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Licensed Unlicensed Requires Authentication Published by De Gruyter January 7, 2021

A novel fractal model for the prediction and analysis of the equivalent thermal conductivity in wood

  • Jingyao Zhao , Liangyan Guo ORCID logo and Yingchun Cai EMAIL logo
From the journal Holzforschung


This study proposes a new fractal model to improve the accuracy of equivalent thermal conductivity (ETC) prediction for wood and determine how the wood’s pore structure influences ETC. Using fractal theory and mercury injection porosimetry data, a fractal model for the geometry of the wood’s pore structure was built. The geometric model was then transformed into an equivalent thermal resistance model to calculate ETC. The calculations produced an explicit expression for ETC derived from the wood’s structural parameters including the minimum and maximum pore apertures, aperture distribution, porosity, and fractal dimension. The model also includes a probability factor. The simulated ETC produced by the model was validated by experiments and it was found to be in good agreement with these. These simulation results will be used to study the influence of several factors on ETC. The proposed model has the potential to be able to predict and analyzing other wood properties such as its electrical conductivity, diffusivity, and permeability and the model can likely also be used to analyze other porous materials.

Corresponding author: Yingchun Cai, Key Laboratory of Bio-based Material Science and Technology (Ministry of Education), Northeast Forestry University, 26 Hexing Road, Harbin, 150040, China, E-mail:

Award Identifier / Grant number: 31901242

Funding source: Heilongjiang Science Foundation Project

Award Identifier / Grant number: LH2020C038


We thank David Frishman, PhD, from Liwen Bianji, Edanz Editing China (, for editing the English text of a draft of this manuscript.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: The project was supported by the National Natural Science Foundation of China and Heilongjiang Science Foundation Project, grant nos. 31901242 and LH2020C038.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.


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Received: 2020-05-27
Accepted: 2020-11-27
Published Online: 2021-01-07
Published in Print: 2021-08-26

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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