Designing a blade-system to generate downburst outflows at boundary layer wind tunnel

Aboutabikh, Moustafa; Ghazal, Tarek; Chen, Jiaxiang; Elgamal, Sameh; Aboshosha, Haitham

doi:10.1016/j.jweia.2019.01.005

Cited by 29 publications

(22 citation statements)

References 37 publications

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“…Figure 11 shows the turbulence intensity profile for downburst simulators D and E. The turbulence intensity increases with height for both system measurements. A similar trend was noticed from the literature by Jubayer et al (2016), Caracoglia (2019), andAboutabikh et al (2019) for different experimental simulations. The test measurements take into consideration smooth terrain for Option D and E as well as the addition of a 10 mm thick surface roughness for Option E. From these average measurements, it can be seen that the turbulence intensity values are higher for the thicker roughness, thus confirming that the increase of surface roughness increases the turbulence near the ground.…”

Section: Turbulence Intensitysupporting

confidence: 90%

“…An average turbulence intensity value of 0.10 was reported by Aboshosha et al (2015) in an open terrain using large-Eddy simulation (LES) models. In the case of Aboutabikh et al, 2019, the turbulence intensity values vary from 0.11 near the ground to a maximum value of 0.13 at a higher height. The values closer to the ground from the studies of Aboshosha et al (2015), Aboutabikh et al (2019), and Le and Caracoglia (2019) are smaller than the turbulence intensity values near the ground presented herein on smooth terrain.…”

Section: Turbulence Intensitymentioning

confidence: 98%

“…Several examples of previous 2-D wall jets used in downburst applications include the addition and/or modification at the lower part of a classic wind tunnel by introducing a slot jet near the wall region (Lin and Savory, 2006;Lin et al, 2007). Other 2-D wall jet examples include the operational control of strategic individual fans from a multifan assembly generating a gust front near the wall (Cao et al, 2002;Sassa et al, 2009;Zhao et al, 2009), the addition of rotating plates (Matsumoto, 1984) or shutters (Matsumoto, 2007), and the addition of blades connected to a stepper motor and rotated at different angles of incidence (Butler and Kareem, 2007;Aboutabikh et al, 2019;Le and Caracoglia, 2019). The advantage of the 2-D wall jet method over the IJ is that it enables the creation of larger sized outflows in smaller spaces.…”

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confidence: 99%

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Examination of different wall jet and impinging jet concepts to produce large-scale downburst outflow

Mejia

Elawady

Vutukuru

et al. 2022

Front. Built Environ.

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Thunderstorm downburst winds are a major cause of severe damage to buildings and other infrastructure. The initiative of the NSF-NHERI Wall of Wind (WOW) Experimental Facility to design and develop a downburst simulator was established to open new horizons for multi-hazard engineering research by extending the current capabilities of the facility to enable the simulation of non-synoptic winds. Five different downburst simulator designs have been tested in the 1:15 small-scale replica of the WOW to identify the optimal design. The design concepts tested herein considered both the 2-D impinging jet and the 2-D wall jet simulation methods. The basic design methodology consists of transforming the available atmospheric boundary layer (ABL) wind simulator into downburst winds by adding an external modification device to the exit of the flow management box. A flow characterization comparison among the five contending downburst simulators, along with comparisons to real downbursts and previous literature findings, has been conducted. The study on the effect of surface roughness length on the height of the peak wind velocity showed that the implementation of a 2-D plane wall jet enables large-scale outflows (higher peak velocity height) with high Reynold numbers, which is advantageous in terms of reducing scaling effects. In general, the current research work showed that four downburst simulation methods were suitable for adoption; however, only one downburst simulator was recommended based on the feasibility of construction in the facility. The chosen downburst simulator consisted of a two louver slat system near the bottom, with a blockage at the top. This configuration enables producing a large rolling vortex passing through the testing section, which would serve adequately in the further study of turbulent flow characterization and testing of larger scale test models.

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Section: Turbulence Intensitysupporting

confidence: 90%

Section: Turbulence Intensitymentioning

confidence: 98%

mentioning

confidence: 99%

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Examination of different wall jet and impinging jet concepts to produce large-scale downburst outflow

Mejia

Elawady

Vutukuru

et al. 2022

Front. Built Environ.

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“…Their radial outflows and ring vortices after touchdown produce strong wind gusts very close to the ground and therefore lead to substantial structural damages (e.g., Yang et al, 2018). Downbursts are typically simulated numerically using CFD (e.g., Mason et al, 2009;Aboshosha et al, 2015;Haines and Taylor 2018;Hao and Wu 2018;Iida and Uematsu 2019) or experimentally using wind tunnels (e.g., Jesson et al, 2015;Jubayer et al, 2016;Hoshino et al, 2018;Aboutabikh et al, 2019;Asano et al, 2019;Junayed et al, 2019;Romanic et al, 2019). Both numerical and experimental approaches to obtain wind fields associated with downbursts are very time consuming (either computational expensive or labor intensive).…”

Section: Downburstsmentioning

confidence: 99%

Applications of Machine Learning to Wind Engineering

Snaiki

2022

Front. Built Environ.

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Advances of the analytical, numerical, experimental and field-measurement approaches in wind engineering offers unprecedented volume of data that, together with rapidly evolving learning algorithms and high-performance computational hardware, provide an opportunity for the community to embrace and harness full potential of machine learning (ML). This contribution examines the state of research and practice of ML for its applications to wind engineering. In addition to ML applications to wind climate, terrain/topography, aerodynamics/aeroelasticity and structural dynamics (following traditional Alan G. Davenport Wind Loading Chain), the review also extends to cover wind damage assessment and wind-related hazard mitigation and response (considering emerging performance-based and resilience-based wind design methodologies). This state-of-the-art review suggests to what extend ML has been utilized in each of these topic areas within wind engineering and provides a comprehensive summary to improve understanding how learning algorithms work and when these schemes succeed or fail. Moreover, critical challenges and prospects of ML applications in wind engineering are identified to facilitate future research efforts.

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“…(3) the airflow field in the wind tunnel should have high turbulence intensity to reproduce the near-surface strong turbulence atmosphere. The numerical simulation of the entire structure and corresponding flow fields of the wind tunnel is an undeniably advanced and effective technique for the optimization of wind tunnel design [43]. This work focuses on 3D computational fluid dynamics (CFD) of the wind tunnel technology.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of sand blown wind tunnel

Yang

Xiaosi

et al. 2020

Automatika

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This work investigates the airflow driven by dual axial-flow fans in an atmospheric boundary layer (ABL) wind tunnel and the expected entrainment of sand movement together. The present study is conducted via 3D numerical simulation based on modelling the entire wind tunnel, including the power fan sections. Three configurations of dual fans in the tunnel are proposed. Simulation results show that the airflow in the tunnel with dual-fan configuration can satisfy the logarithmic distribution law for ABL flows. The airflow driven by the dual fans placed together at the tunnel outlet is highly similar to that in the tunnel with single fans. Although the boundary layer thickness is reduced, the maximum airflow velocity (53.393 m/s) and turbulence intensity (12.02%), which are respectively 1.75 and 1.49 times higher than those under the single-fan configuration, can be reached when dual fans are separately placed at the tunnel inlet and outlet. The simulation and experiment manifest that the separated arrangement of dual fans in the tunnel should be suitable for the experimental study of aeolian sand transport. Some measures, such as wind tunnel construction adjustment and optimal roughness element arrangement, are necessary to guarantee the required boundary layer thickness in the wind tunnel.

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Designing a blade-system to generate downburst outflows at boundary layer wind tunnel

Cited by 29 publications

References 37 publications

Examination of different wall jet and impinging jet concepts to produce large-scale downburst outflow

Examination of different wall jet and impinging jet concepts to produce large-scale downburst outflow

Applications of Machine Learning to Wind Engineering

Optimal design of sand blown wind tunnel

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