Aerodynamic and aeroelastic responses of short gap twin-box decks: Box geometry and gap distance dependent surrogate based design

Nieto, Francisco Javier Fernández; Montoya, Miguel Cid; Hernández, Santiago; Kusano, Ibuki; Casteleiro, Alejandro; Álvarez, A.; Jurado, J. Á.; Fontán, Arturo

doi:10.1016/j.jweia.2020.104147

Cited by 27 publications

(11 citation statements)

References 42 publications

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“…It has been checked that integral parameters obtained for the case of study are very similar to the numerical ones in [11] despite the minor differences in the value of the turbulence intensity and numerical settings. Furthermore, the results obtained herein are aligned with the experimental results reported in the same reference.…”

Section: Interpolation Curves and Stochastic Mean And Variancementioning

confidence: 72%

“…3 COMPUTATIONAL APPROACH The deterministic samples of the quantities of interest, that are latter used in the stochastic collocation, are identified by means of CFD simulations. To this end, 2D URANS simulations are conducted for the short-gap twin-box deck named as g22 case in [11], which has been adopted as case of study (see Section 4 for details). The turbulence model of choice has been k-ω SST.…”

Section: Interpolation Curves and Stochastic Mean And Variancementioning

confidence: 99%

“…The layout of the flow domain is the one reported in the authors' previous work [11]. Similarly, the mesh adopted in this study is the medium grid in the spatial verification study reported in the same reference for the g22 case, using the same type of boundary conditions.…”

Section: Interpolation Curves and Stochastic Mean And Variancementioning

confidence: 99%

See 2 more Smart Citations

Uncertainty quantification of aerodynamic and aeroelastic responses of a short-gap twin-box deck depending on the wind angle of attack

Lobriglio¹,

Álvarez²,

Nieto³

et al. 2022

Int. J. CMEM

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Section: Interpolation Curves and Stochastic Mean And Variancementioning

confidence: 72%

Section: Interpolation Curves and Stochastic Mean And Variancementioning

confidence: 99%

See 1 more Smart Citation

Uncertainty quantification of aerodynamic and aeroelastic responses of a short-gap twin-box deck depending on the wind angle of attack

Lobriglio¹,

Álvarez²,

Nieto³

et al. 2022

Int. J. CMEM

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“…Wang and Dragomirescu [17] tested a new type of multi-box bridge deck in the wind tunnel for static force coefficients, flutter derivatives and critical flutter wind speed. Nieto et al [18,19] performed a parametric investigation on the slotted cross sections to apply the analytical optimum design problem formulation to optimize the cross section. Multi-objective optimization algorithm have been presented in [20] to obtain Pareto optimal solutions for a bridge cross section by using CFD simulations to minimize the aerodynamic forces.…”

Section: Introductionmentioning

confidence: 99%

Framework for a simulation-based aerodynamic shape optimization of bridge decks for different limit state phenomena

Abbas¹,

Kavrakov²,

Morgenthal³

et al. 2022

Preprint

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Wind-induced response governs the design of the long-span bridges. The shape of the deck is one of the most important factors that not only affects the mechanical properties but greatly influences the aerodynamic performance of the bridge. An efficient framework is proposed to perform aerodynamic shape optimization of a bridge deck using Computational Fluid Dynamics (CFD) simulations and response surface strategies considering aeroelastic phenomena such as flutter, buffeting and Vortex-induced Vibrations (VIV). A parameterized cross section of the deck has been utilized ensuring required carriageway width and structural demand. The Particle swarm optimization algorithm has been employed which leads to an optimized shape that performs better as compared to the reference section. The presented aerodynamic shape optimization framework has a great potential to be used in the design of long-span bridges.

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“…It is well known that a complete characterisation of the aeroelastic behaviour of the PV panel is not feasible using URANS simulations. Nevertheless, it is common practice [14] to employ engineering models that are computationally affordable, and it is therefore meaningful to inquire within what limits they are reliably applicable; a similar approach was adopted, for example, in [15]. The use of unsteady RANS simulations to analyse the wind structure interaction is growing in many engineering applications [16][17][18][19]; the majority of these are related to structural studies on fundamental components of rotating machines.…”

Section: Introductionmentioning

confidence: 99%

Wind Flow Characterisation over a PV Module through URANS Simulations and Wind Tunnel Optical Flow Methods

et al. 2021

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Despite their simplicity, photovoltaic (PV) modules are often arranged in structures that can be affected by severe and complex wind loads: in this context, the wind flow and the dynamic excitation induced by vortex shedding can introduce unexpected aeroelastic responses. This work introduces a novel wind tunnel application of experimental techniques to address this issue by the use of flow visualisation and video postprocessing, through the optical flow algorithm. Numerical simulations based on unsteady Reynolds-averaged Navier–Stokes (RANS) models are performed and compared against the experimental wind tunnel tests on a PV panel that was also instrumented with pressure taps. A setup with a 65∘ tilt angle was examined because, based on preliminary analyses, it was considered interesting for the free flow–wake transition associated with the dynamic response of the PV panel. The comparison of the experimental and numerical average wind fields supported that the proposed optical flow method was appropriate for characterising the wake of the panel, because there was enough seeding to perform the video postprocessing. Experiments and numerical predictions were compared as regards the average pressure distribution on the panel surfaces, and the average percentage was in the error of 7%; this supports that the URANS method was capable of reproducing the average behaviour of the panel, as well as for the selected configuration, which is particularly challenging. Furthermore, the simulated and measured power spectral densities of the wind speed were compared, and this resulted in the numerical model quite faithfully reproducing the frequency of the peak at 5 m/s, while the error was in the order of 20% for the 10 m/s case; this supports that, despite the URANS approach being affected by well-known critical points regarding the simulation of instantaneous quantities, it can be employed to elaborate information that can be particularly useful for the structural design of the panel. This kind of result can be considered as a first step, obtained with simplified and affordable methods, towards a characterisation of the dynamic behaviour of a PV panel in a real-world setup.

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Aerodynamic and aeroelastic responses of short gap twin-box decks: Box geometry and gap distance dependent surrogate based design

Cited by 27 publications

References 42 publications

Uncertainty quantification of aerodynamic and aeroelastic responses of a short-gap twin-box deck depending on the wind angle of attack

Uncertainty quantification of aerodynamic and aeroelastic responses of a short-gap twin-box deck depending on the wind angle of attack

Framework for a simulation-based aerodynamic shape optimization of bridge decks for different limit state phenomena

Wind Flow Characterisation over a PV Module through URANS Simulations and Wind Tunnel Optical Flow Methods

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