Recent advances in modeling and simulation of nanofluid flows-Part I: Fundamentals and theory

Mahian, Omid; Kolsi, Lioua; Amani, Mohammad; Estellé, Patrice; Ahmadi, Goodarz; Kleinstreuer, Clement; Marshall, Jeffrey S.; Siavashi, Majid; Taylor, Robert A.; Niazmand, Hamid; Wongwises, Somchai; Hayat, Tasawar; Kolanjiyil, Arun V.; Kasaeian, Alibakhsh; Pop, Ioan

doi:10.1016/j.physrep.2018.11.004

Cited by 775 publications

(238 citation statements)

References 219 publications

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“…The exergy loss by the coolant is given by

{∆ Ex}_{f} = Q - T_{o} true[m_{f} c_{p, f} \ln \frac{T_{f, i}}{T_{f, e}} - m_{f} {∆ p}_{f} / (ρ_{f} T_{f, italicmean}) true],

whereas the exergy gain rate by air is calculated by

Δ E x_{a} = Q - T_{o} true[m_{a} c_{p, a} \ln true(\frac{T_{a, e}}{T_{a, i}} true) - m_{a} R normall normaln true(\frac{p_{a, i}}{p_{a, e}} true) true] .

…”

Section: Mathematical Modelingmentioning

confidence: 99%

“…Nanofluids are a colloidal mixture of nanometer size particle less than 100 nm because all physical bulk solids have a critical length, below which the properties of the bulk solid changes into ordinary fluids to improve the thermal conductivity value and convective heat transfer performance of the base fluid . The dispersion of nanoparticles in the base fluidnot only contirbutes to the increment of thermal conductivity but also increases the heat transfer surface area due to which the convective heat transfer coefficient the heat transfer imrpove . Many researchers have studied the performance of radiators using nanofluids as coolants theoretically and experimentally.…”

Section: Introductionmentioning

confidence: 99%

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Exergy and energy analysis of a wavy fin radiator with variously shaped nanofluids as coolants

Kumar

Sahoo

2019

Heat Trans. Asian Res.

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The present study deals with the energy and exergy analysis of a wavy fin radiator deploying various shapes of Al2O 3‐water as nanocoolant. The effects of radiator effectiveness, pumping power, heat transfer rate, and performance index with variously shaped nanoparticles, mainly spherical, brick, and platelet, on coolant flow rates and air velocities have been investigated. Also, the impacts of entropy, second law efficiency, entropy generation number, and irreversibility on radiator performance analysis have been considered with steady‐state assumptions. Theoretical analysis revealed that the spherical particle–based nanocoolant showed 21.9%, and 18.2% higher effectiveness than platelet and brick nanocoolants. However, minimization in the entropy generation is observed in the platelet shape of the nanoparticle. The second law efficiency is 13% higher for the spherical nanocoolant compared with the brick nanocoolant. An optimum entropy generation number is found at a coolant flow rate of 13 l/min and then gradually decreases with an increase in the coolant flow rate. For all the considered operating parameters, the spherical nanoparticle showed a better performance than brick and platelet nanofluids as a radiator coolant. Due to the enhanced overall performance for the spherical nanofluid, it may be considered as a potential candidate for a radiator coolant.

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“…The exergy loss by the coolant is given by

{∆ Ex}_{f} = Q - T_{o} true[m_{f} c_{p, f} \ln \frac{T_{f, i}}{T_{f, e}} - m_{f} {∆ p}_{f} / (ρ_{f} T_{f, italicmean}) true],

whereas the exergy gain rate by air is calculated by

Δ E x_{a} = Q - T_{o} true[m_{a} c_{p, a} \ln true(\frac{T_{a, e}}{T_{a, i}} true) - m_{a} R normall normaln true(\frac{p_{a, i}}{p_{a, e}} true) true] .

…”

Section: Mathematical Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exergy and energy analysis of a wavy fin radiator with variously shaped nanofluids as coolants

Kumar

Sahoo

2019

Heat Trans. Asian Res.

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“…The overall aim of using nanoﬂuids is to have optimum thermophysical properties at minimum particle volume fractions. Moreover, it has been reported that the thermal conductivity of nanoﬂuids increases with the increase of nanoparticle concentration . It has also been revealed that the thermal conductivity enhancement ratio declines with that of the base fluid, while it is directly proportional to the nanoparticle thermal conductivity .…”

Section: Introductionmentioning

confidence: 97%

“…From the above review, it can be noticed that all the different parameters and mechanisms affecting the thermal conductivity of nanofluids cannot be accounted by the traditional models of particle suspensions . Thus, several models have been proposed, such as the work of Alrashed et al using an enhanced artiﬁcial neural network (EANN) and an adaptive neuro‐fuzzy inference system approach and the results showed a suitable accuracy with experimental data.…”

Section: Introductionmentioning

confidence: 99%

Modeling the effective thermal conductivity of nanofluids using full factorial design analysis

Grine

Benhamza

2019

Heat Trans. Asian Res.

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In this paper, a full factorial design analysis is proposed for predicting nanofluid thermal conductivity ratio (TCR) as well as determining the effects of critical factors and their interactions. A statistical design of experiment approach with three variables (volume fraction, temperature, and nanoparticle diameter) at two levels is carried out. Three types of oxide‐water nanofluids (Al2O3‐water, CuO‐water, and TiO2‐water) are used to evaluate the effectiveness of the proposed mathematical model. The significance and adequacy of the regression model were evaluated by the analysis of variance. The predicted model has a root mean square error equals to 0.0074, R2 = 0.99, and P < .0013, thus showing good results compared to a set of experimental data as well as other mathematical model results. The results illustrate that the TCR of metallic oxide nanoﬂuids increases with temperature and nanoparticles volume fraction but decreases when nanoparticle size intensiﬁes. Furthermore, it is found that the nanoparticles volume fraction has a great impact on the nanoﬂuids thermophysical properties. Finally, the obtained results confirm that the proposed model is considerably accurate and capable of predicting nanoﬂuids thermal conductivity and that it can be used with ease as an alternative to many other models.

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“…In order to reduce the aforementioned shortages of a SBPTSC, a direct‐absorption solar collector (DASC) can be adopted as been widely used in the low temperature regime . In a DASC, the nanoparticles dispersed in the nanofluids directly interact and absorb the solar energy; thus, the absorber coating is no longer needed.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of direct‐absorption parabolic‐trough solar collectors considering concentric nanofluid segmentation

et al. 2020

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Summary Nanofluids have been actively used in direct‐absorption solar collectors. In a direct‐absorption parabolic‐trough solar collector (DAPTSC) for medium‐high temperature regime, the nanofluids used to be contained in a transparent glass receiver located at the focal line of a parabolic mirror so that the solar radiation is concentrated to the glass receiver and absorbed volumetrically inside the collector. In order to further increase the thermal efficiency of a DAPTSC, we propose to insert an extra glass tube inside the nanofluid so that the nanofluid is separated into two concentric segmentations (ie, an inner section and an outer section), and apply a nanofluid of lower concentration in the outer section while a nanofluid of a higher concentration in the inner section. The proposed idea is numerically tested on four pairs of DAPTSCs (the variants obtained depending on whether there is a vacuum envelope and whether there is a reflective semicylindrical coating on the receiver). The results show that at the same particle concentration parameter, the DAPTSCs with two concentric segmentations of nanofluids outperform those with one uniform nanofluid for all configurations considered in this work. For a transparent case without an envelope, this efficiency increase is as high as 12.5% point when the inlet temperature is 350 K. In addition, parametric studies are performed for this best configuration to evaluate the effect of absorption coefficient, mass flow rate, collector length, solar irradiance, and convection heat transfer coefficient on the collector performance.

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Recent advances in modeling and simulation of nanofluid flows-Part I: Fundamentals and theory

Cited by 775 publications

References 219 publications

Exergy and energy analysis of a wavy fin radiator with variously shaped nanofluids as coolants

Exergy and energy analysis of a wavy fin radiator with variously shaped nanofluids as coolants

Modeling the effective thermal conductivity of nanofluids using full factorial design analysis

Comparative analysis of direct‐absorption parabolic‐trough solar collectors considering concentric nanofluid segmentation

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