Investigation on convective heat transfer and friction factor of silver–water nanofluid under laminar flow – an experimental study

Iyahraja, S.; J, Selwin Rajadurai; B, Siva Subramanian; S, Siva Subramanian; Sivasankar, P; Subash, S

doi:10.1007/s00231-019-02640-y

Cited by 13 publications

(4 citation statements)

References 37 publications

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“…The Nusselt number, Nu, is defined as: where h is the heat transfer coefficient and λ n is the thermal conductivity of nano-fluid; T w and T f are the average wall temperature and fluid temperature, respectively. The fanning friction factor, f , is calculated as: (10) where P denotes the pressure drop along channel and L = L − 2L 1 indicates effective channel length. The P in represents inlet pressure and P out represents outlet pressure.…”

Section: B Governing Equations Assumptions and Boundary Conditionsmentioning

confidence: 99%

“…Nanofluid shows great potential of heat transfer enhancement in thermal equipment due to their high thermal conductivity, low pressure resistance and high stability compared [1]- [3]. In addition, abundant experimental and numerical studies found that the heat transfer performance of nanofluid outperforms corresponding base fluid [4]- [10]. Kalteh et al [8] investigated the effects of different parameters including nanoparticle diameter, volume concentration and flow Reynolds number on the convection heat transfer…”

Section: Introductionmentioning

confidence: 99%

“…experimentally. Iyahraja et al [10] conducted an experimental study on the laminar flow forced convective heat transfer and friction factor of silver-water nanofluids in a horizontal circular pipe.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Nanofluid Field Reconstruction in Experimental Analysis

Liu

Xie

et al. 2020

IEEE Access

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Experiment is an important method to study the thermal and flow performance. Nevertheless, only limited information, such as local temperature and pressure, can be obtained through detection machines. Based on deep learning, a general, useful and flexible reconstruction model is proposed to reconstruct global flow field in two-dimension domain with the limited information exploiting from experiments as input information. Besides, the corresponding performance parameters Nu and f are extracted from generated fields. To validate the feasible, stability and accuracy of the framework, the micro channels with nano fluids are taken as validation case. First of all, the comparison between reconstructed fields and original fields are presented. It shows that reconstructed fields are almost the same as original ones and extracted performance parameters also have high precision. Next, the effects of train size, measuring uncertainty and measuring layouts are considered in sensitivity analysis. Higher train size and smaller measuring uncertainty are advantageous to the reconstruction results. Measuring layout has little influence on reconstruction performance and at least 7 local measuring points are enough. INDEX TERMS Deep learning, microchannel, field reconstruction, nanofluid.

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Section: B Governing Equations Assumptions and Boundary Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Nanofluid Field Reconstruction in Experimental Analysis

Liu

Xie

et al. 2020

IEEE Access

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“…Another researcher Karimzadehkhouei et al [13] investigated the effect of inlet temperature for alumina-water nanofluid for thermally developing and hydrodynamically developed zone of laminar fluid flow whereas their results indicated that inlet temperature effects for thermally developing regions were more resulted and significant. Beside them, Singh et al [14], Iyahraja et al [15], Rabby et al [16], He et al [17], Kapıcıoğlu and Esen [18], Kaya et al [19], Ekiciler et al [20] also examined the impact of nanofluids on laminar convective heat transfer and their results also indicate the enhancement and improvement of heat transfer characteristics by the utilisation of nanofluids.…”

Section: Introductionmentioning

confidence: 99%

Numerical simulation on performance evaluation among metal and oxide based nanofluids for power savings application of a circular tube

Rabby

Hossain

Amin

et al. 2021

Journal of Thermal Engineering

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The advancement of heat transfer techniques is a challenge to the researcher in this era. Implementation of nanotechnology is one of the potential techniques which enhance the heat transfer rate in a significant amount. Subsequently, nanotechnology can reduce the requirement of pumping power. However, suspension of nanoparticle with liquid to produce a new working fluid called nanofluid which has better thermal and fluid dynamic properties in comparison to pure liquid is introduced as a typical nanotechnology technique in the heat transfer area. In this study, the thermal performance of two categories of nanofluids metal-based (Cu-water and Ag-water) and oxide-based (Al 2 O 3 -water, CuO-water, BeO-water) with 1-5% volume fractions have been analysed for the laminar flow region of a circular tube which is fully developed under 2D control volume finite element method. The heat transfer was analysed for a range of Reynolds numbers from 100 to 1000 with a constant heat flux of 500 W/m 2 applied on the tube wall. For evaluating the performance among nanofluids, the Figure of Merits (FOM), pumping power, Nusselt number enhancement ratio, and heat transfer coefficient ratio of the base fluid and nanofluids have been calculated and compared. The computational results show that in terms of Nusselt number and heat transfer coefficient, all nanofluids provide higher enhancement compared to pure water. Meanwhile, for this higher enhancement, nanofluids required significantly lower pumping power in comparison to pure water. However, the power has been saved 86.26% for Ag-water nanofluid, 72.84% for Cu-water, 42.36% for CuO-water, 40.99% for Al 2 O 3 -water, and 26.58% for BeO-water. Between the mentioned two categories of nanofluids, metal-based nanofluids provide the highest heat transfer enhancement and lowest pumping power requirement compared to oxide-based because of their higher thermal conductivity and other fluid and

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