In recent years, the advanced operation of building facilities using IoT and machine learning, such as demand response and fault detection/diagnosis, has been promoted. However, only a limited number of buildings are capable of satisfactorily measuring and storing data, and it is difficult to demand advanced operation from ordinary buildings. The solution to this problem is to structure buildings using uniform rules and grammar. In this study, building facilities were structured according to Brick Schema, a metadata schema. In addition, we developed operational applications that can be widely and universally applied to structured building facilities. First, metadata in building equipment were discussed, and then a metadata model of a heat source system, which was the subject of the study, was constructed. Next, we developed and applied an operational application that can be utilized for heat source systems, by using the generality and graph structure of the metadata model, and showed that it can be executed in a portable manner.

One-dimensional simulations to predict the thermal environment of underground stations require inputs such as pressure loss coefficients of flows and heat transfer coefficients between airflows and walls of the stations. Therefore, in this study, a high-accuracy numerical simulation of an H-shaped channel with counterflows and heat transfer, which is a simplified two-dimensional configuration model of underground stations, was performed to determine the heat transfer coefficients between the counterflows and walls of the H-shaped channel. As a second report, this paper presents the results of a numerical simulation of turbulent heat transfer in H-shaped channel flows carried out to determine their heat transfer coefficients. For the proper calculation of turbulent heat transfer, low-Reynolds-number two equation turbulence models for velocity and thermal fields are adopted. As a result, the characteristic behaviors of heat transfer coefficients in three types of H-shaped channel flows are revealed.

In order to reduce the measurement error of the reference pressure in differential pressure control, containers were connected in the middle of a measurement air tube on the reference pressure side, and the measurement error was evaluated when a disturbance wind was blown to the measurement port. When the 100ℓ container was connected and wind was blown at a constant speed of 13.1 m/s, the measurement error was reduced to 87% of that without the container. When wind with a fluctuating speed of 5.8 to 15.5 m/s was blown for a continuous period, the standard deviation ratio of the differential pressure (σ with container/σ without container) was 0.56 for the 20𝓁 container, 0.19 for the 60𝓁 container, and 0.10 for the 100𝓁 container, and the pressure measurement error was considerably reduced. The standard deviation ratio of differential pressure decreases when natural wind blows, but the variation is large.

Potential flow satisfies Laplace's equation and can be easily calculated using the finite difference method (FDM). A formula for calculating the exhaust air volume of circular hoods was derived by combining analytical and numerical solutions of the potential flow. Numerical simulations were carried out with varying flange length. The influence of suction from the rear of the hood was quantified by expressing the exhaust air volume in the form of the analytical solution multiplied by a correction coefficient. In case of a hood without a flange, flow existed from behind the circular hood. However, in case of a flanged hood, the flow from behind the circular hood was cut off. Thus, the exhaust air volume could be reduced by installing the flange. The flow velocity distribution around a circular hood was measured by using hot wire velocimetry. The approximation accuracy of potential flow is approximately 10 % compared to the flow velocity measurement. The exhaust air volume formula derived in this study could be more effective than conventional methods.