Scientific Article

Quality criterion and errors of corrected negative SEA loss factors

¹ KFB Acoustics sp. z o. o., Acoustic Research and Innovation Center, Oławska 8, 55-040 Domasław, Poland
² Wroclaw University of Science and Technology, Department of Acoustics, Multimedia and Signal Processing, Wybrzeże Stanisława Wyspiańskiego 27, 50-370 Wrocław, Poland

^* Corresponding author: p.nieradka@kfb-acoustics.com

Received: 26 February 2024
Accepted: 19 July 2024

Abstract

Statistical Energy Analysis (SEA) is a well-known numerical method for predicting vibroacoustic phenomena in complex systems. The accuracy of SEA models relies on the precise determination of coupling loss factors and damping loss factors. Experimental SEA (E-SEA) methods, such as the Power Injection Method are commonly employed to measure these parameters. However, these techniques may yield negative loss factors, which are considered measurement errors. Monte Carlo Filtering (MCF) is one of the procedures, that allows the correction of negative loss factors, but the quality of the results remains unknown. The knowledge of the loss factors’ quality is directly related to the practical applications of SEA, where good quality of the input model parameters (coupling and damping loss factors) correspond to good quality and precise simulations of complex vibroacoustic systems (like trains, vehicle, airplanes, buildings) responses. In a previous study, a total loss factor (TLF) criterion was proposed as a quality indicator for the corrected loss factors. The current paper validates the TLF criterion through a comprehensive analysis of various numerical examples. By expanding the Monte Carlo sample’s value range (search area) and using different probability density functions, we intentionally introduced errors in the loss factors. The TLF criterion demonstrated resilience to increasing errors in certain scenarios, raising concerns about its sensitivity. Nevertheless, it seems, that the TLF criterion remains a good indicator of population stability and large error occurrence.