Vibration Response Law of Aircraft Taxiing under Random Roughness Excitation
Source abstract
Abstract
Understanding the vibration response patterns of aircraft taxiing under runway roughness excitation is crucial for aircraft design and runway performance evaluation. In this paper, we establish pavement roughness models for both asphalt and concrete surfaces, taking into account their unique structural characteristics. We construct a six-degrees-of-freedom aircraft model using multi-rigid-body system dynamics theory and employ the pseudo-excitation method to examine the influence of pavement roughness types on the steady vibration response of aircraft taxiing at constant speeds. Furthermore, we analyze the non-stationary vibration response patterns of aircraft during takeoff and landing taxiing using the method of instantaneous frequency response function with space frequency. Lastly, we explore the effect of stochastic structural parameters on aircraft vibration response using the Monte Carlo method. Our findings reveal that the roughness power spectrum differs between asphalt and concrete pavements, and the established roughness models in this paper demonstrate a strong fit (R2 > 0.95). The type of pavement roughness has a relatively minor impact on the power spectral density distribution of the aircraft vibration response, suggesting that the same roughness model can be used for both asphalt and concrete pavements when high accuracy is not required. The power spectral density distribution of aircraft vibration response varies across different motion attitudes, with the vibration response during landing being significantly larger than that during takeoff. Among the aircraft structural parameters, the randomness of the sprung mass has the most substantial effect on the aircraft vibration response, potentially causing the variation coefficient of dynamic load on the front landing gear to exceed 0.11. Tire stiffness comes next, which can lead to the variation coefficient of dynamic load on the main landing gear reaching 0.07. The results have a guiding role in optimizing aircraft structure design and ensuring pavement performance.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image and Signal Denoising Methods · Machine Fault Diagnosis Techniques · Probabilistic and Robust Engineering Design
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of Civil Aviation Administration of China, Tongji University, Shanghai 201804, China
Funding
Natural Science Foundation of Shanghai
References (31)
Show source-provided references
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