Automation and New Capabilities in the University of Florida NHERI Boundary Layer Wind Tunnel

Catarelli, Ryan; Fernández-Cabán, Pedro L.; Phillips, Brian M.; Bridge, Jennifer A.; Masters, Forrest J.; Gurley, Kurtis R.; Prevatt, David O.

doi:10.3389/fbuil.2020.558151

Cited by 29 publications

(5 citation statements)

References 24 publications

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“…1 —inlay 3.) replaces the manual roughness adjustment approach with the Terraformer - a unique computer-controlled array of 1116 individual roughness elements (62 rows of 18) with fetch length of 18.3m that rapidly actuate to user-specified configurations 29 – 31 . Catarelli et al 29 describes the characteristics, operation, and limitations of the system.…”

Section: Methodsmentioning

confidence: 99%

“…This benchmark case for which equivalence is being sought creates mean and turbulence intensity profiles that match the standard models and metrics employed to investigate pressures on scale models of bluff body infrastructure (e.g. buildings) 29 , 30 , and are therefore of direct value to the wind engineering community. Each of the active learning components are described below with algorithmic and equipment design details provided in the Supplementary Materials (SM) .…”

Section: Methodsmentioning

confidence: 99%

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Active learning applied to automated physical systems increases the rate of discovery

Shields

Gurley

Catarelli

et al. 2023

Sci Rep

Self Cite

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Active machine learning is widely used in computational studies where repeated numerical simulations can be conducted on high performance computers without human intervention. But translation of these active learning methods to physical systems has proven more difficult and the accelerated pace of discoveries aided by these methods remains as yet unrealized. Through the presentation of a general active learning framework and its application to large-scale boundary layer wind tunnel experiments, we demonstrate that the active learning framework used so successfully in computational studies is directly applicable to the investigation of physical experimental systems and the corresponding improvements in the rate of discovery can be transformative. We specifically show that, for our wind tunnel experiments, we are able to achieve in approximately 300 experiments a learning objective that would be impossible using traditional methods.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Active learning applied to automated physical systems increases the rate of discovery

Shields

Gurley

Catarelli

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…The experiments were carried out at the Natural Hazards Engineering Research Infrastructure (NHERI) Boundary Layer Wind Tunnel of the University of Florida. The tunnel is 6 m wide, 3 m tall, and 40 m long, as shown in Figure 3, where the "terraformers" are automated terrain roughness elements with an adjustable height that can be quickly adjusted by electric actuators [33]. For the conducted tests, the height of the terraformer elements was set to 16 cm, in order to obtain a suburban terrain condition.…”

Section: Methodsmentioning

confidence: 99%

Uncertainty Quantification and Simulation of Wind-Tunnel-Informed Stochastic Wind Loads

Duarte,

Arunachalam,

Subgranon

et al. 2023

Wind

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The simulation of stochastic wind loads is necessary for many applications in wind engineering. The proper-orthogonal-decomposition-(POD)-based spectral representation method is a popular approach used for this purpose, due to its computational efficiency. For general wind directions and building configurations, the data-informed POD-based stochastic model is an alternative that uses wind-tunnel-smoothed auto- and cross-spectral density as input, to calibrate the eigenvalues and eigenvectors of the target load process. Even though this method is straightforward and presents advantages, compared to using empirical target auto- and cross-spectral density, the limitations and errors associated with this model have not been investigated. To this end, an extensive experimental study on a rectangular building model considering multiple wind directions and configurations was conducted, to allow the quantification of uncertainty related to the use of short-duration wind tunnel records for calibration and validation of the data-informed POD-based stochastic model. The results demonstrate that the data-informed model can efficiently simulate stochastic wind loads with negligible model errors, while the errors associated with calibration to short-duration wind tunnel data can be important.

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“…To remove such a pressure gradient in the upper layers of the ABL, the most common practice is to adjust the ceiling height of the wind tunnel, which has been implemented in a few wind tunnels worldwide. [12][13][14][15] A gradually increasing ceiling height in the test section decreased the flow speed over the entire cross-section and produced approximate ZPG flows. However, an adjustable ceiling was not adopted in all the wind tunnels.…”

Section: Introductionmentioning

confidence: 99%

“…In the studies associated with far‐field dispersion or a super high‐rise building, the airflow in the whole depth of the ABL is to be modeled. To remove such a pressure gradient in the upper layers of the ABL, the most common practice is to adjust the ceiling height of the wind tunnel, which has been implemented in a few wind tunnels worldwide 12–15 . A gradually increasing ceiling height in the test section decreased the flow speed over the entire cross‐section and produced approximate ZPG flows.…”

Section: Introductionmentioning

confidence: 99%

Investigation on favorable pressure gradient in an atmospheric boundary layer wind tunnel using large‐eddy simulation and its impact on wind field and surface pressure measurement

Zhang

Ooka

Kikumoto

et al. 2023

Japan Architectural Review

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A constant cross‐section wind tunnel usually experiences a negative pressure gradient (ie, favorable pressure gradient, FPG), resulting in a certain degree of discrepancy between the simulated and idealized atmospheric boundary layer (ABL) flows. To investigate its impact, three large‐eddy simulation (LES) cases were conducted in this study: one simply reproduced a wind tunnel, one removed the FPG by adjusting the ceiling height, and one reduced the FPG by canceling unnecessary boundary layers associated with the tunnel sidewalls and ceiling. The required ceiling height in the second case was estimated based on the pressure gradient in the first case. The FPG was found mainly generated by the contraction effect induced by the boundary layers over the tunnel walls because it can be largely removed in the third case. In addition, we investigated the mean and standard deviation of the surrounding wind field and surface pressure coefficient of an isolated square‐section building model. Because the FPG in the macroscale ABL is negligible compared with the adverse pressure gradient in the microscale near‐wall boundary layers of the building, the influence of the FPG is negligible in a wind tunnel experiment conducted in a relatively large wind tunnel with a safe blockage ratio.

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Automation and New Capabilities in the University of Florida NHERI Boundary Layer Wind Tunnel

Cited by 29 publications

References 24 publications

Active learning applied to automated physical systems increases the rate of discovery

Active learning applied to automated physical systems increases the rate of discovery

Uncertainty Quantification and Simulation of Wind-Tunnel-Informed Stochastic Wind Loads

Investigation on favorable pressure gradient in an atmospheric boundary layer wind tunnel using large‐eddy simulation and its impact on wind field and surface pressure measurement

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