Uncertainty quantification

Predictive large eddy simulations for urban flows: Challenges and opportunities

Computational fluid dynamics predictions of urban flow are subject to several sources of uncertainty, such as the definition of the inflow boundary conditions or the turbulence model. Compared to Reynolds-averaged Navier-Stokes (RANS) simulations, …

Uncertainty quantification for microscale CFD simulations based on input from mesoscale codes

Accurate predictions of wind and dispersion in the atmospheric boundary layer (ABL) can provide essential information to support design and policy decisions for sustainable urban areas. However, computational fluid dynamics (CFD) predictions of the …

Quantifying inflow uncertainties in RANS simulations of urban pollutant dispersion

Numerical simulations of flow and pollutant dispersion in urban environments have the potential to support design and policy decisions that could reduce the population's exposure to air pollution. Reynolds-averaged Navier-Stokes simulations are a …

Quantifying inflow and RANS turbulence model form uncertainties for wind engineering flows

Reynolds-averaged Navier--Stokes (RANS) simulations are often used in the wind engineering practice for the analysis of turbulent bluff body flows. An approach that allows identifying the uncertainty related to the use of reduced-order turbulence …

Quantifying inflow uncertainties for CFD simulations of the flow in downtown Oklahoma City

Computational Fluid Dynamics (CFD) methods are widely used to investigate wind flow and dispersion in urban environments. Validation with field experiments that represent the full complexity of the problem should be performed to assess the predictive …