Simulating pass-by noise of vehicles - Comparison of a ray tracing model using simplified geometrical building models with measurement

Kurzfassung

Noise effects like wake-up reactions strongly depend on single noise events, which are characterized by the maximum sound level and their steepest slope in level time-history. To predict these time and space dependent sound events resulting from ground based transportation in urban areas, detailed simulation methods are required. State of the art 2.5D ray tracing methods for noise prediction from ground based transportation make simplifying assumptions in order to keep the simulation effort manageable. Among others they use simplified geometrical building models (LOD1-models). These LOD1-models consist of the floor plan of a building and an average height of the building. This simplification of roof shape leads to deviations in SPL compared to original roof shape. However, since the ray tracing methods focus on time-averaged noise levels, they can achieve good agreement with energy-equivalent noise levels from measurements. The question rises whether 2.5D ray tracers can adequately predict single noise events from vehicle pass-by. The missing possibility of temporal averaging demands higher quality of the simulation models and the input data. Therefore, in this paper the LOD1 assumption is investigated in terms of its influence on level- time prediction. Measurements have been carried out in the anechoic room of the DLR Göttingen to simulate a sound source passing-by a saddle roof building. In order to determine the physical influence of the roof shape they are compared to measurements with a corresponding LOD1 building. Finally, the measurement results are compared to simulation results of the CNOSSOS-EU model, a ray tracing model which uses the LOD1 assumption. The differences between measurements and the simulation model are discussed in terms of maximum sound pressure level and steepest slope of rise in level-time history and the energy-equivalent sound pressure level.