FlowGAN: A Conditional Generative Adversarial Network for Flow Prediction in Various Conditions

Chen, Donglin; Gao, Xiang; Xu, Chuanfu; Chen, Shizhao; Fang, Jianbin; Wang, Zhenghua; Wang, Zheng

doi:10.1109/ictai50040.2020.00057

Cited by 27 publications

(12 citation statements)

References 17 publications

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“…We perform experiments on all datasets, { 1 ,  2 ,  3 ,  4  5 }, separately. Qualitative results are shown in Figures [12,13,15,16,17], while quantitative measurements are listed in Tables [1,2,3,4,5,6,7,8]. Training time for the proposed method on each dataset are listed in Table 2.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, it can be seen as a better indicator of the quality of a prediction. The metric is commonly used to quantify relative residuals in accuracy validation, and is used by [7,14,8,13,9] and can be found in the CFD literature [31]. MRE is defined as…”

Section: Rmsementioning

confidence: 99%

“…Unlike other methods that use an encoder to map the input to lower-dimensional space, they have a way to parameterize the airfoils as a 14-dimensional vector. While their approach is promising, FlowGAN [13] shows how they customize U-Net as the generator to include the Reynolds number and angle of attack. The flow parameters are concatenated with the geometry parameters extracted by the encoder of U-Net before they are passed to an MLP network to perform a nonlinear input-output mapping.…”

Section: Introductionmentioning

confidence: 99%

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Positional Encoding Augmented GAN for the Assessment of Wind Flow for Pedestrian Comfort in Urban Areas

Hoeiness¹,

Gjerde²,

Oggiano³

et al. 2021

Preprint

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Approximating wind flows using computational fluid dynamics (CFD) methods can be timeconsuming. Creating a tool for interactively designing prototypes while observing the wind flow change requires simpler models to simulate faster. Instead of running numerical approximations resulting in detailed calculations, data-driven methods in deep learning might be able to give similar results in a fraction of the time. This work rephrases the problem from computing 3D flow fields using CFD to a 2D image-to-image translation-based problem on the building footprints to predict the flow field at pedestrian height level. We investigate the use of generative adversial networks (GAN), such as Pix2Pix [1] and CycleGAN [2] representing state-of-the-art for image-to-image translation task in various domains as well as U-Net autoencoder [3]. The models can learn the underlying distribution of a dataset in a data-driven manner, which we argue can help the model learn the underlying Reynolds-averaged Navier-Stokes (RANS) equations from CFD.We experiment on novel simulated datasets on various three-dimensional bluff-shaped buildings with and without height information. Moreover, we present an extensive qualitative and quantitative evaluation of the generated images for a selection of models and compare their performance with the simulations delivered by CFD. We then show that adding positional data to the input can produce more accurate results by proposing a general framework for injecting such information on the different architectures. Furthermore, we show that the models' performances improve by applying attention mechanisms and spectral normalization to facilitate stable training. Finally, the paper displays results on a real urban city environment dataset generated from the capital of Norway, which strongly supports the use of neural network-based methods as the underlying model for an interactive design tool.

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Section: Methodsmentioning

confidence: 99%

Section: Rmsementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Positional Encoding Augmented GAN for the Assessment of Wind Flow for Pedestrian Comfort in Urban Areas

Hoeiness¹,

Gjerde²,

Oggiano³

et al. 2021

Preprint

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show abstract

“…Using conditional generative models, one can formulate the nonlinear functions between shape parameters and high-dimensional flow fields. For example, Chen et al [393] used a conditional GAN to train a prediction model of the velocity field with respect to different shapes and varaious conditons (α ∈ [−22.5 • , 22.5 • ] and Re ∈ [5 × 10 5 , 5 × 10 6 ]). In the prediction of 75 unseen UIUC airfoils, the mean relative error of x-and y-components of velocities in regions of interest were 8.13% and 20.83%, which were smaller than those in CCN-based autoencoder [255] and U-net [389].…”

Section: Flow Field Modelingmentioning

confidence: 99%

Machine Learning in Aerodynamic Shape Optimization

Li¹,

Martins²

2022

Preprint

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Large volumes of experimental and simulation aerodynamic data have been rapidly advancing aerodynamic shape optimization (ASO) via machine learning (ML), whose effectiveness has been growing thanks to continued developments in deep learning. In this review, we first introduce the state of the art and the unsolved challenges in ASO. Next, we present a description of ML fundamentals and detail the ML algorithms that have succeeded in ASO. Then we review ML applications contributing to ASO from three fundamental perspectives: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands like interactive design optimization. However, practical large-scale design optimizations remain a challenge due to the costly ML training expense. A deep coupling of ML model construction with ASO prior experience and knowledge, such as taking physics into account, is recommended to train ML models effectively.

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“…To overcome the aforementioned limitations, researchers have recently borrowed modelling techniques from the field of deep learning, a subset of machine learning. In particular, Convolutional Neural Networks (CNNs), in conjunction with image representations of design inputs and computer simulation results outputs have been employed to preserve prediction informativeness without a mesh based dependency [17,[23][24][25][26][27][28][29]. CNNs are a particular class of neural networks that have gained popularity when working with spatially structured data such as grids or images.…”

Section: Introductionmentioning

confidence: 99%

Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach

Attar

Zhou

Foster

et al. 2021

Journal of Manufacturing Processes

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FlowGAN: A Conditional Generative Adversarial Network for Flow Prediction in Various Conditions

Cited by 27 publications

References 17 publications

Positional Encoding Augmented GAN for the Assessment of Wind Flow for Pedestrian Comfort in Urban Areas

Positional Encoding Augmented GAN for the Assessment of Wind Flow for Pedestrian Comfort in Urban Areas

Machine Learning in Aerodynamic Shape Optimization

Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach

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