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Sensitivity analysis of heat transfer rate for smart roof design by adaptive neuro-fuzzy technique

机译:基于自适应神经模糊技术的智能屋面传热速率敏感性分析

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Thermal comfort in the building, is a major concern among the design expertise especially in tropical countries. Therefore, it is important to design a smart roof system to reduce the heat gain of the building and enhance the resident's thermal comfort. The leading feature of this system is using PVC tubes integrated with a layer of insulation material and fans and located at the underside of the roof to act as a moving-air path (MAP) system. Indoor experiment is carried out on the scale roof model and the heat transfer rate into the attic is determined using experimental data. Next, Adaptive Neuro Fuzzy Inference System (ANFIS) is applied as a soft-computing method determine the predominant variables that affecting the thermal comfort in the building. Training and testing data of the ANFIS model are collected from the experimental measurement. Mass flow rate, solar-air temperature, inlet air temperature, outlet air temperature and ambient temperature are the input parameters used to compute the output parameter which is the attic temperature. The results indicated that the combination of mass flow rate and ambient temperature is the primary factor and the best predictor accuracy for thermal comfort in the building. (C) 2016 Elsevier B.V. All rights reserved.
机译:建筑物中的热舒适性是设计专业知识的主要关注点,尤其是在热带国家。因此,设计智能屋顶系统以减少建筑物的热量吸收并提高居民的热舒适度非常重要。该系统的主要特征是使用PVC管,该PVC管与一层隔热材料和风扇集成在一起,并位于屋顶的下侧,用作移动风道(MAP)系统。在天平屋顶模型上进行室内实验,并使用实验数据确定向阁楼的传热速率。接下来,将自适应神经模糊推理系统(ANFIS)作为一种软计算方法来确定影响建筑物热舒适度的主要变量。从实验测量中收集ANFIS模型的训练和测试数据。质量流率,太阳能温度,进气温度,出气温度和环境温度是用于计算输出参数(阁楼温度)的输入参数。结果表明,质量流量和环境温度的组合是建筑物热舒适性的主要因素和最佳预测精度。 (C)2016 Elsevier B.V.保留所有权利。

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