Experimental and numerical thermal performance analysis with exergy destruction on nanofluid flow in tube with double strip helical screw inserts

Chaurasia, Shashank Ranjan; Sarviya, R. M.

doi:10.1177/0957650920982149

Cited by 6 publications

(2 citation statements)

References 59 publications

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“…Nevertheless, the chemical method is mostly preferred for the large scale production of graphene. [10][11][12] A few Researchers had studied the efficacy of various nanofluids in heat exchangers, like Al 2 O 3 , [13][14][15][16][17][18] TiO 2 , [19][20][21] CuO, [22][23][24][25] ZnO, 26 SiO 2 , 27 CNT, [28][29][30] Al 2 O 3 +PCM, 31 fly ash, 32 Graphite, 33 Graphene [34][35][36] and all of them lead to superior enhancement in thermal performance of heat exchangers when correlated with base fluids. Selvam et al 37,38 conducted experimental trails on the dispersion of graphene nano particles in W+EG based coolant (70:30) in a radiator by altering the inlet temperature and flow rate of a coolant in the range of 35-45°C and 12.5-62.5 g/s.…”

Section: Introductionmentioning

confidence: 99%

Optimization through Taguchi and artificial neural networks on thermal performance of a radiator using graphene based coolant

Nambala¹,

Kishore

Pujari³

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part A:

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As a typical coolant in a radiator, a combination of water and ethylene glycol is often used. Since it has a lower thermal conductivity, the addition of nano particles can increase the performance of the coolant which aids in dwindling the weight and size of the radiator. This paper deals with effect of various input variables like flow rate, inlet temperature of the coolant and Vol% of nano particles (NP) on a radiator’s heat transfer parameters like heat transfer rate (Q), convective heat transfer co-efficient (h), Reynolds number (Re), Nusselt number (Nu) and friction factor (ff) were estimated using optimization methods through Taguchi, ANOVA and ANN. Initially Experimental trials were carried to evaluate heat transfer parameters of a radiator under forced convection by changing inlet temperature, flow rate and NP addition of a coolant. Base fluid water and EG were taken in the ratio of 70:30. To that, Nano particles of graphene were dispersed in the base fluid in the range of 0.1–0.3 Vol%. Coolant inlet temperature, flow rate and addition of nano particles were considered as input parameters (I/P). TheL27 orthogonal array was used as Design of Experiments (DOE). Q, h, Nu, Re and ffwere selected as performance variables. In addition to that, an ANOVA test through Minitab 15 was employed to evaluate the performance parameters in order to estimate each variable and its contribution in percentage. It was established that parameters like Q, h and Nu were largely influenced by the inlet temperature of a coolant, where Re and ff were impacted by flow rate. Estimation of heat transfer parameters like Q, h, Nu, Re and ff were done by using MATLAB through Artificial Neural Networks (ANN) and were found to be in good agreement with experimental data.

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Section: Introductionmentioning

confidence: 99%

Optimization through Taguchi and artificial neural networks on thermal performance of a radiator using graphene based coolant

Nambala¹,

Kishore

Pujari³

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part A:

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“…The main device used in the majority of thermal applications is a heat exchanger. [1][2][3] HT in the heat exchanger can be enhanced by the reduction in thermal resistance. This reduction can be achieved either by the generation of turbulence or by enhancing the effective HT area.…”

Section: Introductionmentioning

confidence: 99%

Heat transfer enhancement with channel surface roughness: A comprehensive review

Sohal

Kumar

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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In various applications such as air conditioning, refrigeration, central heating, and chemical processing, heat losses due to thermal resistance are of considerable amount. To improve the thermo-hydraulic performance of the system involved in such applications, it is required to reduce the thermal resistance. This review article focuses on the works related to heat transfer enhancement using two types of surface roughness that are ribs and corrugations. Ribs and corrugations reduce thermal resistance by increasing surface area and creating perturbation in the flow field. This review article covers various experimental, analytical, and numerical studies related to the thermo-hydraulic performance of different working fluids flowing through the channels incorporated with surface roughness. The effects of using different working fluids that are conventional fluids, nanofluids, and hybrid nanofluids on the heat transfer performance of channels with surface roughness are also studied. Authors believe that this article will provide a basis for the advanced research on the thermo-hydraulic performance assessment of the nanofluids and hybrid nanofluids in the ribbed and corrugated channels, which will help in the enhancement of the performance of the system comprising the application of such channels.

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Thermo-fluid performance for helical coils inserted in a tube using hybrid CFD-ANN approach

Almahmmadi,

Refaey,

Abdelghany

et al. 2024

Thermal Science and Engineering Progress

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Experimental and numerical thermal performance analysis with exergy destruction on nanofluid flow in tube with double strip helical screw inserts

Cited by 6 publications

References 59 publications

Optimization through Taguchi and artificial neural networks on thermal performance of a radiator using graphene based coolant

Optimization through Taguchi and artificial neural networks on thermal performance of a radiator using graphene based coolant

Heat transfer enhancement with channel surface roughness: A comprehensive review

Thermo-fluid performance for helical coils inserted in a tube using hybrid CFD-ANN approach

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