Large eddy simulation of pollutant dispersion in a naturally cross-ventilated model building: Comparison between sub-grid scale models

Bazdidi–Tehrani, Farzad; Masoumi-Verki, Shahin; Gholamalipour, Payam; Kiamansouri, Mohsen

doi:10.1007/s12273-019-0525-5

Cited by 18 publications

(3 citation statements)

References 58 publications

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“…We researched this topic using an empty domain and reported encouraging conclusions [86]. Some other studies propose advanced SGS models to reduce the errors associated with modeling small eddies [149][150][151]. Besides, the point a CFD engineer needs to ponder over before adopting a specific technique, such as LES, is to anticipate how that model would perform for the particular flow problem under consideration.…”

Section: Summary Of Findingsmentioning

confidence: 91%

Assessing aerodynamic loads on low-rise buildings considering Reynolds number and turbulence effects: a review

Khaled

Aly

2022

Adv. Aerodyn.

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This paper presents an extensive review of existing techniques used in estimating design wind pressures considering Reynolds number and turbulence effects, as well as a case study of a reference building investigated experimentally. We shed light on the limitations of current aerodynamic testing techniques, provisions in design standards, and computational fluid dynamics (CFD) methods to predict wind-induced pressures. The paper highlights the reasons for obstructing the standardization of the wind tunnel method. Moreover, we introduce improved experimental and CFD techniques to tackle the identified challenges. CFD provides superior and efficient performance by employing wall-modeled large-eddy simulation (WMLES) and hybrid RANS-LES models. In addition, we tested a large-scale building model and compared the results with published small-scale data. The findings reinforce our hypothesis concerning the scaling issues and Reynolds number effects in aerodynamic testing.

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Section: Summary Of Findingsmentioning

confidence: 91%

Assessing aerodynamic loads on low-rise buildings considering Reynolds number and turbulence effects: a review

Khaled

Aly

2022

Adv. Aerodyn.

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“…The change of ventilation mode means that the indoor thermal environment cannot be considered as perfectly mixed, and obtaining its detailed temperature distribution becomes necessary. Computational fluid dynamics (CFD) has been used as an effective tool for studies of indoor thermal environments, such as airflow movement [9,10], heat transfer between indoor components [11,12] and pollutant dispersion [13,14]. Indoor temperature distribution, however, is affected by various heat sources, which are dynamically changing with time, hence difficult to obtain in practice.…”

Section: Introductionmentioning

confidence: 99%

Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors

et al. 2021

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In practical building control, quickly obtaining detailed indoor temperature distribution is necessary for providing satisfying personal comfort and improving building energy efficiency. The aim of this study is to propose a fast prediction method for indoor temperature distribution without knowing the thermal boundary conditions in practical applications. In this method, the index of contribution ratio of indoor climate (CRI), which represents the independent contribution of each heat source to the temperature distribution, has been combined with the air temperature collected by one mobile sensor at the height of the working area. Based on a typical office model, the effectiveness of using mobile sensors was discussed, and the influence of its acquisition height and acquisition distance on the prediction accuracy was analyzed as well. The results showed that the proposed prediction method was effective. When the sensors fixed on the wall were used to predict the indoor temperature distribution, the maximum average relative error was 27.7%, whereas when the mobile sensor was used to replace the fixed sensors, the maximum average relative error was 4.8%. This indicates that using mobile sensors with flexible acquisition location can help promote both reliability and accuracy of temperature prediction. In the human activity area, data from a set of mobile sensors were used to predict the temperature distribution at four heights. The prediction accuracy was 2.1%, 2.1%, 2.3%, and 2.7%, respectively. However, the influence of acquisition distance of mobile sensors on prediction accuracy cannot be ignored. The distance should be large enough to disperse the distribution of the acquisition points. Due to the influence of airflow, some distance between the acquisition points and the room boundaries should be given.

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“…Wind induced cross-ventilation has been utilized in traditional and modern buildings for air-quality improvement (Lien and Ahmed 2011;Heracleous and Michael 2019;Aflaki et al 2019;Aydin and Mirzaei 2016) and building energy reduction (Mochida et al 2006;Mohammadreza Shirzadi, Naghashzadegan, and Mirzaei 2019;Zhang, Mirzaei, and Jones 2018;Ohba and Lun 2010;Lo and Novoselac 2013;Li et al 2014). Different methods are used for analyzing cross-ventilation, including simplified correlations based on the orifice flow theory (Kobayashi et al 2009;Karava, Stathopoulos, and Athienitis 2004;Martins and da Graça 2016), field measurement (H. Park 2013;Mochida et al 2005), wind tunnel measurement (Tominaga and Blocken 2016;Mohammadreza Shirzadi, Tominaga, and Mirzaei 2019c;Katayama, Tsutsumi, and Ishii 1992; C.-R. Chu and Chiang 2014; C. R. Chu, Chiu, and Wang 2010;Tominaga and Blocken 2015;Mohammadreza Shirzadi, Tominaga, and Mirzaei 2019a;S Murakami 1991), and computational fluid dynamics (CFD) (T van Hooff, Blocken, and Tominaga 2017;Twan van Hooff, Blocken, and Tominaga 2016;Mohammadreza Shirzadi, Tominaga, and Mirzaei 2020;Ramponi and Blocken 2012a;James Lo, Banks, and Novoselac 2013;Kosutova et al 2019;Lo 2011;Hua, Ohbab, and Yoshiec 2006;Bazdidi-Tehrani et al 2019).…”

Section: Introductionmentioning

confidence: 99%

CFD analysis of cross-ventilation flow in a group of generic buildings: Comparison between steady RANS, LES and wind tunnel experiments

2020

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Computational fluid dynamics (CFD) results generated by the steady Reynolds-averaged Navier-Stokes equations (SRANS) model and large eddy simulation (LES) are compared with wind tunnel experiments for investigating a cross-ventilation flow in a group of generic buildings. The mean flow structure and turbulence statistics are compared for SRANS based on different two-equation turbulence models with LES based on the Smagorinsky subgrid-scale turbulence model. The LES results show very close agreement with the experimental results in the prediction of the time-averaged velocity, wind surface pressure around and inside the building, and crossing flow through the openings. In contrast, SRANS fails to predict the most important features of cross-ventilation. LES reproduced well the anisotropic turbulence property around and inside the cross-ventilated building, which is closely related to the transient momentum transfer caused in street canyon flows and has a significant influence on the mean flow structure. In contrast, SRANS could not inherently reproduce such transient fluctuations and anisotropic turbulence property, and it results in low accurate predictions for the time-average velocity components, wind surface pressure distribution and crossing airflow rate up to 100% error.

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Large eddy simulation of pollutant dispersion in a naturally cross-ventilated model building: Comparison between sub-grid scale models

Cited by 18 publications

References 58 publications

Assessing aerodynamic loads on low-rise buildings considering Reynolds number and turbulence effects: a review

Assessing aerodynamic loads on low-rise buildings considering Reynolds number and turbulence effects: a review

Predicting Indoor Temperature Distribution Based on Contribution Ratio of Indoor Climate (CRI) and Mobile Sensors

CFD analysis of cross-ventilation flow in a group of generic buildings: Comparison between steady RANS, LES and wind tunnel experiments

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