Multi-disciplinary & multi-objective design optimisation tools are used more and more in order to help CAE designers and managers in their quest for product higher quality and returns. As far as an optimisation problem is concerned, several strategies can be used to select candidates for evaluation, i.e. designs on the Pareto frontier, as for instance, Preliminary Exploration Methods, DOE, Local Refinement Methods, Special-Purpose Plug-ins or Multi-Objective Optimization Methods that include genetic algorithms and evolution strategies. The goal of the Multi-Objective Optimization Methods consists in locating the designs on the Pareto frontier automatically, letting the user to pick the desired trade-off among them by means of Decision Making tools. For these reasons the use of tools for the integration of different design methodologies and analysis software is a key point for the modern engineering industries. The aim of the application consists in a multi-disciplinary optimisation of a construction machinery cab considering its vibro-acoustic performance. The multi-objective design optimisation code (modeFRONTIER) drives the analysis process flow taking into account the cab parameter structural modifications and carrying out the vibro-acoustic field optimisation. A 3D cavity representing the real cab has been modelled by means of a (Ansys) FE structural mesh. Starting from the cab vibration load experimental acquisition, a (Sysnoise) BEM coupled analysis has been carried out in order to evaluate the cab inner vibro-acoustic field as a function of the physical properties of each structural element. The present paper illustrates the comparison between the results achieved by means of MOGA (Multi Objective Genetic Algorithm) and MOGT (Multi Objective Game Theory) optimisation strategies. Optimisation run variables/objectives dependence and computation process logic have been evaluated in order to understand peculiarities and overcome limits of the methodologies. After all the less tested and more innovating MOGT strategy shows itself to be a robust and fast multi objective optimisation tool too when combined with Evolutionary Algorithms.
MOGA & MOGT Strategies Comparison for a Cab Vibro-acoustic Optimization
Miccoli G;
2006
Abstract
Multi-disciplinary & multi-objective design optimisation tools are used more and more in order to help CAE designers and managers in their quest for product higher quality and returns. As far as an optimisation problem is concerned, several strategies can be used to select candidates for evaluation, i.e. designs on the Pareto frontier, as for instance, Preliminary Exploration Methods, DOE, Local Refinement Methods, Special-Purpose Plug-ins or Multi-Objective Optimization Methods that include genetic algorithms and evolution strategies. The goal of the Multi-Objective Optimization Methods consists in locating the designs on the Pareto frontier automatically, letting the user to pick the desired trade-off among them by means of Decision Making tools. For these reasons the use of tools for the integration of different design methodologies and analysis software is a key point for the modern engineering industries. The aim of the application consists in a multi-disciplinary optimisation of a construction machinery cab considering its vibro-acoustic performance. The multi-objective design optimisation code (modeFRONTIER) drives the analysis process flow taking into account the cab parameter structural modifications and carrying out the vibro-acoustic field optimisation. A 3D cavity representing the real cab has been modelled by means of a (Ansys) FE structural mesh. Starting from the cab vibration load experimental acquisition, a (Sysnoise) BEM coupled analysis has been carried out in order to evaluate the cab inner vibro-acoustic field as a function of the physical properties of each structural element. The present paper illustrates the comparison between the results achieved by means of MOGA (Multi Objective Genetic Algorithm) and MOGT (Multi Objective Game Theory) optimisation strategies. Optimisation run variables/objectives dependence and computation process logic have been evaluated in order to understand peculiarities and overcome limits of the methodologies. After all the less tested and more innovating MOGT strategy shows itself to be a robust and fast multi objective optimisation tool too when combined with Evolutionary Algorithms.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
