Multiobjective Design Optimization Framework for Multicomponent System with Complex Nonuniform Loading

Abstract

To improve the accuracy and efficiency of multiobjective design optimization for a multicomponent system with complex nonuniform loads, an efficient surrogate model (the decomposed collaborative optimized Kriging model, DCOKM) and an accurate optimal algorithm (the dynamic multiobjective genetic algorithm, DMOGA) are presented in this study. Furthermore, by combining DCOKM and DMOGA, the corresponding multiobjective design optimization framework for the multicomponent system is developed. The multiobjective optimization design of the carrier roller system is considered as a study case to verify the developed approach with respect to multidirectional nonuniform loads. We find that the total standard deviation of three carrier rollers is reduced by 92%, where the loading distribution is more uniform after optimization. This study then compares surrogate models (response surface model, Kriging model, OKM, and DCOKM) and optimal algorithms (neighbourhood cultivation genetic algorithm, nondominated sorting genetic algorithm, archive microgenetic algorithm, and DMOGA). The comparison results demonstrate that the proposed multiobjective design optimization framework is demonstrated to hold advantages in efficiency and accuracy for multiobjective optimization.

Real-Time Air-to-Ground Data Communication Technology of Aeroengine Health Management System with Adaptive Rate in the Whole Airspace

“…Although health management technology is the most effective method to ensure the safe operation of the aeroengine and reduce maintenance costs, the timeliness and accuracy of data sources used by it will directly affect the quality of health assessment. In terms of algorithm theory research on aeroengine health management, typical algorithms include methods based on physical models [5,6], methods based on data [7,8], methods based on experience, and fusion methods based on intelligent algorithms [9][10][11].…”