Data-driven assessment of arch vortices in simplified urban flows

Martínez-Sánchez, Álvaro; Lazpita, Eneko; Corrochano, Adrián; Clainche, Soledad Le; Hoyas, Sergio; Vinuesa, Ricardo

doi:10.1016/j.ijheatfluidflow.2022.109101

Cited by 10 publications

(19 citation statements)

References 47 publications

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“…2022; Martínez-Sánchez et al. 2023), with the difference being that in this case the inflow boundary layer is laminar. This database was obtained through direct numerical simulation using the open-source numerical code Nek5000 (Fischer, Lottes & Kerkemeier 2008), which is based on the spectral element method, to solve the incompressible Navier–Stokes equations: where represents the velocity field, is the kinematic viscosity, and denotes the pressure, which includes the constant-density term.…”

Section: Numerical Simulations and Flow Descriptionmentioning

confidence: 94%

“…This division focuses on the identification of two main types of modes: vortex-generator modes (G) and vortex-breaker modes (B). The naming of these modes is purely associated with the shape of their associated structures, which is covered extensively in the works of Lazpita et al (2022) and Martínez-Sánchez et al (2023). The major structures and vortices are produced by the G modes; therefore, they are related to the mechanism that could create the horseshoe and arch vortices.…”

Section: Reduced-order Model For Urban Flowsmentioning

confidence: 99%

“…This temporal resolution is enough to capture accurately the low- and high-frequency flow mechanisms identified in the literature (Martínez-Sánchez et al. 2023).…”

Section: Numerical Simulations and Flow Descriptionmentioning

confidence: 99%

“…(2022) and Martínez-Sánchez et al. (2023). They analysed the near-wake flow of finite square cylinders, which can be described as a combination of four main vortices (Hunt et al.…”

Section: Introductionmentioning

confidence: 97%

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Causality analysis of large-scale structures in the flow around a wall-mounted square cylinder

et al. 2023

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The aim of this work is to analyse the formation mechanisms of large-scale coherent structures in the flow around a wall-mounted square cylinder, due to their impact on pollutant transport within cities. To this end, we assess causal relations between the modes of a reduced-order model obtained by applying proper orthogonal decomposition to high-fidelity simulation data of the flow case under study. The causal relations are identified using conditional transfer entropy, which is an information-theoretical quantity that estimates the amount of information contained in the past of one variable about another. This allows for an understanding of the origins and evolution of different phenomena in the flow, with the aim of identifying the modes responsible for the formation of the main vortical structures. Our approach unveils that vortex-breaker modes are the most causal modes, in particular, over higher-order modes, and no significant causal relationships were found for vortex-generator modes. We validate this technique by determining the causal relations present in the nine-equation model of near-wall turbulence developed by Moehlis et al. (New J. Phys., vol. 6, 2004, p. 56), which are in good agreement with literature results for turbulent channel flows.

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Section: Numerical Simulations and Flow Descriptionmentioning

confidence: 94%

Section: Reduced-order Model For Urban Flowsmentioning

confidence: 99%

“…This temporal resolution is enough to capture accurately the low- and high-frequency flow mechanisms identified in the literature (Martínez-Sánchez et al. 2023).…”

Section: Numerical Simulations and Flow Descriptionmentioning

confidence: 99%

“…(2022) and Martínez-Sánchez et al. (2023). They analysed the near-wake flow of finite square cylinders, which can be described as a combination of four main vortices (Hunt et al.…”

Section: Introductionmentioning

confidence: 97%

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Causality analysis of large-scale structures in the flow around a wall-mounted square cylinder

et al. 2023

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“…Recently, machine-learning-based approaches have been increasingly used in the study of turbulent flows for a variety of tasks related to flow prediction (Duraisamy et al, 2019;Brunton et al, 2020;Guastoni et al, 2021Guastoni et al, , 2022, prediction of temporal dynamics (Srinivasan et al, 2019;Eivazi et al, 2021;Borrelli et al, 2022), extraction of flow patterns (Jiménez, 2018;Eivazi et al, 2022;Martínez-Sánchez et al, 2023), generation of inflow conditions (Fukami et al, 2019;Yousif et al, 2023) or flow control (Rabault et al, 2019;Guastoni et al, 2023) to name a few. In addition, neural network models have started offering new interesting opportunities to formulate efficient data-driven wall models, as highlighted in Vinuesa and Brunton (2022).…”

Section: Introductionmentioning

confidence: 99%

Predicting the wall-shear stress and wall pressure through convolutional neural networks

Balasubramanian¹,

Gastonia²,

Schlatter³

et al. 2023

Preprint

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The objective of this study is to assess the capability of convolution-based neural networks to predict the wall quantities in a turbulent open channel flow, starting from measurements within the flow. Gradually approaching the wall, the first tests are performed by training a fully-convolutional network (FCN) to predict the two-dimensional velocity-fluctuation fields at the inner-scaled wall-normal location y + target , using the sampled velocity fluctuations in wall-parallel planes located farther from the wall, at y + input . The predictions from the FCN are compared against the predictions from a proposed R-Net architecture as a part of the network investigation study. Since the R-Net model is found to perform better than the FCN model, the former architecture is optimized to predict the two-dimensional streamwise and spanwise wall-shear-stress components and the wall pressure from the sampled velocity-fluctuation fields farther from the wall. The data for training and testing is obtained from direct numerical simulation (DNS) of open channel flow at friction Reynolds numbers Re τ = 180 and 550. The turbulent velocity-fluctuation fields are sampled at various inner-scaled wall-normal locations, i.e. y + = {15, 30, 50, 100, 150}, along with the wall-shear stress and the wall pressure. At Re τ = 550, both FCN and R-Net can take advan- *

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Analysis of transient and intermittent flows using a multidimensional empirical mode decomposition

de Souza,

Miotto,

Wolf

2024

Theor. Comput. Fluid Dyn.

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Data-driven assessment of arch vortices in simplified urban flows

Cited by 10 publications

References 47 publications

Causality analysis of large-scale structures in the flow around a wall-mounted square cylinder

Causality analysis of large-scale structures in the flow around a wall-mounted square cylinder

Predicting the wall-shear stress and wall pressure through convolutional neural networks

Analysis of transient and intermittent flows using a multidimensional empirical mode decomposition

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