Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley a...Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.展开更多
With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electro...With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electronic instruments.Therefore,the design and preparation of electromagnetic absorbing composites represent an efficient approach to mitigate the current hazards of electromagnetic radiation.However,traditional electromagnetic absorbers are difficult to satisfy the demands of actual utilization in the face of new challenges,and emerging absorbents have garnered increasing attention due to their structure and performance-based advantages.In this review,several emerging composites of Mxene-based,biochar-based,chiral,and heat-resisting are discussed in detail,including their synthetic strategy,structural superiority and regulation method,and final optimization of electromagnetic absorption ca-pacity.These insights provide a comprehensive reference for the future development of new-generation electromagnetic-wave absorption composites.Moreover,the potential development directions of these emerging absorbers have been proposed as well.展开更多
This study investigates the potential of Prosopis cineraria Leaves Powder(PCLP)as a biosorbent for removing lead(Pb)and zinc(Zn)from aqueous solutions,optimizing the process using Response Surface Methodology(RSM).Pro...This study investigates the potential of Prosopis cineraria Leaves Powder(PCLP)as a biosorbent for removing lead(Pb)and zinc(Zn)from aqueous solutions,optimizing the process using Response Surface Methodology(RSM).Prosopis cineraria,commonly known as Khejri,is a drought-resistant tree with significant promise in environmental applications.The research employed a Central Composite Design(CCD)to examine the independent and combined effects of key process variables,including initial metal ion concentration,contact time,pH,and PCLP dosage.RSM was used to develop mathematical models that explain the relationship between these factors and the efficiency of metal removal,allowing the determination of optimal operating conditions.The experimental results indicated that the Langmuir isotherm model was the most appropriate for describing the biosorption of both metals,suggesting favorable adsorption characteristics.Additionally,the D-R isotherm confirmed that chemisorption was the primary mechanism involved in the biosorption process.For lead removal,the optimal conditions were found to be 312.23 K temperature,pH 4.72,58.5 mg L-1 initial concentration,and 0.27 g biosorbent dosage,achieving an 83.77%removal efficiency.For zinc,the optimal conditions were 312.4 K,pH 5.86,53.07 mg L-1 initial concentration,and the same biosorbent dosage,resulting in a 75.86%removal efficiency.These findings highlight PCLP’s potential as an effective,eco-friendly biosorbent for sustainable heavy metal removal in water treatment.展开更多
Purpose–The precast concrete slab track(PST)has advantages of fewer maintenance frequencies,better smooth rides and structural stability,which has been widely applied in urban rail transit.Precise positioning of prec...Purpose–The precast concrete slab track(PST)has advantages of fewer maintenance frequencies,better smooth rides and structural stability,which has been widely applied in urban rail transit.Precise positioning of precast concrete slab(PCS)is vital for keeping the initial track regularity.However,the cast-in-place process of the self-compacting concrete(SCC)filling layer generally causes a large deformation of PCS due to the water-hammer effect of flowing SCC,even cracking of PCS.Currently,the buoyancy characteristic and influencing factors of PCS during the SCC casting process have not been thoroughly studied in urban rail transit.Design/methodology/approach–In this work,a Computational Fluid Dynamics(CFD)model is established to calculate the buoyancy of PCS caused by the flowing SCC.The main influencing factors,including the inlet speed and flowability of SCC,have been analyzed and discussed.A new structural optimization scheme has been proposed for PST to reduce the buoyancy caused by the flowing SCC.Findings–The simulation and field test results showed that the buoyancy and deformation of PCS decreased obviously after adopting the new scheme.Originality/value–The findings of this study can provide guidance for the control of the deformation of PCS during the SCC construction process.展开更多
Ground source heat pump systems demonstrate significant potential for northern rural heating applications;however,the effectiveness of these systems is often limited by challenging geological conditions.For instance,i...Ground source heat pump systems demonstrate significant potential for northern rural heating applications;however,the effectiveness of these systems is often limited by challenging geological conditions.For instance,in certain regions,the installation of buried pipes for heat exchangers may be complicated,and these pipes may not always serve as efficient low-temperature heat sources for the heat pumps of the system.To address this issue,the current study explored the use of solar-energy-collecting equipment to supplement buried pipes.In this design,both solar energy and geothermal energy provide low-temperature heat to the heat pump.First,a simulation model of a solar‒ground source heat pump coupling system was established using TRNSYS.The accuracy of this model was validated through experiments and simulations on various system configurations,including varying numbers of buried pipes,different areas of solar collectors,and varying volumes of water tanks.The simulations examined the coupling characteristics of these components and their influence on system performance.The results revealed that the operating parameters of the system remained consistent across the following configurations:three buried pipes,burial depth of 20 m,collector area of 6 m^(2),and water tank volume of 0.5 m^(3);four buried pipes,burial depth of 20 m,collector area of 3 m^(2),and water tank volume of 0.5 m^(3);and five buried pipes with a burial depth of 20 m.Furthermore,the heat collection capacity of the solar collectors spanning an area of 3 m^(2)was found to be equivalent to that of one buried pipe.Moreover,the findings revealed that the solar‒ground source heat pump coupling system demonstrated a lower annual cumulative energy consumption compared to the ground source heat pump system,presenting a reduction of 5.31%compared to the energy consumption of the latter.展开更多
To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy...To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,th...Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm.The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control.Then,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy.Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage.Additionally,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm.These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load transients.This strategy effectively mitigated transient voltage and current surges during mode transitions.Consequently,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid inverter.The enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.展开更多
The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the exis...The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.展开更多
In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradien...In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradient method.Under the condition of standard Wolfe line search,the proposed search direction is the descent direction.For general nonlinear functions,the method is globally convergent.Finally,numerical results show that the proposed method is efficient.展开更多
This research presents a novel nature-inspired metaheuristic optimization algorithm,called theNarwhale Optimization Algorithm(NWOA).The algorithm draws inspiration from the foraging and prey-hunting strategies of narw...This research presents a novel nature-inspired metaheuristic optimization algorithm,called theNarwhale Optimization Algorithm(NWOA).The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals,“unicorns of the sea”,particularly the use of their distinctive spiral tusks,which play significant roles in hunting,searching prey,navigation,echolocation,and complex social interaction.Particularly,the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the Arctic ice.These functions provide a strong assessment basis for investigating the algorithm’s prowess at balancing exploration and exploitation,convergence speed,and solution accuracy.The performance of the NWOA is evaluated on 30 benchmark test functions.A comparison study using the Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Perfumer Optimization Algorithm(POA),Candle Flame Optimization(CFO)Algorithm,Particle Swarm Optimization(PSO)Algorithm,and Genetic Algorithm(GA)validates the results.As evidenced in the experimental results,NWOA is capable of yielding competitive outcomes among these well-known optimizers,whereas in several instances.These results suggest thatNWOAhas proven to be an effective and robust optimization tool suitable for solving many different complex optimization problems from the real world.展开更多
The development of high-performance structural and functional materials is vital in many industrial fields.High-and medium-entropy alloys(H/MEAs)with superior comprehensive properties owing to their specific microstru...The development of high-performance structural and functional materials is vital in many industrial fields.High-and medium-entropy alloys(H/MEAs)with superior comprehensive properties owing to their specific microstructures are promising candidates for structural materials.More importantly,multitudinous efforts have been made to regulate the microstructures and the properties of H/MEAs to further expand their industrial applications.The various heterostructures have enormous potential for the development of H/MEAs with outstanding performance.Herein,multiple heterogeneous structures with single and hierarchical heterogeneities were discussed in detail.Moreover,preparation methods for compositional inhomogeneity,bimodal structures,dualphase structures,lamella/layered structures,harmonic structures(core-shell),multiscale precipitates and heterostructures coupled with specific microstructures in H/MEAs were also systematically reviewed.The deformation mechanisms induced by the different heterostructures were thoroughly discussed to explore the relationship between the heterostructures and the optimized properties of H/MEAs.The contributions of the heterostructures and advanced microstructures to the H/MEAs were comprehensively elucidated to further improve the properties of the alloys.Finally,this review discussed the future challenges of high-performance H/MEAs for industrial applications and provides feasible methods for optimizing heterostructures to enhance the comprehensive properties of H/MEAs.展开更多
The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedic...The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach.This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique,specifically designed for early-stage wing configuration.By employing surrogate model trained on small dataset derived from low-fidelity simulations,our method aims to predict outputs within an acceptable range.This strategy significantly mitigates computational costs and expedites the design exploration process.The methodology's validation relies on its successful application in optimizing the box wing of PARSIFAL,serving as a benchmark,while the primary focus remains on optimizing the newly designed box wing by Bionica.Applying this method to the Bionica configuration led to a notable 14%improvement in overall aerodynamic effciency.Furthermore,all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation.This methodology introduces innovative approach that aims to streamline computational processes,potentially reducing the time and resources required compared to traditional optimization methods.展开更多
Aqueous zinc-halogen batteries are promising candidates for large-scale energy storage due to their abundant resources,intrinsic safety,and high theoretical capacity.Nevertheless,the uncontrollable zinc dendrite growt...Aqueous zinc-halogen batteries are promising candidates for large-scale energy storage due to their abundant resources,intrinsic safety,and high theoretical capacity.Nevertheless,the uncontrollable zinc dendrite growth and spontaneous shuttle effect of active species have prohibited their practical implementation.Herein,a double-layered protective film based on zinc-ethylenediamine tetramethylene phosphonic acid(ZEA)artificial film and ZnF2-rich solid electrolyte interphase(SEI)layer has been successfully fabricated on the zinc metal anode via electrode/electrolyte synergistic optimization.The ZEA-based artificial film shows strong affinity for the ZnF2-rich SEI layer,therefore effectively suppressing the SEI breakage and facilitating the construction of double-layered protective film on the zinc metal anode.Such double-layered architecture not only modulates Zn2+flux and suppresses the zinc dendrite growth,but also blocks the direct contact between the metal anode and electrolyte,thus mitigating the corrosion from the active species.When employing optimized metal anodes and electrolytes,the as-developed zinc-(dual)halogen batteries present high areal capacity and satisfactory cycling stability.This work provides a new avenue for developing aqueous zinc-(dual)halogen batteries.展开更多
Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian O...Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian Optimization utilizes Gaussian Process (GP) as its core model and employs an adaptive search strategy to efficiently explore and identify optimal combinations of operational parameters within a limited parameter space, thereby enabling rapid optimization of the required parameters to achieve the target droplet size. Traditional methods typically rely on manually selecting a series of operational parameters and conducting multiple simulations to gradually approach the target droplet size. This process is time-consuming and prone to getting trapped in local optima. In contrast, Bayesian Optimization adaptively adjusts its search strategy, significantly reducing computational costs and effectively exploring global optima, thus greatly improving optimization efficiency. Additionally, the study investigates the impact of rectangular rib structures within the T-junction microchannel on droplet generation, revealing how the channel geometry influences droplet formation and size. After determining the target droplet size, we further applied Bayesian Optimization to refine the rib geometry. The integration of Bayesian Optimization with computational fluid dynamics (CFD) offers a promising tool and provides new insights into the optimal design of microfluidic devices.展开更多
Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high-and low-pressure wells within the same development block,which leads to issues involving mixed-p...Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high-and low-pressure wells within the same development block,which leads to issues involving mixed-pressure flows.Traditional pipeline optimization methods used in conventional gas well blocks fail to address the unique needs of shale gas wells,such as the precise planning of airflow paths,pressure distribution,and compression.This study proposes a pressure-controlled production optimization strategy specifically designed for shale gas wells operating under mixed-pressure flow conditions.The strategy aims to improve production stability and optimize system efficiency.The decline in production and pressure for individual wells over time is forecasted using a predictive model that accounts for key factors of system optimization,such as reservoir depletion,wellbore conditions,and equipment performance.Additionally,the model predicts the timing and impact of liquid loading,which can significantly affect production.The optimization process involves analyzing the existing gathering pipeline network to determine the most efficient flow directions and compression strategies based on these predictions,while the strategy involves adjusting compressor settings,optimizing flow rates,and planning pressure distribution across the network to maximize productivity while maintaining system stability.By implementing these strategies,this study significantly improves gas well productivity and enhances the adaptability and efficiency of the gathering and transportation system.The proposed approach provides systematic technical solutions and practical guidance for the efficient development and stable production of shale gas fields,ensuring more robust and sustainable pipeline operations.展开更多
The optimization of polymer structures aims to determine an optimal sequence or topology that achieves a given target property or structural performance.This inverse design problem involves searching within a vast com...The optimization of polymer structures aims to determine an optimal sequence or topology that achieves a given target property or structural performance.This inverse design problem involves searching within a vast combinatorial phase space defined by components,se-quences,and topologies,and is often computationally intractable due to its NP-hard nature.At the core of this challenge lies the need to evalu-ate complex correlations among structural variables,a classical problem in both statistical physics and combinatorial optimization.To address this,we adopt a mean-field approach that decouples direct variable-variable interactions into effective interactions between each variable and an auxiliary field.The simulated bifurcation(SB)algorithm is employed as a mean-field-based optimization framework.It constructs a Hamiltonian dynamical system by introducing generalized momentum fields,enabling efficient decoupling and dynamic evolution of strongly coupled struc-tural variables.Using the sequence optimization of a linear copolymer adsorbing on a solid surface as a case study,we demonstrate the applica-bility of the SB algorithm to high-dimensional,non-differentiable combinatorial optimization problems.Our results show that SB can efficiently discover polymer sequences with excellent adsorption performance within a reasonable computational time.Furthermore,it exhibits robust con-vergence and high parallel scalability across large design spaces.The approach developed in this work offers a new computational pathway for polymer structure optimization.It also lays a theoretical foundation for future extensions to topological design problems,such as optimizing the number and placement of side chains,as well as the co-optimization of sequence and topology.展开更多
Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the dela...Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the delays arising from the residence time for materials passing through production units during the process with guaranteed constraint satisfaction, an asynchronous distributed parameter projection algorithm with gradient tracking method is introduced. Besides, the heavy ball momentum and Nesterov momentum are incorporated into the proposed algorithm in order to achieve double acceleration properties. The experimental results show that the proposed asynchronous algorithm can achieve a faster convergence compared with the synchronous algorithm.展开更多
In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focus...Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focusing solely on wear and not addressing fatigue in profile optimization can lead to the propagation of rail cracks,the peeling of material off the rail,and even rail fractures.Therefore,we propose an optimization approach that balances rail wear and fatigue for heavy-haul railway rails to mitigate rail fatigue damage.Initially,we performed a field investigation to acquire essential data and understand the characteristics of track damage.Based on theory and measured data,a simulation model for wear and fatigue was then established.Subsequently,the control points of the rail profile according to cubic non-uniform rational B-spline(NURBS)theory were set as the research variables.The rail’s wear rate and fatigue crack propagation rate were adopted as the objective functions.A multi-objective,multi-variable,and multi-constraint nonlinear optimization model was then constructed,specifically using a Levenberg Marquardt-back propagation neural network as optimized by the particle swarm optimization algorithm(PSO-LM-BP neural network).Ultimately,optimal solutions from the model were identified using a chaos microvariation adaptive genetic algorithm,and the effectiveness of the optimization was validated using a dynamics model and a rail damage model.展开更多
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.
基金supported by the Surface Project of Local De-velopment in Science and Technology Guided by Central Govern-ment(No.2021ZYD0041)the National Natural Science Founda-tion of China(Nos.52377026 and 52301192)+3 种基金the Natural Science Foundation of Shandong Province(No.ZR2019YQ24)the Taishan Scholars and Young Experts Program of Shandong Province(No.tsqn202103057)the Special Financial of Shandong Province(Struc-tural Design of High-efficiency Electromagnetic Wave-absorbing Composite Materials and Construction of Shandong Provincial Tal-ent Teams)the“Sanqin Scholars”Innovation Teams Project of Shaanxi Province(Clean Energy Materials and High-Performance Devices Innovation Team of Shaanxi Dongling Smelting Co.,Ltd.).
文摘With the increasing complexity of the current electromagnetic environment,excessive microwave radi-ation not only does harm to human health but also forms various electromagnetic interference to so-phisticated electronic instruments.Therefore,the design and preparation of electromagnetic absorbing composites represent an efficient approach to mitigate the current hazards of electromagnetic radiation.However,traditional electromagnetic absorbers are difficult to satisfy the demands of actual utilization in the face of new challenges,and emerging absorbents have garnered increasing attention due to their structure and performance-based advantages.In this review,several emerging composites of Mxene-based,biochar-based,chiral,and heat-resisting are discussed in detail,including their synthetic strategy,structural superiority and regulation method,and final optimization of electromagnetic absorption ca-pacity.These insights provide a comprehensive reference for the future development of new-generation electromagnetic-wave absorption composites.Moreover,the potential development directions of these emerging absorbers have been proposed as well.
文摘This study investigates the potential of Prosopis cineraria Leaves Powder(PCLP)as a biosorbent for removing lead(Pb)and zinc(Zn)from aqueous solutions,optimizing the process using Response Surface Methodology(RSM).Prosopis cineraria,commonly known as Khejri,is a drought-resistant tree with significant promise in environmental applications.The research employed a Central Composite Design(CCD)to examine the independent and combined effects of key process variables,including initial metal ion concentration,contact time,pH,and PCLP dosage.RSM was used to develop mathematical models that explain the relationship between these factors and the efficiency of metal removal,allowing the determination of optimal operating conditions.The experimental results indicated that the Langmuir isotherm model was the most appropriate for describing the biosorption of both metals,suggesting favorable adsorption characteristics.Additionally,the D-R isotherm confirmed that chemisorption was the primary mechanism involved in the biosorption process.For lead removal,the optimal conditions were found to be 312.23 K temperature,pH 4.72,58.5 mg L-1 initial concentration,and 0.27 g biosorbent dosage,achieving an 83.77%removal efficiency.For zinc,the optimal conditions were 312.4 K,pH 5.86,53.07 mg L-1 initial concentration,and the same biosorbent dosage,resulting in a 75.86%removal efficiency.These findings highlight PCLP’s potential as an effective,eco-friendly biosorbent for sustainable heavy metal removal in water treatment.
文摘Purpose–The precast concrete slab track(PST)has advantages of fewer maintenance frequencies,better smooth rides and structural stability,which has been widely applied in urban rail transit.Precise positioning of precast concrete slab(PCS)is vital for keeping the initial track regularity.However,the cast-in-place process of the self-compacting concrete(SCC)filling layer generally causes a large deformation of PCS due to the water-hammer effect of flowing SCC,even cracking of PCS.Currently,the buoyancy characteristic and influencing factors of PCS during the SCC casting process have not been thoroughly studied in urban rail transit.Design/methodology/approach–In this work,a Computational Fluid Dynamics(CFD)model is established to calculate the buoyancy of PCS caused by the flowing SCC.The main influencing factors,including the inlet speed and flowability of SCC,have been analyzed and discussed.A new structural optimization scheme has been proposed for PST to reduce the buoyancy caused by the flowing SCC.Findings–The simulation and field test results showed that the buoyancy and deformation of PCS decreased obviously after adopting the new scheme.Originality/value–The findings of this study can provide guidance for the control of the deformation of PCS during the SCC construction process.
基金supported by 2024 Central Guidance Local Science and Technology Development Fund Project"Study on the mechanism and evaluation method of thermal pollution in water bodies,as well as research on thermal carrying capacity".(Grant 246Z4506G)Key Research and Development Project in Hebei Province:"Key Technologies and Equipment Research and Demonstration of Multiple Energy Complementary(Electricity,Heat,Cold System)for Solar Energy,Geothermal Energy,Phase Change Energy"(Grant 236Z4310G)the Hebei Academy of Sciences Key Research and Development Program"Research on Heat Transfer Mechanisms and Efficient Applications of Intermediate and Deep Geothermal Energy"(22702)。
文摘Ground source heat pump systems demonstrate significant potential for northern rural heating applications;however,the effectiveness of these systems is often limited by challenging geological conditions.For instance,in certain regions,the installation of buried pipes for heat exchangers may be complicated,and these pipes may not always serve as efficient low-temperature heat sources for the heat pumps of the system.To address this issue,the current study explored the use of solar-energy-collecting equipment to supplement buried pipes.In this design,both solar energy and geothermal energy provide low-temperature heat to the heat pump.First,a simulation model of a solar‒ground source heat pump coupling system was established using TRNSYS.The accuracy of this model was validated through experiments and simulations on various system configurations,including varying numbers of buried pipes,different areas of solar collectors,and varying volumes of water tanks.The simulations examined the coupling characteristics of these components and their influence on system performance.The results revealed that the operating parameters of the system remained consistent across the following configurations:three buried pipes,burial depth of 20 m,collector area of 6 m^(2),and water tank volume of 0.5 m^(3);four buried pipes,burial depth of 20 m,collector area of 3 m^(2),and water tank volume of 0.5 m^(3);and five buried pipes with a burial depth of 20 m.Furthermore,the heat collection capacity of the solar collectors spanning an area of 3 m^(2)was found to be equivalent to that of one buried pipe.Moreover,the findings revealed that the solar‒ground source heat pump coupling system demonstrated a lower annual cumulative energy consumption compared to the ground source heat pump system,presenting a reduction of 5.31%compared to the energy consumption of the latter.
基金Supported by State Grid Zhejiang Electric Power Co.,Ltd.Science and Technology Project Funding(No.B311DS230005).
文摘To address the issue of coordinated control of multiple hydrogen and battery storage units to suppress the grid-injected power deviation of wind farms,an online optimization strategy for Battery-hydrogen hybrid energy storage systems based on measurement feedback is proposed.First,considering the high charge/discharge losses of hydrogen storage and the low energy density of battery storage,an operational optimization objective is established to enable adaptive energy adjustment in the Battery-hydrogen hybrid energy storage system.Next,an online optimization model minimizing the operational cost of the hybrid system is constructed to suppress grid-injected power deviations with satisfying the operational constraints of hydrogen storage and batteries.Finally,utilizing the online measurement of the energy states of hydrogen storage and batteries,an online optimization strategy based on measurement feedback is designed.Case study results show:before and after smoothing the fluctuations in wind power,the time when the power exceeded the upper and lower limits of the grid-injected power accounted for 24.1%and 1.45%of the total time,respectively,the proposed strategy can effectively keep the grid-injected power deviations of wind farms within the allowable range.Hydrogen storage and batteries respectively undertake long-term and short-term charge/discharge tasks,effectively reducing charge/discharge losses of the Battery-hydrogen hybrid energy storage systems and improving its operational efficiency.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金received funding from the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1633)2023 University Student Innovation and Entrepreneurship Training Program(202311463009Z)+1 种基金Changzhou Science and Technology Support Project(CE20235045)Open Project of Jiangsu Key Laboratory of Power Transmission&Distribution Equipment Technology(2021JSSPD12).
文摘Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm.The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control.Then,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy.Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage.Additionally,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm.These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load transients.This strategy effectively mitigated transient voltage and current surges during mode transitions.Consequently,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid inverter.The enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability.
基金funded by the State Grid Corporation Science and Technology Project(5108-202218280A-2-391-XG).
文摘The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.
基金Supported by the Science and Technology Project of Guangxi(Guike AD23023002)。
文摘In this paper,we propose a three-term conjugate gradient method for solving unconstrained optimization problems based on the Hestenes-Stiefel(HS)conjugate gradient method and Polak-Ribiere-Polyak(PRP)conjugate gradient method.Under the condition of standard Wolfe line search,the proposed search direction is the descent direction.For general nonlinear functions,the method is globally convergent.Finally,numerical results show that the proposed method is efficient.
文摘This research presents a novel nature-inspired metaheuristic optimization algorithm,called theNarwhale Optimization Algorithm(NWOA).The algorithm draws inspiration from the foraging and prey-hunting strategies of narwhals,“unicorns of the sea”,particularly the use of their distinctive spiral tusks,which play significant roles in hunting,searching prey,navigation,echolocation,and complex social interaction.Particularly,the NWOA imitates the foraging strategies and techniques of narwhals when hunting for prey but focuses mainly on the cooperative and exploratory behavior shown during group hunting and in the use of their tusks in sensing and locating prey under the Arctic ice.These functions provide a strong assessment basis for investigating the algorithm’s prowess at balancing exploration and exploitation,convergence speed,and solution accuracy.The performance of the NWOA is evaluated on 30 benchmark test functions.A comparison study using the Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Perfumer Optimization Algorithm(POA),Candle Flame Optimization(CFO)Algorithm,Particle Swarm Optimization(PSO)Algorithm,and Genetic Algorithm(GA)validates the results.As evidenced in the experimental results,NWOA is capable of yielding competitive outcomes among these well-known optimizers,whereas in several instances.These results suggest thatNWOAhas proven to be an effective and robust optimization tool suitable for solving many different complex optimization problems from the real world.
基金National Natural Science Foundation of China(52261032,51861021,51661016)Science and Technology Plan of Gansu Province(21YF5GA074)+2 种基金Public Welfare Project of Zhejiang Natural Science Foundation(LGG22E010008)Wenzhou Basic Public Welfare Scientific Research Project(G2023020)Incubation Program of Excellent Doctoral Dissertation-Lanzhou University of Technology。
文摘The development of high-performance structural and functional materials is vital in many industrial fields.High-and medium-entropy alloys(H/MEAs)with superior comprehensive properties owing to their specific microstructures are promising candidates for structural materials.More importantly,multitudinous efforts have been made to regulate the microstructures and the properties of H/MEAs to further expand their industrial applications.The various heterostructures have enormous potential for the development of H/MEAs with outstanding performance.Herein,multiple heterogeneous structures with single and hierarchical heterogeneities were discussed in detail.Moreover,preparation methods for compositional inhomogeneity,bimodal structures,dualphase structures,lamella/layered structures,harmonic structures(core-shell),multiscale precipitates and heterostructures coupled with specific microstructures in H/MEAs were also systematically reviewed.The deformation mechanisms induced by the different heterostructures were thoroughly discussed to explore the relationship between the heterostructures and the optimized properties of H/MEAs.The contributions of the heterostructures and advanced microstructures to the H/MEAs were comprehensively elucidated to further improve the properties of the alloys.Finally,this review discussed the future challenges of high-performance H/MEAs for industrial applications and provides feasible methods for optimizing heterostructures to enhance the comprehensive properties of H/MEAs.
基金The funding for this publication was provided by Johannes Kepler University(JKU),Linz.Special thanks to Prof.Zongmin DENG from Beihang University for his invaluable guidance,insightful feedback,and constructive criticism,which greatly enhanced the quality of this manuscript.We extend our heartfelt gratitude to the PARSIFAL team for providing the supporting materials,which inspired this study.
文摘The optimization of wings typically relies on computationally intensive high-fidelity simulations,which restrict the quick exploration of design spaces.To address this problem,this paper introduces a methodology dedicated to optimizing box wing configurations using low-fidelity data driven machine learning approach.This technique showcases its practicality through the utilization of a tailored low-fidelity machine learning technique,specifically designed for early-stage wing configuration.By employing surrogate model trained on small dataset derived from low-fidelity simulations,our method aims to predict outputs within an acceptable range.This strategy significantly mitigates computational costs and expedites the design exploration process.The methodology's validation relies on its successful application in optimizing the box wing of PARSIFAL,serving as a benchmark,while the primary focus remains on optimizing the newly designed box wing by Bionica.Applying this method to the Bionica configuration led to a notable 14%improvement in overall aerodynamic effciency.Furthermore,all the optimized results obtained from machine learning model undergo rigorous assessments through the high-fidelity RANS analysis for confirmation.This methodology introduces innovative approach that aims to streamline computational processes,potentially reducing the time and resources required compared to traditional optimization methods.
基金support from the National Natural Science Foundation of China(22209089,22178187)Natural Science Foundation of Shandong Province(ZR2022QB048,ZR2021MB006)+2 种基金Excellent Youth Science Foundation of Shandong Province(Overseas)(2023HWYQ-089)the Taishan Scholars Program of Shandong Province(tsqn201909091)Open Research Fund of School of Chemistry and Chemical Engineering,Henan Normal University.
文摘Aqueous zinc-halogen batteries are promising candidates for large-scale energy storage due to their abundant resources,intrinsic safety,and high theoretical capacity.Nevertheless,the uncontrollable zinc dendrite growth and spontaneous shuttle effect of active species have prohibited their practical implementation.Herein,a double-layered protective film based on zinc-ethylenediamine tetramethylene phosphonic acid(ZEA)artificial film and ZnF2-rich solid electrolyte interphase(SEI)layer has been successfully fabricated on the zinc metal anode via electrode/electrolyte synergistic optimization.The ZEA-based artificial film shows strong affinity for the ZnF2-rich SEI layer,therefore effectively suppressing the SEI breakage and facilitating the construction of double-layered protective film on the zinc metal anode.Such double-layered architecture not only modulates Zn2+flux and suppresses the zinc dendrite growth,but also blocks the direct contact between the metal anode and electrolyte,thus mitigating the corrosion from the active species.When employing optimized metal anodes and electrolytes,the as-developed zinc-(dual)halogen batteries present high areal capacity and satisfactory cycling stability.This work provides a new avenue for developing aqueous zinc-(dual)halogen batteries.
基金support from National Key Research and Development Program of China(2023YFC3905400)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA0490102)National Natural Science Foundation of China(22178354,2242100322408374).
文摘Integrating Bayesian Optimization with Volume of Fluid (VOF) simulations, this work aims to optimize the operational conditions and geometric parameters of T-junction microchannels for target droplet sizes. Bayesian Optimization utilizes Gaussian Process (GP) as its core model and employs an adaptive search strategy to efficiently explore and identify optimal combinations of operational parameters within a limited parameter space, thereby enabling rapid optimization of the required parameters to achieve the target droplet size. Traditional methods typically rely on manually selecting a series of operational parameters and conducting multiple simulations to gradually approach the target droplet size. This process is time-consuming and prone to getting trapped in local optima. In contrast, Bayesian Optimization adaptively adjusts its search strategy, significantly reducing computational costs and effectively exploring global optima, thus greatly improving optimization efficiency. Additionally, the study investigates the impact of rectangular rib structures within the T-junction microchannel on droplet generation, revealing how the channel geometry influences droplet formation and size. After determining the target droplet size, we further applied Bayesian Optimization to refine the rib geometry. The integration of Bayesian Optimization with computational fluid dynamics (CFD) offers a promising tool and provides new insights into the optimal design of microfluidic devices.
基金supported by the National Natural Science Foundation of China under Grant 52325402,52274057 and 52074340the National Key R&D Program of China under Grant 2023YFB4104200+1 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028.
文摘Shale gas wells often face challenges in maintaining continuous and stable production due to their coexistence with high-and low-pressure wells within the same development block,which leads to issues involving mixed-pressure flows.Traditional pipeline optimization methods used in conventional gas well blocks fail to address the unique needs of shale gas wells,such as the precise planning of airflow paths,pressure distribution,and compression.This study proposes a pressure-controlled production optimization strategy specifically designed for shale gas wells operating under mixed-pressure flow conditions.The strategy aims to improve production stability and optimize system efficiency.The decline in production and pressure for individual wells over time is forecasted using a predictive model that accounts for key factors of system optimization,such as reservoir depletion,wellbore conditions,and equipment performance.Additionally,the model predicts the timing and impact of liquid loading,which can significantly affect production.The optimization process involves analyzing the existing gathering pipeline network to determine the most efficient flow directions and compression strategies based on these predictions,while the strategy involves adjusting compressor settings,optimizing flow rates,and planning pressure distribution across the network to maximize productivity while maintaining system stability.By implementing these strategies,this study significantly improves gas well productivity and enhances the adaptability and efficiency of the gathering and transportation system.The proposed approach provides systematic technical solutions and practical guidance for the efficient development and stable production of shale gas fields,ensuring more robust and sustainable pipeline operations.
基金supported by the Fundamental Research Funds for the Central Universities(No.2024JBZX029)Shijiazhuang High Level Science and Technology Innovation and Entrepreneurship Talent Project(No.08202307)the National Natural Science Foundation of China(NSFC)(No.22173004).
文摘The optimization of polymer structures aims to determine an optimal sequence or topology that achieves a given target property or structural performance.This inverse design problem involves searching within a vast combinatorial phase space defined by components,se-quences,and topologies,and is often computationally intractable due to its NP-hard nature.At the core of this challenge lies the need to evalu-ate complex correlations among structural variables,a classical problem in both statistical physics and combinatorial optimization.To address this,we adopt a mean-field approach that decouples direct variable-variable interactions into effective interactions between each variable and an auxiliary field.The simulated bifurcation(SB)algorithm is employed as a mean-field-based optimization framework.It constructs a Hamiltonian dynamical system by introducing generalized momentum fields,enabling efficient decoupling and dynamic evolution of strongly coupled struc-tural variables.Using the sequence optimization of a linear copolymer adsorbing on a solid surface as a case study,we demonstrate the applica-bility of the SB algorithm to high-dimensional,non-differentiable combinatorial optimization problems.Our results show that SB can efficiently discover polymer sequences with excellent adsorption performance within a reasonable computational time.Furthermore,it exhibits robust con-vergence and high parallel scalability across large design spaces.The approach developed in this work offers a new computational pathway for polymer structure optimization.It also lays a theoretical foundation for future extensions to topological design problems,such as optimizing the number and placement of side chains,as well as the co-optimization of sequence and topology.
基金supported by National Key Research and Development Program of China(2022YFB3305900)National Natural Science Foundation of China(62394343,62394345)+1 种基金Major Science and Technology Projects of Longmen Laboratory(NO.LMZDXM202206)Shanghai Rising-Star Program under Grant 24QA2706100.
文摘Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the delays arising from the residence time for materials passing through production units during the process with guaranteed constraint satisfaction, an asynchronous distributed parameter projection algorithm with gradient tracking method is introduced. Besides, the heavy ball momentum and Nesterov momentum are incorporated into the proposed algorithm in order to achieve double acceleration properties. The experimental results show that the proposed asynchronous algorithm can achieve a faster convergence compared with the synchronous algorithm.
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
基金supported by the National Natural Science Foundation of China(No.52388102)the Sichuan Science and Technology Program(No.2023ZDZX0008)China.The authors would like to thank the Guoneng Shuo-Huang Railway Development Company,China for providing vehicle parameters and line data for this project.The authors would also like to acknowledge the Xplorer Prize for sponsoring the project.
文摘Rail profile optimization is a critical strategy for mitigating wear and extending service life.However,damage at the wheel-rail contact surface goes beyond simple rail wear,as it also involves fatigue phenomena.Focusing solely on wear and not addressing fatigue in profile optimization can lead to the propagation of rail cracks,the peeling of material off the rail,and even rail fractures.Therefore,we propose an optimization approach that balances rail wear and fatigue for heavy-haul railway rails to mitigate rail fatigue damage.Initially,we performed a field investigation to acquire essential data and understand the characteristics of track damage.Based on theory and measured data,a simulation model for wear and fatigue was then established.Subsequently,the control points of the rail profile according to cubic non-uniform rational B-spline(NURBS)theory were set as the research variables.The rail’s wear rate and fatigue crack propagation rate were adopted as the objective functions.A multi-objective,multi-variable,and multi-constraint nonlinear optimization model was then constructed,specifically using a Levenberg Marquardt-back propagation neural network as optimized by the particle swarm optimization algorithm(PSO-LM-BP neural network).Ultimately,optimal solutions from the model were identified using a chaos microvariation adaptive genetic algorithm,and the effectiveness of the optimization was validated using a dynamics model and a rail damage model.
