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Towards extraction ...
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
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- Eivazi, Hamidreza (författare)
- KTH,Linné Flow Center, FLOW,Teknisk mekanik
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- Le Clainche, Soledad (författare)
- Univ Politecn Madrid, Sch Aerosp Engn, Madrid 28040, Spain.
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- Hoyas, Sergio (författare)
- Univ Politecn Valencia, Inst Univ Matemat Pura & Aplicada, Valencia 46022, Spain.
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- Vinuesa, Ricardo (författare)
- KTH,Linné Flow Center, FLOW,SeRC - Swedish e-Science Research Centre,Strömningsmekanik och Teknisk Akustik
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(creator_code:org_t)
- Elsevier BV, 2022
- 2022
- Engelska.
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Ingår i: Expert systems with applications. - : Elsevier BV. - 0957-4174 .- 1873-6793. ; 202, s. 117038-
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Modal-decomposition techniques are computational frameworks based on data aimed at identifying a low-dimensional space for capturing dominant flow features: the so-called modes. We propose a deep probabilistic-neural-network architecture for learning a minimal and near-orthogonal set of non-linear modes from high-fidelity turbulent-flow data useful for flow analysis, reduced-order modeling and flow control. Our approach is based on beta-variational autoencoders (beta-VAEs) and convolutional neural networks (CNNs), which enable extracting 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 flow-field 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, where the energy percentage captured by the proposed method from five modes is equal to 87.36% against 32.41% of the POD. Moreover, we compare our method with available AE-based models. We show the ability of our approach in the extraction of near-orthogonal modes with the determinant of the correlation matrix equal to 0.99, which may lead to interpretability.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Strömningsmekanik och akustik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Fluid Mechanics and Acoustics (hsv//eng)
Nyckelord
- Non-linear mode decomposition
- Turbulent flows
- Variational autoencoders
- Convolutional neural networks
- Machine learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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