Velocity-pressure field of cross ventilation with open windows analyzed by wind tunnel and numerical simulation

Kato, Shinsuke; Murakami, Shuzo; Mochida, Akashi; Akabayashi, Shin ichi; Tominaga, Yoshihide

doi:10.1016/0167-6105(92)90049-g

Cited by 118 publications

(80 citation statements)

References 4 publications

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“…Indeed, in such cases, the sealed-building assumption does not hold (e.g. [63]). In addition, as stated in the introduction, the actual volume flow rate is difficult to obtain from the orifice equation due to the uncertainty in the discharge coefficients (e.g.…”

Section: Driving Pressure Differences Obtained From Sealed Building Mmentioning

confidence: 99%

“…When flow contact is present jet momentum is conserved and reaches the leeward opening, resulting in different values of the discharge coefficient for the windward and leeward opening (e.g. [62,63]). In the past 50 years, CFD has developed into a powerful tool for studies in urban physics and building aerodynamics [64], including natural ventilation in buildings, as demonstrated by review and overview papers [30,[64][65][66][67][68][69][70][71].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the coupled approach (e.g. [29,30,42,47,48,50,63,72]) is employed using the 3D [27]. For more information related to the wind-tunnel experiments the reader is referred to [27].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

CFD analysis of cross-ventilation of a generic isolated building with asymmetric opening positions: Impact of roof angle and opening location

Perén

Hooff

Leite

et al. 2015

Building and Environment

151

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Section: Driving Pressure Differences Obtained From Sealed Building Mmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CFD analysis of cross-ventilation of a generic isolated building with asymmetric opening positions: Impact of roof angle and opening location

Perén

Hooff

Leite

et al. 2015

Building and Environment

151

View full text Add to dashboard Cite

“…[6,7,9,10,13,[19][20][21][22][23][24][25][26][27][28][29]), analytical and/or semiempirical formulae (e.g. [9][10][11]13,15,[30][31][32][33][34]), simulations with zonal and multizone network models (e.g.…”

Section: Introductionmentioning

confidence: 99%

CFD simulation of cross-ventilation for a generic isolated building: Impact of computational parameters

Ramponi

Blocken

2012

Building and Environment

452

271

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• Extensive literature review on numerical cross-ventilation studies in overview table.• Detailed sensitivity analysis for CFD simulations of a cross-ventilated building model.• The SST k-ω turbulence model shows the best agreement with PIV measurements.• The turbulent kinetic energy strongly influences the convergence and the results.• The prediction of the outdoor standing vortex largely affects the indoor airflow. AbstractAccurate CFD simulation of coupled outdoor wind flow and indoor air flow is essential for the design and evaluation of natural cross-ventilation strategies for buildings. It is widely recognized that CFD simulations can be very sensitive to the large number of computational parameters that have to be set by the user. Therefore, detailed and generic sensitivity analyses of the impact of these parameters on the simulation results are important to provide guidance for the execution and evaluation of future CFD studies. A detailed review of the literature indicates that there is a lack of extensive generic sensitivity studies for CFD simulation of natural cross-ventilation. In order to provide such a study, this paper presents a series of coupled 3D steady RANS simulations for a generic isolated building. The CFD simulations are validated based on detailed wind tunnel experiments with Particle Image Velocimetry. The impact of a wide range of computational parameters is investigated, including the size of the computational domain, the resolution of the computational grid, the inlet turbulent kinetic energy profile of the atmospheric boundary layer, the turbulence model, the order of the discretization schemes and the iterative convergence criteria. Specific attention is given to the problem of oscillatory convergence that was observed during some of these coupled CFD simulations. Based on this analysis, the paper identifies the most important parameters. The intention is to contribute to improved accuracy, reliability and evaluation of coupled CFD simulations for cross-ventilation assessment.

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“…CFD is-among others-used for natural ventilation studies (e.g. Kato et al 1992;Jiang and Chen 2002;Kurabuchi et al 2004;Hu et al 2005;van Hooff and Blocken 2010a,b;Novoselac 2011, 2013;Ramponi and Blocken 2012a,b;Ai and Mak 2014a,b;Perén et al 2015;Tong et al 2016a,b), for indoor airflow studies (e.g. Gan and Awbi 1994;Chen 1995;Nielsen 1998;Zhang et al 2007;Wang and Chen 2009;Cao and Meyers 2013;Liu et al 2013Liu et al , 2016aYou et al 2016) and for indoor pollutant concentration studies (e.g.…”

Section: Introductionmentioning

confidence: 99%

Low-Reynolds number mixing ventilation flows: Impact of physical and numerical diffusion on flow and dispersion

Hooff

Blocken

2017

Build. Simul.

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Quality assurance in computational fluid dynamics (CFD) is essential for an accurate and reliable assessment of complex indoor airflow. Two important aspects are the limitation of numerical diffusion and the appropriate choice of inlet conditions to ensure the correct amount of physical diffusion. This paper presents an assessment of the impact of both numerical and physical diffusion on the predicted flow patterns and contaminant distribution in steady Reynolds-averaged NavierStokes (RANS) CFD simulations of mixing ventilation at a low slot Reynolds number (Re≈2,500). The simulations are performed on five different grids and with three different spatial discretization schemes; i.e. first-order upwind (FOU), second-order upwind (SOU) and QUICK. The impact of physical diffusion is assessed by varying the inlet turbulence intensity (TI) that is often less known in practice. The analysis shows that: (1) excessive numerical and physical diffusion leads to erroneous results in terms of delayed detachment of the wall jet and locally decreased velocity gradients; (2) excessive numerical diffusion by FOU schemes leads to deviations (up to 100%) in mean velocity and concentration, even on very high-resolution grids; (3) difference between SOU and FOU on the coarsest grid is larger than difference between SOU on coarsest grid and SOU on 22 times finer grid; (4) imposing TI values from 1% to 100% at the inlet results in very different flow patterns (enhanced or delayed detachment of wall jet) and different contaminant concentrations (deviations up to 40%); (5) impact of physical diffusion on contaminant transport can markedly differ from that of numerical diffusion.

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Velocity-pressure field of cross ventilation with open windows analyzed by wind tunnel and numerical simulation

Cited by 118 publications

References 4 publications

CFD analysis of cross-ventilation of a generic isolated building with asymmetric opening positions: Impact of roof angle and opening location

CFD analysis of cross-ventilation of a generic isolated building with asymmetric opening positions: Impact of roof angle and opening location

CFD simulation of cross-ventilation for a generic isolated building: Impact of computational parameters

Low-Reynolds number mixing ventilation flows: Impact of physical and numerical diffusion on flow and dispersion

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