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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.

The power injection method (PIM) is an experimental method used to identify the statistical energy analysis (SEA) parameters (called loss factors -- LFs) of a vibroacoustic system. By definition, LFs are positive real numbers. However, it is not uncommon to obtain negative LFs during experiments, which is considered a measurement error. To date, a recently proposed method, called Monte Carlo filtering (MCF), of correcting negative coupling loss factors (CLFs) has been validated for systems that meet SEA assumptions. In this article, MCF was validated for point connections and in conditions where SEA assumptions are not met (systems with low modal overlap, non-conservative junctions, strong coupling). The effect of removing MCF bias on the results was also examined. During the experiments, it was observed that the bias is inversely proportional to the damping loss factor of the examined subsystems. The obtained results confirm that the PIM, combined with MCF, allows to determine non-negative SEA parameters in all considered cases. [ABSTRACT FROM AUTHOR]

Monte Carlo Filtering (MCF) is one of the methods of Experimental Statistical Energy Analysis (E-SEA), which allows the correction of negative LFs (Loss Factors). In this article, a modification of the MCF method, called DESA (Diagonal Expansion of the Search Area), is proposed. The technique applies a non-uniform extension of the search area when generating a population of normalized energy matrices. The degree of expansion of the search area is controlled by the Diagonal Penalty Factor (DPF). The authors demonstrated the method's effectiveness on a system that could not be identified in several frequency bands by the classical MCF method. After applying DESA, it was possible to fill in the problematic bands that were missing CLF (coupling loss factor) and DLF (damping loss factor) values. The paper also proposes a way to minimize the errors introduced by using overly high DPF values. [ABSTRACT FROM AUTHOR]