• 论文与研究报告 •

### 木质地采暖地板蓄热性能检测及反演方法

1. 1. 山东建筑大学热能工程学院 济南 250101;
2. 山东建筑大学信息与电气工程学院 济南 250101
• 收稿日期:2018-04-02 修回日期:2018-10-29 出版日期:2018-11-25 发布日期:2018-12-04
• 基金资助:
泰山学者优势特色学科人才团队（2015162）；山东建筑大学校内博士基金（X18006Z）。

### Measurement and Inverse Prediction Methods of Heat Storage Performance for Wood Flooring with Geothermal System

Zhou Shiyu1, Du Guangyue2, Cao Zhengbin2, Liu Xiaoping2, Zhou Yucheng2

1. 1. School of Thermal Engineering, Shandong Jianzhu University Jinan 250101;
2. School of Information and Electrical Engineering, Shandong Jianzhu University Jinan 250101
• Received:2018-04-02 Revised:2018-10-29 Online:2018-11-25 Published:2018-12-04

Abstract: [Objective] Based on the study method for the inverse heat transfer problem, BP neural network technique is adopted for the inverse calculation of the heat storage of wood flooring with geothermal system. Therefore, provides theoretical and method ological support for the analysis of heat storage performance of wood floor.[Method] Firstly, the numerical model of the testing cavity is established by 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℃). The data are divided into training set and testing set of neural network model. The data of the initial temperature of 50,60,70,80,90,100,110,120,130℃ are used as the training set, while the data of the initial temperature of 55,65,75,85,95,105,115,125℃ are used as the testing set.[Result] 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:mean relative error (MRE)=0.68%, maximum relative error (MAE)=19.51%, mean square error (MSE)=1.18%, fitting degree (R2)=0.98. Based on this model, Betula platyphylla, Fraxinus mandshurica, Betula alnoides and Quercus mongolica are selected as the four typical solid wood floor samples for the inversion calculation of the heat storage. The results showed that the heat storage performances of the four kinds of solid wood floor are as follows:Quercus mongolica > Betula alnoides > Fraxinus mandshurica > Betula platyphylla.[Conclusion] It can be concluded that the well trained neural network model could effectively predict out the heat storage performance of different wood floor samples, verifying the feasibility of the method based on BP neural network technology to retrieve the thermal storage performance of the wood floor.