On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

Torres, Pablo; Clainche, Soledad Le; Vinuesa, Ricardo

doi:10.3390/en14051310

Cited by 24 publications

(19 citation statements)

References 175 publications

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“…Furthermore, we compared the performance of the CNN-βVEs in terms of the quality of the reconstructions and orthogonality of the extracted modes with that of the CNN-AEs and CNN-HAEs. The flow-field data were obtained from a well-resolved large-eddy simulation (LESs) database Torres et al (2021); Torres (2021). Our results from modal decomposition using POD indicates that 247 modes required to obtain 99% of the energy from the reconstruction.…”

Section: Discussionmentioning

confidence: 99%

“…We employ a database (Torres et al, 2021;Torres, 2021) of the flow through a simplified urban environment to obtain the training data. This database was obtained through well-resolved large-eddy simulation (LES) using the opensource numerical code Nek5000 (Paul F. Fischer and Kerkemeier, 2008), which is based on the spectral-element method (SEM), to solve the incompressible Navier-Stokes equations:…”

Section: Training Datamentioning

confidence: 99%

“…Therefore, there is a pressing need for improved prediction and assessment methods to tackle these challenges and enable urban sustainability in the near future (Vinuesa et al, 2020). Furthermore, it is necessary to gain further insight into the physics responsible for the pollutant-concentration and thermal distributions within cities (Torres et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows

Eivazi¹,

Clainche²,

Hoyas³

et al. 2021

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We propose a deep probabilistic-neural-network architecture for learning a minimal and near-orthogonal set of non-linear modes from high-fidelity turbulentflow-field data useful for flow analysis, reduced-order modeling, and flow control. Our approach is based on β-variational autoencoders (β-VAEs) and convolutional neural networks (CNNs), which allow us to extract non-linear modes from multi-scale turbulent flows while encouraging the learning of independent latent variables and penalizing the size of the latent vector. Moreover, we introduce an algorithm for ordering VAE-based modes with respect to their contribution to the reconstruction. We apply this method for non-linear mode decomposition of the turbulent flow through a simplified urban environment, where the flowfield data is obtained based on well-resolved large-eddy simulations (LESs). We demonstrate that by constraining the shape of the latent space, it is possible to motivate the orthogonality and extract a set of parsimonious modes sufficient for high-quality reconstruction. Our results show the excellent performance of the method in the reconstruction against linear-theory-based decompositions. Moreover, we compare our method with available AE-based models. We show the ability of our approach in the extraction of near-orthogonal modes that may lead to interpretability.

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Section: Discussionmentioning

confidence: 99%

Section: Training Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows

Eivazi¹,

Clainche²,

Hoyas³

et al. 2021

Preprint

Self Cite

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“…Vortex-breaker modes Briefly, we have simulated the turbulent flow in a simplified urban environment consisting of two obstacles, where different regimes are observed when varying the separation between them 13 . According to the classification by Oke (1988) 14 , the first regime is the so-called skimming-flow (SF) configuration, in which the distance between the buildings is the lowest hence the second building hinders the development of the wake of the first one.…”

Section: Vortex-generator Modesmentioning

confidence: 99%

On the generation and destruction mechanisms of arch vortices in urban fluid flows

Lazpita,

Martínez-Sánchez,

Corrochano

et al. 2022

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Studying and interpreting the different flow patterns present in urban areas is becoming essential since they help develop new approaches to fight climate change through an improved understanding of the dynamics of the pollutants in urban environments. This study uses higher order dynamic mode decomposition (HODMD) to analyze a high-fidelity database of the turbulent flow in various simplified urban environments. The geometry simulated consists of two buildings separated by a certain distance. Three different cases have been studied, corresponding to the three different regimes identified in the bibliography. We recognize the characteristics of the well-known arch vortex forming on the leeward side of the first building and document possible generation and destruction mechanisms of this vortex based on the resulting temporal modes. These so-called vortex-generating and vortex-breaking modes are further analyzed via proper-orthogonal decomposition. We show that the arch vortex plays a prominent role in the dispersion of pollutants in urban environments, where its generation leads to an increase in their concentration; therefore, the reported mechanisms are of extreme importance in the context of urban sustainability.

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“…From DayMET, we focus on the reconstruction of the daily maximum temperature over North America. We also consider the problem of far-field flow-state estimation from cross-sectional data using a threedimensional square cylinder wake [21] and urban flow [67].…”

Section: Introductionmentioning

confidence: 99%

Assessments of model-form uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

Morimoto,

Fukami,

Maulik

et al. 2021

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We use Gaussian stochastic weight averaging (SWAG) to assess the model-form uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG approximates a posterior Gaussian distribution of each weight, given training data, and a constant learning rate. Having access to this distribution, it is able to create multiple models with various combinations of sampled weights, which can be used to obtain ensemble predictions. The average of such an ensemble can be regarded as the 'mean estimation', whereas its standard deviation can be used to construct 'confidence intervals', which enable us to perform uncertainty quantification (UQ) with regard to the training process of neural networks. We utilize representative neural-network-based function approximation tasks for the following cases: (i) a two-dimensional circular-cylinder wake; (ii) the DayMET dataset (maximum daily temperature in North America); (iii) a three-dimensional square-cylinder wake; and (iv) urban flow, to assess the generalizability of the present idea for a wide range of complex datasets. SWAG-based UQ can be applied regardless of the network architecture, and therefore, we demonstrate the applicability of the method for two types of neural networks: (i) global field reconstruction from sparse sensors by combining convolutional neural network (CNN) and multi-layer perceptron (MLP); and (ii) far-field state estimation from sectional data with two-dimensional CNN. We find that SWAG can obtain physically-interpretable confidence-interval estimates from the perspective of model-form uncertainty. This capability supports its use

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On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

Cited by 24 publications

References 175 publications

Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows

Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows

On the generation and destruction mechanisms of arch vortices in urban fluid flows

Assessments of model-form uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

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