Assessment of aircraft landing gear cumulative stroke to develop a new runway roughness evaluation index

ABSTRACT

This study was undertaken to develop a new index, the Main Landing Gear Cumulative Stroke (MLGCS) index, to evaluate airport runway roughness. Using ADAMS/Aircraft software, we first developed and validated a virtual prototype model of the Boeing B737-800 aircraft and then employed the model to predict the aircraft’s dynamic responses with regard to roughness. Using this prototype model and comparing it to the International Roughness Index (IRI) that is designed to evaluate roadway roughness, we established a landing gear cumulative stroke (LGCS) model to represent runway roughness. The results show that taxiing speed is an important factor using the LGCS model. According to the most unfavorable scenario, we determined the LGCS of the nose landing gear to be 100 km/h and that of the main landing gear to be 60 km/h. These results underscore that, based on correlation analysis, using the cumulative stroke of the main landing gear is more reasonable than using that of the nose landing gear to evaluate runway roughness. Furthermore, based on measured data for 37 runways, a comparison of commonly used roughness indices indicates that the MLGCS index proposed in this paper is superior to both the Boeing Bump Index and the IRI.

KEYWORDS:

• Runway roughness
• evaluation index
• landing gear cumulative stroke (LGCS)
• correlation analysis

Acknowledgement

This study was supported by China Postdoctoral Science Foundation funded project (No.2020M681392), and the National Natural Science Foundation of China (grant number U1833123 and No. U1933113). The authors appreciate the help of Zhekai Zhang, Junjun Chen, Penghui Li, et al.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by China Postdoctoral Science Foundation: [Grant Number 2020M681392]; the National Natural Science Foundation of China: [Grant Number U1833123]; the National Natural Science Foundation of China: [Grant Number U1933113].