Rolling stock manufacturers are finding structural solutions to reduce power required by the vehicles,and the lightweight design of the car body represents a possible solution.Optimization processes and innovative mat...Rolling stock manufacturers are finding structural solutions to reduce power required by the vehicles,and the lightweight design of the car body represents a possible solution.Optimization processes and innovative materials can be combined in order to achieve this goal.In this framework,we propose the redesign and optimization process of the car body roof for a light rail vehicle,introducing a sandwich structure.Bonded joint was used as a fastening system.The project was carried out on a single car of a modern tram platform.This preliminary numerical work was developed in two main steps:redesign of the car body structure and optimization of the innovated system.Objective of the process was the mass reduction of the whole metallic structure,while the constraint condition was imposed on the first frequency of vibration of the system.The effect of introducing a sandwich panel within the roof assembly was evaluated,focusing on the mechanical and dynamic performances of the whole car body.A mass saving of 63%on the optimized components was achieved,corresponding to a 7.6%if compared to the complete car body shell.In addition,a positive increasing of 17.7%on the first frequency of vibration was observed.Encouraging results have been achieved in terms of weight reduction and mechanical behaviour of the innovated car body.展开更多
A topological structural design approach is presented which is based upon the implementation of a two phase evolutionary optimization algorithm in conjunction with a finite element analysis code. The first phase utili...A topological structural design approach is presented which is based upon the implementation of a two phase evolutionary optimization algorithm in conjunction with a finite element analysis code. The first phase utilizes a conventional genetic approach which performs a global search for the optimal design topology. Dual level material properties are specified within the genetic encoding and are applied to each individual element in the design mesh to represent either design material or a void. The second phase introduces a rule based refinement which allows for user design intent to accelerate the solution process and eliminate obvious design discrepancies resulting from the phase one search. A series of plate design problems are presented where the objective is to minimize the overall volume of the structure under predefined loading and constraint conditions. The constraints include both stress and deflection considerations where stress is calculated through the use of a commercial finite element package. The initial plate example incorporates a coarse mesh, but a gradual decrease in element size was employed for the remaining cases examined. Replacement of the phase one search with a set of randomly generated designs is demonstrated in order to form a greatly reduced design space which drastically increases the efficiency of the solution process. Comparison results are drawn between the conventional genetic algorithm and the two phase procedure.展开更多
Simulation and optimization are the key points of virtual product development (VPD). Traditional engineering simulation software and optimization methods are inadequate to analyze the optimization problems because of ...Simulation and optimization are the key points of virtual product development (VPD). Traditional engineering simulation software and optimization methods are inadequate to analyze the optimization problems because of its computational inefficiency. A systematic design optimization strategy by using statistical methods and mathematical optimization technologies is proposed. This method extends the design of experiments (DOE) and the simulation metamodel technologies. Metamodels are built to in place of detailed simulation codes based on effectively DOE, and then be linked to optimization routines for fast analysis, or serve as a bridge for integrating simulation software across different domains. A design optimization of composite material structure is used to demonstrate the newly introduced methodology.展开更多
An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent cros...An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent crossover outperforms the traditional genetic algorithm combined with a rule-based approach utilizing domain specific knowledge developed by Webb, et al. [1]. The encoding of the improved crossover consists of two chromosome strings within the genetic algorithm where the first string represents the design or solution string, and the second string represents chromosome crossover string intelligence. This improved crossover methodology saves the best population members or designs evaluated from each generation and applies crossover chromosome intelligence to the best saved population members paired with globally selected parents. Enhanced features of this crossover methodology employ the random selection of the best designs from the prior generation as a potential parent coupled with alternating intelligence pairing methods. In addition to this approach, two globally selected parents possess the ability to mate utilizing crossover chromosome string intelligence maintaining the integrity of a global GA search. Overall, the final population following crossover employs both global and best generation design chromosome strings to maximize creativity while enhancing the solution search. This is a modification to a conventional GA that can be translated into GA encoding. This technique is explored initially through a Base 10 mathematical application followed by the examination of plate structural optimization considering stress and displacement constraints. Results from crossover intelligence are compared with the conventional genetic algorithm and from Webb, et al. [1] which illustrates the outcome of a two phase genetic optimization algorithm.展开更多
Virtual product development (VPD) is essentially based on simulation. Due tocomputational inefficiency, traditional engineering simulation software and optimization methods areinadequate to analyze optimization proble...Virtual product development (VPD) is essentially based on simulation. Due tocomputational inefficiency, traditional engineering simulation software and optimization methods areinadequate to analyze optimization problems in VPD. Optimization method based on simulationmetamodel for virtual product development is proposed to satisfy the needs of complex optimaldesigns driven by VPD. This method extends the current design of experiments (DOE) by variousmetamodeling technologies. Simulation metamodels are built to approximate detailed simulation codes,so as to provide link between optimization and simulation, or serve as a bridge for simulationsoftware integration among different domains. An example of optimal design for composite materialstructure is used to demonstrate the newly introduced method.展开更多
文摘Rolling stock manufacturers are finding structural solutions to reduce power required by the vehicles,and the lightweight design of the car body represents a possible solution.Optimization processes and innovative materials can be combined in order to achieve this goal.In this framework,we propose the redesign and optimization process of the car body roof for a light rail vehicle,introducing a sandwich structure.Bonded joint was used as a fastening system.The project was carried out on a single car of a modern tram platform.This preliminary numerical work was developed in two main steps:redesign of the car body structure and optimization of the innovated system.Objective of the process was the mass reduction of the whole metallic structure,while the constraint condition was imposed on the first frequency of vibration of the system.The effect of introducing a sandwich panel within the roof assembly was evaluated,focusing on the mechanical and dynamic performances of the whole car body.A mass saving of 63%on the optimized components was achieved,corresponding to a 7.6%if compared to the complete car body shell.In addition,a positive increasing of 17.7%on the first frequency of vibration was observed.Encouraging results have been achieved in terms of weight reduction and mechanical behaviour of the innovated car body.
文摘A topological structural design approach is presented which is based upon the implementation of a two phase evolutionary optimization algorithm in conjunction with a finite element analysis code. The first phase utilizes a conventional genetic approach which performs a global search for the optimal design topology. Dual level material properties are specified within the genetic encoding and are applied to each individual element in the design mesh to represent either design material or a void. The second phase introduces a rule based refinement which allows for user design intent to accelerate the solution process and eliminate obvious design discrepancies resulting from the phase one search. A series of plate design problems are presented where the objective is to minimize the overall volume of the structure under predefined loading and constraint conditions. The constraints include both stress and deflection considerations where stress is calculated through the use of a commercial finite element package. The initial plate example incorporates a coarse mesh, but a gradual decrease in element size was employed for the remaining cases examined. Replacement of the phase one search with a set of randomly generated designs is demonstrated in order to form a greatly reduced design space which drastically increases the efficiency of the solution process. Comparison results are drawn between the conventional genetic algorithm and the two phase procedure.
文摘Simulation and optimization are the key points of virtual product development (VPD). Traditional engineering simulation software and optimization methods are inadequate to analyze the optimization problems because of its computational inefficiency. A systematic design optimization strategy by using statistical methods and mathematical optimization technologies is proposed. This method extends the design of experiments (DOE) and the simulation metamodel technologies. Metamodels are built to in place of detailed simulation codes based on effectively DOE, and then be linked to optimization routines for fast analysis, or serve as a bridge for integrating simulation software across different domains. A design optimization of composite material structure is used to demonstrate the newly introduced methodology.
文摘An intelligent crossover methodology within the genetic algorithm (GA) is explored within both mathematical and finite element arenas improving both design and solution convergence time. This improved intelligent crossover outperforms the traditional genetic algorithm combined with a rule-based approach utilizing domain specific knowledge developed by Webb, et al. [1]. The encoding of the improved crossover consists of two chromosome strings within the genetic algorithm where the first string represents the design or solution string, and the second string represents chromosome crossover string intelligence. This improved crossover methodology saves the best population members or designs evaluated from each generation and applies crossover chromosome intelligence to the best saved population members paired with globally selected parents. Enhanced features of this crossover methodology employ the random selection of the best designs from the prior generation as a potential parent coupled with alternating intelligence pairing methods. In addition to this approach, two globally selected parents possess the ability to mate utilizing crossover chromosome string intelligence maintaining the integrity of a global GA search. Overall, the final population following crossover employs both global and best generation design chromosome strings to maximize creativity while enhancing the solution search. This is a modification to a conventional GA that can be translated into GA encoding. This technique is explored initially through a Base 10 mathematical application followed by the examination of plate structural optimization considering stress and displacement constraints. Results from crossover intelligence are compared with the conventional genetic algorithm and from Webb, et al. [1] which illustrates the outcome of a two phase genetic optimization algorithm.
基金National Natural Science Foundation of China (No.5988950)
文摘Virtual product development (VPD) is essentially based on simulation. Due tocomputational inefficiency, traditional engineering simulation software and optimization methods areinadequate to analyze optimization problems in VPD. Optimization method based on simulationmetamodel for virtual product development is proposed to satisfy the needs of complex optimaldesigns driven by VPD. This method extends the current design of experiments (DOE) by variousmetamodeling technologies. Simulation metamodels are built to approximate detailed simulation codes,so as to provide link between optimization and simulation, or serve as a bridge for simulationsoftware integration among different domains. An example of optimal design for composite materialstructure is used to demonstrate the newly introduced method.
