Springer Science+Business Media
About: Building Simulation is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Airflow & Energy consumption. It has an ISSN identifier of 1996-3599. Over the lifetime, 1181 publications have been published receiving 19053 citations. The journal is also known as: Jianzhu moni.
Papers published on a yearly basis
TL;DR: Why RANS is still frequently used and whether this is justified or not is illustrated by examples for five application areas in building simulation: pedestrian-level wind comfort, near-field pollutant dispersion, urban thermal environment, natural ventilation of buildings and indoor airflow.
Abstract: Large Eddy Simulation (LES) undeniably has the potential to provide more accurate and more reliable results than simulations based on the Reynolds-averaged Navier-Stokes (RANS) approach. However, LES entails a higher simulation complexity and a much higher computational cost. In spite of some claims made in the past decades that LES would render RANS obsolete, RANS remains widely used in both research and engineering practice. This paper attempts to answer the questions why this is the case and whether this is justified, from the viewpoint of building simulation, both for outdoor and indoor applications. First, the governing equations and a brief overview of the history of LES and RANS are presented. Next, relevant highlights from some previous position papers on LES versus RANS are provided. Given their importance, the availability or unavailability of best practice guidelines is outlined. Subsequently, why RANS is still frequently used and whether this is justified or not is illustrated by examples for five application areas in building simulation: pedestrian-level wind comfort, near-field pollutant dispersion, urban thermal environment, natural ventilation of buildings and indoor airflow. It is shown that the answers vary depending on the application area but also depending on other—less obvious—parameters such as the building configuration under study. Finally, a discussion and conclusions including perspectives on the future of LES and RANS in building simulation are provided.
TL;DR: Wang et al. as discussed by the authors reviewed the development history, state-of-the-art on the development of building simulation technology and introduced the main objective, structure, and core programs of DeST.
Abstract: Many building simulation programs have been developed all over the world since the computer-aided simulation technology was first applied in the 1960s In early 1980s, Tsinghua University has started to develop a new building simulation tool DeST with the aims to benefit for practical and research use of building simulation related applications in China DeST can be used to simulate and analyze both building energy consumption and HVAC (heating, ventilation and air-conditioning) system It has been designed to aim improving the reliability of system design, to ensure the quality of the system performance, and to reduce energy consumption of buildings This paper reviews the development history, state-of-the-art on the development of building simulation technology and introduces the main objective, structure, and core programs of DeST The analytical verifications, inter-program comparisons, and empirical validations of DeST are also presented in this paper The application of DeST will be introduced in part II of the companion paper
TL;DR: This study estimated the association between the infection probability and ventilation rates with the Wells-Riley equation, where the quantum generation rate (q) by a COVID-19 infector was obtained using a reproductive number-based fitting approach.
Abstract: A growing number of cases have proved the possibility of airborne transmission of the coronavirus disease 2019 (COVID-19). Ensuring an adequate ventilation rate is essential to reduce the risk of infection in confined spaces. In this study, we estimated the association between the infection probability and ventilation rates with the Wells-Riley equation, where the quantum generation rate (q) by a COVID-19 infector was obtained using a reproductive number-based fitting approach. The estimated q value of COVID-19 is 14-48 h-1. To ensure an infection probability of less than 1%, a ventilation rate larger than common values (100-350 m3/h per infector and 1200-4000 m3/h per infector for 0.25 h and 3 h of exposure, respectively) is required. If the infector and susceptible person wear masks, then the ventilation rate ensuring a less than 1% infection probability can be reduced to a quarter respectively, which is easier to achieve by the normal ventilation mode applied in typical scenarios, including offices, classrooms, buses, and aircraft cabins. Strict preventive measures (e.g., wearing masks and preventing asymptomatic infectors from entering public spaces using tests) that have been widely adopted should be effective in reducing the risk of infection in confined spaces.
TL;DR: In this paper, a Markov chain-based occupancy model is used to simulate the occupancy of an office building for a typical workday with key statistical properties of occupancy such as the time of morning arrival and night departure, lunch time, periods of intermediate walking-around, etc.
Abstract: Building occupancy is an important basic factor in building energy simulation but it is hard to represent due to its temporal and spatial stochastic nature. This paper presents a novel approach for building occupancy simulation based on the Markov chain. In this study, occupancy is handled as the straightforward result of occupant movement processes which occur among the spaces inside and outside a building. By using the Markov chain method to simulate this stochastic movement process, the model can generate the location for each occupant and the zone-level occupancy for the whole building. There is no explicit or implicit constraint to the number of occupants and the number of zones in the model while maintaining a simple and clear set of input parameters. From the case study of an office building, it can be seen that the model can produce realistic occupancy variations in the office building for a typical workday with key statistical properties of occupancy such as the time of morning arrival and night departure, lunch time, periods of intermediate walking-around, etc. Due to simplicity, accuracy and unrestraint, this model is sufficient and practical to simulate occupancy for building energy simulations and stochastic analysis of building heating, ventilation, and air conditioning (HVAC) systems.
TL;DR: In this article, a Nonlinear Model Predictive Control (NMPC) is designed and implemented in real-time based on dynamic programming for integrated building heating and cooling control to reduce energy consumption and maintain indoor temperature setpoint.
Abstract: Current research studies show that building heating, cooling and ventilation energy consumption account for nearly 40% of the total building energy use in the U.S. The potential for saving energy through building control systems varies from 5% to 20% based on recent market surveys. This papers introduces and illustrates a methodology for integrated building heating and cooling control to reduce energy consumption and maintain indoor temperature set-point, based on the prediction of occupant behavior patterns and local weather conditions. Advanced machine learning methods including Adaptive Gaussian Process, Hidden Markov Model, Episode Discovery and Semi-Markov Model are modified and implemented into this study. A Nonlinear Model Predictive Control (NMPC) is designed and implemented in real-time based on dynamic programming. The experiment test-bed is setup in the Solar House, with over 100 sensor points measuring indoor environmental parameters, power consumption and ambient conditions. The experiments are carried out for two continuous months in the heating season and for a week in the cooling season. The results show that there is a 30.1% measured energy reduction in the heating season compared with the conventional scheduled temperature set-points, and 17.8% energy reduction in the cooling season.