Particle image velocimetry technique and ultrasound method to obtain the modulus of elasticity of bertholletia excelsa wood

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Abstract

Non-destructive techniques for characterizing materials in-service have been increasing in importance. Thus, it is relevant to assess the potential of non-destructive techniques for solid materials. This work aimed to determine the modulus of elasticity of Bertholletia excelsa wood using the particle image velocimetry technique and the ultrasound method to compare the results with the conventional methodology. For this purpose, samples of Bertholletia excelsa were made using a circular saw. The samples were evaluated for sound propagation to calculate the modulus of elasticity using ultrasound equipment. Subsequently, they were subjected to the compression parallel to grain test in a universal testing machine. The samples were marked and monitored during the loading session, with the repeated capture of images using a professional camera. The deformation values obtained were used to estimate the modulus of elasticity using the particle image velocimetry technique. The mean values of modulus of elasticity found were 17403 MPa for ultrasound, 15589 MPa for the particle image velocimetry technique, and 15333 MPa for the universal testing machine. The particle image velocimetry technique was considered to be statistically similar (Tukey α = 0,05) to the other methods tested. The linear coefficient of determination (R2) between the particle image velocimetry technique and the universal testing machine was 0,95, a high and satisfactory value. Thus, the particle image velocimetry technique and the ultrasound method are valid to estimate the modulus of elasticity of Bertholletia excelsa wood and possibly of woods with similar technological characteristics.

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APA

de Novais Miranda, E. H., Ferreira, R. P., Pereira, R. A., Guedes, T. O., Rivera, F. P., & Gomes, D. A. C. (2022). Particle image velocimetry technique and ultrasound method to obtain the modulus of elasticity of bertholletia excelsa wood. Maderas: Ciencia y Tecnologia, 24, 227–234. https://doi.org/10.4067/S0718-221X2022000100413

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