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The Efficient Multi-Objective Optimization of Finite Element Analysis Model Using ModelCenter

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The multi-objective optimization for a nested flying vehicle (NFV) of space science experiments is carried out aiming at the launch weight, frequency response and vacuum effect. The parametric model and finite element analysis are adopted to implement the structural analysis. The NFV is optimized to enhance the performance in the space environment where the lunch weight and structural strength are key constraints to concern about. The CAX software, analysis models and algorithms are integrated based on ModelCenter framework which makes modeling, analyzing and optimization more convenient and efficient. The optimizer of ModelCenter is chosen to optimize the structural performance of NFV, including the total mass, maximum deformation caused by vacuum environment and frequency response. As to validate the results, both weighting method with gradient optimization algorithm and Genetic Algorithm (GA) for multi-objective optimization are used. The optimization results of NFV verify the approaches proposed in this paper can improve the performance of NFV and apply to the finite element analysis model.
Title: The Efficient Multi-Objective Optimization of Finite Element Analysis Model Using ModelCenter
Description:
The multi-objective optimization for a nested flying vehicle (NFV) of space science experiments is carried out aiming at the launch weight, frequency response and vacuum effect.
The parametric model and finite element analysis are adopted to implement the structural analysis.
The NFV is optimized to enhance the performance in the space environment where the lunch weight and structural strength are key constraints to concern about.
The CAX software, analysis models and algorithms are integrated based on ModelCenter framework which makes modeling, analyzing and optimization more convenient and efficient.
The optimizer of ModelCenter is chosen to optimize the structural performance of NFV, including the total mass, maximum deformation caused by vacuum environment and frequency response.
As to validate the results, both weighting method with gradient optimization algorithm and Genetic Algorithm (GA) for multi-objective optimization are used.
The optimization results of NFV verify the approaches proposed in this paper can improve the performance of NFV and apply to the finite element analysis model.

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