Speaker
Description
Early design simulative cabin noise assessment is indispensable when designing novel aircraft. When considering an early design stage, many crucial parameters needed for the simulation, such as material parameters and excitation, are not known yet and make the noise assessment challenging. Therefore, uncertainties should be considered in the simulation chain, in order to obtain a more robust early noise assessment.
This contribution builds on a recently published work of the authors \linebreak (https://doi.org/10.48550/arXiv.2408.08402) that allows for an inclusion of stochastic excitations in the noise assessment process and yields efficient approximations of the mean sound pressure and its covariance. In this contribution, the existing framework is extended to also include material uncertainties. Here, a low-rank decomposition of the covariance matrix of the excitation is used as input for a model order reduction algorithm as to yield rapidly evaluable models for many different excitations. When we introduce uncertainties in the material parameters, however, we also have to consider the change in the underlying system matrix. Here, the covariance of the output is approximated by the First Order Second Moment method (FOSM). FOSM not only requires the covariance matrix of the excitation, but also the derivatives of the system matrices with respect to the uncertain parameters and the covariance of the uncertain parameters. While the covariance of the excitation can be computed efficiently through the previously proposed method, the latter requires efficient methods for evaluating matrix products and for solving linear systems, on which this contribution will also shed light. Subsequently, the covariance of the output is composed of a sum of these matrix products depending on how many uncertain material parameters are considered.
The method is developed for a simple vibroacoustic problem, a plate cavity system as prior step to applying it to a full-scale aircraft cabin noise model. As a starting point the Young’s modulus is chosen as only uncertain parameter. The excitation is chosen to be a realistic turbulent boundary layer excitation, meaning it includes inherent underlying uncertainties. With the proposed method we can predict the sound pressure inside a cavity and its covariance for changing material parameters and stochastic excitations, while maintaining a reasonable computational cost.
