Agradecimentos: The authors of this work would like to acknowledge Fundac¸~ao de Amparo a Pesquisa do Estado de S~ao Paulo, FAPESP, for supporting the present work under research grants No. 2013/08293-7 and 2013/07375-0. The first author is supported by FAPESP scholarships No. 2018/11410-9 and...

Agradecimentos: The authors of this work would like to acknowledge Fundac¸~ao de Amparo a Pesquisa do Estado de S~ao Paulo, FAPESP, for supporting the present work under research grants No. 2013/08293-7 and 2013/07375-0. The first author is supported by FAPESP scholarships No. 2018/11410-9 and 2019/18809-7, and the second author is supported by a scholarship from Conselho Nacional de Desenvolvimento Cient ıfico, CNPq

An assessment of physics-based and data-driven reduced-order models (ROMs) is presented for the study of convective heat transfer in a rectangular cavity. Despite the simple geometrical configuration, the current setup offers increasingly rich dynamics as the thermal forcing is increased, thus...

An assessment of physics-based and data-driven reduced-order models (ROMs) is presented for the study of convective heat transfer in a rectangular cavity. Despite the simple geometrical configuration, the current setup offers increasingly rich dynamics as the thermal forcing is increased, thus making it a suitable candidate to evaluate the performance of ROMs. First, flow simulations are performed using a high-order spectral element method that will feed the ROMs with well-resolved temporal and spatial information. Proper orthogonal decomposition (POD) is applied to reduce the problem dimensionality for all models. The class of tested physics-based models include the Galerkin and least-squares Petrov–Galerkin (LSPG) methods that rely on projection of the Navier–Stokes and energy equations being solved. On the other hand, the data-driven methods applied in this work rely on regression of the governing equations, which are treated as a nonlinear dynamical system. The data-driven methods tested here include the sparse identification of nonlinear dynamics (SINDy) approach and a method recently proposed in literature based on deep neural networks (DNNs). All ROMs are able to represent the periodical temporal dynamics of a low Rayleigh number flow. However, the physics-based approaches demonstrate a better performance for a moderate Rayleigh number case with more complex flow dynamics, when several frequencies are excited in a non-periodical fashion

CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ

FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP

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