Application of metaheuristic algorithms in optimum thermal design analysis of a rectangular porous fin subjected to both insulated and convective tip conditions
Restricted accessResearch articleFirst published online December, 2020
Application of metaheuristic algorithms in optimum thermal design analysis of a rectangular porous fin subjected to both insulated and convective tip conditions
In this novel optimization analysis, a rectangular porous fin subjected to convective tip conditions along with insulated end condition is studied. The aim is to optimize the important variables responsible for transferring heat from fin with insulated as well as convective tip to the surrounding in order to get a higher rate of heat transfer from the fin surface. Temperature-dependent solid and fluid thermal conductivities as well as variable internal heat generation is considered. To obtain heat dissipation rate through porous fin, the nonlinear governing equation is first solved analytically using Adomian decomposition method. Then the heat transfer rate, i.e., objective function of this problem is optimized with three powerful metaheuristic techniques, namely firefly algorithm, particle swarm optimization and gravitational search algorithm. A comparative analysis has revealed that fins with convective end show significantly higher heat transfer rate compared to their insulated end counterparts. Particle swarm optimization showed marginally higher heat transfer values compared to firefly algorithm but firefly algorithm took less computational effort to reach the optimum value.
KiwanSAl-NimrMA. Using porous fins for heat transfer enhancement. J Heat Trans2001; 123: 790–795.
2.
KiwanS. Thermal analysis of natural convection porous fins. Trans Porous Media2007; 67: 17–29.
3.
KiwanS. Effect of radiative losses on the heat transfer from porous fins. Int J Therm Sci2007; 46: 1046–1055.
4.
HatamiMJafaryarMGanjiDD, et al.Optimization of finned-tube heat exchangers for diesel exhaust waste heat recovery using CFD and CCD techniques. Int Comm Heat Mass Trans2014; 57: 254–263.
5.
HatamiMGanjiDDGorji-BandpyM. Experimental and numerical analysis of the optimized finned-tube heat exchanger for OM314 diesel exhaust exergy recovery. Energy Conv Manag2015; 97: 26–41.
6.
HatamiMGanjiDDGorji-BandpyM. Numerical study of finned type heat exchangers for ICEs exhaust waste heat recovery. Case Stud Therm Eng2014; 4: 53–64.
7.
HatamiMGanjiDDGorji-BandpyM. Experimental and thermodynamical analyses of the diesel exhaust vortex generator heat exchanger for optimizing its operating condition. Appl Therm Eng2015; 75: 580–591.
8.
KunduBBhanjaD. An analytical prediction for performance and optimum design analysis of porous fins. Int J Refrig2011; 34: 337–352.
9.
KunduBBhanjaDLeeKS. A model on the basis of analytics for computing maximum heat transfer in porous fins. Int J Heat Mass Transfer2012; 55: 7611–7622.
10.
HatamiMGanjiDD. Investigation of refrigeration efficiency for fully wet circular porous fins with variable sections by combined heat and mass transfer analysis. Int J Refrig2014; 40: 140–151.
11.
HatamiMHasanpourAGanjiDD. Heat transfer study through porous fins (Si3N4 and AL) with temperature-dependent heat generation. Energy Conv Manag2013; 74: 9–16.
12.
GhasemiSEValipourPHatamiM, et al.Heat transfer study on solid and porous convective fins with temperature-dependent heat generation using efficient analytical method. J Central South Univ2014; 21: 4592–4598.
13.
ArdahaieSSAmiriAJAmoueiA, et al.Investigating the effect of adding nano particles to the blood flow in presence of magnetic field in a porous blood arterial. Info Med Unlocked2018; 10: 71–81.
14.
DogonchiaASHatamiMHosseinzadehKh, et al.Non-spherical particles sedimentation in an incompressible Newtonian medium by Padé approximation. Case Stud Therm Eng2015; 14: 100439–100439.
15.
HatamiMHosseinzadehKhDomairryG, et al.Numerical study of MHD two-phase Couette flow analysis for fluid-particle suspension between moving parallel plates. J Taiwan Inst Chem Eng2014; 45: 2238–2245.
16.
AtoueiSAHosseinzadehKhHatamiM, et al.Heat transfer study on convective–radiative semi-spherical fins with temperature-dependent properties and heat generation using efficient computational methods. Appl Therm Eng2015; 89: 299–305.
17.
RahimiJGanjiDDKhakiM, et al.Solution of the boundary layer flow of an Eyring-Powell non-Newtonian fluid over a linear stretching sheet by collocation method. Alexandria Eng J2017; 56: 621–627.
18.
HatamiMZhouJGengJ, et al.Optimization of a lid-driven T-shaped porous cavity to improve the nanofluids mixed convection heat transfer. J Mol Liquid2017; 231: 620–631.
19.
HatamiMJingD. Optimization of wavy direct absorber solar collector (WDASC) using Al2O3-water nanofluid and RSM analysis. Appl Therm Eng2017; 121: 1040–1050.
20.
HatamiMSongDJingD. Optimization of a circular-wavy cavity filled by nano fluid under the natural convection heat transfer condition. Int J Heat Mass Transfer2016; 98: 758–767.
21.
BhanjaDKunduBAzizA. Enhancement of heat transfer from a continuously moving porous fin exposed in convective–radiative environment. Energy Convers Manage2014; 88: 842–853.
22.
BhanjaDKunduB. Thermal analysis of a constructal T-shaped porous fin with radiation effects. Int J Refrig2011; 34: 1483–1496.
23.
HazarikaSADeshamukhyaTBhanjaD, et al.Thermal analysis of a constructal T-shaped porous fin with simultaneous heat and mass transfer. Chin J Chem Eng2017; 25: 1121–1136.
24.
VahabzadehAGanjiDDAbbasiM. Analytical investigation of porous pin fins with variable section in fully-wet conditions. Case Stud Therm Eng2015; 5: 1–12.
25.
DeshamukhyaTBhanjaDNathS, et al.Analytical study of temperature distribution in a rectangular porous fin considering both insulated and convective tip. AIP Conf Proc2017; 1859: 020031-1–6.
26.
DasROoiKT. Predicting multiple combination of parameters for designing a porous fin subjected to a given temperature requirement. Energy Conv Manag2013; 66: 211–219.
27.
DasR. Forward and inverse solutions of a conductive, convective and radiative cylindrical porous fin. Energy Conv Manag2014; 87: 96–106.
28.
DasRPrasadDK. Prediction of porosity and thermal diffusivity in a porous fin using differential evolution algorithm. Swarm Evol Comput2015; 23: 27–39.
29.
TalbiEGYalaouiFAmodeoL. Metaheuristics for production systems, 1st ed. New York: Springer, 2016.
30.
EsfeMHMahianOHajmohammadMH, et al.Design of a heat exchanger working with organic nanofluids using multi-objective particle swarm optimization algorithm and response surface method. Int J Heat Mass Transfer2018; 119: 922–930.
31.
DastmalchiMSheikhzadehGAArefmaneshA. Optimization of micro-finned tubes in double pipe heat exchangers using particle swarm algorithm. Appl Therm Eng2017; 119: 1–9.
32.
MarianiVCDuckARKGuerraFA, et al.A chaotic quantum-behaved particle swarm approach applied to optimization of heat exchangers. Appl Therm Eng2012; 42: 119–128.
33.
PatelVKRaoRV. Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique. Appl Therm Eng2010; 30: 1417–1425.
34.
SadeghzadehHEhyaeiMARosenMA. Techno-economic optimization of a shell and tube heat exchanger by genetic and particle swarm algorithms. Energy Conv Manage2015; 93: 84–91.
35.
SoleimaniSGanjiDDGorjiM, et al.Optimal location of a pair heat source-sink in an enclosed square cavity with natural convection through PSO algorithm. Int Comm Heat Mass Trans2011; 38: 652–658.
36.
TurgutOE. Hybrid chaotic quantum behaved particle swarm optimization algorithm for thermal design of plate fin heat exchangers. Appl Math Model2016; 40: 50–69.
37.
HamadnehNKhanWASathasivamS, et al.Design optimization of pin fin geometry using particle swarm optimization algorithm. PLoS ONE2013; 8: 1–9.
38.
RashediENezamabadi-PourHSaryazdiS. GSA: a gravitational search algorithm. Info Sci2009; 179: 2232–2248.
39.
LiCZhouJ. Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm. Energy Conv Manag2011; 52: 374–381.
40.
ChenZYuanXTianH, et al.Improved gravitational search algorithm for parameter identification of water turbine regulation system. Energy Conv Manag2014; 78: 306–315.
41.
MohantyDK. Gravitational search algorithm for economic optimization design of a shell and tube heat exchanger. Appl Therm Eng2016; 107: 184–193.
42.
DumanSGüvençUSönmezY, et al.Optimal power flow using gravitational search algorithm. Energy Conv Manag2012; 59: 86–95.
43.
RashediENezamabadi-PourHSaryazdiS. Filter modeling using gravitational search algorithm. Eng Appl Artif Int2011; 24: 117–122.
44.
YangXSHosseiniSSSGandomiAH. Firefly algorithm for solving nonconvex economic dispatch problems with valve loading effect. Appl Soft Comput2011; 12: 1180–1186.
45.
DekhiciLBornePKhaledB. Firefly algorithm for economic power dispatching with pollutants emission. Info Eco2012; 16: 45–57.
46.
ZhangYWuL. A novel method for rigid image registration based on firefly algorithm. Int J Res Rev Soft Int Comput2012; 2: 141–146.
47.
HassanzadehTVojodiHMoghadamA. An image segmentation approach based on maximum variance intra-cluster method and firefly algorithm. IEEE Seventh Int Conf Natural Comput2011; 3: 1817–1821.
48.
KazemzadehA. Optimum design of structures using an improved firefly algorithm. Int J Optim Civil Eng2011; 2: 327–340.
49.
GholizadehSBaratiH. A comparative study of three metaheuristics for optimum design of trusses. Int J Optim Civil Eng2012; 3: 423–441.
50.
SeverinSRossmannJ. A comparison of different metaheuristic algorithms for optimizing blended PTP movements for industrial robots. Int Robo App2012; ▪▪: 321–330.
51.
DeshamukhyaTHazarikaSABhanjaD, et al.An optimization study to investigate non-linearity in thermal behaviour of porous fin having temperature dependent internal heat generation with and without tip loss. Commun Nonlinear Sci Numer Simulat2019; 67: 351–365.
52.
DeshamukhyaTBhanjaDNathS, et al.Prediction of optimum design variables for maximum heat transfer through a rectangular porous fin using particle swarm optimization. J Mech Sci Tech2018; 32: 4495–4502.
53.
DeshamukhyaTNathRHazarikaSA, et al.A modified firefly algorithm to maximize heat dissipation of a rectangular porous fin in heat exchangers exposed to both convective and radiative environment. Proc IMechE, Part E: J Process Mechanical Engineering2019; 233: 1203–1216.
54.
WolpertDMacreadyW. No free lunch theorems for optimization. IEEE Trans Evol Comput1997; 1: 67–82.
55.
YangXS. Review of meta-heuristics and generalised evolutionary walk algorithm. Int J Bio-Inspired Comput2011; 3: 77–84.
