The present research investigates heat transfer and the flow characteristics of periodically corrugated wavy channels numerically under pulsatile flow conditions. The numerical method used here is Lattice Boltzmann Method (LBM), and the validation of the study is done by Ansys-Fluent which is finite volume based commercial Computational Fluid Dynamics (CFD) code. For modeling walls, bounce-back method, namely, staircase method and three different curved boundary treatments, which are extrapolation, Filippova-Hänel (FH) and Mei-Luo-Shy (MLS), are used. For modeling constant temperature at walls, staircase method and the same curved wall treatments are used. Corrugated channels have a sharp wavy peak, and its inclination angle is 30°. Two different minimum channel heights are considered, which are 5 and 10 mm in corrugated channels. Flow regime is assumed as laminar (50 < Re < 300) and Prandtl number is kept as 0.7. Four kinds of different sinusoidal pulsatile flows are used with a combination of two different dimensionless frequencies and dimensionless amplitudes. For varying Reynolds number range, Nusselt number and friction factor are calculated. Narrow channel creates higher Nusselt number and friction factor than wide channel. At low Reynolds number, Nusselt number does not changed with pulsatile flow conditions, however at high Reynolds number cases of lower dimensionless frequency and higher dimensionless amplitude produce higher Nusselt numbers. Lower dimensionless frequency cases produce higher Nusselt number than higher dimensionless frequency cases. Pulsatile flow conditions have no effect on friction factor and for narrow and wide channel. Nusselt number prediction of FVM is close to STR and EXT for all cases of narrow channels, and close to the FH, MLS and EXT for all cases wide channel. Correlation equations for Nusselt number and friction factor are constructed by deep neural network (DNN) algorithm.
SheikholeslamiMGorji-BandpyMGanjiDD. Review of heat transfer enhancement methods: Focus on passive methods using swirl flow devices. Renew Sustain Energ Rev2015; 49: 444–469.
2.
AlamTKimMH. A comprehensive review on single phase heat transfer enhancement techniques in heat exchanger applications. Renew Sustain Energ Rev2018; 81: 813–839.
3.
ElshafeiEAMAwadMMEl-NegiryE, et al. Heat transfer and pressure drop in corrugated channels. Energy2010; 35: 101–110.
4.
PanJBianYLiuY, et al. Characteristics of flow behavior and heat transfer in the grooved channel for pulsatile flow with a reverse flow. Int J Heat Mass Transf2020; 147: 118932.
5.
ZhangFBianYLiuY, et al. Experimental and numerical analysis of heat transfer enhancement and flow characteristics in grooved channel for pulsatile flow. Int J Heat Mass Transf2019; 141: 1168–1180.
6.
TokgozNTunayTSahinB. Effect of corrugated channel phase shifts on flow structures and heat transfer rate. Exp Therm Fluid Sci2018; 99: 374–391.
7.
KurtulmuşNSahinB. Experimental investigation of pulsating flow structures and heat transfer characteristics in sinusoidal channels. Int J Mech Sci2020; 167: 105268.
8.
ZontulHŞahinB. Experimental investigation of convective heat transfer performance and hydrodynamics of pulsating flow through the rectangular grooved channel. Exp Therm Fluid Sci2023; 141: 110796.
9.
KurtulmuşNSahinB. A review of hydrodynamics and heat transfer through corrugated channels. Int Commun Heat Mass Transf2019; 108: 104307.
10.
HamzahHSahinB. Analysis of SWCNT-water nanofluid flow in wavy channel under turbulent pulsating conditions: investigation of homogeneous and discrete phase models. Int J Therm Sci2023; 184: 108011.
11.
SucciS. The lattice Boltzmann equation. Oxford: Oxford Univ. Press, 2001.
12.
ChenSDoolenGD. Lattice Boltzmann method for fluid flows. Annu Rev Fluid Mech1998; 30: 329–364.
13.
BenziRSucciSVergassolaM. The lattice Boltzmann equation: theory and applications. Phys Rep1992; 222: 145–197.
14.
ÖzsabanMAslanEGüvenHR. Numerical investigation of incompressible forced convection in a channel with a rectangular prism. Sak Uni J Sci2020; 24(1): 10–18.
15.
XiongWZhangJ. A two-dimensional lattice Boltzmann model for uniform channel flows. Compt Math Appl2011; 61(12): 3453–3460.
16.
AfrouziHHAhmadianMMoshfeghA, et al. Statistical analysis of pulsating non-Newtonian flow in a corrugated channel using lattice-Boltzmann method. Physica A2019; 535: 122486.
17.
HekmatMHSaharkhizS. Effect of nanofluid flows on heat transfer intensification of corrugated channels with an oscillating blade. Chem Eng Process Process Intensification2022; 179: 109072.
18.
AroniLTKunjaJMSIslamAKMS. Study of laminar convective heat transfer through a sinusoidal corrugated channel with a zero degree phase shift using different nanofluids. Mater Today Proc2021; 44(6): 4816–4821.
19.
EzzatneshanESalehiAVaseghniaH. Study of micro-heater shape and wettability effects on inception of boiling phenomenon using a multiphase lattice Boltzmann method. Int J Therm Sci2023; 184: 107913.
20.
EzzatneshanESalehiAVaseghniaH. Study on forcing schemes in the thermal lattice Boltzmann method for simulation of natural convection flow problems. Heat Transf2021; 50: 7604–7631.
21.
EzzatneshanE. Implementation of a curved wall- and an absorbing open-boundary condition for the D3Q19 lattice Boltzmann method for simulation of incompressible fluid flows. Sci Iran2018; 26(4): 2329–2341.
22.
AslanEOzsabanMOzcelikG, et al. A numerical analysis of convection heat transfer and friction factor for oscillating corrugated channel flows. Heat Transf Eng2021; 42(3–4): 181–190.
23.
AslanETaymazIBenimAC. Investigation of LBM curved boundary treatments for unsteady flows. Eur J Mech BFluids2015; 51: 68–74.
24.
GuoZZhengCShiB. An extrapolation method for boundary conditions in lattice Boltzmann method. Phys Fluids2002; 14(6): 2007–2010.
MeiRLuoLSShyyW. An accurate curved boundary treatment in the lattice Boltzmann method. J Comput Phys1999; 155: 307–330.
27.
DegrignyJCaiS-GBoussugeJ-F, et al. Improved wall model treatment for aerodynamic flows in LBM. Comput Fluids2021; 227: 105041.
28.
TaoSXuAHeQ, et al. A curved lattice Boltzmann boundary scheme for thermal convective flows with Neumann boundary condition. Int J Heat Mass Transf2020; 150: 118119345.
29.
JafariMFarhadiMSedighiK. Pulsating flow effects on convection heat transfer in a corrugated channel: A LBM approach. Int Commun Heat Mass Transf2013; 45: 146–154.
30.
KwonBEjazFHwangLK. Machine learning for heat transfer correlations. Int Commun Heat Mass Transf2020; 116: 104694.
31.
PaiSSVisariaDWeibelJA. A machine-learning-based surrogate model for internal flow Nusselt number and friction factor in various channel cross cections. In: 20th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, San Diego, CA, USA, 1–4 June 2021, pp.1024–1029.
32.
IslamogluYKurtA. Heat transfer analysis using ANNs with experimental data for air flowing in corrugated channels. Int J Heat Mass Transf2004; 47: 1361–1365.
33.
BeigzadehROzairyR. Developing predictive models for analysis the heat transfer in sinusoidal wavy channels. Therm Sci Eng Prog2019; 14: 100425.
34.
KorolevaAPKuzmenkovNVFrantcuzovMS. Application of machine learning methods for investigating the heat transfer enhancement performance in a circular tube with artificial roughness. J Phys Conf Ser2020; 1675: 012008.
35.
DurgamSBhosaleABhosaleV, et al. Temperature prediction of heat sources using machine learning techniques. Heat Transf2021; 50(8): 7817–7838.
36.
SouayehBBhattacharyyaSHdhiriN, et al. Heat and fluid flow analysis and ANN-based prediction of a novel spring corrugated tape. Sustainability2021; 13(6): 3023.
37.
SkrypnikANShchelchkovAVGortyshovYF, et al. Artificial neural networks application on friction factor and heat transfer coefficients prediction in tubes with inner helical-finning. Appl Therm Eng2022; 206: 118049.
38.
LiYLiuTXieY. Thermal fluid fields reconstruction for nanofluids convection based on physics-informed deep learning. Sci Rep2022; 12: 12567.
39.
TandirogluA. Artificial neural network approach for transient forced convective heat transfer optimization. Int J Mech Eng Appl2016; 4(6): 212–225.
40.
HuangHLeeTSShuC. Thermal curved boundary treatment for the thermal lattice Boltzmann equation. Int J Mod Phys C2006; 17(05): 631–643.
NičenoBNobileE. Numerical analysis of fluid flow and heat transfer in periodic wavy channels. Int J Heat Fluid Flow2001; 22(2): 156–167.
45.
BhatnagarPLGrossEPKrookM. A model for collision processes in gases. I. Small amplitude processes in charged and neutral one-component systems. Phys Rev1954; 94: 511–525.
46.
HeXLuoLS. Lattice Boltzmann model for the incompressible Navier–Stokes equation. J Stat Phys1997; 88: 927–944.
47.
GuoZZhaoTS. A lattice Boltzmann model for convection heat transfer in porous media. Num Heat Transf B2005; 47(2): 157–177.
48.
BenimACAslanETaymazI. Lattice Boltzmann method for laminar forced convection in a channel with a triangular prism. Heat Transf Res2011; 42(4): 359–377.
49.
MohammadAA. Lattice Boltzmann method – fundamentals and engineering applications with computer codes. London: Springer, 2019.
50.
ZouQHeX. On pressure and velocity boundary conditions for the lattice Boltzmann BGK model. Phys Fluids1997; 9(6): 1591–1598.
51.
Ansys-Fluent. Version 192. Canonsburg, PA: Ansys-Inc, 2018.
52.
IssaRI. Solution of the implicitly discretised fluid flow equations by operator-splitting. J Comput Phys1986; 62: 40–65.
53.
AslanEBenimI, AC, Investigation of the lattice Boltzmann SRT and MRT stability for lid driven cavity flow. Int J Mater Mech Manuf2014; 2(4): 317–324.
54.
BenimACAslanETaymazI. Investigation into LBM analysis of incompressible laminar flows at high Reynolds numbers. Wseas Trans Fluid Mech2009; 4(4): 107–116.
55.
BengioY. Learning deep architectures for AI. Found Trends Mach Learn2009; 2(1): 1–127.
56.
LeCunYBengioYHintonG. Deep learning. Nature2015; 521: 436–444.
57.
DengLDongY. Deep learning: methods and applications. Found Trends Signal Process2014; 7(3–4): 197–387.
58.
MohanrajMJayarajSMuraleedharanC. Applications of artificial neural networks for thermal analysis of heat exchangers – A review. Int J Therm Sci2015; 90: 150–172.
59.
NwankpaCIjomahWGachaganA, et al. Activation functions: comparison of trends in practice and research for deep learning, 2018. (accessed date: 28.03.2023) http://arxiv.org/abs/1811.03378.
60.
AslanETaymazIIslamogluY. Finite volume simulation for convective heat transfer in wavy channels. Heat Mass Transf2016; 52: 483–497.
61.
LoyJ. Neural network projects with Python. Birmingham: Packt Publishing, 2019.
