Original article

Physico-chemical indicators of inter-specific variability in vibration damping of wood

¹ Laboratoire de Mécanique et Génie Civil, CNRS, Université Montpellier 2, Place E. Bataillon, cc 048, 34095 Montpellier Cedex 5, France
² Laboratory of Forest Resources Circulatory System, Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, Kyoto 606-8522, Japan
³ Production et valorisation des bois tropicaux et méditerranéens, CIRAD PERSYST Department, TA B40/16 BP 5035, 34398 Montpellier Cedex 5, France
⁴ CNRS, UMR Ecologie des Forêts de Guyane, BP 316 - 97379 Kourou Cedex, French Guyana

^* Corresponding author: iris_bremaud@hotmail.com

Received: 2 November 2009
Accepted: 13 February 2010

Abstract

• The vibration damping coefficient (tanδ) of wood is an important property for acoustical uses, including musical instruments. Current difficulties in the availability of some of the preferred species call for diversification, but this comes up against the lack of systematic damping coefficient data.

• Keeping in mind the possible factors affecting tanδ, could we predict its variations between species, by using indicators that are either easily measured and/or readily available for many species?

• Vibrational properties, equilibrium moisture content and colorimetric parameters were assessed on 94 wood types belonging to 76 species. Experimental results were then related to data on chemical contents and physical properties from the CIRAD database. The “standard” relationship between tanδ and specific modulus of elasticity (E’/ρ) explained only half of the variations. Deviations from this trend were correlated to extractives content, yet effects were not directly quantitative. Damping deviations were also correlated to colour and moisture-related properties, especially so with fibre saturation point.

• By taking into account a combination of moisture-related properties, colour – or extractives content, and the “standard” relationship between tanδ and E’/ρ, we could propose simple predictive models which explain up to 89% of observed variations in tanδ between 48 species.