The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of...The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.展开更多
The spoke as a key component has a significant impact on the performance of the non-pneumatic tire(NPT).The current research has focused on adjusting spoke structures to improve the single performance of NPT.Few studi...The spoke as a key component has a significant impact on the performance of the non-pneumatic tire(NPT).The current research has focused on adjusting spoke structures to improve the single performance of NPT.Few studies have been conducted to synergistically improve multi-performance by optimizing the spoke structure.Inspired by the concept of functionally gradient structures,this paper introduces a functionally gradient honeycomb NPT and its optimization method.Firstly,this paper completes the parameterization of the honeycomb spoke structure and establishes the numerical models of honeycomb NPTs with seven different gradients.Subsequently,the accuracy of the numerical models is verified using experimental methods.Then,the static and dynamic characteristics of these gradient honeycomb NPTs are thoroughly examined by using the finite element method.The findings highlight that the gradient structure of NPT-3 has superior performance.Building upon this,the study investigates the effects of key parameters,such as honeycomb spoke thickness and length,on load-carrying capacity,honeycomb spoke stress and mass.Finally,a multi-objective optimization method is proposed that uses a response surface model(RSM)and the Nondominated Sorting Genetic Algorithm-II(NSGA-II)to further optimize the functional gradient honeycomb NPTs.The optimized NPT-OP shows a 23.48%reduction in radial stiffness,8.95%reduction in maximum spoke stress and 16.86%reduction in spoke mass compared to the initial NPT-1.The damping characteristics of the NPT-OP have also been improved.The results offer a theoretical foundation and technical methodology for the structural design and optimization of gradient honeycomb NPTs.展开更多
Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help...Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.展开更多
The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increas...The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increases with cement sand ratio(CSR),slurry concentration(SC),and curing age(CA),while flow resistance(FR)increases with SC and backfill flow rate(BFR),and decreases with CSR.Then the regression models of UCS and FR as response values were established through RSM.Multi-factor interaction found that CSR-CA impacted UCS most,while SC-BFR impacted FR most.By introducing the desirability function,the optimal backfill parameters were obtained based on RSM-DF(CSR is 1:6.25,SC is 69%,CA is 11.5 d,and BFR is 90 m^(3)/h),showing close results of Design Expert and high reliability for optimization.For a copper mine in China,RSM-DF optimization will reduce cement consumption by 4758 t per year,increase tailings consumption by about 6700 t,and reduce CO_(2)emission by about 4758 t.Thus,RSM-DF provides a new approach for backfill parameters optimization,which has important theoretical and practical values.展开更多
A new hang-off system has been proposed to improve the security of risers in hang-off modes during typhoons.However,efficient anti-typhoon evacuation strategies have not been investigated.Optimiza-tion model and metho...A new hang-off system has been proposed to improve the security of risers in hang-off modes during typhoons.However,efficient anti-typhoon evacuation strategies have not been investigated.Optimiza-tion model and method for the anti-typhoon evacuation strategies should be researched.Therefore,multi-objective functions are proposed based on operation time,evacuation speed stability,and steering stability.An evacuation path model and a dynamic model of risers with the new hang-off system are developed for design variables and constraints.A multi-objective optimization model with high-dimensional variables and complex constraints is established.Finally,a three-stage optimization method based on genetic algorithm,least square method,and the penalty function method is proposed to solve the multi-objective optimization model.Optimization results show that the operation time can be reduced through operation parameter optimization,especially evacuation heading optimization.The optimal anti-typhoon strategy is evacuation with all risers suspended along a variable path when the direction angle is large,while evacuation with all risers suspended along a straight path at another di-rection angle.Besides,the influencing factors on anti-typhoon evacuation strategies indicate that the proposed optimization model and method have strong applicability to working conditions and remarkable optimization effects.展开更多
Thin-walled structures are widely used in cars due to their lightweight construction and energy-absorbing properties.However,issues such as high initial stress and lowenergy-absorbing efficiency arise.This study propo...Thin-walled structures are widely used in cars due to their lightweight construction and energy-absorbing properties.However,issues such as high initial stress and lowenergy-absorbing efficiency arise.This study proposes a novel energy-absorbing structure inwhich a straight tube is combinedwith a conical tube and a bamboo-inspired bulkhead structure is introduced.This configuration allows the conical tube to flip outward first and then fold together with the straight tube.This deformation mode absorbs more energy and less peak force than the conical tube sinking and flipping inward.Through finite element numerical simulation,the specific energy absorption capacity of the structure is increased by 26%compared to that of a regular circular cross-section tube.Finally,the impact resistance of the bionic straight tapered tube structure is further improved through multi-objective optimization,promoting the engineering application and lightweight design of hybrid cross-section tubes.展开更多
A novel dual-pressure organic Rankine cycle system(DPORC)with a dual-stage ejector(DE-DPORC)is proposed.The system incorporates a dual-stage ejector that utilizes a small amount of extraction steam from the highpressu...A novel dual-pressure organic Rankine cycle system(DPORC)with a dual-stage ejector(DE-DPORC)is proposed.The system incorporates a dual-stage ejector that utilizes a small amount of extraction steam from the highpressure expander to pressurize a large quantity of exhaust gas to performwork for the low-pressure expander.This innovative approach addresses condensing pressure limitations,reduces power consumption during pressurization,minimizes heat loss,and enhances the utilization efficiency of waste heat steam.A thermodynamic model is developed with net output work,thermal efficiency,and exergy efficiency(W_(net,ηt,ηex))as evaluation criteria,an economicmodel is established with levelized energy cost(LEC)as evaluation index,anenvironmentalmodel is created with annual equivalent carbon dioxide emission reduction(AER)as evaluation parameter.A comprehensive analysis is conducted on the impact of heat source temperature(T_(S,in)),evaporation temperature(T_(2)),entrainment ratio(E_(r1),E_(r2)),and working fluid pressure(P_(5),P_(6))on system performance.It compares the comprehensive performance of the DE-DPORC system with that of the DPORC system at TS,in of 433.15 K and T2 of 378.15 K.Furthermore,multi-objective optimization using the dragonfly algorithm is performed to determine optimal working conditions for the DE-DPORC system through the TOPSIS method.The findings indicate that the DEDPORC system exhibits a 5.34%increase inWnet andηex,a 58.06%increase inηt,a 5.61%increase in AER,and a reduction of 47.67%and 13.51%in the heat dissipation of the condenser andLEC,compared to theDPORCsystem,highlighting the advantages of this enhanced system.The optimal operating conditions are TS,in=426.74 K,T_(2)=389.37 K,E_(r1)=1.33,E_(r2)=3.17,P_(5)=0.39 MPa,P_(6)=1.32 MPa,which offer valuable technical support for engineering applications;however,they are approaching the peak thermodynamic and environmental performance while falling short of the highest economic performance.展开更多
With the high speed, the rotor of magnetically suspended permanent magnet synchronous motor(MSPMSM) suffers great thermal stress and mechanical stress resulting from the temperature rise problem caused by rotor losses...With the high speed, the rotor of magnetically suspended permanent magnet synchronous motor(MSPMSM) suffers great thermal stress and mechanical stress resulting from the temperature rise problem caused by rotor losses, which leads to instability and inefficiency.In this paper, the mechanical–temperature field coupling analysis is conducted to analyze the relationship between the temperature field and structure, and multi-objective optimization of a rotor is performed to improve the design reliability and efficiency. Firstly, the temperature field is calculated by the 2 D finite element model of MSPMSM and the method of applying the 2 D temperature result to the 3 D finite element model of the motor rotor equivalently is proposed. Then the thermal–structure coupling analysis is processed through mathematic method and finite element method(FEM),in which the 3 D finite element model is established precisely in a way and approaches the practical operation state further. Moreover, the impact produced by the temperature and structure on the mechanical strength is analyzed in detail. Finally, the optimization mathematical model of the motor rotor is established with Sequential Quadratic Programming-NLPQL selected in the optimization scheme. Through optimization, the strength of the components in the motor rotor increases obviously and satisfies the design requirement, which to a great extend enhances the service life of the MSPMSM rotor.展开更多
The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-obje...The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming （MINLP）. A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.展开更多
By avoiding or reducing the production of waste, waste minimization is an effective approach to solve the pollution problem in chemical industry. Process integration supported by multi-objective optimization provides ...By avoiding or reducing the production of waste, waste minimization is an effective approach to solve the pollution problem in chemical industry. Process integration supported by multi-objective optimization provides a framework for process design or process retrofit by simultaneously optimizing on the aspects of environment and economics. Multi-objective genetic algorithm is applied in this area as the solution approach for the multi-objective optimization problem.展开更多
In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （S...In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.展开更多
Thin-walled structures have been used in many fields due to their superior mechanical properties.In this paper,two types of hierarchical multi-cell tubes,inspired by the self-similarity of Pinus sylvestris,are propose...Thin-walled structures have been used in many fields due to their superior mechanical properties.In this paper,two types of hierarchical multi-cell tubes,inspired by the self-similarity of Pinus sylvestris,are proposed to enhance structural energy absorption performance.The finite element models of the hierarchical structures are established to validate the crashworthiness performance under axial dynamic load.The theoreticalmodel of themean crushing force is also derived based on the simplified super folded element theory.The finite element results demonstrate that the energy absorption characteristics and deformation mode of the bionic hierarchical thin-walled tubes are further improved with the increase of hierarchical sub-structures.It can be also obtained that the energy absorption performance of corner self-similar tubes is better than edge self-similar tubes.Furthermore,multiobjective optimization of the hierarchical tubes is constructed by employing the response surface method and genetic algorithm,and the corresponding Pareto front diagram is obtained.This research provides a new idea for the crashworthiness design of thin-walled structures.展开更多
The pressurizing pipeline of hot press resonates under the excitation load,which poses a serious hidden danger to the safety of the equipment and the operator.In order to increase the natural frequency of the pressuri...The pressurizing pipeline of hot press resonates under the excitation load,which poses a serious hidden danger to the safety of the equipment and the operator.In order to increase the natural frequency of the pressurizing pipeline,modal analysis of the pressurizing pipeline is carried out to study the mechanism of pipeline vibration and common vibration reduction measures.A method of increasing the natural frequency of the pressurizing pipeline was analyzed.The influence of pipeline clamp assembly stiffness,pipeline clamp number and pipeline clamp installation position on the mode of the pressurizing pipeline is studied.Sensitivity analysis is carried out to study the influence of the various parameters on the mode of the pressurizing pipeline.Genetic algorithm based on Pareto optimality is introduced for multi-objective optimization of pressurizing pipeline.The optimization results show that the natural frequency of the pressurizing pipeline increases by 2.4%and the displacement response is reduced by 17.7%.展开更多
Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mos...Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.展开更多
In order to improve the crashworthiness of thin-walled columns, the energy absorption characteristics of three columns under quasi-static axial crushing loads were analyzed through LS-DYNA. Numerical results show that...In order to improve the crashworthiness of thin-walled columns, the energy absorption characteristics of three columns under quasi-static axial crushing loads were analyzed through LS-DYNA. Numerical results show that the energy absorption capability of the bitubular hexagonal columns with middle to middle(MTM) ribs is the best, followed by the bitubular hexagonal columns with corner to corner(CTC) ribs and the bitubular hexagonal columns without(NOT) ribs, respectively. Then, the MTM rib was optimized by using multi-objective particle swarm optimization algorithm. Through the analysis of the Pareto front for specific energy absorption(SEA, A_(se)) and peak crushing force(PCF, F_(pc)), it is found that there is a vertex on the Pareto front. The vertex has the design parameters of t_1=1.2 mm, t_2=1.2 mm, A_(se)=11.3729 k J/kg, F_(pc)=235.8491 kN. When the PCF is in a certain size, on the left of the vertex, the point with t_2=1.2 mm has the biggest SEA, meanwhile on the right of the vertex, the point with t_1=1.2 mm has the biggest SEA. Finally, the global sensitivity analysis was conducted to investigate the effect of two design parameters. The result is obtained that both SEA and PCF for MTM are more sensitive to t_1 rather than t_2 in the design domain.展开更多
A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably m...A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.展开更多
The experimental analysis presented aims at the selection of the most optimal machining parameter combination for wire electrical discharge machining （WEDM） of 5083 aluminum alloy. Based on the Taguchi experimental ...The experimental analysis presented aims at the selection of the most optimal machining parameter combination for wire electrical discharge machining （WEDM） of 5083 aluminum alloy. Based on the Taguchi experimental design （L9 orthogonal array） method, a series of experiments were performed by considering pulse-on time, pulse-off time, peak current and wire tension as input parameters. The surface roughness and cutting speed were considered responses. Based on the signal-to-noise （S/N） ratio, the influence of the input parameters on the responses was determined. The optimal machining parameters setting for the maximum cutting speed and minimum surface roughness were found using Taguchi methodology. Then, additive model was employed for prediction of all （34） possible machining combinations. Finally, a handy technology table has been reported using Pareto optimality approach.展开更多
To solve the constraints of multi-objective optimization of the driver system and high nonlinear problems, according to the relevant dimensions of a car, we build a simulation model with Hybrid Ⅲ 50th dummy driver co...To solve the constraints of multi-objective optimization of the driver system and high nonlinear problems, according to the relevant dimensions of a car, we build a simulation model with Hybrid Ⅲ 50th dummy driver constraint system. The comparison of the driver mechanics index of the experimental data with the simulation data in the frontal crash shows that the accuracy of simulation model meets the requirements. The optimal Latin test design is adopted, and the global sensitivity analysis of the design parameters is carried out based on the Kriging model. The four most sensitive parameters are selected, and the parameters are solved by a multi-island genetic algorithm.And then the nonlinear programming quadratic line(NLPQL) algorithm is used to search for accurate optimization. The optimal parameters of the occupant restraint system are determined: the limiting force value of force limiter 2 985.603 N, belt extension 12.684%, airbag point explosion time 27.585 ms, and airbag vent diameter 27.338 mm, with the weighted injury criterion(WIC) decreased by 12.97%, the head injury decreased by 22.60%, and the chest compression decreased by 7.29%. The results show that the system integration of passive safety devices such as seat belts and airbags can effectively protect the driver.展开更多
Space solar power station is a novel renewable energy equipment in space to provide the earth with abundant and continuous power.The Orb-shaped Membrane Energy Gathering Array,one of the alternative construction schem...Space solar power station is a novel renewable energy equipment in space to provide the earth with abundant and continuous power.The Orb-shaped Membrane Energy Gathering Array,one of the alternative construction schemes in China,is promising for collecting space sunlight with a large-scale spherical concentrator.Both the structural and optical performances such as root mean square deformation,natural frequency,system mass,and sunlight blocking rate have significant influences on the system property of the concentrator.Considering the comprehensive performance of structure and optic,this paper proposes a novel mesh grid based on normal polyhedron projection and spherical arc bisection for the supporting structure to deal with the challenge of the large-scale structural modular design.For both achieving low system mass and high surface precision,a multilayer and multi-objective optimization model is proposed by classifying the supporting structure into different categories and optimizing their internal and external diameters.The Particle Swarm Optimization algorithm is adopted to find optimal sectional dimensions of the different kinds of supporting structure.The infinite model is also established and structural analysis is carried out,which are expected to provide a certain reference for the subsequent detailed structural design.The numerical results indicate that the spherical concentrator designed by the novel mesh grid would obtain as high as 94.37%sunlight collection efficiency.The supporting structure constructed with the multiple layers would reduce the system quality by 6.92%,sunlight blocking rate by 28.54%,maximum deformation by 41.50%,and root mean square by 9.48%to the traditional single layer,respectively.展开更多
基金funded by the National Key Research and Development Program of China(2024YFE0106800)Natural Science Foundation of Shandong Province(ZR2021ME199).
文摘The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.
基金Supported by National Natural Science Foundation of China(Grant Nos.52072156,52272366)Postdoctoral Foundation of China(Grant No.2020M682269).
文摘The spoke as a key component has a significant impact on the performance of the non-pneumatic tire(NPT).The current research has focused on adjusting spoke structures to improve the single performance of NPT.Few studies have been conducted to synergistically improve multi-performance by optimizing the spoke structure.Inspired by the concept of functionally gradient structures,this paper introduces a functionally gradient honeycomb NPT and its optimization method.Firstly,this paper completes the parameterization of the honeycomb spoke structure and establishes the numerical models of honeycomb NPTs with seven different gradients.Subsequently,the accuracy of the numerical models is verified using experimental methods.Then,the static and dynamic characteristics of these gradient honeycomb NPTs are thoroughly examined by using the finite element method.The findings highlight that the gradient structure of NPT-3 has superior performance.Building upon this,the study investigates the effects of key parameters,such as honeycomb spoke thickness and length,on load-carrying capacity,honeycomb spoke stress and mass.Finally,a multi-objective optimization method is proposed that uses a response surface model(RSM)and the Nondominated Sorting Genetic Algorithm-II(NSGA-II)to further optimize the functional gradient honeycomb NPTs.The optimized NPT-OP shows a 23.48%reduction in radial stiffness,8.95%reduction in maximum spoke stress and 16.86%reduction in spoke mass compared to the initial NPT-1.The damping characteristics of the NPT-OP have also been improved.The results offer a theoretical foundation and technical methodology for the structural design and optimization of gradient honeycomb NPTs.
基金supported by National Key Research and Development Program of China (2023YFB3307800)National Natural Science Foundation of China (Key Program: 62136003, 62373155)+1 种基金Major Science and Technology Project of Xinjiang (No. 2022A01006-4)the Fundamental Research Funds for the Central Universities。
文摘Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.
基金Funded by the Deep Underground National Science&Technology Major Project gram of China(No.2024ZD1003704)the National Natural Science Foundation of China(Nos.51834001 and 52374111)。
文摘The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increases with cement sand ratio(CSR),slurry concentration(SC),and curing age(CA),while flow resistance(FR)increases with SC and backfill flow rate(BFR),and decreases with CSR.Then the regression models of UCS and FR as response values were established through RSM.Multi-factor interaction found that CSR-CA impacted UCS most,while SC-BFR impacted FR most.By introducing the desirability function,the optimal backfill parameters were obtained based on RSM-DF(CSR is 1:6.25,SC is 69%,CA is 11.5 d,and BFR is 90 m^(3)/h),showing close results of Design Expert and high reliability for optimization.For a copper mine in China,RSM-DF optimization will reduce cement consumption by 4758 t per year,increase tailings consumption by about 6700 t,and reduce CO_(2)emission by about 4758 t.Thus,RSM-DF provides a new approach for backfill parameters optimization,which has important theoretical and practical values.
基金supported by the National Natural Science Foundation of China(Grant No:52271300,52071337)National Key Research and Development Program of China(2022YFC2806501)+1 种基金High-tech Ship Research Projects Sponsored by MIIT(CBG2N21-4-25)Program for Changjiang Scholars and Innovative Research Team in University(Grant No.IRT14R58).
文摘A new hang-off system has been proposed to improve the security of risers in hang-off modes during typhoons.However,efficient anti-typhoon evacuation strategies have not been investigated.Optimiza-tion model and method for the anti-typhoon evacuation strategies should be researched.Therefore,multi-objective functions are proposed based on operation time,evacuation speed stability,and steering stability.An evacuation path model and a dynamic model of risers with the new hang-off system are developed for design variables and constraints.A multi-objective optimization model with high-dimensional variables and complex constraints is established.Finally,a three-stage optimization method based on genetic algorithm,least square method,and the penalty function method is proposed to solve the multi-objective optimization model.Optimization results show that the operation time can be reduced through operation parameter optimization,especially evacuation heading optimization.The optimal anti-typhoon strategy is evacuation with all risers suspended along a variable path when the direction angle is large,while evacuation with all risers suspended along a straight path at another di-rection angle.Besides,the influencing factors on anti-typhoon evacuation strategies indicate that the proposed optimization model and method have strong applicability to working conditions and remarkable optimization effects.
文摘Thin-walled structures are widely used in cars due to their lightweight construction and energy-absorbing properties.However,issues such as high initial stress and lowenergy-absorbing efficiency arise.This study proposes a novel energy-absorbing structure inwhich a straight tube is combinedwith a conical tube and a bamboo-inspired bulkhead structure is introduced.This configuration allows the conical tube to flip outward first and then fold together with the straight tube.This deformation mode absorbs more energy and less peak force than the conical tube sinking and flipping inward.Through finite element numerical simulation,the specific energy absorption capacity of the structure is increased by 26%compared to that of a regular circular cross-section tube.Finally,the impact resistance of the bionic straight tapered tube structure is further improved through multi-objective optimization,promoting the engineering application and lightweight design of hybrid cross-section tubes.
基金supported by the Foundation of Liaoning Provincial Key Laboratory of Energy Storage and Utilization(Grant Nos.CNWK202304 and CNNK202315)the Introduction of TalentResearch Start-Up Funding Projects ofYingkou Institute of Technology(Grant No.YJRC202107).
文摘A novel dual-pressure organic Rankine cycle system(DPORC)with a dual-stage ejector(DE-DPORC)is proposed.The system incorporates a dual-stage ejector that utilizes a small amount of extraction steam from the highpressure expander to pressurize a large quantity of exhaust gas to performwork for the low-pressure expander.This innovative approach addresses condensing pressure limitations,reduces power consumption during pressurization,minimizes heat loss,and enhances the utilization efficiency of waste heat steam.A thermodynamic model is developed with net output work,thermal efficiency,and exergy efficiency(W_(net,ηt,ηex))as evaluation criteria,an economicmodel is established with levelized energy cost(LEC)as evaluation index,anenvironmentalmodel is created with annual equivalent carbon dioxide emission reduction(AER)as evaluation parameter.A comprehensive analysis is conducted on the impact of heat source temperature(T_(S,in)),evaporation temperature(T_(2)),entrainment ratio(E_(r1),E_(r2)),and working fluid pressure(P_(5),P_(6))on system performance.It compares the comprehensive performance of the DE-DPORC system with that of the DPORC system at TS,in of 433.15 K and T2 of 378.15 K.Furthermore,multi-objective optimization using the dragonfly algorithm is performed to determine optimal working conditions for the DE-DPORC system through the TOPSIS method.The findings indicate that the DEDPORC system exhibits a 5.34%increase inWnet andηex,a 58.06%increase inηt,a 5.61%increase in AER,and a reduction of 47.67%and 13.51%in the heat dissipation of the condenser andLEC,compared to theDPORCsystem,highlighting the advantages of this enhanced system.The optimal operating conditions are TS,in=426.74 K,T_(2)=389.37 K,E_(r1)=1.33,E_(r2)=3.17,P_(5)=0.39 MPa,P_(6)=1.32 MPa,which offer valuable technical support for engineering applications;however,they are approaching the peak thermodynamic and environmental performance while falling short of the highest economic performance.
基金co-supported by the Excellent Youth Science Foundation of China(No.51722501)the China Postdoctoral Science Foundation(No.2016M600027)+1 种基金the National Natural Science Foundation of China(Nos.51575025 and 61703022)the Preliminary Exploration of Project of China(No.7131474)
文摘With the high speed, the rotor of magnetically suspended permanent magnet synchronous motor(MSPMSM) suffers great thermal stress and mechanical stress resulting from the temperature rise problem caused by rotor losses, which leads to instability and inefficiency.In this paper, the mechanical–temperature field coupling analysis is conducted to analyze the relationship between the temperature field and structure, and multi-objective optimization of a rotor is performed to improve the design reliability and efficiency. Firstly, the temperature field is calculated by the 2 D finite element model of MSPMSM and the method of applying the 2 D temperature result to the 3 D finite element model of the motor rotor equivalently is proposed. Then the thermal–structure coupling analysis is processed through mathematic method and finite element method(FEM),in which the 3 D finite element model is established precisely in a way and approaches the practical operation state further. Moreover, the impact produced by the temperature and structure on the mechanical strength is analyzed in detail. Finally, the optimization mathematical model of the motor rotor is established with Sequential Quadratic Programming-NLPQL selected in the optimization scheme. Through optimization, the strength of the components in the motor rotor increases obviously and satisfies the design requirement, which to a great extend enhances the service life of the MSPMSM rotor.
基金Supported by the National High Technology Research and Development Program of China （2008AA042902, 2009AA04Z162）, the Program of Introducing Talents of Discipline to University （B07031） and the National Natural Science Foundation of China （21106129）.
文摘The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming （MINLP）. A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.
文摘By avoiding or reducing the production of waste, waste minimization is an effective approach to solve the pollution problem in chemical industry. Process integration supported by multi-objective optimization provides a framework for process design or process retrofit by simultaneously optimizing on the aspects of environment and economics. Multi-objective genetic algorithm is applied in this area as the solution approach for the multi-objective optimization problem.
文摘In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively.
基金The authors are grateful to the National Natural Science Foundation of China(Grant No.11902183)the Doctoral Research Foundation of Shandong University of Technology(Grant No.4041/418017).
文摘Thin-walled structures have been used in many fields due to their superior mechanical properties.In this paper,two types of hierarchical multi-cell tubes,inspired by the self-similarity of Pinus sylvestris,are proposed to enhance structural energy absorption performance.The finite element models of the hierarchical structures are established to validate the crashworthiness performance under axial dynamic load.The theoreticalmodel of themean crushing force is also derived based on the simplified super folded element theory.The finite element results demonstrate that the energy absorption characteristics and deformation mode of the bionic hierarchical thin-walled tubes are further improved with the increase of hierarchical sub-structures.It can be also obtained that the energy absorption performance of corner self-similar tubes is better than edge self-similar tubes.Furthermore,multiobjective optimization of the hierarchical tubes is constructed by employing the response surface method and genetic algorithm,and the corresponding Pareto front diagram is obtained.This research provides a new idea for the crashworthiness design of thin-walled structures.
文摘The pressurizing pipeline of hot press resonates under the excitation load,which poses a serious hidden danger to the safety of the equipment and the operator.In order to increase the natural frequency of the pressurizing pipeline,modal analysis of the pressurizing pipeline is carried out to study the mechanism of pipeline vibration and common vibration reduction measures.A method of increasing the natural frequency of the pressurizing pipeline was analyzed.The influence of pipeline clamp assembly stiffness,pipeline clamp number and pipeline clamp installation position on the mode of the pressurizing pipeline is studied.Sensitivity analysis is carried out to study the influence of the various parameters on the mode of the pressurizing pipeline.Genetic algorithm based on Pareto optimality is introduced for multi-objective optimization of pressurizing pipeline.The optimization results show that the natural frequency of the pressurizing pipeline increases by 2.4%and the displacement response is reduced by 17.7%.
基金This work is supported in part by the National Science Foundation of China(Grant Nos.61672392,61373038)in part by the National Key Research and Development Program of China(Grant No.2016YFC1202204).
文摘Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.
基金Projects(U1334208,51405516,51275532)supported by the National Natural Science Foundation of ChinaProjects(2015ZZTS210,2015ZZTS045)supported by the Fundamental Research Funds for the Central Universities,China
文摘In order to improve the crashworthiness of thin-walled columns, the energy absorption characteristics of three columns under quasi-static axial crushing loads were analyzed through LS-DYNA. Numerical results show that the energy absorption capability of the bitubular hexagonal columns with middle to middle(MTM) ribs is the best, followed by the bitubular hexagonal columns with corner to corner(CTC) ribs and the bitubular hexagonal columns without(NOT) ribs, respectively. Then, the MTM rib was optimized by using multi-objective particle swarm optimization algorithm. Through the analysis of the Pareto front for specific energy absorption(SEA, A_(se)) and peak crushing force(PCF, F_(pc)), it is found that there is a vertex on the Pareto front. The vertex has the design parameters of t_1=1.2 mm, t_2=1.2 mm, A_(se)=11.3729 k J/kg, F_(pc)=235.8491 kN. When the PCF is in a certain size, on the left of the vertex, the point with t_2=1.2 mm has the biggest SEA, meanwhile on the right of the vertex, the point with t_1=1.2 mm has the biggest SEA. Finally, the global sensitivity analysis was conducted to investigate the effect of two design parameters. The result is obtained that both SEA and PCF for MTM are more sensitive to t_1 rather than t_2 in the design domain.
文摘A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.
文摘The experimental analysis presented aims at the selection of the most optimal machining parameter combination for wire electrical discharge machining （WEDM） of 5083 aluminum alloy. Based on the Taguchi experimental design （L9 orthogonal array） method, a series of experiments were performed by considering pulse-on time, pulse-off time, peak current and wire tension as input parameters. The surface roughness and cutting speed were considered responses. Based on the signal-to-noise （S/N） ratio, the influence of the input parameters on the responses was determined. The optimal machining parameters setting for the maximum cutting speed and minimum surface roughness were found using Taguchi methodology. Then, additive model was employed for prediction of all （34） possible machining combinations. Finally, a handy technology table has been reported using Pareto optimality approach.
基金Supported by Natural Science and Technology Research Project of the Jiangxi Education Department(GJJ202002, GJJ2202620)。
文摘To solve the constraints of multi-objective optimization of the driver system and high nonlinear problems, according to the relevant dimensions of a car, we build a simulation model with Hybrid Ⅲ 50th dummy driver constraint system. The comparison of the driver mechanics index of the experimental data with the simulation data in the frontal crash shows that the accuracy of simulation model meets the requirements. The optimal Latin test design is adopted, and the global sensitivity analysis of the design parameters is carried out based on the Kriging model. The four most sensitive parameters are selected, and the parameters are solved by a multi-island genetic algorithm.And then the nonlinear programming quadratic line(NLPQL) algorithm is used to search for accurate optimization. The optimal parameters of the occupant restraint system are determined: the limiting force value of force limiter 2 985.603 N, belt extension 12.684%, airbag point explosion time 27.585 ms, and airbag vent diameter 27.338 mm, with the weighted injury criterion(WIC) decreased by 12.97%, the head injury decreased by 22.60%, and the chest compression decreased by 7.29%. The results show that the system integration of passive safety devices such as seat belts and airbags can effectively protect the driver.
基金the National Natural Science Foundation of China[No.52105275]the Natural Science Foundation of Shaanxi Province[2020JQ-595]Open Fund of Shaanxi Key Laboratory of Space Solar Power Station System in Xidian University.
文摘Space solar power station is a novel renewable energy equipment in space to provide the earth with abundant and continuous power.The Orb-shaped Membrane Energy Gathering Array,one of the alternative construction schemes in China,is promising for collecting space sunlight with a large-scale spherical concentrator.Both the structural and optical performances such as root mean square deformation,natural frequency,system mass,and sunlight blocking rate have significant influences on the system property of the concentrator.Considering the comprehensive performance of structure and optic,this paper proposes a novel mesh grid based on normal polyhedron projection and spherical arc bisection for the supporting structure to deal with the challenge of the large-scale structural modular design.For both achieving low system mass and high surface precision,a multilayer and multi-objective optimization model is proposed by classifying the supporting structure into different categories and optimizing their internal and external diameters.The Particle Swarm Optimization algorithm is adopted to find optimal sectional dimensions of the different kinds of supporting structure.The infinite model is also established and structural analysis is carried out,which are expected to provide a certain reference for the subsequent detailed structural design.The numerical results indicate that the spherical concentrator designed by the novel mesh grid would obtain as high as 94.37%sunlight collection efficiency.The supporting structure constructed with the multiple layers would reduce the system quality by 6.92%,sunlight blocking rate by 28.54%,maximum deformation by 41.50%,and root mean square by 9.48%to the traditional single layer,respectively.
