Uncertainty Quantification for RANS Predictions of Wind Loads on Buildings

  • G. LambertiEmail author
  • C. Gorlé
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 27)


Computational fluid dynamics simulations to calculate wind pressure loads on buildings can be strongly influenced by uncertainty in the inflow boundary conditions and the turbulence model. In the present work we investigate these uncertainties in Reynolds-averaged Navier-Stokes predictions for wind pressure coefficients of a high-rise building, and compare the results to wind tunnel measurements. The uncertainty in the inflow boundary condition is characterized using three uncertain parameters, the reference velocity, roughness length and model orientation, and propagated to the quantities of interest using a non-intrusive polynomial chaos expansion approach. The results indicate that the uncertainty in the inflow conditions is non negligible, but insufficient to explain the discrepancy with the wind tunnel data, in particular where flow separation occurs. The uncertainty related to the turbulence model is investigated by introducing perturbations in the Reynolds stress tensor. The results confirm that the turbulence model form uncertainty is dominant near the separation region that forms downstream of the windward building edge.


Computational Fluid-Dynamics (CFD) Atmospheric Boundary Layer (ABL) Wind loading Uncertainty Quantification (UQ) 



This material is based upon work supported by the National Science Foundation under Grant Number 1635137, and used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number CI-1548562.


  1. 1.
    Amerio L (2018) Experimental high resolution analysis of the pressure peaks on a building scale model façades. PhD thesis, Politecnico di MilanoGoogle Scholar
  2. 2.
    Smith RC (2013) Uncertainty quantification: theory, implementation, and applications, vol 12. SIAMGoogle Scholar
  3. 3.
    Gorlé C, Emory M, Larsson J, Iaccarino G (2012) Epistemic uncertainty quantification for RANS modeling of the flow over a wavy wall. In: Center for turbulence research, annual research briefsGoogle Scholar
  4. 4.
    Emory M, Larsson J, Iaccarino G (2013) Modeling of structural uncertainties in Reynolds-averaged Navier-Stokes closures. Phys Fluids 25(11):110822CrossRefGoogle Scholar
  5. 5.
    Richards PJ, Hoxey RP (1993) Appropriate boundary conditions for computational wind engineering models using the \(k-\varepsilon \) turbulence model. J Wind Eng Ind Aerodyn 46:145–153CrossRefGoogle Scholar
  6. 6.
    Parente A, Gorle C, van Beeck J, Benocci C (2011) Improved kappa-epsilon model and wall function formulation for the RANS simulation of ABL flows. J Wind Eng Ind Aerodyn 99:267–278CrossRefGoogle Scholar
  7. 7.
    Bohnhoff WJ, Dalbey KR, Eddy, JP, Frye JR, Hooper RW, Hu KT, Hough PD, Khalil M, Ridgeway EM, Rushdi A (2009) Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: version 6.6 user’s manual. Sandia National Laboratories, Technical report SAND2010-2183Google Scholar
  8. 8.
    Gorle C, Garcia-Sanchez C, Iaccarino G (2015) Quantifying inflow and RANS turbulence model form uncertainties for wind engineering flows. J Wind Eng Ind Aerodyn 144:202–212CrossRefGoogle Scholar
  9. 9.
    Iaccarino G, Mishra AA, Ghili S (2017) Eigenspace perturbations for uncertainty estimation of single-point turbulence closures. Phys Rev Fluids 2:024605CrossRefGoogle Scholar
  10. 10.
    Ng LWT, Eldred MS (2012) Multifidelity uncertainty quantification using non-intrusive polynomial chaos and stochastic collocation. In: 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conferenceGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringStanford UniversityStanfordUSA

Personalised recommendations