Numerical simulations and optimization of impinging jet configuration

• Published in: International journal of numerical methods for heat & fluid flow Vol. 31; no. 1; pp. 1 - 25
• Main Authors: Singh, Alankrita, Chakravarthy, Balaji, Prasad, BVSSS
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Group Publishing Limited 12.01.2021
• Subjects: Aerodynamics; Artificial neural networks; Asymmetry; Computational fluid dynamics; Computer simulation; Configurations; Cooling; Curvature; Diameters; Dimensions; Experiments; Friction; Gas turbines; Geometry; Heat transfer; Impingement; Jet impingement; Kinetic energy; Locations (working); Mathematical models; Optimization; Parameters; Reynolds number; Simulation; Turbines; Turbulence; Turbulence models; Velocity; Viscosity; Vortices
• DOI: 10.1108/HFF-01-2020-0053
• ISSN: 0961-5539

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, d p , 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.