Evaluation and analysis of nanofluid and surfactant impact on photovoltaic-thermal systems

Al‐Waeli, Ali H.A.; Chaichan, Miqdam T.; Kazem, Hussein A.; Sopian, Kamaruzzaman

doi:10.1016/j.csite.2019.100392

Cited by 93 publications

(50 citation statements)

References 46 publications

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“…Therefore, the lower the SDBS concentration, the greater the propensity of Al-NPs to aggregate and later deposit, which is consistent with the literature. 41 …”

Section: Resultsmentioning

confidence: 99%

Formation Damage Induced by Water-Based Alumina Nanofluids during Enhanced Oil Recovery: Influence of Postflush Salinity

et al. 2020

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Injecting nanofluids (NFs) has been proven to be a potential method to enhance oil recovery. Stranded oil is produced by wettability alteration where nanoparticles form a wedge film on pore wall surfaces, which is thought to shrink the pore space of the reservoir. Furthermore, ensuring the stability of the injected NF during the application is a major challenge. A low permeability reservoir and salinity of water make the response of NF injection to the formation damage more difficult. This article, therefore, studied the formation damage induced by the injection of alumina nanofluids (Al-NFs) in a relatively low permeability (7.1 mD) sandstone core. The salinity of the postflush water was also considered to mitigate the destructive impact. Al-NF was formulated by dispersing alumina nanoparticles (Al-NPs) in an aqueous solution of sodium dodecylbenzene sulfonate (SDBS) at its critical micelle concentration (CMC, 0.1 wt %). The formation damage, inherent to Al-NF injection, was evaluated by core-flooding tests. The assays consisted of the injection of 1 PV Al-NF (0.05 wt %) at the trail of which postflush at different salinities was flooded. The study found that the salinity of the postflush has an effect on the formation damage and oil recovery factor (RF). A chase water with a salinity concentration of 3 wt % sodium chloride (NaCl) produced an RF of 8.7% compared to a base case of water-flooding with a pressure drop of up to 13 MPa across the core (70 mm in length). These results pertained to the deposition of Al-NPs at the injection end. However, lowering the postflush salinity to 1 wt % NaCl mitigated the formation damage as evidenced by the decrease in pressure (35%) and an increase in RF to 17.2%.

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“…Therefore, the lower the SDBS concentration, the greater the propensity of Al-NPs to aggregate and later deposit, which is consistent with the literature. 41 …”

Section: Resultsmentioning

confidence: 99%

Formation Damage Induced by Water-Based Alumina Nanofluids during Enhanced Oil Recovery: Influence of Postflush Salinity

et al. 2020

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“…The nanofluid is rotated in its closed system starting from its container through the copper pipe to the nano‐PCM tank to get the heat from it, causing reduction in the nano‐PCM temperature and thus sustaining the removal of heat from the PV panel to the tank. The nano‐SiC‐water mass flow rate used was 0.15 kg/s depending on Al‐Waeli et al results. The hot nanofluid flows out of the tank through the heat exchanger to exchange the heat in this case with the water flowing in the heat exchanger, which reduces the temperature of the nanofluid and makes it able to continue its work efficiently.…”

Section: The Study Methodologymentioning

confidence: 99%

“…To prepare the nanofluid in the required concentrations, a surfactant called cetyl trimethyl ammonium (CTAB) has been added by about 0.1 mL to water, depending on References procedures. The PCM used was organic paraffin because of its widespread use as a low‐cost energy storage material available in local markets; the price of its kilogram is less than US$ 2.…”

Section: The Study Methodologymentioning

confidence: 99%

Mathematical and neural network models for predicting the electrical performance of a PV/T system

et al. 2019

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Summary There are many photovoltaic/thermal (PV/T) systems' designs that are used mainly to reduce the temperature of the PV cell by using a thermal medium to cool the photovoltaic module. In this study, a PV/T system uses nano‐phase change material (PCM) and nanofluid cooling system was adopted. Three cooling models were compared using nanofluid (SiC‐water) and nano‐PCM to improve the performance and productivity of the PV/T system. Three mathematical models were developed for linear prediction, and their results were compared with the predicted artificial neural network results, results were verified, and experimental results were appropriate. Three common evaluation criteria were adopted to compare that the results of proposed forecasting models with other models developed in many research studies are done, including the R2, mean square error (MSE), and root‐mean‐square error (RMSE). Besides, different experiments were implemented using varying number of hidden layers to ensure that the proposed neural network models achieved the best results. The best neural prediction models deployed in this study resulted in good R2 score of 0.81 and MSE of 0.0361 and RMSE and RMSE rate is 0.371. Mathematical models have proven their high potential to easily determine the future outcomes with the preferable circumstances for any PV/T system in a precise way to reduce the error rate to the lowest level.

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“…The development of energy‐efficient heat transfer fluids has numerous limitations due to their low thermal conductivity. To overcome this challenge, a new kind of fluid has been proposed; the so‐called nanofluid . Nanofluids are a class of heat transfer fluids created by the suspension of small amounts of nanoscale metallic, nonmetallic, or polymetallic particles in a base fluid (water, glycerine oil, ethylene glycol (EG)) having high thermal conductivity.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome this challenge, a new kind of fluid has been proposed; the so-called nanofluid. 1,2 Nanofluids are a class of heat transfer fluids created by the suspension of small amounts of nanoscale metallic, nonmetallic, or polymetallic particles in a base fluid (water, glycerine oil, ethylene glycol (EG)) having high thermal conductivity. They find applications in various engineering fields such as transportation, biomedical, nuclear energy, and electronic cooling.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of thermal performance of single‐walled carbon nanotube nanofluid under turbulent flow conditions

Fadodun

Amosun

Salau

et al. 2019

Engineering Reports

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In this paper, a numerical study is performed to investigate the effect of Reynolds number and nanoparticle concentration on the thermal performance of single‐walled carbon nanotubes (SWCNTs) nanofluids flowing through a straight pipe with constant heat flux in a turbulent flow regime. The governing equations (continuity, momentum, energy, rate of turbulent production, and rate of turbulent dissipation) are solved using a computational fluid dynamic approach (ie, finite volume method). In the sensitivity analysis, the Reynolds number is varied between (10 000‐200 000) and nanoparticle concentration between (0%‐0.25%). The simulation results show that convective heat transfer (average Nusselt number) increases by 7.48% while the pressure drop and pumping power increase by 119% and 199%, respectively. In addition, at low Reynolds number (104), increase in nanoparticle concentration (0%‐0.25%) decreases entropy production rate by 16.95%. However, at higher Reynolds number (2*105), the entropy production rate increases by 149.77%. Furthermore, other results such as entropy generation number, Bejan number, second thermodynamic law efficiency, and thermal performance factor, which combine the first and second law of thermodynamics, are presented as functions of Reynolds number and nanoparticle's concentration. The overall result shows that SWCNT nanofluid will only be beneficial at low Reynolds number of (10 000‐50 000).

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Evaluation and analysis of nanofluid and surfactant impact on photovoltaic-thermal systems

Cited by 93 publications

References 46 publications

Formation Damage Induced by Water-Based Alumina Nanofluids during Enhanced Oil Recovery: Influence of Postflush Salinity

Formation Damage Induced by Water-Based Alumina Nanofluids during Enhanced Oil Recovery: Influence of Postflush Salinity

Mathematical and neural network models for predicting the electrical performance of a PV/T system

Numerical investigation of thermal performance of single‐walled carbon nanotube nanofluid under turbulent flow conditions

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