Effects on thermophysical properties of carbon based nanofluids: Experimental data, modelling using regression, ANFIS and ANN

Al‐Rashed, Abdullah A.A.A.; Gharibdousti, Maryam Soltanpour; Goodarzi, Marjan; Oliveira, Letícia Raquel de; Safaei, Mohammad Reza; Filho, Ênio Pedone Bandarra

doi:10.1016/j.ijheatmasstransfer.2018.04.142

Cited by 190 publications

(53 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This fact agrees with conventional models such as the Maxwell model. On the other hand, it has been shown by Alrashed et al that nanoﬂuids thermal conductivity enhancement is independent of the base ﬂuid.…”

Section: Introductionmentioning

confidence: 95%

“…One of the recent technologies of improving heat transfer is suspending nano‐sized solid particles of metallic or oxide‐metallic with high intrinsic thermal conductivity in a base fluid. This mixture is called a nanofluid …”

Section: Introductionmentioning

confidence: 99%

“…This mixture is called a nanofluid. [1][2][3][4][5] Nanofluids thermophysical properties can be controlled by chemical and physical specifications of the base fluid and nanoparticles. Nanofluids thermal conductivity is one of the factors that play an important role in heat transfer performance.…”

Section: Introductionmentioning

confidence: 99%

“…14,27 A summary of experimental studies on nanofluids thermal conductivity can be found in Rashmi et al 28 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 GRINE AND BENHAMZA | 2931 of particle suspensions. 20 Thus, several models have been proposed, such as the work of Alrashed et al 5,29 using an enhanced artificial neural network (EANN) and an adaptive neurofuzzy inference system approach and the results showed a suitable accuracy with experimental data. Hosseini et al 9 presented a nanofluid thermal conductivity model as a function of nanoparticle thermal conductivity, taking into consideration the working fluid, temperature, nanoparticle volume fraction, and size as well as the properties of the interfacial shell.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Grine

Benhamza

2019

Heat Trans. Asian Res.

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Grine

Benhamza

2019

Heat Trans. Asian Res.

View full text Add to dashboard Cite

show abstract

“…Nanofluid, characterized by a significant increase in a number of properties compared to conventional engineered fluid [1], is found to serve in many practical applications, for example, petroleum engineering [2][3][4][5], power industry [6,7], and medical science [8,9], which has drawn particular attentions for cancer treatments in recent years. Cancer covers a huge group of diseases which can damage any portion of the body.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Analysis of Entropy Generation in the Flow of Viscoelastic Nanofluid through an Annular Region of Two Asymmetric Annuli Having Flexible Surfaces

et al. 2020

View full text Add to dashboard Cite

In this manuscript, the authors developed the mathematical model for entropy generation analysis during the peristaltic propulsion of Jeffrey nanofluids passing in a midst of two eccentric asymmetric annuli. The model was structured by implementation of lubrication perspective and dimensionless strategy. Entropy generation caused by the irreversible influence of heat and mass transfer of nanofluid and viscous dissipation of the considered liquid was taken into consideration. The governing equations were handled by a powerful analytical technique (HPM). The comparison of total entropy with the partial entropy was also invoked by discussing Bejan number results. The influence of various associated variables on the profiles of velocity, temperature, nanoparticle concentration, entropy generation and Bejan number was formulated by portraying the figures. Mainly from graphical observations, we analyzed that, in the matter of thermophoresis parameter and Brownian motion parameter, entropy generation is thoroughly enhanced while inverse readings were reported for the temperature difference parameter and the ratio of temperature to concentration parameters.

show abstract

Quality Evaluation of Asparagus officinalis by Profile of Amino Acids and Mineral Elements in Different Parts Combined with Chemometrics Methods

Liu,

Sun,

et al. 2024

Chemistry & Biodiversity

View full text Add to dashboard Cite

Asparagus officinalis has a homologous value in medicine and vegetables. Its immature stem, commonly called asparagus, is a central edible part. Asparagus skin and leaf also contain rich nutrients. However, these parts are often discarded. This study investigated amino acid and mineral elements in immature stem, skinless asparagus, asparagus skin, and leaf. Their quality was further evaluated by chemometrics methods such as principal component analysis and neural network analysis. The results showed amino acid content was high in immature stem and skinless asparagus and low in leaf, whereas the mineral elements were in four parts. Quality evaluation results showed four parts were divided into three grades. Immature stem and skinless asparagus were grouped into cluster 1 with the best quality as high‐quality raw materials in food and health‐care products. Meanwhile, three AA (Cys, His, Arg) and two mineral elements (Na, Cr) were identified as quality evaluation iconic substances.

show abstract

Effects on thermophysical properties of carbon based nanofluids: Experimental data, modelling using regression, ANFIS and ANN

Cited by 190 publications

References 47 publications

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

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

Mathematical Analysis of Entropy Generation in the Flow of Viscoelastic Nanofluid through an Annular Region of Two Asymmetric Annuli Having Flexible Surfaces

Quality Evaluation of Asparagus officinalis by Profile of Amino Acids and Mineral Elements in Different Parts Combined with Chemometrics Methods

Contact Info

Product

Resources

About