Building energy simulation considering spatial temperature distribution for nonuniform indoor environment
Building energy simulation considering spatial temperature distribution for nonuniform indoor environment
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DOI：
10.1016/j.buildenv.2013.02.007
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发表时间：
2013-05
影响因子：
7.4
通讯作者：
Weirong Zhang;K. Hiyama;S. Kato;Y. Ishida
中科院分区：
文献类型：
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作者：
Weirong Zhang;K. Hiyama;S. Kato;Y. Ishida
建筑能耗模拟大多采用网络或多区域模型，不考虑室内空气温度和速度分布。然而，最近引进的个人通风，地板采暖系统，置换通风积极利用不均匀的室内环境，以满足能源效率和热舒适的需求。因此，预计建筑物能源模拟结果将受到选择适当的参考空气温度以计算通过建筑材料的热传递的显著影响。这意味着在进行能量模拟时，需要考虑非均匀环境下的空气温度分布。该问题的另一种方法是将计算流体动力学（CFD）模拟直接与网络模型结合联合收割机;然而，不幸的是，这种方法在计算上太耗时。在本研究中，我们提出了一种可接受的快速模拟方法，该方法将从CFD结果中提取并指示所有热因素的个体影响的室内气候（CRI）贡献率与网络模型相结合，以实现包含能量的模拟。温度分布。通过CRI的引入，可以实现与CFD一样高的精度和与网络模型一样快的计算速度。采用CRI耦合方法对一个办公室的热负荷进行了模拟计算。采用CRI耦合方法计算的能源需求结果比非耦合网络模拟的结果低15-20%。
Building energy simulations are mostly implemented using network or multi-zone models that do not consider indoor air temperature and velocity distribution. However, the recent introduction of personal ventilation, floor-heating systems, and displacement ventilation positively utilizes a nonuniform indoor environment to meet the demand for both energy efficiency and thermal comfort. Therefore, it is expected that building energy simulation results will be significantly impacted by the choice of an appropriate reference air temperature for the calculation of heat transfer through constructed materials. This means that the air temperature distribution needs to be taken into account for nonuniform environments when carrying out energy simulations. An alternative approach to this problem is to combine a computational fluid dynamics (CFD) simulation directly with a network model; however, this approach is unfortunately too computationally time-consuming. In this study, we propose an acceptably fast simulation method that couples the contribution ratio of indoor climate (CRI), which is extracted from CFD results and indicates the individual impact of all heat factors, with the network model to implement an energy simulation that incorporates a temperature distribution. With the introduction of CRI, it is possible to achieve a precision as high as that of CFD and a calculation speed as fast as that of the network model. A case study simulating the thermal load of a single office room was carried out with the CRI-coupled method. The energy demand result calculated by CRI-coupled method was 15–20% lower than that of a non-coupled network simulation.