Quantification of Uncertainty in Computational Fluid Dynamics

Roache, Patrick J.

doi:10.1146/annurev.fluid.29.1.123

Cited by 1,784 publications

(808 citation statements)

References 78 publications

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“…It further highlights the importance of experimental validation data from which the most appropriate turbulence model for a particular geometry can be chosen. The grid-dependence of the solutions obtained can be quantified more precisely using Richardson extrapolation [17,18] which predicts the accuracy of CFD predictions compared to 'ideal' values based on a hypothetical zero grid spacing. Table 1 shows the grid-induced error in the flow rate through the rear vent aperture using Richardson extrapolation.…”

Section: Cfd Analysis Of Wind Tunnel Flows: Verification and Validationmentioning

confidence: 99%

An experimental and computational study of the aerodynamic and passive ventilation characteristics of small livestock trailers

Gilkeson

Thompson

Wilson

et al. 2009

Journal of Wind Engineering and Industrial Aerodynamics

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Section: Cfd Analysis Of Wind Tunnel Flows: Verification and Validationmentioning

confidence: 99%

An experimental and computational study of the aerodynamic and passive ventilation characteristics of small livestock trailers

Gilkeson

Thompson

Wilson

et al. 2009

Journal of Wind Engineering and Industrial Aerodynamics

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“…In addition, several previously published guidelines stated that a grid-sensitivity analysis should be conducted to make sure the results obtained are (nearly) grid independent and do not suffer from excessive amounts of numerical diffusion (e.g. Jones and Whittle 1992;Chen and Srebric 2002;Chen and Zhai 2004;Roache 1997;Li and Nielsen 2011;Blocken 2015). Sørensen and Nielsen (2003) stated that-at that time-obtaining a grid-independent solution for 3D cases was very difficult due to the lack of computational resources.…”

Section: Grid Resolutionmentioning

confidence: 99%

“…with S c the contaminant source rate (= 0.1 kg/(m 3 ·s)), V the volume of the contaminant source (=1.25 × 10 −7 m 3 ), h inlet To quantify the grid-sensitivity results, the gridconvergence index (GCI) by Roache (1997) is used to determine the error when the fine grid is used:…”

Section: Grid-sensitivity Analysismentioning

confidence: 99%

“…with F s the safety factor, taken to be 1.25, which is the recommended value when three or more grids are considered in the grid-sensitivity analysis (Roache 1997), r the linear grid refinement factor (r = 2 ), p the formal order of accuracy which is assigned a value of 2 due to the SOU discretization schemes used for the reference case simulations, and f 2 and f 1 the solutions obtained on the fine and finest grid, respectively, which correspond to…”

Section: Grid-sensitivity Analysismentioning

confidence: 99%

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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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“…[16][17][18][19][20][21][22][23][24][25] Consistent with this importance, a procedure for verifying the conceptual development and numerical implementation of integration algorithms used to determine pF with temperature-dependent delays is now described. This procedure also provides the basis for an alternative approach to the numerical approximation of pF.…”

Section: Verification Of Numerical Proceduresmentioning

confidence: 99%

Effect of delayed link failure on probability of loss of assured safety in temperature-dependent systems with multiple weak and strong links

Helton

Johnson²,

Oberkampf

2009

Reliability Engineering & System Safety

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Weak link (WL)/strong link (SL) systems constitute important parts of the overall operational design of high consequence systems, with the SL system designed to permit operation of the system only under intended conditions and the WL system designed to prevent the unintended operation of the system under accident conditions. Degradation of the system under accident conditions into a state in which the WLs have not deactivated the system and the SLs have failed in the sense that they are in a configuration that could permit operation of the system is referred to as loss of assured safety. The probability of such degradation conditional on a specific set of accident conditions is referred to as probability of loss of assured safety (PLOAS). Previous work has developed computational procedures for the calculation of PLOAS under fire conditions for a system involving multiple WLs and SLs and with the assumption that a link fails instantly when it reaches its failure temperature. Extensions of these procedures are obtained for systems in which there is a temperature-dependent delay between the time at which a link reaches its failure temperature and the time at which that link actually fails.

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Quantification of Uncertainty in Computational Fluid Dynamics

Cited by 1,784 publications

References 78 publications

An experimental and computational study of the aerodynamic and passive ventilation characteristics of small livestock trailers

An experimental and computational study of the aerodynamic and passive ventilation characteristics of small livestock trailers

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

Effect of delayed link failure on probability of loss of assured safety in temperature-dependent systems with multiple weak and strong links

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