Unsteady-RANS simulation of conjugate heat transfer in a cavity with a vertical semitransparent wall

Noh-Pat, F.; Xamán, J.; Álvarez, G.; Gijón-Rivera, M.; Hernández-Pérez, I.; Arce, J.; Villanueva-Vega, E.

doi:10.1016/j.compfluid.2015.05.023

Cited by 7 publications

(1 citation statement)

References 30 publications

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“…CFD numerical simulation mainly includes direct numerical simulation (DNS) [12][13][14], Reynolds averaged Navier-Stokes (RANS) [15,16], and large eddy simulation (LES) [17][18][19]. DNS is a direct high-fidelity method for solving Navier-Stokes equations, which requires very fine grids.…”

Section: Introductionmentioning

confidence: 99%

Reduced‐Order Modeling of Cavity Flow Oscillations across Multi‐Mach Numbers Using Deep Learning

Liu

Ning

Ding

et al. 2021

Shock and Vibration

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The reduced-order model can accurately and efficiently predict unsteady problems in many aerospace engineering applications. The traditional reduced-order model based on proper orthogonal decomposition (POD) and Galerkin projection has poor robustness and large error in predicting complex problems. In this paper, a reduced-order model combining POD and deep learning is proposed to predict cavity flow oscillations under different flow conditions. Firstly, POD modes and corresponding coefficients are obtained by POD. Then, two deep learning frameworks, including multilayer perceptron (MLP) and long short-term memory (LSTM) neural networks, are used to predict the future POD coefficients, respectively. Finally, the cavity flow oscillations across multi-Mach numbers are predicted by the POD modes and the future coefficients. The results show that both of these frameworks can accurately predict cavity flow oscillations when the flow conditions change, and the time cost is reduced by order of magnitude. In addition, due to the performance of LSTM is better than that of MLP, its calculation speed is faster.

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Section: Introductionmentioning

confidence: 99%

Reduced‐Order Modeling of Cavity Flow Oscillations across Multi‐Mach Numbers Using Deep Learning

Liu

Ning

Ding

et al. 2021

Shock and Vibration

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Analysis of Combined Natural Convection and Radiation Heat Transfer in a Partitioned Rectangular Enclosure with Semitransparent Walls

Zhou

Liu

Huang

et al. 2019

Trans. Tianjin Univ.

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In this paper, we present a two-dimensional numerical analysis of the conjugate natural convection and radiation heat transfer in a double-space enclosure with two semitransparent walls. Two kinds of boundary conditions are considered, the first being the isothermal process of the opaque wall, and the other the incidence of a constant radiation flux in the left semitransparent wall. The renormalization group k-ε model is adopted to simulate the turbulent flow in the enclosure. To compute the radiation heat transfer in a semitransparent medium, the discrete ordinates model is used. We compare the behaviors of enclosures with single and double semitransparent walls and determine the difference in the results obtained for semitransparent and opaque partitions. The results indicate that a semitransparent partition facilitates a reduction in the heat loss or obtains a higher temperature distribution. The transmittance of a semitransparent wall has a great effect on the thermal and flow characteristics in an enclosure. The change of wall temperature is found to be significant when the thermal conductivity values range from 0.05 to 0.5 W/(m•K), and to be small when ranging from 0.5 to 10 W/(m•K). These conclusions are helpful for green design and energy saving in solar buildings.

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Thermal performance of a room with a double glazing window using glazing available in Mexican market

Aguilar

Xamán

Olazo-Gómez

et al. 2017

Applied Thermal Engineering

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Unsteady-RANS simulation of conjugate heat transfer in a cavity with a vertical semitransparent wall

Cited by 7 publications

References 30 publications

Reduced‐Order Modeling of Cavity Flow Oscillations across Multi‐Mach Numbers Using Deep Learning

Reduced‐Order Modeling of Cavity Flow Oscillations across Multi‐Mach Numbers Using Deep Learning

Analysis of Combined Natural Convection and Radiation Heat Transfer in a Partitioned Rectangular Enclosure with Semitransparent Walls

Thermal performance of a room with a double glazing window using glazing available in Mexican market

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