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.

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.

Dissipative splitter silencers are widely used in industry for the reduction of propagated sound waves in ducts. Even though these systems are effective from the acoustics point of view when they are properly designed, they also introduce a pressure loss in the system, due to the modification of the properties of the flow circulating inside the duct. This effect is not desired in some industrial applications, so it is necessary to be able to predict the pressure loss as precisely as possible to design silencers according to the needs. Nevertheless, the prediction made by standards are usually limited to given geometries or flow speed. In this work, we present a comparative study on the results obtained for the pressure loss by means of the standards ISO 14163 and VDI 1801-1, numerical simulations with the finite element method, and experimental measurements. Additionally, two different profile shapes and four input face velocities are tested in order to know the influence of these parameters in the variations of the flow and the accuracy of the prediction of the different methods.

Monte Carlo Filtering (MCF) is one of the methods of Experimental Statistical Energy Analysis (E-SEA), which allows the correction of a negative LF (Loss Factor). In this article, a modification of the MCF method, called DESA (Diagonal Extension 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 and DLF values. The paper also proposes a way to minimize the errors introduced by using overly high DPF values.