Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex str...Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex structural optimization problems, if the structural reanalysis technique is not adopted, the more the number of finite element analysis (FEA) is, the more the consuming time is. In the conventional structural optimization the number of FEA can be reduced by the structural reanalysis technique based on the approximation techniques and sensitivity analysis. With these techniques, this paper provides a new approximation model-segment approximation model, adopted for the GA application. This segment approximation model can decrease the number of FEA and increase the convergence rate of GA. So it can apparently decrease the computation time of GA. Two examples demonstrate the availability of the new segment approximation model.展开更多
Our differential and grading toothed roll crusher blends the advantages of a toothed roll crusher and a jaw crusher and possesses characteristics of great crushing,high breaking efficiency,multi-sieving and has,for th...Our differential and grading toothed roll crusher blends the advantages of a toothed roll crusher and a jaw crusher and possesses characteristics of great crushing,high breaking efficiency,multi-sieving and has,for the moment,made up for the short- comings of the toothed roll crusher.The moving jaw of the crusher is a crank-rocker mechanism.For optimizing the dynamic per- formance and improving the cracking capability of the crusher,a mathematical model was established to optimize the transmission angleγand to minimize the travel characteristic value m of the moving jaw.Genetic algorithm is used to optimize the crusher crank-rocker mechanism for multi-object design and an optimum result is obtained.According to the implementation,it is shown that the performance of the crusher and the cracking capability of the moving jaw have been improved.展开更多
The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the slid...The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.展开更多
In silico approaches for metabolites optimization have been derived from the flood of sequenced and annotated genomes. However, there exist still numerous degrees of freedom in terms of optimization algorithm approach...In silico approaches for metabolites optimization have been derived from the flood of sequenced and annotated genomes. However, there exist still numerous degrees of freedom in terms of optimization algorithm approaches that can be exploited in order to enhance yield of processes which are based on biological reactions. Here, we propose an evolutionary approach aiming to suggest different mutant for augmenting ethanol yield using glycerol as substrate in Escherichia coli. We found that this algorithm, even though is far from providing the global optimum, is able to uncover genes that a global optimizer would be incapable of. By over-expressing accB, eno, dapE, and accA mutants in ethanol production was augmented up to 2 fold compared to its counterpart E. coli BW25113.展开更多
As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weigh...As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weight under burst pressure con- straint. The actual burst pressure is calculated using the arc-length and restart analysis in finite element analysis (FEA). A penalty function in the fitness function is proposed to deal with the constrained problem. The effects of the population size and the number of generations in the GA on the weight and burst pressure of the vessel are explored. The optimization results using the proposed GA are also compared with those using the simple GA and the conventional Monte Carlo method.展开更多
Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters...Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,展开更多
Due to the severe and costly problems caused by asphaltene precipitation in petroleum industry,developing a quick and accurate model,to predict the asphaltene precipitation under different conditions,seems crucial.In ...Due to the severe and costly problems caused by asphaltene precipitation in petroleum industry,developing a quick and accurate model,to predict the asphaltene precipitation under different conditions,seems crucial.In this study,a new model,namely genetic algorithm e support vector regression(GA-SVR)is proposed,which is applied to predict the amount of asphaltene precipitation.GA is used to select the best optimal values of SVR parameters and kernel parameter,simultaneously,to increase the generalization performance of the SVR.The GA-SVR model is trained and tested on the experimental data sets reported in literature.The performance of the GASVR model is compared with two scaling equation models,using statistical error measures and graphical analyses.The results show that the prediction performance of the proposed model,is highly reliable and satisfactory.展开更多
Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensiv...Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.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
Large-scale solar sails can provide power to spacecraft for deep space exploration.A new type of telescopic tubular mast(TTM)driven by a bistable carbon fiber-reinforced polymer tube was designed in this study to solv...Large-scale solar sails can provide power to spacecraft for deep space exploration.A new type of telescopic tubular mast(TTM)driven by a bistable carbon fiber-reinforced polymer tube was designed in this study to solve the problem of contact between the sail membrane and the spacecraft under light pressure.Compared with the traditional TTM,it has a small size,light weight,high extension ratio,and simple structure.The anti-blossoming and self-unlocking structure of the proposed TTM was described.We aimed to simplify the TTM with a complex structure into a beam model with equal linear mass density,and the simulation results showed good consistency.The dynamic equation was derived based on the equivalent model,and the effects of different factors on the vibration characteristics of the TTM were analyzed.The performance parameters were optimized based on a multiobjective genetic algorithm,and prototype production and load experiments were conducted.The results show that the advantages of the new TTM can complete the deployment of large-scale solar sails,which is valuable for future deep space exploration.展开更多
The characteristics of normal inflatable antennas are described in this paper.For its deficiency such as low stiffness,a new-style of rigid-flexible coupling inflatable antenna is introduced.The advantages and system-...The characteristics of normal inflatable antennas are described in this paper.For its deficiency such as low stiffness,a new-style of rigid-flexible coupling inflatable antenna is introduced.The advantages and system-composition are presented.Airbag is an important component,and the shape and stress distribution of airbag is important for the whole structure.Thus,shape-state analysis is performed.The uniformity of constrained force at joint between airbag and rib will impact the state of joint,or even the deployment process.Therefore,a shape optimization of airbag is developed on the base of genetic algorithms,and the best shape of airbag is finally obtained.Therefore,the stress distribution of airbags will be uniformed and the antenna structure system will be more reliability.展开更多
Fine particulate matter produced during the rapid industrialization over the past decades can cause significant harm to human health.Twin-fluid atomization technology is an effective means of controlling fine particul...Fine particulate matter produced during the rapid industrialization over the past decades can cause significant harm to human health.Twin-fluid atomization technology is an effective means of controlling fine particulate matter pollution.In this paper,the influences of the main parameters on the droplet size,effective atomization range and sound pressure level(SPL)of a twin-fluid nozzle(TFN)are investigated,and in order to improve the atomization performance,a multi-objective synergetic optimization algorithm is presented.A multi-physics coupled acousticmechanics model based on the discrete phase model(DPM),large eddy simulation(LES)model,and Ffowcs Williams-Hawkings(FW-H)model is established,and the numerical simulation results of the multi-physics coupled acoustic-mechanics method are verified via experimental comparison.Based on the analysis of the multi-physics coupled acoustic-mechanics numerical simulation results,the effects of the water flow on the characteristics of the atomization flow distribution were obtained.A multi-physics coupled acoustic-mechanics numerical simulation result was employed to establish an orthogonal test database,and a multi-objective synergetic optimization algorithm was adopted to optimize the key parameters of the TFN.The optimal parameters are as follows:A gas flow of 0.94 m^(3)/h,water flow of 0.0237 m^(3)/h,orifice diameter of the self-excited vibrating cavity(SVC)of 1.19 mm,SVC orifice depth of 0.53 mm,distance between SVC and the outlet of nozzle of 5.11 mm,and a nozzle outlet diameter of 3.15 mm.The droplet particle size in the atomization flow field was significantly reduced,the spray distance improved by 71.56%,and the SPL data at each corresponding measurement point decreased by an average of 38.96%.The conclusions of this study offer a references for future TFN research.展开更多
Preventative maintenance (PM) measures for bridges are proactive maintenance actions which aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economi...Preventative maintenance (PM) measures for bridges are proactive maintenance actions which aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds since it can extend the life of the bridge and avoid the need for unplanned essential/corrective maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed a two-stage evolutionary optimization methodology based on genetic algorithm (GA) principles which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. In this paper, the sensitivity of the methodology to various key input parameters of the optimization methodology is examined in order to quantify their effects and identify possible trends in the optimum PM intervention profiles. The results of the sensitivity studies highlight the combined use of both proactive and reactive PM measures in deriving optimum strategy solutions. The precise mix and sequence of PM measures is clearly a function of the relative effectiveness and cost of the different available PM options as well as the various key parameters such as discount rate, target probability of failure, initial probability of failure and service life period examined. While the results highlight the need for more reliable data they also demonstrate the robustness and usefulness of the methodology;in the case where data is limited it can be used as a comparative tool to improve understanding of the effects of various strategies and enhance the decision making process.展开更多
The goal of this effort was to provide a static and dynamic collaborative optimization （CO） model for the design of ship hull structure. The CO model integrated with static, mode and dynamic analyses. In the system-...The goal of this effort was to provide a static and dynamic collaborative optimization （CO） model for the design of ship hull structure. The CO model integrated with static, mode and dynamic analyses. In the system-level optimization model, a new objective function was advised, integrating all the subsystem-levels＇ objective functions, so as to eliminate the effects of dimensions and magnitude order. The proposed CO architecture enabled multi-objectives of the system and subsystem-level to be considered at both levels during optimization. A bi-level optimization strategy was advised, using the multi-island genetic algorithm. The proposed model was demonstrated with a deck optimization problem of container ship stern. The analysis progress and results of example show that the CO strategy is not only feasible and reliable, but also well suited for use in actual optimization problems of ship design.展开更多
In order to find out the optimal press bend forming path in fabricating aircraft integral panels, this article proposes a new method on the basis of the authors＇ previous work. It is composed of the finite element me...In order to find out the optimal press bend forming path in fabricating aircraft integral panels, this article proposes a new method on the basis of the authors＇ previous work. It is composed of the finite element method （FEM） equivalent model, the surface curvature analysis, the artificial neural network response surface and the genetic algorithm. The method begins with analyzing the objective＇s shape curvature to determine the bending position. Then it optimizes the punch travel at each bending position by the following steps： （1） Establish a multi-step press bend forming FEM equivalent model, with which the FEM ex- periments designed with the Taguchi method are performed. （2） Construct a back-propagation （BP） neural network response surface with the data from the FEM experiments. （3） Use the genetic algorithm to optimize the neural network response surface as the objective function. Finally, this method is verified by press bending a complicated double-curvature grid-type stiffened panel and bears out its effectiveness and intrinsic worth in designing the press bend forming path.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
文摘Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex structural optimization problems, if the structural reanalysis technique is not adopted, the more the number of finite element analysis (FEA) is, the more the consuming time is. In the conventional structural optimization the number of FEA can be reduced by the structural reanalysis technique based on the approximation techniques and sensitivity analysis. With these techniques, this paper provides a new approximation model-segment approximation model, adopted for the GA application. This segment approximation model can decrease the number of FEA and increase the convergence rate of GA. So it can apparently decrease the computation time of GA. Two examples demonstrate the availability of the new segment approximation model.
基金Project 50574091 supported by the National Natural Science Foundation of China
文摘Our differential and grading toothed roll crusher blends the advantages of a toothed roll crusher and a jaw crusher and possesses characteristics of great crushing,high breaking efficiency,multi-sieving and has,for the moment,made up for the short- comings of the toothed roll crusher.The moving jaw of the crusher is a crank-rocker mechanism.For optimizing the dynamic per- formance and improving the cracking capability of the crusher,a mathematical model was established to optimize the transmission angleγand to minimize the travel characteristic value m of the moving jaw.Genetic algorithm is used to optimize the crusher crank-rocker mechanism for multi-object design and an optimum result is obtained.According to the implementation,it is shown that the performance of the crusher and the cracking capability of the moving jaw have been improved.
文摘The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.
基金the support of the National BioResource Project(NIG,Japan):E.coli Strain for kindly providing us with the Keio Collection using for our experimental sectionAlso this work is funded by Vicerrectoria de investigaciones at Universidad de los Andes.
文摘In silico approaches for metabolites optimization have been derived from the flood of sequenced and annotated genomes. However, there exist still numerous degrees of freedom in terms of optimization algorithm approaches that can be exploited in order to enhance yield of processes which are based on biological reactions. Here, we propose an evolutionary approach aiming to suggest different mutant for augmenting ethanol yield using glycerol as substrate in Escherichia coli. We found that this algorithm, even though is far from providing the global optimum, is able to uncover genes that a global optimizer would be incapable of. By over-expressing accB, eno, dapE, and accA mutants in ethanol production was augmented up to 2 fold compared to its counterpart E. coli BW25113.
基金Project (Nos. 2006BAK04A02-02 and 2006BAK02B02-08) sup-ported by the National Key Technology R&D Program, China
文摘As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weight under burst pressure con- straint. The actual burst pressure is calculated using the arc-length and restart analysis in finite element analysis (FEA). A penalty function in the fitness function is proposed to deal with the constrained problem. The effects of the population size and the number of generations in the GA on the weight and burst pressure of the vessel are explored. The optimization results using the proposed GA are also compared with those using the simple GA and the conventional Monte Carlo method.
文摘Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,
文摘Due to the severe and costly problems caused by asphaltene precipitation in petroleum industry,developing a quick and accurate model,to predict the asphaltene precipitation under different conditions,seems crucial.In this study,a new model,namely genetic algorithm e support vector regression(GA-SVR)is proposed,which is applied to predict the amount of asphaltene precipitation.GA is used to select the best optimal values of SVR parameters and kernel parameter,simultaneously,to increase the generalization performance of the SVR.The GA-SVR model is trained and tested on the experimental data sets reported in literature.The performance of the GASVR model is compared with two scaling equation models,using statistical error measures and graphical analyses.The results show that the prediction performance of the proposed model,is highly reliable and satisfactory.
基金supported by National Natural Science Foundation of China (Grant No.60572007)National Basic Research Program of China(973 Program,Grant No.613580202)
文摘Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.
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Supported by National Key R&D Program of China (Grant No.2018YFB1304600)National Natural Science Foundation of China (Grant No.51905527)+1 种基金CAS Interdisciplinary Innovation Team of China (Grant No.JCTD-2018-11)State Key Laboratory of Robotics Foundation of China (Grant No.Y91Z0303)。
文摘Large-scale solar sails can provide power to spacecraft for deep space exploration.A new type of telescopic tubular mast(TTM)driven by a bistable carbon fiber-reinforced polymer tube was designed in this study to solve the problem of contact between the sail membrane and the spacecraft under light pressure.Compared with the traditional TTM,it has a small size,light weight,high extension ratio,and simple structure.The anti-blossoming and self-unlocking structure of the proposed TTM was described.We aimed to simplify the TTM with a complex structure into a beam model with equal linear mass density,and the simulation results showed good consistency.The dynamic equation was derived based on the equivalent model,and the effects of different factors on the vibration characteristics of the TTM were analyzed.The performance parameters were optimized based on a multiobjective genetic algorithm,and prototype production and load experiments were conducted.The results show that the advantages of the new TTM can complete the deployment of large-scale solar sails,which is valuable for future deep space exploration.
基金Sponsored by the Program for New Century Excellent Talents in University(Grant No. NCET-08-0150)
文摘The characteristics of normal inflatable antennas are described in this paper.For its deficiency such as low stiffness,a new-style of rigid-flexible coupling inflatable antenna is introduced.The advantages and system-composition are presented.Airbag is an important component,and the shape and stress distribution of airbag is important for the whole structure.Thus,shape-state analysis is performed.The uniformity of constrained force at joint between airbag and rib will impact the state of joint,or even the deployment process.Therefore,a shape optimization of airbag is developed on the base of genetic algorithms,and the best shape of airbag is finally obtained.Therefore,the stress distribution of airbags will be uniformed and the antenna structure system will be more reliability.
基金Supported by National Natural Science Foundation of China (Grant No.U21A20122)Zhejiang Provincial Natural Science Foundation of China (Grant No.LY22E050012)+2 种基金China Postdoctoral Science Foundation (Grant Nos.2023T160580,2023M743102)Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems of China (Grant No.GZKF-202225)Students in Zhejiang Province Science and Technology Innovation Plan of China (Grant No.2023R403073)。
文摘Fine particulate matter produced during the rapid industrialization over the past decades can cause significant harm to human health.Twin-fluid atomization technology is an effective means of controlling fine particulate matter pollution.In this paper,the influences of the main parameters on the droplet size,effective atomization range and sound pressure level(SPL)of a twin-fluid nozzle(TFN)are investigated,and in order to improve the atomization performance,a multi-objective synergetic optimization algorithm is presented.A multi-physics coupled acousticmechanics model based on the discrete phase model(DPM),large eddy simulation(LES)model,and Ffowcs Williams-Hawkings(FW-H)model is established,and the numerical simulation results of the multi-physics coupled acoustic-mechanics method are verified via experimental comparison.Based on the analysis of the multi-physics coupled acoustic-mechanics numerical simulation results,the effects of the water flow on the characteristics of the atomization flow distribution were obtained.A multi-physics coupled acoustic-mechanics numerical simulation result was employed to establish an orthogonal test database,and a multi-objective synergetic optimization algorithm was adopted to optimize the key parameters of the TFN.The optimal parameters are as follows:A gas flow of 0.94 m^(3)/h,water flow of 0.0237 m^(3)/h,orifice diameter of the self-excited vibrating cavity(SVC)of 1.19 mm,SVC orifice depth of 0.53 mm,distance between SVC and the outlet of nozzle of 5.11 mm,and a nozzle outlet diameter of 3.15 mm.The droplet particle size in the atomization flow field was significantly reduced,the spray distance improved by 71.56%,and the SPL data at each corresponding measurement point decreased by an average of 38.96%.The conclusions of this study offer a references for future TFN research.
文摘Preventative maintenance (PM) measures for bridges are proactive maintenance actions which aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds since it can extend the life of the bridge and avoid the need for unplanned essential/corrective maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed a two-stage evolutionary optimization methodology based on genetic algorithm (GA) principles which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. In this paper, the sensitivity of the methodology to various key input parameters of the optimization methodology is examined in order to quantify their effects and identify possible trends in the optimum PM intervention profiles. The results of the sensitivity studies highlight the combined use of both proactive and reactive PM measures in deriving optimum strategy solutions. The precise mix and sequence of PM measures is clearly a function of the relative effectiveness and cost of the different available PM options as well as the various key parameters such as discount rate, target probability of failure, initial probability of failure and service life period examined. While the results highlight the need for more reliable data they also demonstrate the robustness and usefulness of the methodology;in the case where data is limited it can be used as a comparative tool to improve understanding of the effects of various strategies and enhance the decision making process.
基金Knowledge-based Ship-design Hyper-integrated Platform(KSHIP) of Ministry of Education and Ministry of Finance,P. R. China(No.200512)
文摘The goal of this effort was to provide a static and dynamic collaborative optimization （CO） model for the design of ship hull structure. The CO model integrated with static, mode and dynamic analyses. In the system-level optimization model, a new objective function was advised, integrating all the subsystem-levels＇ objective functions, so as to eliminate the effects of dimensions and magnitude order. The proposed CO architecture enabled multi-objectives of the system and subsystem-level to be considered at both levels during optimization. A bi-level optimization strategy was advised, using the multi-island genetic algorithm. The proposed model was demonstrated with a deck optimization problem of container ship stern. The analysis progress and results of example show that the CO strategy is not only feasible and reliable, but also well suited for use in actual optimization problems of ship design.
基金Specialized Research Fund for the Doctoral Program of High Education of China (20091102110021)
文摘In order to find out the optimal press bend forming path in fabricating aircraft integral panels, this article proposes a new method on the basis of the authors＇ previous work. It is composed of the finite element method （FEM） equivalent model, the surface curvature analysis, the artificial neural network response surface and the genetic algorithm. The method begins with analyzing the objective＇s shape curvature to determine the bending position. Then it optimizes the punch travel at each bending position by the following steps： （1） Establish a multi-step press bend forming FEM equivalent model, with which the FEM ex- periments designed with the Taguchi method are performed. （2） Construct a back-propagation （BP） neural network response surface with the data from the FEM experiments. （3） Use the genetic algorithm to optimize the neural network response surface as the objective function. Finally, this method is verified by press bending a complicated double-curvature grid-type stiffened panel and bears out its effectiveness and intrinsic worth in designing the press bend forming path.
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.
