This paper presents an improved virtual coupling train set(VCTS)operation control framework to deal with the lack of opti-mization of speed curves in the traditional techniques.The framework takes into account the tem...This paper presents an improved virtual coupling train set(VCTS)operation control framework to deal with the lack of opti-mization of speed curves in the traditional techniques.The framework takes into account the temporary speed limit on the railway line and the communication delay between trains,and it uses a VCTS consisting of three trains as an experimental object.It creates the virtual coupling train tracking and control process by improving the driving strategy of the leader train and using the leader-follower model.The follower train uses the improved speed curve of the leader train as its speed refer-ence curve through knowledge migration,and this completes the multi-objective optimization of the driving strategy for the VCTS.The experimental results confirm that the deep reinforcement learning algorithm effectively achieves the optimization goal of the train driving strategy.They also reveal that the intrinsic curiosity module prioritized experience replay dueling double deep Q-network(ICM-PER-D3QN)algorithm outperforms the deep Q-network(DQN)algorithm in optimizing the driving strategy of the leader train.The ICM-PER-D3QN algorithm enhances the leader train driving strategy by an average of 57%when compared to the DQN algorithm.Furthermore,the particle swarm optimization(PSO)-based model predictive control(MPC)algorithm has also demonstrated tracking accuracy and further improved safety during VCTS operation,with an average increase of 37.7%in tracking accuracy compared to the traditional MPC algorithm.展开更多
Impinging jet arrays are extensively used in numerous industrial operations,including the cooling of electronics,turbine blades,and other high-heat flux systems because of their superior heat transfer capabilities.Opt...Impinging jet arrays are extensively used in numerous industrial operations,including the cooling of electronics,turbine blades,and other high-heat flux systems because of their superior heat transfer capabilities.Optimizing the design and operating parameters of such systems is essential to enhance cooling efficiency and achieve uniform pressure distribution,which can lead to improved system performance and energy savings.This paper presents two multi-objective optimization methodologies for a turbulent air jet impingement cooling system.The governing equations are resolved employing the commercial computational fluid dynamics(CFD)software ANSYS Fluent v17.The study focuses on four controlling parameters:Reynolds number(Re),swirl number(S),jet-to-jet separation distance(Z/D),and impingement height(H/D).The effects of these parameters on heat transfer and impingement pressure distribution are investigated.Non-dominated Sorting Genetic Algorithm(NSGA-II)and Weighted Sum Method(WSM)are employed to optimize the controlling parameters for maximum cooling performance.The aim is to identify optimal design parameters and system configurations that enhance heat transfer efficiency while achieving a uniform impingement pressure distribution.These findings have practical implications for applications requiring efficient cooling.The optimized design achieved a 12.28%increase in convective heat transfer efficiency with a local Nusselt number of 113.05 compared to 100.69 in the reference design.Enhanced convective cooling and heat flux were observed in the optimized configuration,particularly in areas of direct jet impingement.Additionally,the optimized design maintained lower wall temperatures,demonstrating more effective thermal dissipation.展开更多
Double-wall effusion cooling coupled with thermal barrier coating(TBC)is an important way of thermal protection for gas turbine vanes and blades of next-generation aero-engine,and formation of discrete crater holes by...Double-wall effusion cooling coupled with thermal barrier coating(TBC)is an important way of thermal protection for gas turbine vanes and blades of next-generation aero-engine,and formation of discrete crater holes by TBC spraying is an approved design.To protect both metal and TBC synchronously,a recommended geometry of crater is obtained through a fully automatic multi-objective optimization combined with conjugate heat transfer simulation in this work.The length and width of crater(i.e.,L/D and W/D)were applied as design variables,and the area-averaged overall effectiveness of the metal and TBC surfaces(i.e.,Φ_(av) and τ_(av))were selected as objective functions.The optimization procedure consists of automated geometry and mesh generation,conjugate heat transfer simulation validated by experimental data and Kriging surrogated model.The results showed that the Φ_(av) and τ_(av) are successfully increased respectively by 9.1%and 6.0%through optimization.Appropriate enlargement of the width and length of the crater can significantly improve the film coverage effect,since that the beneficial anti-CRVP is enhanced and the harmful CRVP is weakened.展开更多
Rockburst is a common dynamic geological hazard,frequently occurring in underground engineering(e.g.,TBM tunnelling and deep mining).In order to achieve rockburst monitoring and warning,the microseismic moni-toring te...Rockburst is a common dynamic geological hazard,frequently occurring in underground engineering(e.g.,TBM tunnelling and deep mining).In order to achieve rockburst monitoring and warning,the microseismic moni-toring technique has been widely used in the field.However,the microseismic source location has always been a challenge,playing a vital role in the precise prevention and control of rockburst.To this end,this study proposes a novel microseismic source location model that considers the anisotropy of P-wave velocity.On the one hand,it assigns a unique P-wave velocity to each propagation path,abandoning the assumption of a homogeneous ve-locity field.On the other hand,it treats the P-wave velocity as a co-inversion parameter along with the source location,avoiding the predetermination of P-wave velocity.To solve this model,three various metaheuristic multi-objective optimization algorithms are integrated with it,including the whale optimization algorithm,the butterfly optimization algorithm,and the sparrow search algorithm.To demonstrate the advantages of the model in terms of localization accuracy,localization efficiency,and solution stability,four blasting cases are collected from a water diversion tunnel project in Xinjiang,China.Finally,the effect of the number of involved sensors on the microseismic source location is discussed.展开更多
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 lack of systematic and scientific top-level arrangement in the field of civil aircraft flight test leads to the problems of long duration and high cost.Based on the flight test activity,mathematical models of flig...The lack of systematic and scientific top-level arrangement in the field of civil aircraft flight test leads to the problems of long duration and high cost.Based on the flight test activity,mathematical models of flight test duration and cost are established to set up the framework of flight test process.The top-level arrangement for flight test is optimized by multi-objective algorithm to reduce the duration and cost of flight test.In order to verify the necessity and validity of the mathematical models and the optimization algorithm of top-level arrangement,real flight test data is used to make an example calculation.Results show that the multi-objective optimization results of the top-level flight arrangement are better than the initial arrangement data,which can shorten the duration,reduce the cost,and improve the efficiency of flight test.展开更多
The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy b...The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.展开更多
This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is establish...This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.展开更多
In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelli...In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.展开更多
CO_(2) Water-Alternating-Gas(CO_(2)-WAG)injection is not only a method to enhance oil recovery but also a feasible way to achieve CO_(2) sequestration.However,inappropriate injection strategies would prevent the attai...CO_(2) Water-Alternating-Gas(CO_(2)-WAG)injection is not only a method to enhance oil recovery but also a feasible way to achieve CO_(2) sequestration.However,inappropriate injection strategies would prevent the attainment of maximum oil recovery and cumulative CO_(2) storage.Furthermore,the optimization of CO_(2)-WAG is computationally expensive as it needs to frequently call the compositional simulation model that involves various CO_(2) storage mechanisms.Therefore,the surrogate-assisted evolutionary optimization is necessary,which replaces the compositional simulator with surrogate models.In this paper,a surrogate-based multi-objective optimization algorithm assisted by the single-objective pre-search method is proposed.The results of single-objective optimization will be used to initialize the solutions of multi-objective optimization,which accelerates the exploration of the entire Pareto front.In addition,a convergence criterion is also proposed for the single-objective optimization during pre-search,and the gradient of surrogate models is adopted as the convergence criterion.Finally,the method proposed in this work is applied to two benchmark reservoir models to prove its efficiency and correctness.The results show that the proposed algorithm achieves a better performance than the conventional ones for the multi-objective optimization of CO_(2)-WAG.展开更多
This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic(FRP)fishing vessels to address critical limitations of conventional designs,including excessive weight,mate...This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic(FRP)fishing vessels to address critical limitations of conventional designs,including excessive weight,material inefficiency,and performance redundancy.By integrating surrogate modeling techniques with a multi-objective genetic algorithm(MOGA),we have developed a systematic approach that encompasses parametric modeling,finite element analysis under extreme operational conditions,and multi-fidelity performance evaluation.Through a 10-t electric winch case study,the methodology’s effectiveness is demonstrated via parametric characterization of structural integrity,stiffness behavior,and mass distribution.The comparative analysis identified optimal surrogate models for predicting key performance metrics,which enabled the construction of a robust multi-objective optimization model.The MOGA-derived Pareto solutions produced a design configuration achieving 7.86%mass reduction,2.01%safety factor improvement,and 23.97%deformation mitigation.Verification analysis confirmed the optimization scheme’s reliability in balancing conflicting design requirements.This research establishes a generalized framework for marine deck machinery modernization,particularly addressing the structural compatibility challenges in FRP vessel retrofitting.The proposed methodology demonstrates significant potential for facilitating sustainable upgrades of fishing vessel equipment through systematic performance optimization.展开更多
Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-i...Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-inspired materials which have excellent properties not present in conventional composites.To create such materials with desirable mechanical properties,the optimum structural parameters combination must be selected.Moreover,the optimal design of bio-inspired composites needs to take into account the trade-offs between various mechanical properties.In this paper,multi-objective optimization models were developed using structural parameters as design variables and mechanical properties as optimization objectives,including stiffness,strength,toughness,and dynamic damping.Using the NSGA-II optimization algorithm,a set of optimal solutions were solved.Additionally,three different structures in natural nacre were introduced in order to utilize the better structure when design bio-inspired materials.The range of optimal solutions that obtained using results from previous research were examined and explained why this collection of optimal solution ranges is better.Also,optimal solutions were compared with the structural features and mechanical properties of real nacre and artificial biomimetic composites to validate our models.Finally,the optimum design strategies can be obtained for nacre-like composites.Our research methodically proposes an optimization method for achieving load-bearing bio-inspired materials with excellent properties and creates a set of optimal solutions from which designers can select the one that best suits their preferences,allowing the fabricated materials to demonstrate preferred performance.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
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
In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave pow...In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave power beam,a technology known as microwave power transmission(MPT).Due to the vast transmission distance of tens of thousands of kilometers,the power transmitting antenna array must span up to 1 kilometer in diameter.At the same time,the size of the rectifying array on the ground should extend over a few kilometers.This makes the MPT system of SSPSs significantly larger than the existing aerospace engineering system.To design and operate a rational MPT system,comprehensive optimization is required.Taking the space MPT system engineering into consideration,a novel multi-objective optimization function is proposed and further analyzed.The multi-objective optimization problem is modeled mathematically.Beam collection efficiency(BCE)is the primary factor,followed by the thermal management capability.Some tapers,designed to solve the conflict between BCE and the thermal problem,are reviewed.In addition to these two factors,rectenna design complexity is included as a functional factor in the optimization objective.Weight coefficients are assigned to these factors to prioritize them.Radiating planar arrays with different aperture illumination fields are studied,and their performances are compared using the multi-objective optimization function.Transmitting array size,rectifying array size,transmission distance,and transmitted power remaine constant in various cases,ensuring fair comparisons.The analysis results show that the proposed optimization function is effective in optimizing and selecting the MPT system architecture.It is also noted that the multi-objective optimization function can be expanded to include other factors in the future.展开更多
The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this ...The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this process still encounters numerous challenges in attaining optimal economic and environmental performance. Therefore, an ensemble learning (EL) framework is proposed for the BCP process in this study to optimize the synergistic benefits while minimizing negative environmental impacts. Six different ensemble learning models are developed to investigate the impact of input features, such as biomass characteristics, coal characteristics, and pyrolysis conditions on the product profit and CO_(2) emissions of the BCP processes. The Optuna method is further employed to automatically optimize the hyperparameters of BCP process models for enhancing their predictive accuracy and robustness. The results indicate that the categorical boosting (CAB) model of the BCP process has demonstrated exceptional performance in accurately predicting its product profit and CO_(2) emission (R2>0.92) after undergoing five-fold cross-validation. To enhance the interpretability of this preferred model, the Shapley additive explanations and partial dependence plot analyses are conducted to evaluate the impact and importance of biomass characteristics, coal characteristics, and pyrolysis conditions on the product profitability and CO_(2) emissions of the BCP processes. Finally, the preferred model coupled with a reference vector guided evolutionary algorithm is carried to identify the optimal conditions for maximizing the product profit of BCP process products while minimizing CO_(2) emissions. It indicates the optimal BCP process can achieve high product profits (5290.85 CNY·t−1) and low CO_(2) emissions (7.45 kg·t^(−1)).展开更多
This work attempts to optimize Graphene nanoplatelets(GPLs)distribution in the face sheet of sandwich plates to pursue the minimum thermal deflection and transverse shear stresses at interfaces.Thus,an Improved Legend...This work attempts to optimize Graphene nanoplatelets(GPLs)distribution in the face sheet of sandwich plates to pursue the minimum thermal deflection and transverse shear stresses at interfaces.Thus,an Improved Legendre Higher-order plate Theory combined with Isogeometric Analysis(ILHT-IGA)is,first,proposed to accurately predict thermomechanical behaviors of GPLs-reinforced sandwich plates,which can ensure the reliability of the optimized results.Then,an accelerated multi-objective optimization approach is proposed to optimize thermomechanical behaviors.The trained machine learning algorithm based on ILHT-IGA is employed as a surrogate model to accelerate the optimization process.Finally,X-shaped GPLs distribution can provide the maximum stiffness to resist thermal expansion.However,X-shaped GPLs distribution on face sheets will result in large difference of stiffnesses at adjacent surfaces of face sheets and core layer.Thus,transverse shear stresses at interfaces are obviously increased.To avoid a sudden increase of transverse shear stresses at interfaces,an alternative optimized GPLs distribution has been obtained,where GPLs gradually increase toward the upper and lower surfaces of face sheets and suddenly decrease near the surface of face sheets.Such distributions can effectively enhance the stiffness of sandwich plates to resist thermal expansion behaviors and decrease transverse shear stresses at interfaces.展开更多
基金supported by the National Natural Science Foundation of China under Grant 52162050.
文摘This paper presents an improved virtual coupling train set(VCTS)operation control framework to deal with the lack of opti-mization of speed curves in the traditional techniques.The framework takes into account the temporary speed limit on the railway line and the communication delay between trains,and it uses a VCTS consisting of three trains as an experimental object.It creates the virtual coupling train tracking and control process by improving the driving strategy of the leader train and using the leader-follower model.The follower train uses the improved speed curve of the leader train as its speed refer-ence curve through knowledge migration,and this completes the multi-objective optimization of the driving strategy for the VCTS.The experimental results confirm that the deep reinforcement learning algorithm effectively achieves the optimization goal of the train driving strategy.They also reveal that the intrinsic curiosity module prioritized experience replay dueling double deep Q-network(ICM-PER-D3QN)algorithm outperforms the deep Q-network(DQN)algorithm in optimizing the driving strategy of the leader train.The ICM-PER-D3QN algorithm enhances the leader train driving strategy by an average of 57%when compared to the DQN algorithm.Furthermore,the particle swarm optimization(PSO)-based model predictive control(MPC)algorithm has also demonstrated tracking accuracy and further improved safety during VCTS operation,with an average increase of 37.7%in tracking accuracy compared to the traditional MPC algorithm.
文摘Impinging jet arrays are extensively used in numerous industrial operations,including the cooling of electronics,turbine blades,and other high-heat flux systems because of their superior heat transfer capabilities.Optimizing the design and operating parameters of such systems is essential to enhance cooling efficiency and achieve uniform pressure distribution,which can lead to improved system performance and energy savings.This paper presents two multi-objective optimization methodologies for a turbulent air jet impingement cooling system.The governing equations are resolved employing the commercial computational fluid dynamics(CFD)software ANSYS Fluent v17.The study focuses on four controlling parameters:Reynolds number(Re),swirl number(S),jet-to-jet separation distance(Z/D),and impingement height(H/D).The effects of these parameters on heat transfer and impingement pressure distribution are investigated.Non-dominated Sorting Genetic Algorithm(NSGA-II)and Weighted Sum Method(WSM)are employed to optimize the controlling parameters for maximum cooling performance.The aim is to identify optimal design parameters and system configurations that enhance heat transfer efficiency while achieving a uniform impingement pressure distribution.These findings have practical implications for applications requiring efficient cooling.The optimized design achieved a 12.28%increase in convective heat transfer efficiency with a local Nusselt number of 113.05 compared to 100.69 in the reference design.Enhanced convective cooling and heat flux were observed in the optimized configuration,particularly in areas of direct jet impingement.Additionally,the optimized design maintained lower wall temperatures,demonstrating more effective thermal dissipation.
基金Anhui Provincial Natural Science Foundation of China(2108085ME176)the Natural Science Foundation of China(52276043)。
文摘Double-wall effusion cooling coupled with thermal barrier coating(TBC)is an important way of thermal protection for gas turbine vanes and blades of next-generation aero-engine,and formation of discrete crater holes by TBC spraying is an approved design.To protect both metal and TBC synchronously,a recommended geometry of crater is obtained through a fully automatic multi-objective optimization combined with conjugate heat transfer simulation in this work.The length and width of crater(i.e.,L/D and W/D)were applied as design variables,and the area-averaged overall effectiveness of the metal and TBC surfaces(i.e.,Φ_(av) and τ_(av))were selected as objective functions.The optimization procedure consists of automated geometry and mesh generation,conjugate heat transfer simulation validated by experimental data and Kriging surrogated model.The results showed that the Φ_(av) and τ_(av) are successfully increased respectively by 9.1%and 6.0%through optimization.Appropriate enlargement of the width and length of the crater can significantly improve the film coverage effect,since that the beneficial anti-CRVP is enhanced and the harmful CRVP is weakened.
基金supported by the National Natural Science Founda-tion of China under Grant Nos.42472351,42177140,52404127,and 42207235the Natural Science Foundation of Hubei Province under Grant No.2024AFD359+1 种基金the Young Elite Scientist Sponsorship Program by CAST under Grant No.YESS20230742the China Postdoctoral Science Foundation Program under Grant No.2024T170684.
文摘Rockburst is a common dynamic geological hazard,frequently occurring in underground engineering(e.g.,TBM tunnelling and deep mining).In order to achieve rockburst monitoring and warning,the microseismic moni-toring technique has been widely used in the field.However,the microseismic source location has always been a challenge,playing a vital role in the precise prevention and control of rockburst.To this end,this study proposes a novel microseismic source location model that considers the anisotropy of P-wave velocity.On the one hand,it assigns a unique P-wave velocity to each propagation path,abandoning the assumption of a homogeneous ve-locity field.On the other hand,it treats the P-wave velocity as a co-inversion parameter along with the source location,avoiding the predetermination of P-wave velocity.To solve this model,three various metaheuristic multi-objective optimization algorithms are integrated with it,including the whale optimization algorithm,the butterfly optimization algorithm,and the sparrow search algorithm.To demonstrate the advantages of the model in terms of localization accuracy,localization efficiency,and solution stability,four blasting cases are collected from a water diversion tunnel project in Xinjiang,China.Finally,the effect of the number of involved sensors on the microseismic source location is discussed.
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supported by the National Natural Science Foundation of China(62073267,61903305)the Fundamental Research Funds for the Central Universities(HXGJXM202214).
文摘The lack of systematic and scientific top-level arrangement in the field of civil aircraft flight test leads to the problems of long duration and high cost.Based on the flight test activity,mathematical models of flight test duration and cost are established to set up the framework of flight test process.The top-level arrangement for flight test is optimized by multi-objective algorithm to reduce the duration and cost of flight test.In order to verify the necessity and validity of the mathematical models and the optimization algorithm of top-level arrangement,real flight test data is used to make an example calculation.Results show that the multi-objective optimization results of the top-level flight arrangement are better than the initial arrangement data,which can shorten the duration,reduce the cost,and improve the efficiency of flight test.
基金supported by the National Natural Science Foundation of China(71901212)the Science and Technology Innovation Program of Hunan Province(2020RC4046).
文摘The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.
基金Supported by the Natural Science Foundation of Zhejiang Province(No.LQ22F030015).
文摘This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.
基金supported by the National Natural Science Foundation of China(Grant No.52179105)China Postdoctoral Science Foundation(Grant No.2024M762193)。
文摘In tunnel construction,tunnel boring machine(TBM)tunnelling typically relies on manual experience with sub-optimal control parameters,which can easily lead to inefficiency and high costs.This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multiobjective optimization(MOO).First,the effective TBM operation dataset is obtained through data preprocessing of the Songhua River(YS)tunnel project in China.Next,the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters(i.e.total thrust and cutterhead torque),rock mass classification,and hazard risks(i.e.tunnel collapse and shield jamming).Then,considering three optimal objectives,(i.e.,penetration rate,rock-breaking energy consumption,and cutterhead hob wear),the MOO framework and corresponding mathematical expression are established.The Pareto optimal front is solved using DE-NSGA-II algorithm.Finally,the optimal control parameters(i.e.,advance rate and cutterhead rotation speed)are obtained by the satisfactory solution determination criterion,which can balance construction safety and efficiency with satisfaction.Furthermore,the proposed method is validated through 50 cases of TBM tunnelling,showing promising potential of application.
基金financial support provided by the National Key R&D Program of China(No.2023YFB4104203 and No.2022YFE0129900)financial support from the National Natural Science Foundation of China(No.U22B2075)The funding from the Shandong Postdoctoral Science Foundation(No.SDBX2023017)is also greatly appreciated.
文摘CO_(2) Water-Alternating-Gas(CO_(2)-WAG)injection is not only a method to enhance oil recovery but also a feasible way to achieve CO_(2) sequestration.However,inappropriate injection strategies would prevent the attainment of maximum oil recovery and cumulative CO_(2) storage.Furthermore,the optimization of CO_(2)-WAG is computationally expensive as it needs to frequently call the compositional simulation model that involves various CO_(2) storage mechanisms.Therefore,the surrogate-assisted evolutionary optimization is necessary,which replaces the compositional simulator with surrogate models.In this paper,a surrogate-based multi-objective optimization algorithm assisted by the single-objective pre-search method is proposed.The results of single-objective optimization will be used to initialize the solutions of multi-objective optimization,which accelerates the exploration of the entire Pareto front.In addition,a convergence criterion is also proposed for the single-objective optimization during pre-search,and the gradient of surrogate models is adopted as the convergence criterion.Finally,the method proposed in this work is applied to two benchmark reservoir models to prove its efficiency and correctness.The results show that the proposed algorithm achieves a better performance than the conventional ones for the multi-objective optimization of CO_(2)-WAG.
基金supported by the Basic Public Welfare Research Program of Zhejiang Province(No.LGN22E050005).
文摘This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic(FRP)fishing vessels to address critical limitations of conventional designs,including excessive weight,material inefficiency,and performance redundancy.By integrating surrogate modeling techniques with a multi-objective genetic algorithm(MOGA),we have developed a systematic approach that encompasses parametric modeling,finite element analysis under extreme operational conditions,and multi-fidelity performance evaluation.Through a 10-t electric winch case study,the methodology’s effectiveness is demonstrated via parametric characterization of structural integrity,stiffness behavior,and mass distribution.The comparative analysis identified optimal surrogate models for predicting key performance metrics,which enabled the construction of a robust multi-objective optimization model.The MOGA-derived Pareto solutions produced a design configuration achieving 7.86%mass reduction,2.01%safety factor improvement,and 23.97%deformation mitigation.Verification analysis confirmed the optimization scheme’s reliability in balancing conflicting design requirements.This research establishes a generalized framework for marine deck machinery modernization,particularly addressing the structural compatibility challenges in FRP vessel retrofitting.The proposed methodology demonstrates significant potential for facilitating sustainable upgrades of fishing vessel equipment through systematic performance optimization.
基金Supported by National Natural Science Foundation of China(Grant Nos.52222505,52321002)Shanghai Municipal Natural Science Foundation o China(Grant No.23ZR1415500)。
文摘Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-inspired materials which have excellent properties not present in conventional composites.To create such materials with desirable mechanical properties,the optimum structural parameters combination must be selected.Moreover,the optimal design of bio-inspired composites needs to take into account the trade-offs between various mechanical properties.In this paper,multi-objective optimization models were developed using structural parameters as design variables and mechanical properties as optimization objectives,including stiffness,strength,toughness,and dynamic damping.Using the NSGA-II optimization algorithm,a set of optimal solutions were solved.Additionally,three different structures in natural nacre were introduced in order to utilize the better structure when design bio-inspired materials.The range of optimal solutions that obtained using results from previous research were examined and explained why this collection of optimal solution ranges is better.Also,optimal solutions were compared with the structural features and mechanical properties of real nacre and artificial biomimetic composites to validate our models.Finally,the optimum design strategies can be obtained for nacre-like composites.Our research methodically proposes an optimization method for achieving load-bearing bio-inspired materials with excellent properties and creates a set of optimal solutions from which designers can select the one that best suits their preferences,allowing the fabricated materials to demonstrate preferred performance.
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.
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.
文摘In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave power beam,a technology known as microwave power transmission(MPT).Due to the vast transmission distance of tens of thousands of kilometers,the power transmitting antenna array must span up to 1 kilometer in diameter.At the same time,the size of the rectifying array on the ground should extend over a few kilometers.This makes the MPT system of SSPSs significantly larger than the existing aerospace engineering system.To design and operate a rational MPT system,comprehensive optimization is required.Taking the space MPT system engineering into consideration,a novel multi-objective optimization function is proposed and further analyzed.The multi-objective optimization problem is modeled mathematically.Beam collection efficiency(BCE)is the primary factor,followed by the thermal management capability.Some tapers,designed to solve the conflict between BCE and the thermal problem,are reviewed.In addition to these two factors,rectenna design complexity is included as a functional factor in the optimization objective.Weight coefficients are assigned to these factors to prioritize them.Radiating planar arrays with different aperture illumination fields are studied,and their performances are compared using the multi-objective optimization function.Transmitting array size,rectifying array size,transmission distance,and transmitted power remaine constant in various cases,ensuring fair comparisons.The analysis results show that the proposed optimization function is effective in optimizing and selecting the MPT system architecture.It is also noted that the multi-objective optimization function can be expanded to include other factors in the future.
基金support from the National Natural Science Foundation of China(22108052).
文摘The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this process still encounters numerous challenges in attaining optimal economic and environmental performance. Therefore, an ensemble learning (EL) framework is proposed for the BCP process in this study to optimize the synergistic benefits while minimizing negative environmental impacts. Six different ensemble learning models are developed to investigate the impact of input features, such as biomass characteristics, coal characteristics, and pyrolysis conditions on the product profit and CO_(2) emissions of the BCP processes. The Optuna method is further employed to automatically optimize the hyperparameters of BCP process models for enhancing their predictive accuracy and robustness. The results indicate that the categorical boosting (CAB) model of the BCP process has demonstrated exceptional performance in accurately predicting its product profit and CO_(2) emission (R2>0.92) after undergoing five-fold cross-validation. To enhance the interpretability of this preferred model, the Shapley additive explanations and partial dependence plot analyses are conducted to evaluate the impact and importance of biomass characteristics, coal characteristics, and pyrolysis conditions on the product profitability and CO_(2) emissions of the BCP processes. Finally, the preferred model coupled with a reference vector guided evolutionary algorithm is carried to identify the optimal conditions for maximizing the product profit of BCP process products while minimizing CO_(2) emissions. It indicates the optimal BCP process can achieve high product profits (5290.85 CNY·t−1) and low CO_(2) emissions (7.45 kg·t^(−1)).
基金supported by the National Natural Sciences Foundation of China(No.12172295)。
文摘This work attempts to optimize Graphene nanoplatelets(GPLs)distribution in the face sheet of sandwich plates to pursue the minimum thermal deflection and transverse shear stresses at interfaces.Thus,an Improved Legendre Higher-order plate Theory combined with Isogeometric Analysis(ILHT-IGA)is,first,proposed to accurately predict thermomechanical behaviors of GPLs-reinforced sandwich plates,which can ensure the reliability of the optimized results.Then,an accelerated multi-objective optimization approach is proposed to optimize thermomechanical behaviors.The trained machine learning algorithm based on ILHT-IGA is employed as a surrogate model to accelerate the optimization process.Finally,X-shaped GPLs distribution can provide the maximum stiffness to resist thermal expansion.However,X-shaped GPLs distribution on face sheets will result in large difference of stiffnesses at adjacent surfaces of face sheets and core layer.Thus,transverse shear stresses at interfaces are obviously increased.To avoid a sudden increase of transverse shear stresses at interfaces,an alternative optimized GPLs distribution has been obtained,where GPLs gradually increase toward the upper and lower surfaces of face sheets and suddenly decrease near the surface of face sheets.Such distributions can effectively enhance the stiffness of sandwich plates to resist thermal expansion behaviors and decrease transverse shear stresses at interfaces.
