Evaluation of aircraft quality characteristic parameters based on Monte Carlo data mining

Xuan Chen; Hongyang Li; Jun Chen; Haixing Wang

doi:10.1117/12.3048329

3 October 2024 Evaluation of aircraft quality characteristic parameters based on Monte Carlo data mining

Xuan Chen, Hongyang Li, Jun Chen, Haixing Wang

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132721Q (2024) https://doi.org/10.1117/12.3048329
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

The quality characteristics and deviations of an aircraft are extremely important for the design and manufacturing of the aircrafts, which are important inputs for the aircraft propulsion and control systems. Therefore, before participating in flight tests, the aircraft must undergo mass characteristic measurements to obtain of the quality characteristic parameters of the mass, the center coordinates of the mass, rotary inertia and product of inertia for aircrafts. This article proposes a method for evaluating the quality characteristics of aircrafts based on Monte Carlo random error generation. Large scale simulation data of the quality parameters of various components of the aircrafts are obtained based on the random error generation. The relationships between the quality deviation of each component of the aircraft and the overall quality deviation of the aircraft is established through data mining between the characteristic parameters of each component and the overall characteristic parameters. The quality parameter evaluation of each component with errors is obtained through Monte Carlo simulation, achieving high-precision evaluation of the overall rotational inertia and inertia product of the aircraft.

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

Citation Download Citation

Xuan Chen, Hongyang Li, Jun Chen, and Haixing Wang "Evaluation of aircraft quality characteristic parameters based on Monte Carlo data mining", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132721Q (3 October 2024); https://doi.org/10.1117/12.3048329

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