Price-performance evaluation of thermal conductivity enhancement of nanofluids with different particle sizes

Alirezaie, Ali; Hajmohammad, Mohammad Hadi; Ahangar, Mohammad Reza Hassani; Esfe, Mohammad Hemmat

doi:10.1016/j.applthermaleng.2017.08.143

Cited by 113 publications

(23 citation statements)

References 23 publications

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“…The enhancement of nanofluid thermal conductivity depands on volume concentration and working temperature. Higher thermal conductivity of nanofluid observed at higher volume concentration and temperature as reported by Alirezaie et al, [50]. However, the thermal conductivity decreased with the increase in the percentage of Ethylene Glycol in the mixture of Ethylene Glycol/water as a base fluid [51].…”

Section: Thermal Conductivity Of Nanofluidssupporting

confidence: 54%

“…It essential that thermal conductivity measurement should be conducted for a new introduction of nanofluid which the objective is to discover the capability of the liquid to carry and dissipate the heat. In order to measure nanofluids thermal conductivity, thermal properties analyzer such as KD2 Pro and TC3010 can be used [14,29,[49][50][51][52]. It is essential to ensure that nanofluid is stable and no particle sedimentation found when measuring thermal conductivity.…”

Section: Thermal Conductivity Of Nanofluidsmentioning

confidence: 99%

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Thermal Conductivity and Dynamic Viscosity of Nanofluids: A Review

Safiei

Rahman

Kulkarni

et al. 2020

ARFMTS

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Thermal conductivity is one of the rheology properties that vital for engineering fluid which indispensable for heat transfer enhancement. For this reason, nanofluid is getting wider attention nowadays due to the presence of nanoparticles in the base fluid can further improve thermal conductivity and dynamic viscosity. These are two important properties for new engineering fluid in providing better cooling and lubricating effects, especially in mechanical and tribology applications. In this paper, specifically, nanofluids thermal conductivity and dynamic viscosity are discussed comprehensively. Both properties' thermal conductivity and viscosity of nanofluids are improved over the base fluid. Furthermore, these two properties increase when more volume concentrations of nanoparticles are added. In addition, the thermal conductivity also improved with increasing the temperature. From the literature review, the maximum enhancement of thermal conductivity for single nanofluid is recorded 36% of MWCNTs in distilled water. On the other hand, the maximum enhancement of viscosity is recorded 39% of Al2O3 in water-ethylene glycol over base fluid. The hybrid nanofluids that consist of more than one type of nanoparticles exhibit better thermal conductivity where the maximum enhancement is recorded 68% of Cu-TiO2 in deionized water. For dynamic viscosity measurement, the maximum enhancement of hybrid nanofluids is recorded 168% of MgO-MWCNT in ethylene glycol. Therefore, to sum up, hybrid nanofluids are really promising to enhance heat transfer performance especially for heating and cooling applications. The potential of these nanofluids should be explored extensively to discover its advantages over conventional working fluid.

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Section: Thermal Conductivity Of Nanofluidssupporting

confidence: 54%

Section: Thermal Conductivity Of Nanofluidsmentioning

confidence: 99%

Thermal Conductivity and Dynamic Viscosity of Nanofluids: A Review

Safiei

Rahman

Kulkarni

et al. 2020

ARFMTS

View full text Add to dashboard Cite

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“…No calculation was done in the referred article, as the comparison was done via chart where no exact numerical was visible. In the work of Alirezaie et al [18], the so-called price performance factor (PPF) was calculated for two nanofluids of various particle sizes. The coefficient was calculated as the thermal conductivity ratio divided by the price of the nanofluid multiplied by 1000.…”

Section: K Value (Price-performance Coefficient) Calculationmentioning

confidence: 99%

Cost/Performance Analysis of Commercial-Grade Organic Phase-Change Materials for Low-Temperature Heat Storage

et al. 2019

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Alkanes are widely used as phase change materials (PCMs), especially for thermal energy storage (TES), due to their high thermal capacity, stability, availability, and non-corrosiveness. However, the drawbacks of alkanes are low heat conductivity and high cost. Our aim was to explore alternative organic PCMs for TES and to compare such compounds based on the relationship between their performance and cost. For this purpose, we analysed several commercially available products, including long chain alkanes, alcohols, monocarboxylic acid, amines, ethers and esters in high purities. Differential scanning calorimetry and thermogravimetry (DSC and TGA) were used to measure the melting point, melting enthalpy and thermal stability of these compounds. The materials were classified according to their melting temperature. In order to compare the compounds, we calculated from the measured enthalpies and the price list provided by producers a coefficient that represents factors in both the performance and cost of the material. This method was used to identify the most suitable organic compound for thermal energy storage in each temperature range. As the main result of this work, it has been revealed that various organic compounds can be considered as a vital alternative to the alkanes in temperatures from −10 to 50 °C. On top of that, alcohols and carboxylic acids can cover the temperature range from 50 to 75 °C, which cannot be covered by alkanes.

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“…The findings that emerged as a result of the study showed that the values obtained from this study were higher than the results of other studies. In the study conducted by Alirezaie et al, 19 thermal performances of two different nanofluids with different particle sizes in ethylene glycol base fluid were analyzed and compared. A new correlation was also obtained to calculate thermal conductivity using the experimental results obtained.…”

Section: Introductionmentioning

confidence: 99%

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

Çolak

2020

Int J Energy Res

100

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In this study, the effect of the amount of data used in the design of artificial neural networks (ANNs) on the predictive accuracy of ANNs was investigated. Five different ANNs were designed using the experimentally measured specific heat data of the Al 2 O 3 /water nanofluid prepared at volumetric concentrations of 0.0125, 0.025, 0.05, 0.1 and 0.2 using the Al 2 O 3 nanoparticle. The developed ANN is a multi-layer perceptron, feedforward and backpropagation model. In each ANN with 15 neurons in the hidden layer, the volumetric concentration (φ) and temperature (T) values were nominated as input layer factors and the specific heat value was estimated as the output value. With the aim of survey the effect of the amount of data on the predicted results of the ANN, a different amount of datasets were used in each developed ANN. In this context, in total 260 data were used in the Model 1 ANN. Subsequently, the total amount of data was reduced by 20% in each developed neural network and 55 data were used in the ANN named Model#5. The results obtained show that ANNs are highly talented of predicting the specific heat values of Al 2 O 3 /water nanofluid. However, in the comparisons, it was evaluated that the amount of data used had a share on the prediction performance of the ANN and that the decrease in the amount of data with the prediction performance of the ANN decreased.

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Price-performance evaluation of thermal conductivity enhancement of nanofluids with different particle sizes

Cited by 113 publications

References 23 publications

Thermal Conductivity and Dynamic Viscosity of Nanofluids: A Review

Thermal Conductivity and Dynamic Viscosity of Nanofluids: A Review

Cost/Performance Analysis of Commercial-Grade Organic Phase-Change Materials for Low-Temperature Heat Storage

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

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