Large-eddy simulations to define building-specific similarity relationships for natural ventilation flow rates

Abstract

Natural ventilation can play an important role towards preventing the spread of airborne infections in indoor environments. However, quantifying natural ventilation flow rates is a challenging task due to significant variability in the boundary conditions that drive the flow. In the current study, we propose and validate an efficient strategy for using computational fluid dynamics to assess natural ventilation flow rates under variable conditions, considering the test case of a single-room home in a dense urban slum. The method characterizes the dimensionless ventilation rate as a function of the dimensionless ventilation Richardson number and the wind direction. First, the high-fidelity large-eddy simulation (LES) predictions are validated against full-scale ventilation rate measurements. Next, simulations with identical Richardson numbers, but varying dimensional wind speeds and temperatures, are compared to verify the proposed similarity relationship. Last, the functional form of the similarity relationship is determined based on 32 LES. Validation of the surrogate model against full-scale measurements demonstrates that the proposed strategy can efficiently inform accurate building-specific similarity relationships for natural ventilation flow rates in complex urban environments.

A Review of Large-Eddy Simulation Cell Size Requirements for Indoor Flows

“…That is why in these studies the building is placed in a wind tunnel (see Figure 2a). The scale of the open area could vary from a small enclosure [26] through a repetitive pattern [7,48,49] to a section of a city [50,51].…”

“…The ∆ is typically less than 50 mm (see Table 1) but it can be as low as 1 mm depending on the size of the domain, which necessitates minimal computational demand. However, when the scale is robust, the ∆ can reach 1 m, as demonstrated by Hwang et al [50], who created an urban scale model to evaluate a single room. 1 NV SR is modelled using a block array Adachi et al [52] 10 NV Laetitia et al [55] 28.8 NV Multiple room Bazdidi-Tehrani et al [46] 13.9 NV SR Zhang et al [22] 42.5 NV SR Arinami et al [49] 18.2 NV SR with various orientations and guide vanes Dai et al [48] 5 NV Hawendi et al [54] 1.5 NV Multiple rooms NV: natural ventilation, SR: single room.…”

“…So as to give a recommendation for mesh size, it would be relevant to know what we should take into account. Firstly, the mean value of the screened studies could be misleading due to the peaking studies [50,67,68], since the mean values of cell size of the categories were: ∆ meanNFT = 132 mm, ∆ meanDFT = 29 mm, and ∆ meanTFT = 38 mm. However, the 25% and 75% quartile values for the categories were ∆ quartileNFT = 5-42.5 mm, ∆ quartileDFT = 10.3-36.2 mm, and ∆ quartileTFT = 11.9-19.3 mm.…”

“…13, x FOR PEER REVIEW 8 of 12 misleading due to the peaking studies[50,67,68] , since the mean values of cell size of the categories were: ΔmeanNFT = 132 mm, ΔmeanDFT = 29 mm, and ΔmeanTFT = 38 mm. However, the 25% and 75% quartile values for the categories were ΔquartileNFT = 5-42.5 mm, ΔquartileDFT = 10.3-36.2 mm, and ΔquartileTFT = 11.9-19.3 mm.…”