Simulation and test data association analysis for aircraft design process optimization

Baoning Ji; Xiaobo Zhou; Jie Zhang; Xiaodong Jiang

doi:10.1117/12.3029369

22 May 2024 Simulation and test data association analysis for aircraft design process optimization

Baoning Ji, Xiaobo Zhou, Jie Zhang, Xiaodong Jiang

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 1317626 (2024) https://doi.org/10.1117/12.3029369
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

The aircraft development process involves a large number of simulation and testing tasks, as well as simulation and testing data. Establishing a platform to analyze these data is important to accelerate the iterative optimization of product design. Therefore, this paper proposes a simulation and test data correlation analysis framework for the optimization of the whole process of aircraft design. The framework starts with an analysis of the functions that a data correlation analysis platform should have based on the application requirements for each phase of aircraft design. Then, the architecture of the data association analysis platform is proposed based on the functional analysis, and each key technology supporting the architecture is introduced. Finally, typical data association analysis objectives and their regulated analysis processes are summarized.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Baoning Ji, Xiaobo Zhou, Jie Zhang, and Xiaodong Jiang "Simulation and test data association analysis for aircraft design process optimization", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 1317626 (22 May 2024); https://doi.org/10.1117/12.3029369

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