Abstract:

Objective: In this paper, the volume specific heat, radial and tangential thermal conductivity and thermal diffusion coefficient of wood obtained from experimental measurement were used to establish the wood volume specific heat model and wood anisotropic thermal conductivity model based on fuzzy least absolute nonlinear regression method, analyze the law of wood thermophysical parameters, and provide a basis for the study of wood thermal conductivity law. It was expected to provide a theoretical basis and data support for the formulation of wood thermophysical property evaluation standards. Method: The experimental samples were obtained by cutting 130 kinds of common woods. Among them, the experimental samples used to measure the volume specific heat of wood were 18 mm in diameter and 2 mm in thickness; the experimental samples for measuring the thermal conductivity and thermal diffusion coefficient of wood were two rectangular samples of 50 mm×50 mm×20 mm. Firstly, the Hot Disk thermal constants analyser was used to test the thermal physical parameters of the experimental samples, and the volume specific heat, radial heat conduction rate, radial thermal diffusion coefficient, axial thermal diffusivity and axial thermal diffusion coefficient of wood samples were obtained. The experimental data were divided into two parts: training set and validation set. Furthermore, a fuzzy least absolute nonlinear regression method suitable for regression analysis of small sample data sets was proposed, and the wood volume specific heat model and anisotropic heat conduction model of wood were established. Firstly, Gaussian membership function was constructed to blur the data. Then a singleton fuzzier was constructed to generate the fuzzy rule base. The product inference engine was used to carry out fuzzy reasoning on the input space elements, and the reasoning results were obtained. Finally, the least absolute regression criterion was used to optimize the obtained results, and a class of nonlinear wood radial and axial thermal diffusivity and thermal conductivity models were established. The model was used to analyze and predict the thermal diffusion and thermal conductivity processes of anisotropic wood in real time. Result: The results showed that the fitting degree of the fuzzy least absolute nonlinear regression (FLANR) was 0.997 6, and the mean velative error(MRE), maximum relative error(MARE) and mean square error(MSE) were 0.026 0%, 0.049 1% and 0.035 2%, respectively. In comparison, the fitting degree of ANFIS prediction was 0.963 1, and the MRE, MARE and MSE were 0.189 3%, 2.176 2% and 0.799 3%, respectively. For the wood anisotropic heat conduction model, a single comparison one of the output variables (the wood axial heat conduction rate) in the wood anisotropic thermal conductivity model showed that the fitting degree of the predicted results of FLANR was 0.958 1, and the MRE, MARE and MSE were 0.190 2%, 0.348 1% and 0.085 3%, respectively. In comparison, the fitting degree of FLS prediction was 0.604 5, and the MRE, MARE and MSE were 2.169 4%, 5.260 9% and 2.910 6%, respectively. The fitting error of FLANR prediction was obviously less than those of ANFIS and FLS, and the model had a good fitting effect and generalization. Conclusion: It might be feasible to use the fuzzy least absolute nonlinear regression to model the specific heat of wood volume and anisotropic thermal conductivity of wood. The calculation time of this method was short and the generalization was good. The established wood thermal physical property parameter model would provide guidance for the follow-up research on wood thermal conductivity.

Key words: wood, thermal property parameters, fuzzy regression, plane source method