in order to optimize the utilization sequence of energy, and improving the energy medium system by themselves. Finally it is clearly pointed out that the steel industry should pay more consideration about the great deal of energy system which they have used now and an ideal energy evaluation methodology and standard should be built as soon as possible if they want to take full usage of the real role and function of energy in all aspects.
文摘With the development of fast communication technology between ego vehicle and other traffic participants,and automated driving technology,there is a big potential in the improvement of energy efficiency of hybrid electric vehicles(HEVs).Moreover,the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on the hilly streets.This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope.Firstly,a rule-based framework is developed to guarantee the success of automated driving in the traffic scenario.Then a constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle.Both speed planning and torque split of hybrid powertrain are provided by the proposed approach.Moreover,the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything.The optimal solution is derived through the Pontryagin’s maximum principle.Finally,to verify the effectiveness of the proposed algorithm,a traffic-in-the-loop powertrain platform with data from real world traffic environment is built.It is found that the fuel economy for the proposed energy management strategy improves in average 17.0%in scenarios of different traffic densities,compared to the energy management strategy without prediction of preceding vehicle speed.
基金Projects(51775445,52175435)supported by the National Natural Science Foundation of ChinaProject(CX2023051)supported by the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China。
文摘On-machine measurement(OMM)stands out as a pivotal technology in complex curved surface adaptive machining.However,the complex structure inherent in workpieces poses a significant challenge as the stylus orientation frequently shifts during the measurement process.Consequently,a substantial amount of time is allocated to calibrating pre-travel error and probe movement.Furthermore,the frequent movement of machine tools also increases the influence of machine errors.To enhance both accuracy and efficiency,an optimization strategy for the OMM process is proposed.Based on the kinematic chain of the machine tools,the relationship between the angle combination of rotary axes,the stylus orientation,and the calibration position of pre-travel error is disclosed.Additionally,an OMM efficiency optimization model for complex curved surfaces is developed.This model is solved to produce the optimal efficiency angle combinations for each to-be-measured point.Within each angle combination,the effects of positioning errors on measurement results are addressed by coordinate system offset and measurement result compensation method.Finally,the experiments on an impeller are used to demonstrate the practical utility of the proposed method.
基金the Nature Foundation(Basic Research)Special Project of Shenyang(22-315-6-20)Liaoning Province Artificial Intelligence Innovation and Development Program Project(2023JH26/10300014)Basic Research Program of Shenyang Institute of Automation,Chinese Academy of Sciences(2023JC2K03).
文摘Optimizing chemical reaction parameters is an expensive optimization problem. Each experiment takes a long time and the raw materials are expensive. High-throughput methods combined with the parallel Efficient Global Optimization algorithm can effectively improve the efficiency of the search for optimal chemical reaction parameters. In this paper, we propose a multi-objective populated expectation improvement criterion for providing multiple near-optimal solutions in high-throughput chemical reaction optimization. An l-NSGA2, employing the Pseudo-power transformation method, is utilized to maximize the expected improvement acquisition function, resulting in a Pareto solution set comprising multiple designs. The approximation of the cost function can be calculated by the ensemble Gaussian process model, which greatly reduces the cost of the exact Gaussian process model. The proposed optimization method was tested on a SNAr benchmark problem. The results show that compared with the previous high-throughput experimental methods, our method can reduce the number of experiments by almost half. At the same time, it theoretically enhances temporal and spatial yields while minimizing by-product formation, potentially guiding real chemical reaction optimization.
基金This work was supported by the Fundamental Research Funds for the Central Universities[Grant number 2019XJ01].
文摘In this paper,an improved discharging circuit was proposed to quicken the decay of the current in the drive coil in a reluctance accelerator when the armature reaches the center of the coil.The aim of this is to prevent the suck-back effect caused by the residual current in drive coil.The method is adding a reverse charging branch with a small capacitor in the traditional pulsed discharging circuit.The results under the traditional circuit and the improved circuit were compared in a simulation.The experiment then verified the simulations and they had good agreement.Simulation and experiment both demonstrated the improved circuit can effectively prevent the suck-back effect and increase the efficiency.At the voltage of 800 V,an efficiency increase of 36.34% was obtained.
基金Supported by the National Science and Technology Support Program(2013BAG12B01)Foundational and Advanced Research Program General Project of Chongqing City(cstc2013jcyjjq60002)
文摘A novel method to calculate fuel-electric conversion factor for full hybrid electric vehicle（HEV）equipped with continuously variable transmission（CVT）is proposed.Based on consideration of the efficiency of pivotal components,electric motor,system efficiency optimization models are developed.According to the target of instantaneous optimization of system efficiency,operating ranges of each mode of power-train are determined,and the corresponding energy management strategies are established.The simulation results demonstrate that the energy management strategy proposed can substantially improve the vehicle fuel economy,and keep battery state of charge（SOC）change in a reasonable variation range.
基金supported by the Natural Science Foundation of China(No.52174232)the Project was supported by Open Research Grant of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining(Nos.EC2022003 and EC2023005)+1 种基金Anhui University of Science and Technology 2023 Graduate Student Innovation Fund(No.2023cx2106)Open Research Grant of Anhui Engineering Research Center for Coal Clean Processing and Carbon Emission Reduction(No.CCCE-2023003).
文摘This study delves into the intricate relationship between iron(Fe)content in kaolinite and its impact on the adsorption behavior of sodium oleate.The effects of different iron concentrations on adsorption energy,hydrogen bond kinetics and adsorption efficiency were studied through simulation and experimental verification.The results show that the presence of iron in the kaolinite structure significantly improves the adsorption capacity of sodium oleate.Kaolinite samples with high iron content have better adsorption properties,lower adsorption energy levels and shorter and stronger hydrogen bonds than pure kaolinite.The optimal concentration of oleic acid ions for achieving maximum adsorption efficiency was identified as 1.2 mmol/L across different kaolinite samples.At this concentration,the adsorption rates and capacities reach their peak,with Fe-enriched kaolinite samples exhibiting notably higher flotation recovery rates.This optimal concentration represents a balance between sufficient oleic acid ion availability for surface interactions and the prevention of self-aggregation phenomena that could hinder adsorption.This study offers promising avenues for optimizing the flotation process in mineral processing applications.
基金Project(60874114) supported by the National Natural Science Foundation of China
文摘By combing the properties of chaos optimization method and genetic algorithm,an adaptive mutative scale chaos genetic algorithm(AMSCGA) was proposed by using one-dimensional iterative chaotic self-map with infinite collapses within the finite region of [-1,1].Some measures in the optimization algorithm,such as adjusting the searching space of optimized variables continuously by using adaptive mutative scale method and making the most circle time as its control guideline,were taken to ensure its speediness and veracity in seeking the optimization process.The calculation examples about three testing functions reveal that AMSCGA has both high searching speed and high precision.Furthermore,the average truncated generations,the distribution entropy of truncated generations and the ratio of average inertia generations were used to evaluate the optimization efficiency of AMSCGA quantificationally.It is shown that the optimization efficiency of AMSCGA is higher than that of genetic algorithm.
基金Supported by Shanghai Municipal Natural Science Foundation of China (Grant No.19ZR1418600)。
文摘Axial flux permanent magnet synchronous motors(AFPMSMs)have been widely used in wind-power generation,electric vehicles,aircraft,and other renewable-energy applications owing to their high power density,operating efficiency,and integrability.To facilitate comprehensive research on AFPMSM,this article reviews the developments in the research on the design and control optimization of AFPMSMs.First,the basic topologies of AFPMSMs are introduced and classified.Second,the key points of the design optimization of core and coreless AFPMSMs are summarized from the aspects of parameter design,structure design,and material optimization.Third,because efficiency improvement is an issue that needs to be addressed when AFPMSMs are applied to electric or other vehicles,the development status of efficiency-optimization control strategies is reviewed.Moreover,control strategies proposed to suppress torque ripple caused by the small inductance of disc coreless permanent magnet synchronous motors(DCPMSMs)are summarized.An overview of the rotor-synchronization control strategies for disc contra-rotating permanent magnet synchronous motors(CRPMSMs)is presented.Finally,the current difficulties and development trends revealed in this review are discussed.
基金Deanship of Scientific Research (DSR) at King Abdulaziz University,Jeddah,Saudi Arabia,under grant no.G:734-611-1441.
文摘With the rapid development of wireless technologies,wireless access networks have entered their Fifth-Generation(5G)system phase.The heterogeneous and complex nature of a 5G system,with its numerous technological scenarios,poses significant challenges to wireless resource management,making radio resource optimization an important aspect of Device-to-Device(D2D)communication in such systems.Cellular D2D communication can improve spectrum efficiency,increase system capacity,and reduce base station communication burdens by sharing authorized cell resources;however,can also cause serious interference.Therefore,research focusing on reducing this interference by optimizing the configuration of shared cellular resources has also grown in importance.This paper proposes a novel algorithm to address the problems of co-channel interference and energy efficiency optimization in a long-term evolution network.The proposed algorithm uses the fuzzy clustering method,which employs minimum outage probability to divide D2D users into several groups in order to improve system throughput and reduce interference between users.An efficient power control algorithm based on game theory is also proposed to optimize user transmission power within each group and thereby improve user energy efficiency.Simulation results show that these proposed algorithms can effectively improve system throughput,reduce co-channel interference,and enhance energy efficiency.
文摘Two new techniques for efficiency-optimization control(EOC) of induction motor drives were proposed. The first method combined Loss Model and "golden section technique", which was faster than the available methods. Secondly, the low-frequency ripple torque due to decrease of rotor flux was compensated in a feedforward manner. If load torque or speed command changed, the efficiency search algorithm would be abandoned and the rated flux would be established to get the best transient response. The close agreement between the simulation and the experimental results confirmed the validity and usefulness of the proposed techniques.
基金the financial support of the National Natural Science Foundation of China(Nos.52076180,51876176 and 51906204)National Science and Technology Major Project,China(No.2017-I0001-0001)。
文摘The Efficient Global Optimization(EGO)algorithm has been widely used in the numerical design optimization of engineering systems.However,the need for an uncertainty estimator limits the selection of a surrogate model.In this paper,a Sequential Ensemble Optimization(SEO)algorithm based on the ensemble model is proposed.In the proposed algorithm,there is no limitation on the selection of an individual surrogate model.Specifically,the SEO is built based on the EGO by extending the EGO algorithm so that it can be used in combination with the ensemble model.Also,a new uncertainty estimator for any surrogate model named the General Uncertainty Estimator(GUE)is proposed.The performance of the proposed SEO algorithm is verified by the simulations using ten well-known mathematical functions with varying dimensions.The results show that the proposed SEO algorithm performs better than the traditional EGO algorithm in terms of both the final optimization results and the convergence rate.Further,the proposed algorithm is applied to the global optimization control for turbo-fan engine acceleration schedule design.
基金supported partly by the National Science and Technology Major Project of China(Grant No.2016ZX05025-001006)Major Science and Technology Project of CNPC(Grant No.ZD2019-183-007)
文摘Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development.
基金National Natural Science Foundation of China under Grant Nos.51639006 and 51725901
文摘Finite element（FE） is a powerful tool and has been applied by investigators to real-time hybrid simulations（RTHSs）. This study focuses on the computational efficiency, including the computational time and accuracy, of numerical integrations in solving FE numerical substructure in RTHSs. First, sparse matrix storage schemes are adopted to decrease the computational time of FE numerical substructure. In this way, the task execution time（TET） decreases such that the scale of the numerical substructure model increases. Subsequently, several commonly used explicit numerical integration algorithms, including the central difference method（CDM）, the Newmark explicit method, the Chang method and the Gui-λ method, are comprehensively compared to evaluate their computational time in solving FE numerical substructure. CDM is better than the other explicit integration algorithms when the damping matrix is diagonal, while the Gui-λ（λ = 4） method is advantageous when the damping matrix is non-diagonal. Finally, the effect of time delay on the computational accuracy of RTHSs is investigated by simulating structure-foundation systems. Simulation results show that the influences of time delay on the displacement response become obvious with the mass ratio increasing, and delay compensation methods may reduce the relative error of the displacement peak value to less than 5% even under the large time-step and large time delay.
基金Supported by Science and Technology Project of Guiyang Company of Guizhou Provincial Tobacco Company"Study on Cultivation of New Type Tobacco Operation Entities in Guiyang Tobacco Area"(2022-06)Students’Platform for Innovation and Entrepreneurship Training Program of Colleges and Universities in Henan Province"Study on Cultivation of New Professional Tobacco Farmers with Family Farms as the Carrier"(202210466045)。
文摘The larger the difference between the willingness scale of tobacco family farmers and the optimal scale of efficiency,the greater the degree of irrationality,and the higher the decision making risk.With the aid of DEA model,this study calculated the optimal scale of efficiency of Guiyang tobacco family farms.Using the ratio of willingness scale and efficiency optimal scale,it measured the degree of irrationality of family farmers.In addition,with the help of multiple linear regression model,it explained the irrational decision making mechanism of family farmers.Finally,it made a portrait of farmers who tend to make irrational decisions,to find specific farmers and guide them in their production and operation,reduce the risk of planting scale decision making and stabilize the sustainable development of the tobacco industry.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11105062 and 11265014the Fundamental Research Funds for the Central Universities under Grant Nos LZUJBKY-2011-57 and LZUJBKY-2015-119
文摘Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculate the mutual information, energy cost, and energy efficiency of an array of these bistable units. We find that the optimal number of units could maximize this array＇s energy efficiency in encoding pulse inputs, which depends on the fixed energy cost. We conclude that demand for energy efficiency in biological systems may strongly influence the size of these systems under the pressure of natural selection.
基金Financial support from the National Natural Science Foundation of China(Grant No.21978171)the China Postdoctoral Science Foundation(Grant No.2020M671171)is greatly appreciated.
文摘This paper proposes an innovative simultaneous optimization approach for single and multi-component mass exchanger network synthesis (MENS). A retrofitted stage-wise superstructure and a parallelized random walk algorithm with compulsive evolution (RWCE) are adopted. An iterative calculation method is designed to satisfy the requirements of multi-component mass transfer, with a relaxation for the outlet composition of the lean streams. The parametric analysis shows that the relaxation coefficient plays a major role in driving the convergence of the method. To improve the robustness of the established model, an adaptive relaxation coefficient strategy is implemented for multi-component MENS problems. In a divergence situation, the outlet concentration of the lean stream can be adjusted automatically by a random relaxation coefficient. Finally, three industrial MENS examples are considered in this work, whose total annual cost (TAC) are reduced by 7179, 2212, and 551 $·year^(-1). The corresponding optimization times are obtained to be 336, 125, and 145 s. The results indicate improvements in the economy and time, demonstrating that the parallelized RWCE can yield an optimal TAC and optimization efficiency compared to previous results. Overall, the adaptive relaxation coefficient strategy enhances the convergence for multi-component MENS problems.
