Empirical investigation of the cooling performance of a new designed Trombe wall in combination with solar chimney and water spraying system

作     者：Mehran Rabani Vali Kalantar Ali A.Dehghan Ahmadreza K.Faghih 侯恩哲 

作者机构：《建筑节能》杂志社 

出 版 物：《建筑节能》 (BUILDING ENERGY EFFICIENCY)

年 卷 期：2015年第43卷第9期

页      面：7-7页

学科分类：081302[工学-建筑设计及其理论] 08[工学] 0813[工学-建筑学] 

主　　题：建筑设计 建筑节能 建筑光学 建筑材料 

摘      要：This paper presents an experimental study of a new designed Trombe wall in combination with solar chimney and water spraying system in a test room under Yazd(Iran) desert *** Trombe wall area is 50% of that of the southern wall of the building that occupies less space and reduces the implementation costs. The new design of the channel has caused the absorber to receive the solar radiation from three directions. Based on the results, the optimum mass flow rate and the nozzle diameter of the water spraying system has been obtained 10 l/h and 30 μm, respectively. The results indicate that the water spraying system decreases indoor temperature and increases indoor relative humidity by about 8 ℃ and 17%, respectively. The most effect of outdoor relative humidity variation is on indoor relative humidity, rather than indoor temperature. When outdoor temperature increases, both indoor relative humidity and the difference between indoor and outdoor relative humidity decreases. The results also showed that theTrombe wall; Solar chimney; Water spraying system(2) Prediction of energy performance of residential buildings:A genetic programming approach, P67-74, by Mauro Castelli,Leonardo Trujillo, Leonardo Vanneschi, Ale觢 Popovic Abstract: Energy consumption has long been emphasized as an important policy issue in today s economies. In particular, the energy efficiency of residential buildings is considered a top priority of a country s energy policy. The paper proposes a genetic programming-based framework for estimating the energy performance of residential buildings. The objective is to build a model able to predict the heating load and the cooling load of residential buildings. An accurate prediction of these parameters facilitates a better control of energy consumption and, moreover, it helps choosing the energy supplier that better fits the energy needs,which is considered an important issue in the deregulated energy market. The proposed framework blends a recently