This paper describes the Buildings library, a free open-source library that is implemented in Modelica, an equation-based object-oriented modeling language. The library supports rapid prototyping, as well as design and operation of building energy and control systems.First, we describe the scope of the library, which covers HVAC systems, multi-zone heat transfer and multi-zone airflow and contaminant transport. Next, we describe differentiability requirements and address how we implemented them. We describe the class hierarchy that allows implementing component models by extending partial implementations of base models of heat and mass exchangers, and by instantiating basic models for conservation equations and flow resistances. We also describe associated tools for pre-and post-processing, regression tests, co-simulation and real-time data exchange with building automation systems.The paper closes with an example of a chilled water plant, with and without water-side economizer, in which we analyzed the system-level efficiency for different control setpoints.

Real time flow simulation is crucial for emergency management in buildings, such as fire and accidental or intentional release of chemical/biological agents (contaminants). The simulation results can then be used to impose proper measures to minimize casualties. The computational fluid dynamics (CFD) is accurate, but too time consuming. Nodal models are fast, but not informative. To obtain a fast and informative solution, this study proposes an intermediate approach between nodal models and CFD by introducing a Fast Fluid Dynamics (FFD) method. This investigation used the FFD methods with and without turbulence treatments to study systematically four basic flows in buildings and compared the numerical results with the corresponding CFD results and the data from the literature. The results show that, on one side, the FFD can offer much richer flow information than nodal models do, but less accurate results than CFD does. On the other side, the FFD is 50 times faster than the CFD. The results also show that the FFD with the laminar assumption has the best overall performance on both accuracy and speed. It is possible to conduct faster-than-real-time flow simulations with detailed flow information by using the FFD method. Practical ImplicationThe paper introduces a Fast Fluid Dynamics (FFD) method, which can simulate airflow and contaminant dispersion in buildings with real time or fast-than-real-time speed and provide informative solutions. As an intermediate approach between nodal models and the CFD, the FFD can be a very useful tool for emergency management in case of fire and accidental or intentional release of chemical or biological agents in a building or around the buildings. The FFD can also be used as a preliminary test tool for fast assessment of indoor airflows before a detailed CFD analysis.

This paper presents a comprehensive review of the open literature on motivations, methods and applications of linking stratified airflow simulation to building energy simulation (BES). First, we review the motivations for coupling prediction models for building energy and indoor environment. This review classified various exchanged data in different applications as interface data and state data, and found that choosing different data sets may lead to varying performance of stability, convergence, and speed for the co-simulation. Second, our review shows that an external coupling scheme is substantially more popular in implementations of co-simulation than an internal coupling scheme. The external coupling is shown to be generally faster in computational speed, as well as easier to implement, maintain and expand than the internal coupling. Third, the external coupling can be carried out in different data synchronization schemes, including static coupling and dynamic coupling. In comparison, the static coupling that performs data exchange only once is computationally faster and more stable than the dynamic coupling. However, concerning accuracy, the dynamic coupling that requires multiple times of data exchange is more accurate than the static coupling. Furthermore, the review identified that the implementation of the external coupling can be achieved through customized interfaces, middleware, and standard interfaces. The customized interface is straightforward but may be limited to a specific coupling application. The middleware is versatile and user-friendly but usually limited in data synchronization schemes. The standard interface is versatile and promising, but may be difficult to implement. Current applications of the co-simulation are mainly energy performance evaluation and control studies. Finally, we discussed the limitations of the current research and provided an overview for future research.

This article describes the development and implementation of the Functional Mock-up Unit (FMU) for cosimulation import interface in EnergyPlus. This new capability allows EnergyPlus to conduct co-simulation with various simulation programs that are packaged as FMUs. For example, one can model an innovative Heating, Ventilation, and Air Conditioning (HVAC) system and its controls in Modelica, export the HVAC system and the control algorithm as an FMU, and link it to a model of the building envelope in EnergyPlus for run-time data exchange.The formal of FMUs is specified in the Functional Mock-up Interface (FMI) standard, an open standard designed to enable links between disparate simulation programs. An FMU may contain models, model description, source code, and executable programs for multiple platforms. A master simulator-in this case, EnergyPlusimports and simulates the FMUs, controlling simulation time and coordinating the exchange of data between the different FMUs.This article describes the mathematical basis of the FMI standard, discusses its application to EnergyPlus, and describes the architecture of the EnergyPlus implementation. It then presents a typical workflow, including pre-processing and co-simulation. The article concludes by presenting two use cases in which models of a ventilation system and a shading controller are imported in EnergyPlus as an FMU.

In this paper, a novel framework is proposed to enable a predictive deployment of unmanned aerial vehicles (UAVs) as temporary flying base stations (BSs) to complement ground cellular systems in face of downlink traffic overload. First, a novel learning approach, based on the weighted expectation maximization (WEM) algorithm, is proposed to estimate the user distribution and the downlink traffic demand. Next, to guarantee a truthful information exchange between the BS and UAV operators, using the framework of contract theory, a traffic offload contract is developed, and the sufficient and necessary conditions for having a feasible contract are analytically derived. Subsequently, an optimization problem is formulated to deploy an optimal UAV onto the hotspot area in a way that the utilities of the overloaded ground BSs are maximized. Simulation and analytical results show that the proposed WEM approach yields a prediction error which is lower than 12%, and compared with a conventional expectation maximization approach, the WEM method yields a significant advantage on the prediction accuracy, as the traffic load in the cellular system becomes spatially uneven. Furthermore, compared with a baseline, event-driven allocation method, the proposed predictive deployment approach enables UAV operators to provide efficient downlink service for hotspot users, and improves the revenues of both the BS and UAV network operators significantly.