Assessing long-term performance of centralized thermal energy storage system
Assessing long-term performance of centralized thermal energy storage system
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DOI：
10.1016/j.applthermaleng.2013.09.047
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发表时间：
2014-01
影响因子：
6.4
通讯作者：
Azeldin El-Sawi;F. Haghighat;H. Akbari
中科院分区：
文献类型：
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作者：
Azeldin El-Sawi;F. Haghighat;H. Akbari
采用经过验证的计算流体动力学仿真工具对集中式潜热储能系统的长期性能进行了研究。LHTES系统与建筑机械通风系统集成。采用石蜡RT 20作为相变材料，并在相变材料中加入翅片以提高相变材料的相变性能，采用人工神经网络（ANN）建立LHTES输入输出参数之间的关系，以减少计算时间。进行了大量的CFD模拟，以确定所有的影响参数的发展人工神经网络。他们包括相变温度范围，空气流量，几何配置的LHTES系统，翅片尺寸，和单位的长度。进一步的CFD模拟进行了适当的训练和测试的人工神经网络提供足够的数据。人工神经网络模型被用来预测LHTES的出口空气温度。结果表明，人工神经网络模型的预测结果与CFD模型的预测结果吻合较好，并将人工神经网络模型应用于蒙特利尔一个LHTES的全年性能研究。我们发现，使用集中式LHTES系统，以减少冷却负荷的潜力是高的相变温度范围较宽。当集中式LHTES系统的长度以1.5 m/s的流速从500 mm增加到650 mm时，集中式LHTES系统有助于将冷却负荷从21%减少到36%。
A validated computational fluid dynamics simulation tool is used to study the long-term performance of a centralized latent heat thermal energy storage system (LHTES). The LHTES system is integrated with a building mechanical ventilation system. Paraffin RT20 was used as a phase change material (PCM) and fins are used to enhance its performance.To reduce the computational time, artificial neural networks (ANN) was used to relate the relationships between the LHTES inputs and output parameters. Extensive CFD simulations were carried out to identify all the influential parameters for the development of ANN. They include phase change temperature range, air flow rate, the geometrical configuration of a LHTES system, fin size, and the unit's length. Further CFD simulations were carried out to provide sufficient data for proper training and testing of the ANN. The ANN model was used to predict the LHTES's outlet air-temperature. There was a good agreement between the ANN prediction and CFD model's prediction.The ANN model then was used to study the annual performance of a LHTES for application in Montreal. We found that the potential of use the centralized LHTES system to reduce the cooling load is high with a wider phase change temperature range. The centralized LHTES system contributes to reducing the cooling load from 21% to 36% when the length of the centralized LHTES system is increased from 500 to 650 mm at a flow rate of 1.5 m/s.