Prediction and uncertainty quantification of structure-borne sound radiation into a diffuse field

Reynders, Edwin; Wang, Pengchao; hoorickx, Cédric Van; Lombaert, Geert

doi:10.1016/j.jsv.2019.114984

Cited by 16 publications

(10 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is also known that impact sound curves have important low-frequency content with probably greater measurement uncertainty, because of the laboratory receiving room effects. Those effects rely on the location of the Schröder frequency above which the diffuse field assumptions hold, but below that there can be unpredictable influence from the room's sound field during measurements [76,77]. Finally, the normalized error distribution across frequency bands seems to be equally random across low-, middle-and high-frequency bands.…”

Section: Prediction Of Impact Sound Insulationmentioning

confidence: 99%

Prediction of Sound Insulation Using Artificial Neural Networks—Part I: Lightweight Wooden Floor Structures

et al. 2022

View full text Add to dashboard Cite

The artificial neural networks approach is applied to estimate the acoustic performance for airborne and impact sound insulation curves of different lightweight wooden floors. The prediction model is developed based on 252 standardized laboratory measurement curves in one-third octave bands (50–5000 Hz). Physical and geometric characteristics of each floor structure (materials, thickness, density, dimensions, mass and more) are utilized as network parameters. The predictive capability is satisfactory, and the model can estimate airborne sound better than impact sound cases especially in the middle-frequency range (250–1000 Hz), while higher frequency bands often show high errors. The forecast of the weighted airborne sound reduction index Rw was calculated with a maximum error of 2 dB. However, the error increased up to 5 dB in the worse case prediction of the weighted normalized impact sound pressure level Ln,w. The model showed high variations near the fundamental and critical frequency areas which affect the accuracy. A feature attribution analysis explored the essential parameters on estimation of sound insulation. The thickness of the insulation materials, the density of cross-laminated timber slab and the concrete floating floors and the total density of floor structures seem to affect predictions the most. A comparison between wet and dry floor solution systems indicated the importance of the upper part of floors to estimate airborne and impact sound in low frequencies.

show abstract

Section: Prediction Of Impact Sound Insulationmentioning

confidence: 99%

Prediction of Sound Insulation Using Artificial Neural Networks—Part I: Lightweight Wooden Floor Structures

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The Young's modulus was estimated by modal testing in unmounted, free-free boundary conditions, yielding a value of 1:65 GPa. 44 Both the PMMA baseplate and the steel stiffeners are modeled with four-node Mindlin-Reissner shell elements. The plate and stiffeners are fully coupled in their plane of contact, with the offset of the stiffeners with respect to the plate taken into account.…”

Section: E Application 3: Plane Radiatormentioning

confidence: 99%

“…The stiffened PMMA plate was excited repetitively using an instrumented impact hammer of the type PCB086C03 at f0:315 m; 0:970 mg T (on a rib) and for a total duration of 20 s. The measured average double-sided power spectral density of the repetitive hammer impact force was employed (which is presented in Ref. 44).…”

Section: E Application 3: Plane Radiatormentioning

confidence: 99%

Numerical realization of diffuse sound pressure fields using prolate spheroidal wave functions

hoorickx

Reynders

2022

The Journal of the Acoustical Society of America

Self Cite

View full text Add to dashboard Cite

A diffuse sound field is conventionally defined as a zero-mean circularly symmetric complex Gaussian random field. A more recent, generalized definition is that of a sound field having mode shapes that are diffuse in the conventional sense, and eigenfrequencies that conform to the Gaussian orthogonal ensemble. Such a generalized diffuse sound field can represent a random ensemble of sound fields that share gross features, such as modal density and total absorption, but otherwise have any possible arrangement of local wave scattering features. The problem of generating realizations or Monte Carlo samples of a conventional diffuse sound field or, equivalently, of the mode shapes of a generalized diffuse sound field, is addressed here. Such realizations can be obtained from an eigenvalue decomposition of the spatial correlation function. A discrete decomposition is numerically expensive when the sound pressures at many locations are of interest, so a fast analytical decomposition based on prolate spheroidal wave functions is developed. The approach is numerically validated by comparison with a detailed room model, where random wave scatterers are explicitly modeled as acoustic point masses with random positions, and good correspondence is observed. Furthermore, applications involving correlated sound sources and sound-structure interaction are presented.

show abstract

“…A diffuse field composed of a large number of statistically independent plane waves, the spatial phase of which is uniformly distributed and independent from the amplitude. As described in [78], the diffuse field may represent the sound field of a conceptual ensemble of rooms with the same modal density and total absorption, however, any possible arrangement of boundaries and small objects that scatter incoming sound waves. For example, if we choose any point in the closed space, sound waves arriving at this point have random phases and there is an equal probability of sound waves arriving from any direction.…”

Section: Sound Field In Roomsmentioning

confidence: 99%

“…Adopting a diffuse field model therefore inherently implies that uncertainty due to random wave scattering is present in the computed results. This uncertainty can be large, especially at low frequencies [78]. In practice there is always a dissipation of energy, therefore, there may not be an equal energy flow in all directions.…”

Section: Sound Field In Roomsmentioning

confidence: 99%

Virtual Building Acoustics

Imran¹

2022

View full text Add to dashboard Cite

show abstract

Prediction and uncertainty quantification of structure-borne sound radiation into a diffuse field

Cited by 16 publications

References 21 publications

Prediction of Sound Insulation Using Artificial Neural Networks—Part I: Lightweight Wooden Floor Structures

Prediction of Sound Insulation Using Artificial Neural Networks—Part I: Lightweight Wooden Floor Structures

Numerical realization of diffuse sound pressure fields using prolate spheroidal wave functions

Virtual Building Acoustics

Contact Info

Product

Resources

About