Aerodynamic shape optimization of civil structures: A CFD-enabled Kriging-based approach

Bernardini, Enrica; Spence, Seymour M.J.; Wei, Daniel; Kareem, Ahsan

doi:10.1016/j.jweia.2015.03.011

Cited by 111 publications

(47 citation statements)

References 32 publications

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“…Given its prior and widespread application, CFD simulation shows promise for assessing aerodynamic performance. [11] Blocken [12] considered the impact of CFD simulation on wind engineering. Blocken et al [13] noted that CFD offers important advantages over field tests and reduced-scale wind tunnel testing.…”

Section: Discussionmentioning

confidence: 99%

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Using computational fluid dynamics to improve the drag coefficient estimates for tall buildings under wind loading

Wahrhaftig

Silva

2017

Structural Design Tall Build

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Summary Wind is the main horizontal force acting on tall buildings. This force is proportional to the drag coefficient. The drag coefficient is an important factor in their structural design. Designers have historically relied upon experimental wind tunnel results to estimate the drag coefficient. However, this process is both expensive and time consuming. In this study, we alternatively computed the drag coefficient (apart from the pressure, force, and bending moment) using computational fluid dynamics for a typical 93‐m‐high residential building. The simulation considers the actual building geometry, as well as the neighborhood roughness effects. We compared these results with the conventional estimates contained in the Brazilian code NBR‐6123/1988 and Eurocode EC1. The results indicated that the pressures obtained herein near the top of the building were lower compared to those obtained using conventional estimation methods given in codes. Comparatively, the obtained bending moment relative to the base of the building was higher, indicating the existence of significant drag forces not considered in codes. In fluid dynamics simulations, the drag coefficient is determined for each terrain condition. Computational fluid dynamics can effectively simulate the drag force and resultant forces in the direction of the flow, as well as the vortices that result during coating detachment and other types of damage.

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Section: Discussionmentioning

confidence: 99%

“…The peak of the speed pressure q p (z) at height z, which includes mean and short-term speed floatation, should be computed using Equation 11.…”

Section: European Codementioning

confidence: 99%

Using computational fluid dynamics to improve the drag coefficient estimates for tall buildings under wind loading

Wahrhaftig

Silva

2017

Structural Design Tall Build

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“…Universal Kriging defines the trend function as a multivariate polynomial, as described in Eq. (11). Simple Kriging refers to the use of a known constant parameter as regression function, i.e.…”

Section: Uncertainty Quantification Methods: Generalised Polynomial Cmentioning

confidence: 99%

“…Kriging is a frequent approach in CFD. It is actually a well known method in optimisation studies because of its ability to deal with many variables or complex scenarios, as in aircraft wing optimisation involving 45 shape parameters [49], in [11] applied to aerodynamic optimisation of civil structures, and in CFD optimization of aeronautical combustion chambers [25] by means of Kriging predictors combined with the NSGA-II optimisation algorithm [21]. Kriging is not only popular in optimisation, but it is also frequently used in UQ.…”

Section: Introductionmentioning

confidence: 99%

On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust

et al. 2019

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A classic approach to computational fluid dynamics is to perform simulations with a fixed set of variables in order to account for parameters and boundary conditions. However, experiments and real-life performance are subject to variability in their conditions. In recent years, the interest of performing simulations under uncertainty is increasing, but this is not yet a common rule, and simulations with lack of information are still taking place. This procedure could be missing details such as whether sources of uncertainty affect dramatic parts in the simulation of the flow. One of the reasons of avoiding to quantify uncertainties is that they usually require to run an unaffordable number of CFD simulations to develop the study. To face this problem, Non-Intrusive Uncertainty Quantification (UQ) has been applied to 3D Reynolds-Averaged Navier-Stokes simulations of an under-expanded jet from an aircraft exhaust with the Spalart-Allmaras turbulent model, in order to assess the impact of inaccuracies and quality in the simulation. To save a large number of computations, sparse grids are used to compute the integrals and built surrogates for UQ. Results show that some regions of the jet plume can be more sensitive than others to variance in both physical and turbulence model parameters. The Spalart-Allmaras turbulent model is demonstrated to have an accurate performance with respect to other turbulent models in RANS, LES and experimental data, and the contribution of a large variance in its parameter is analysed. This investigation explicitly outlines, exhibits and proves the details of the relationship between diverse sources of input uncertainty, the sensitivity of different quantities of interest to said uncertainties and the spatial distribution arising due to their propagation in the simulation of the high-speed jet flow. This analysis represents first numerical study that provides evidence for this heuristic observation.

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“…This technique is nowadays one of the most widespread methods and its performance is widely demonstrated in the literature, for instance, in [21]. In the field of aerodynamics, some applications can be found, as for instance, the work of [22], which employs Kriging surrogate models for optimizing the shape of a building by means of changing the corners of the shape.…”

Section: Aerodynamic Response By Means Of Surrogate Modelingmentioning

confidence: 99%

The role of surrogate models in combined aeroelastic and structural optimization of cable-stayed bridges with single box deck

Montoya¹,

Hernández²,

Kusano³

et al. 2016

WIT Transactions on the Built Environment

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The aeroelastic phenomena represent a crucial issue in the design of long span bridges and their relevance grows as the length of the spans is increased, which is the current trend in these mega-structures. The search for the best performing deck cross-section, as well as the search of the most economic bridge design, are two of the most relevant goals for bridge designers. Hence, the pursuit of good aerodynamic performance of the bridge deck cross-section and the structural optimization of the bridge are destined to converge in a unified process to achieve more efficient bridge designs, given their interdependence. This paper presents an integrated methodology based on a surrogate-based optimization process to obtain an optimum bridge design considering structural and aeroelastic constraints. The deck cross-section used in this work is the well-known G1 section, in which two deck shape variables are considered. The aerodynamic behavior of the cross sections analyzed by the optimization algorithm is given by a surrogate model trained with a set of designs calculated with CFD techniques. The structural performance is analyzed by means of finite element analysis. The areas and prestressing forces of the stays and the deck shape and plate thickness are considered as design variables, and the optimization of the bridge is carried out by a gradient-based optimization algorithm.

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Aerodynamic shape optimization of civil structures: A CFD-enabled Kriging-based approach

Cited by 111 publications

References 32 publications

Using computational fluid dynamics to improve the drag coefficient estimates for tall buildings under wind loading

Using computational fluid dynamics to improve the drag coefficient estimates for tall buildings under wind loading

On the influence of uncertainty in computational simulations of a high-speed jet flow from an aircraft exhaust

The role of surrogate models in combined aeroelastic and structural optimization of cable-stayed bridges with single box deck

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