Genetic algorithm based correlations for heat transfer calculation on concave surfaces

Abstract

Laboratory experiments were carried out on a boundary layer over a concave wall subject to Görtler instability in the presence of forced wavelengths.

On Using Quasi-Newton Algorithms of the Broyden Class for Model-to-Test Correlation

“…In contrast to most correlation methods developed (Jouffroy, 2007;De Palo et al, 2011;Momayez et al, 2009;Harvatine and DeMauro, 1994;Roscher, 2006;van Zijl, 2013;WenLong et al, 2011;Mareschi et al, 2005), this method will not attempt to minimize the length of the vector ||F(p) -t mes ||, but searches a root of the equation system:…”

“…Many methods have been developed and analyzed to perform model-to-measurement correlation (Jouffroy, 2007;De Palo et al, 2011;Momayez et al, 2009;Harvatine and De Mauro, 1994;Roscher, 2006;van Zijl, 2013;WenLong et al, 2011;Mareschi et al, 2005). Most methods are based on stochastic optimization algorithms and often require several hundred iterations to converge.…”

Flow structure and heat transfer induced by embedded vorticity

“…Such vortices can be readily generated by flow separation behind vortex generators or turbulence promoters (such as transient structures caused by Kelvin-Helmholtz instabilities and counteror co-rotating vortices caused by pressure differences across the tabs) [1][2][3][4], by surface curvature where the centrifugal force produces streamwise vortices (such as Dean and Görtler instabilities) [5][6][7], and by turbulent or laminar jets (such as ring vortices) [8,9].…”

Prediction of thermal and fluid flow characteristics in helically coiled tubes using ANFIS and GA based correlations

“…This method can be used in energy and cost optimization [13] or developing the correlations by optimizing the correlation constants [14,15]. Artificial neural networks were developed to modeling heat transfer and flow characteristic in coiled tubes in previous work [16].…”