Uncertainty Quantification in Aircraft Noise Calculation: Current Status and Challenges at DLR

Abstract

At DLR, scientific aircraft noise prediction of single flights is performed with the code PANAM, which uses parametric and componential noise source modeling. PANAM is used to assess the noise (a) from different aircraft configurations, (b) along flight procedures, and (c) the effect of novel retrofit technologies. In such comparisons, uncertainty quantification is important to assess the reliability of the simulations; especially when noise source models for different technologies come from various research groups and differ in their fidelity and complexity. Another important application of uncertainty quantification is the benchmarking of simulation results with measurement data. Recently, the influence of the model uncertainty and the input parameter uncertainty on the simulation results has been investigated in a cooperation with TU Braunschweig, theUniversity of Alabama, and Empa. The investigations have been carried out both at the level of the individual components and at the level of the entire aircraft. The influence of the atmosphere on the sound propagation was also considered. A first-order second-moment approach was compared to Monte Carlo simulations and to an approach using nonintrusive polynomial chaos with least square. These initial studies were limited to a simplified assessment of the problem, i.e., the noise descriptor was not an integral level and did not incorporate complex frequency based weightings or corrections. Since uncertainty assessment in PANAM is an ongoing research topic, this presentation provides (1) a brief overview of the current status and (2) discusses the current challenges that need to be addressed to ultimately quantify the uncertainties associated with the predicted levels for typical PANAM applications, i.e., points (a) to (c).