Scaling effects on peak wind load estimation for low-rise buildings: An experimental and analytical study

Abstract

Though scaling effects on estimation of peak wind loads are extensively documented in the literature, there is a lack of systematic comparison of such scaling effects across different model scales tested in wind tunnels. Moreover, the limited studies on large-scale physical models accepted the inadequacy of simulating the large turbulent eddies rather than addressing the missing low-frequency turbulence simulation inherent in such large-scale testing. This study provides an in-depth comparison of estimates of peak wind loads obtained using a range of experimental scaled models (1:100 and 1:6 model scales) of the Wind Engineering Research Field Laboratory (WERFL) building while applying a Partial Turbulence Simulation (PTS) methodology to incorporate the effects of turbulence. Large-scale wind tunnel testing of structures provides the advantage of achieving an improved Reynolds number (Re) similarity, more accurate geometric similarity, and enhanced resolution of flow details such as corner vortices, than is possible with the use of smaller model scales. However, as the model scale increases, achieving turbulence similarity becomes challenging due to limitations in simulating the low-frequency turbulent eddies in the wind tunnel. The PTS method has been used in the recent past to account for the low-frequency turbulence effects in the peak wind load estimation. The method is referred to as 1DPTS when considering longitudinal turbulence components only and 2DPTS when considering both longitudinal and lateral turbulence components in the analysis. The computationally intensive 2DPTS can be further simplified by using a weighted average method as described in this paper as “weighted average PTS.” The current study explores the effect of scaling on peak wind load estimation by using the 1DPTS, 2DPTS, and weighted average PTS methods. Results from the multi-scale experimental tests demonstrate that using larger model scales and compensating for the low-frequency turbulence deficit in the post-test analysis significantly improve the agreement with the prototype data owing to the simulation of a higher Reynolds number. Furthermore, comparing the results of the proposed weighted average method with full-scale data reveals its potential as a substitute for the original 2DPTS method, offering enhanced accuracy, particularly at the roof corner for oblique wind directions. While the effects of Re are well known, the findings of this study explore its impact in conjunction with the application of the PTS methods on peak Cp estimation across five different model scales with Re ranging from 3.65 × 104 to 2.43 × 106, the latter being closest to the full-scale (field) Re. The findings provide new insights into how large-scale physical model testing in high Re flows, supported by analytical PTS-based compensation, can enhance the accuracy of peak wind load estimations for critical scenarios including cornering winds that generate conical vortices.