Enhanced heat transfer in a labyrinth channels with ribs of different shape

Du, Wei; Li, Luo; Wang, Songtao; Liu, Jian; Sundén, Bengt

doi:10.1108/hff-05-2019-0393

Cited by 4 publications

(2 citation statements)

References 32 publications

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“…The physical model of the present study is inspired from previous studies (Gau and Lee, 1992; Gau and Lee, 2000; Yan et al , 2005; Du et al , 2019; Park et al , 2003; Agrawal and Bower, 1982; Rady and Arquis, 2006). The model and its associated meshing are shown in Figure 1.…”

Section: Computational Methodologymentioning

confidence: 99%

“…They found both an increase and a decrease in the heat transfer with ribs over a flat surface, compared to a smooth flat surface, depending on the inclination of ribs. Du et al (2019) considered eight different cases of dimple depth and diameter for a double wall cooling surface. They obtained only a minor effect of the ribs on the overall thermal performance.…”

Section: Introductionmentioning

confidence: 99%

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Numerical simulations and optimization of impinging jet configuration

Singh

Balaji

Prasad

2020

HFF

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Purpose Numerical simulations are performed to determine the heat transfer characteristics of slot jet impingement of air on a concave surface. The purpose of this paper is to investigate the effect of protrusions on the heat transfer by placing semi-circular protrusions on the concave surface at several positions. After identifying appropriate locations where the heat transfer is a maximum, multiple protrusions are placed at desired locations on the plate. The gap ratio, curvature ratio (d/D) and the dimensions of the plate are varied so as to obtain heat transfer data. The curvature ratio is varied first, keeping the concave diameter (D) fixed followed by a fixed slot width (d). A surrogate model based on an artificial neural network is developed to determine optimum locations of the protrusions that maximize the heat transfer from the concave surface. Design/methodology/approach The scope and objectives of the present study are two-dimensional numerical simulations of the problem by considering all the geometrical parameters (H/d, dp, Re, θ) affecting heat transfer characteristics with the help of networking tool and numerical simulation. Development of a surrogate forward model with artificial neural networks (ANNs) with a view to explore the full parametric space. To quantitatively ascertain if protrusions hurt or help heat transfer for an impinging jet on a concave surface. Determination of the location of protrusions where higher heat transfer could be achieved by using exhaustive search with the surrogate model to replace the time consuming forward model. Findings A single protrusion has nearly no effect on the heat transfer. For a fixed diameter of concave surface, a smaller jet possesses high turbulence kinetic energy with greater heat transfer. ANN is a powerful tool to not only predict impingement heat transfer characteristics by considering multiple parameters but also to determine the optimum configuration from many thousands of candidate solutions. A maximum increase of 8 per cent in the heat transfer is obtained by the best configuration constituting of multiple protrusions, with respect to the baseline smooth configuration. Even this can be considered as marginal and so it can be concluded that first cut results for heat transfer for an impinging jet on a concave surface with protrusions can be obtained by geometrically modeling a much simpler plain concave surface without any significant loss of accuracy. Originality/value The heat transfer during impingement cooling depends on various geometrical parameters but, not all the pertinent parameters have been varied comprehensively in previous studies. It is known that a rough surface may improve or degrade the amount of heat transfer depending on their geometrical dimensions of the target and the rough geometry and the flow conditions. Furthermore, to the best of authors’ knowledge, scarce studies are available with inclusion of protrusions over a concave surface. The present study is devoted to development of a surrogate forward model with ANNs with a view to explore the full parametric space.

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