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Journal of Physics: Conference Series

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Numerical study on the mixed convection with nanofluids in vertical channel asymmetrically heated

Maria Barbato1, Bernardo Buonomo1, Oronzio Manca1 and Gianluca Sarli1

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series, Volume 2648, 78th ATI Annual Congress (Energy transition: Research and innovation for industry, communities and the territory) (ATI-2023) 14/09/2023 - 15/09/2023 Carpi, Italy Citation Maria Barbato et al 2023 J. Phys.: Conf. Ser. 2648 012068 DOI 10.1088/1742-6596/2648/1/012068

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Abstract

Mixed convection heat transfer in cavities is of great interest in many iConjugate heat transfer performance of stepped lid-driven cavity with Al2O3/water nanofluid under forced and mixed convection

• Research Article
• Open access
• Published: 01 May 2021
• Volume 3, article number 605, (2021)
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📷 SN Applied Sciences Aims and scope Submit manuscript

Conjugate heat transfer performance of stepped lid-driven cavity with Al2O3/water nanofluid under forced and mixed convection

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• Naveen Janjanam,
• Rajesh Nimmagadda,
• Lazarus Godson Asirvatham,
• R. Harish &
• Somchai Wongwises
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Abstract Two-dimensional conjugate heat transfer performance of stepped lid-driven cavity was numerically investigated in the present study under forced and mixed convection in laminar regime. Pure water and Aluminium oxide (Al2O3)/water nanofluid with three different nanoparticle volume concentrations were considered. All the numerical simulations were performed in ANSYS FLUENT using homogeneous heat transfer model for Reynolds number, Re = 100 to 500 and Grashof number, Gr = 5000, 13,000 and 20,000. Effective thermal conductivity of the Al2O3/water nanofluid was evaluated by considering the Brownian motion of nanoparticles which results in 20.56% higher value for 3 vol.% Al2O3/water nanofluid in comparison with the lowest thermal conductivity value obtained in the present study. A solid region made up of silicon is present underneath the fluid region of the cavity in three geometrical configurations (forward step, backward step and no step) which results in conjugate heat transfer. For higher Re values (Re = 500), no much difference in the average Nusselt number (Nuavg) is observed between forced and mixed convection. Whereas, for Re = 100 and Gr = 20,000, Nuavg value of mixed convection is 24% higher than that of forced convection. Out of all the three configurations, at Re = 100, forward step with mixed convection results in higher heat transfer performance as the obtained interface temperature is lower than all other cases. Moreover, at Re = 500, 3 vol.% Al2O3/water nanofluid enhances the heat transfer performance by 23.63% in comparison with pure water for mixed convection with Gr = 20,000 in forward step.

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1 Introduction Rapid growth in the usage of high-performance electronic devices with compact size requires efficient cooling systems [1, 2]. Apart from this, the system should also be capable of preventing the leakage associated with the circulating coolant. This leakage problem will be well eliminated when one switches to closed cooling system rather than the open type [3]. This is because the circulating coolant will never leave the system there by causing no leakage into the surrounding electronic circuits. Moreover, efficient coolants are also essentially needed when compared with conventional heat transfer fluids so that the large quantities of heat generated from the electronic devices can be easily dissipated. Dispersing nanoparticles within the conventional heat transfer fluids such as water or ethylene glycol will enhance the effective thermal conductivity of the coolant thereby enhancing the heat transfer performance of the cooling system. These coolants with nanoparticles are named as “nanofluids,” and they have wide variety of applications [4, 5].Many classical thermal conductivity models were developed in eighteenth and nineteenth centuries for predicting the effective thermal conductivity of solid–liquid mixtures. Maxwell [6] developed a static thermal conductivity model for mixtures with low volume concentration of spherical particles. This model fails when the concentration of particles is sufficiently higher. This failure is avoided by the thermal conductivity model developed by Bruggeman [7], which can be applied even to high volume concentration of nanoparticles which are of spherical shape. The particle shape, size and particle interactions for thermal conductivity evaluation were not considered in these models. Hamilton and Crosser [8] moved a step higher by developing a thermal conductivity model with inclusion of particle shape by introducing empirical shape factor (n). Lu and Lin [9] thermal conductivity model consider pair interactions that is obtained by solving a boundary value problem. Chon et al. [10] took nanoparticle diameter into consideration and developed an experimental thermal conductivity correlation for Al2O3/water nanofluids. Experimental investigation along with theoretical determination of thermal conductivity of Al2O3/water nanofluid was carried out by Chandrasekar et al. [11]. Khanafer and Vafai [12] observed that the obtained experimental results reported by several researchers for the effective thermal conductivity disagree and requirement of additional experimental and theoretical research studies is very essential in order to clarify the mechanisms responsible for nanofluid heat transfer enhancement.Xuan et al. [13] developed a theoretical model to predict the thermal conductivity of nanofluids by considering properties of nanoparticles and base fluid, as well as the structure of the nanoparticles and aggregates. Patel et al. [14] developed thermal conductivity model for nanofluids by considering micro-convection phenomena involving Brownian motion as well as increment in the specific area. The developed thermal conductivity model will give good insight on phenomena involved at nanoscale. According to the observations of Prasher et al. [15], no concrete conclusions have been reached among various thermal conductivity models and mechanisms proposed in the literature. He concluded that Brownian motion is mainly responsible for nanofluid thermal conductivity enhancement. Incorporation of Brownian motion into the thermal conductivity model along with particle size was carried out by Dong and Chen [16]. Goudarzi et al. [17] numerically investigated migration of nanoparticles under Brownian diffusion and concludes it as an important enhancing phenomenon in heat transfer. Abdelmalek et al. [18] as well as Harish and Sivakumar [19] observed that both the effects of thermophoresis and Brownian motion surges the thermal energy of fluid and are significantly important. Higher heat transfer enhancement is observed with the use of Maxwell model when compared with that of traditional model in the numerical investigation carried out by Lin and Jiang [20].Gibanov et al. [21, 22] carried out numerical investigation on ferro fluid and alumina nanofluid mixed convection in a lid-driven cavity with backward step heat conducting solid region. The observations proved that the sizes and conductivity of the backward step will essentially change both the flow behavior as well as heat transfer patterns. Numerical study has been carried out by Al-Amiri et al. [23] on lid-driven cavity with a sinusoidal wavy bottom surface for analyzing the mixed convection heat transfer. It is observed from the study that by increasing the amplitude of the wavy surface and Reynolds number, the average Nusselt number increases. Khanafer and Aithal [24] also investigated mixed convection heat transfer flow phenomena in a lid-driven cavity fitted with a circular body. The study reported that by placing cylinder near the bottom wall of the cavity, an optimal heat transfer performance is obtained. Mixed convection in lid driven cavity with heated triangular block is numerically studied by Gangawane [25]. The study concluded that the convection heat transfer can be controlled by inserting triangular body into the lid-driven cavity. Khan et al. [26] numerically observed that heat transfer rate in a split lid-driven cavity enhances by placing a Y-shaped obstacle. Unsteady mixed convection in lid-driven enclosure with oscillatory walls was numerically investigated by Valizadeh Ardalan et al. [27] by using Cu/water nanofluid. The study concluded that reduction in the frequency of wall oscillation leads to enhancement in the heat transfer.However, from the above-mentioned literature [12, 15], it can be observ

It is better to do validation with experimental correlations then calculate how much the error. If the error is high, then you need to refine the current conditions. Otherwise, you can consider the current assumptions.

Thank you for your answer,

If I use a PRESTO and second order to solve pressure , Momentum and energy equation with simple algorithm for velocity-pressure copling. How i can set up the underrelaxation factor?