This study aims to develop and apply a unified stochastic–deterministic framework for assessing ride comfort and load-sharing of a large commercial aircraft model with eight-degree of freedom (DOF) under correlated multi-wheel runway roughness.
A power spectral density (PSD) based correlated multi-wheel roughness model generates spatially coherent runway excitation. The 8-DOF system is assembled from physically consistent mass, stiffness and damping distributions, transformed via modal decomposition and integrated in time using the Newmark-ß scheme in modal coordinates. A parametric Monte Carlo campaign (gear stiffness/damping scaling, mass-distribution scenarios, multiple taxiing speeds and PSD levels; ten realizations per case) yields ensemble statistics for root mean square (RMS) acceleration, vibration dose value (VDV) and peak wheel loads.
Modal and time-domain results show dominant fuselage modes in the approximately 1–10 Hz band. Increasing landing-gear damping substantially reduces fuselage RMS acceleration and VDV (observed reductions in tested cases up to approximately 15%), whereas increased stiffness amplifies vibration transmission and load imbalance across gears. Stochastic variability coefficient of variation for key metrics remained below 8% for the tested realizations, indicating relatively low sensitivity to random runway phase variations.
Model neglects nonlinear shock-strut and detailed tire contact; results are for the linearized 8-DOF framework.
The paper integrates correlated multi-wheel PSD excitation, modal Newmark integration and Monte Carlo simulation into a single, computationally efficient platform for simultaneous comfort and load-sharing assessment useful for landing-gear tuning and preliminary design optimization.
