The numerical investigation of heat transfer and pressure drop of turbulent flow in a triangular microchannel

Rezaei, Omid; Akbari, Omid Ali; Marzban, Ali; Toghraie, Davood; Pourfattah, Farzad; Mashayekhi, Ramin

doi:10.1016/j.physe.2017.06.013

Cited by 125 publications

(46 citation statements)

References 67 publications

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“…The forced CHT in a GHD is intimately related to the TKE . Increasing TKE in a fluid could result in better CHT and thus can improve the homogeneity of the air temperature inside the greenhouse.…”

Section: Resultsmentioning

confidence: 99%

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Computational fluid dynamics analysis of heat transfer in a greenhouse solar dryer “chapel‐type” coupled to an air solar heating system

Román-Roldán

López‐Ortiz

Ituna-Yudonago

et al. 2019

Energy Science & Engineering

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The distribution of turbulent kinetic energy (TKE), temperature, and velocity of humid air inside the greenhouse solar dryer (GHSD) was numerically investigated using 3D CFD ANSYS FLUENT code. The effect of solar radiation was coupled with the energy equations using the discrete ordinate model. Numerical simulations were based on two geometric models: Real model and model with reduced height, the solution was in good agreement with experimental data of temperature. The results of the real model showed that the TKE is ranged between 1.27 m2/s2 and 6 m2/s2 with an average of about 1.6 m2/s2 for the entire greenhouse dryer (GHD) volume. The greatest TKE magnitude is in the paths of the diffusers, which caused a temperature drop of about 2 K in the areas near the walls. Consequently, almost homogeneous temperature distribution was obtained in the entire volume of the GHD, although the average temperature was 315 K, and a gradient with respect to ambient temperature was of 14 K, that is, suitable for drying. Also, the average air velocity at 1 m height was 0.71 m/s, which is a value near the lowest limit (0.6 m/s) of forced convection drying. The improvement in the GHD by 36.5% volume reduction allowed an increase in the average TKE of 3.8 m2/s2 (2.4 times more than the previous one) located in the middle of the greenhouse; the average temperature reached 316.5 K with a gradient of 15.5 K, which represents an increase of 1.5 K (11%) compared to the real geometric model. The air velocity at 1 m height increased to 0.9 m/s in the improved geometric model (a growth of 35.7% compared with the previous geometry). More than 95% of the improved GHD volume has a uniform temperature, which is very suitable for a good quality drying process with higher speed.

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Section: Resultsmentioning

confidence: 99%

“…Another important aspect that is not wisely investigated is the air turbulent flow behavior in the GHD and its effect on the temperature distribution. As it knows, the CHT is intimately related to the TKE . Increasing TKE in a fluid results in better CHT .…”

Section: Introductionmentioning

confidence: 99%

Computational fluid dynamics analysis of heat transfer in a greenhouse solar dryer “chapel‐type” coupled to an air solar heating system

Román-Roldán

López‐Ortiz

Ituna-Yudonago

et al. 2019

Energy Science & Engineering

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“…Mahian [18] asserted that it is crucial to use measured data for thermophysical properties to predict the heat transfer coefficient with high accuracy. The analysis reported in [19] revealed that the effect of adding nanoparticles to the base fluid has a strong influence on the heat transfer enhancement by increasing the Reynolds number of flows and increasing the heat transfer. Heydari [20] used water as the base fluid and investigated the effect of the volume fraction of nanoparticles (TiO 2 ) on the heat transfer and physical properties of the fluid.…”

Section: Introductionmentioning

confidence: 99%

Statistical Modeling for Nanofluid Flow: A Stretching Sheet with Thermophysical Property Data

Jedi

Shamsudeen

Razali

et al. 2020

Colloids and Interfaces

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This paper reports the use of a numerical solution of nanofluid flow. The boundary layer flow over a stretching sheet in combination of two nanofluids models is studied. The partial differential equation that governs this model was transformed into a nonlinear ordinary differential equation by using similarity variables, and the numerical results were obtained by applying the shooting technique. Copper (Cu) nanoparticles (water-based fluid) were used in this study. This paper presents and discusses all numerical results, including those for the local Sherwood number and the local Nusselt number. Additionally, the effects of the nanoparticle volume fraction, Brownian motion Nb, and thermophoresis Nt on the performance of heat transfer are discussed. The results show that the stretching sheet has a unique solution: as the nanoparticle volume fraction φ (φ = 0), Nt (Nt = 0.1), and Nb decrease, the rate of heat transfer increases. Furthermore, as φ (φ = 0) and Nb decrease, the rate of mass transfer increases. The data of the Nusselt and Sherwood numbers were tested using different statistical distributions, and it is found that both datasets fit the Weibull distribution for different values of Nt and rotating φ.

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“…For hydrothermal features of cooling methods, many investigations have assessed different micro HSs for laminar and turbulent flow employing air, water, and nanofluids as Newtonian and non‐Newtonian are commonly used. These cooling approaches have been developed by the following techniques—straight flat plate fins, wavy plate fins, vortex generator, grooved tubes, inclined solid ribs, porous media, two‐phase flow, and magnetic field . All these attempts are enhanced by the thermohydraulic performance of different HS designs for more reduction in the weight and volume of electronic, electrical, and solar systems.…”

Section: Introductionmentioning

confidence: 99%

Three‐dimensional computational comparison of mini‐pinned heat sinks using different nanofluids: Part two—energy and exergy characteristics

Al‐damook

Alfellag

Khalil

2019

Heat Trans. Asian Res.

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The energy and exergy characteristics of 3D-pinned heat sink (HS) designs have been computationally compared as the second part of a three-part investigation. Different pin profiles, such as circular, square, triangular, strip and elliptic pins, and without pin HS are conducted with three different types of nanofluids-Al 2 O 3 -water, SiO 2 -water, and CuO-water for laminar forced convection. The concentrations of nanofluids vary from 0 to 5 vol% with different Reynolds numbers ranging between 100 and 1000. The finite volume method employing the SIMPLE algorithm for a computational solution is applied to solve the Navier-Stokes and energy equations. Four criterions studies are explained-energy efficiency, exergy loss, and exergy efficiency of HSs with pressure drop. The results showed that the highest energy and exergy efficiencies are nearly 76% and 57%, respectively, for elliptic-pinned HSs using pure water, while about 82% and 62% using 5 vol% of SiO 2 -water nanofluids. Besides, the elliptic-pinned HSs have a favorable reduction in the exergy loss, nearly 17% using 5 vol% of SiO 2 -water nanofluids. Subsequently, the elliptic-pinned HS is recommended to apply with pure water considering the development in pressure drop required. However, the elliptic-pinned HSs could be employed with 5 vol% of SiO 2 -water nanofluids regardless of the development in pressure drop required for thermal energy dissipation applications with more exergy efficiency and reduction of exergy loss. K E Y W O R D Sdifferent nanofluids, different pinned heat sinks, energy and exergy characteristics, exergy loss, thermal energy enhancement

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The numerical investigation of heat transfer and pressure drop of turbulent flow in a triangular microchannel

Cited by 125 publications

References 67 publications

Computational fluid dynamics analysis of heat transfer in a greenhouse solar dryer “chapel‐type” coupled to an air solar heating system

Computational fluid dynamics analysis of heat transfer in a greenhouse solar dryer “chapel‐type” coupled to an air solar heating system

Statistical Modeling for Nanofluid Flow: A Stretching Sheet with Thermophysical Property Data

Three‐dimensional computational comparison of mini‐pinned heat sinks using different nanofluids: Part two—energy and exergy characteristics

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