Abstract
Wood heating floor has been widely used today, but the performance evaluation system still needs to be further improved. The author's team developed the equipment for testing heat storage efficiency of wood floor. The basic principle is to calculate the heat storage efficiency of test samples by temperature field distribution which is measured by the sensor in closed cavity. Based on the study method for the inverse heat transfer problem, this paper proposed an inversion calculation method for the heat storage efficiency of the test sample according to the measured temperature field. BP neural network technique is adopted for the inversion calculation which is a nonlinear problem. Numerical model of the testing cavity is established with CFD software. The temperature field data of a single structure sample under different initial temperature range of 50 ℃ ~ 130 ℃ are obtained by simulation (different simulation conditions are divided by interval of 5 ℃). After repeated training, a better neural network model is obtained. The average values of the calculation error and the fitting degree of the testing set are MRE = 0.67%, MAE = 19.68%, MSE = 1.16%, R2 = 0.97. It can be seen that, the well trained BP neural network model could predict out the heat storage of different wood floor samples, and provides support for the analysis of heat storage efficiency for wood heating floor.
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References
Shen, B.H., Jiang, J., Sun, W.S., et al.: Status review of heating flooring in China. China Wood-Based Panels 11, 5–8 (2012)
Chen, Q.H., Pang, L., Meng, L.M., et al.: A method for calculation of the thermal diffusivity of a solid material based on inverse heat conduction problem analysis. J. Beijing Univ. Chem. Technol. (Natural Science Edition) 41(5), 76–82 (2014)
Han, W.W., Wu, J., Liu, C.L., et al.: Inversion of the third boundary condition on the inner wall of a two-dimensional pipe based on inverse heat conduction problems. J. Mech. Eng. 51(16), 171–176 (2015)
Song, X., Zhang, Y.W., Ma, J.Y.: Inverse heat conduction problem for inner wall temperature fluctuation inversion of high temperature chamber. J. Therm. Sci. Technol. 15(2), 104–108 (2016)
Ahamad, S.I., Balaji, C.: Inverse conjugate mixed convection in a vertical substrate with protruding heat sources: a combined experimental and numerical study. Heat Mass Transf. 52(6), 1243–1254 (2015)
Tahavvor, A.R., Yaghoubi, M.: Natural cooling of horizontal cylinder using artificial neural network (ANN). Int. Commun. Heat Mass Transfer 35(9), 1196–1203 (2008)
Rajeev, R.M., Balaji, C.: Optimization of the location of multiple discrete heat sources in a ventilated cavity using artificial neural networks and genetic algorithm. Int. Commun. Heat Mass Transfer 51(9–10), 2299–2312 (2008)
Kumar, A., Balaji, C.: A principal component analysis and neural network based non-iterative method for inverse conjugate natural convection. Int. Commun. Heat Mass Transfer 53(21–22), 4684–4695 (2010)
Ozgur, A.S., Demir, H., Agra, O.: Application of artificial neural networks for prediction of natural convection from a heated horizontal cylinder. Int. Commun. Heat Mass Transfer 37(1), 68–73 (2010)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. Parallel distributed processing: Explorations in macrostructure of cognition. Badford Books, Cambridge (1986)
Montana, D.J., Davis, L.: Training feed-forward neutral networks using genetic algorithms. In: Proceeding of the International Joint Conference on Artificial Intelligence, Los Altos, pp. 762–767 (1989)
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Du, G. (2021). Study of Measurement and Inverse Prediction Methods of Heat Storage Efficiency for the Wood Heating Floor. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-82562-1_35
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DOI: https://doi.org/10.1007/978-3-030-82562-1_35
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