Weirong Zhang

A Review: Coupled Simulation of CFD and Network Model for Heat and Contaminant Transport in a Building

In this study, the coupled simulation of CFD (Computational Fluid Dynamics) and network model of building energy (heat) and airflow simulation is reviewed. The network model is a tool to describe the whole and macroscopic characteristics of heat or airflow transport in buildings, where heat flow or airflow is modeled as a network of one-dimensional flow elements. It can be conducted with comparatively less computational cost. The temperature or contaminant concentration of the room air is often not uniform but spatially distributed in a room, when the mixing efficiency of room airflow is not high enough, or when sources and respective sinks of heat or contaminant are located comparatively apart in a room. The CFD is a tool to describe these distribution characteristics precisely. It requires considerable computation amount and computational time. It is not light or simple to do compared with the network model computation. If one wants to analyze the whole and macroscopic characteristics of heat or flow transports in buildings including the distribution features of temperature or contaminant in a certain room, or if one wants to analyze the room airflow with the consideration of whole building heat and flow transport, the two simulations should be done simultaneously. In other words, they should be solved with the coupled manner. There are many methods or ways for coupling the two simulations. In this review, the authors divided the coupled simulations into two groups. One is the group of which the CFD analysis is the main objective, while the other is the group with network simulation as the main objective. In the latter case, even the major objective is obtained from the results of the network simulation, and the full coupling CFD and network simulation requires an enormous amount of computation and time compared with the single network simulation. The full coupling is practically absurd and is sometimes impossible because of its enormous computation amount. Further simplification of the coupling with CFD has to be pursued. The authors have reviewed the papers published in the recent 10 years on this topic and classified relevant works into the two categories mentioned above.

Study on Statistical Prediction and Design Method for Indoor Thermal Environment

The indoor thermal environment is affected by many heat factors such as occupants′ activities and outdoor climate changes. Because each of them varies asynchronously, the heat factors′ variation shows a nonsingle degree of freedom overall. Moreover, the heat factors′ impact on space is not uniform. Therefore, there always exist spatial distributions and temporal variations. In practice, the number of degrees of freedom of an air-conditioning system is usually limited and less than that of the object zone. The control performance of an air-conditioning system designed under a perfect mixing method cannot be satisfactory because the spatial distribution is neglected. For energy conservation and thermal comfort improvement, it is necessary to propose an air-conditioning system design method which can positively use the spatial distribution and temporal variation of the indoor thermal environment. In this study, methods for indoor temperature fluctuation analysis and optimization of air-conditioner sensor location are proposed. To represent the temperature field structure, an index called CRI is integrated. With this method, the spatial distribution and temporal variation can be calculated immediately and analyzed statistically; furthermore, the indoor temperature and outdoor climate′s correlation can be concisely visualized. A case study is conducted to verify the validity of this method.