Application of artificial neural network for performance prediction of a nanofluid-based direct absorption solar collector

Delfani, Shahram; Esmaeili, Mohammad; Karami, Maryam

doi:10.1016/j.seta.2019.100559

Cited by 30 publications

(22 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A combined PVT/PCM system using nanofluids has proved to be an effective coolant in enhancing the thermal conductivity of PVT collectors [202]. Other studies related to nanofluids in solar collectors investigated the photothermal properties of various mono and hybrid nanofluids [203][204][205][206][207][208][209][210][211][212], the impact of magnetic fields on the thermal performance of nanofluids in a solar collector [213], the forced convective behaviour of nanoparticles inside a solar collector [214] and more recently the application of ANN models for the prediction of nanofluids performance in solar collectors [215][216][217]. Other studies investigating the application of nanofluids in various solar collectors are presented in Table 7.…”

Section: Photovoltaic Thermal Collectors (Pvt)mentioning

confidence: 99%

An updated review of nanofluids in various heat transfer devices

Okonkwo

Wole‐Osho

Almanassra

et al. 2020

J Therm Anal Calorim

239

View full text Add to dashboard Cite

The field of nanofluids has received interesting attention since the concept of dispersing nanoscaled particles into a fluid was first introduced in the later part of the twentieth century. This is evident from the increased number of studies related to nanofluids published annually. The increasing attention on nanofluids is primarily due to their enhanced thermophysical properties and their ability to be incorporated into a wide range of thermal applications ranging from enhancing the effectiveness of heat exchangers used in industries to solar energy harvesting for renewable energy production. Owing to the increasing number of studies relating to nanofluids, there is a need for a holistic review of the progress and steps taken in 2019 concerning their application in heat transfer devices. This review takes a retrospective look at the year 2019 by reviewing the progress made in the area of nanofluids preparation and the applications of nanofluids in various heat transfer devices such as solar collectors, heat exchangers, refrigeration systems, radiators, thermal storage systems and electronic cooling. This review aims to update readers on recent progress while also highlighting the challenges and future of nanofluids as the next-generation heat transfer fluids. Finally, a conclusion on the merits and demerits of nanofluids is presented along with recommendations for future studies that would mobilise the rapid commercialisation of nanofluids.

show abstract

Section: Photovoltaic Thermal Collectors (Pvt)mentioning

confidence: 99%

An updated review of nanofluids in various heat transfer devices

Okonkwo

Wole‐Osho

Almanassra

et al. 2020

J Therm Anal Calorim

239

View full text Add to dashboard Cite

show abstract

“…Working using assumptions different from traditional prediction methods, ANNs have become one of the AI tools that are often used by researchers lately 14 . The use of ANN, which can learn the connection between irregular time series and nonlinear functions, has provided higher performance compared to traditional mathematical tools.…”

Section: Introductionmentioning

confidence: 99%

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid

Çolak

2021

Int J Energy Res

View full text Add to dashboard Cite

Summary In this study, the effect of the number of neurons on the predictive performance of artificial neural networks (ANN) has been investigated using experimental data. For this purpose, 6 different ANN have been developed by using a total of 60 experimental data of ZrO2/water nanofluid obtained from the literature. In ANN developed with the number of 5, 10, 15, 20, 25, and 30 neurons, all other parameters have been kept constant, and the effect of only the number of neurons on the prediction performance has been investigated. The performance of each ANN has been calculated separately and then their performance has been analyzed by comparing them with each other. As a consequence of the study, it has been seen that the model with the most ideal predictive performance has been developed with 5 neurons with an average error rate of 0.001%, and the highest margin of error the model has been developed with 15 neurons and had an error rate of 0.026%. In the light of the obtained data, it has been concluded that ANN are generally high performance predictive tools, and it is not possible to reach a standard correlation to regulate the number of neurons to be used in the optimization of ANN.

show abstract

“…So, the total layer hardness of the FGC is higher after the aging process. 55,56 Therefore, various aging periods of 2,4,6,8,10,12,14,16,24,48, and 96 h have been selected in our experimental study. 36 It should be noted from Table 6 that the time required to reach peak hardness in the L2 is lower than required both the upper surface and other layers of the FGC in the case study.…”

Section: Environmentmentioning

confidence: 99%

“…Then, these values can be used for the prediction of the output data corresponding to different input data. 14–17…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network (ANN) solution to the prediction of age-hardening and corrosion behavior of an Al/TiC functional gradient material (FGM)

Dikici

Tuntaş

2020

Journal of Composite Materials

View full text Add to dashboard Cite

In this theoretical study, the prediction of the corrosion resistance and age-hardening behavior of an Al/TiC functional gradient material (FGM) has been investigated by using the artificial neural network (ANN). The input parameters have been selected as TiC volume fraction of the composite layers, aging periods of the composite, environmental conditions, and applied potential during the corrosion tests. Current and microhardness were used as the one output in the proposed network. Also, a new three-layered composite has been imaginarily designed to demonstrate the predictive capability and flexibilities of the ANN model as a case study. Artificially aging (T6) process and potentiodynamic scanning (PDS) tests were used for heat-treating and corrosion response of the FGS, respectively. The results showed that the generated PDS curves of the FGM and calculated corrosion parameters of the case study are quite near and in acceptable limits for similar composites obtained values in experimental studies. Besides, this study has been a great success in predicting peak-aging times and its corresponding hardness values more precisely.

show abstract

Application of artificial neural network for performance prediction of a nanofluid-based direct absorption solar collector

Cited by 30 publications

References 33 publications

An updated review of nanofluids in various heat transfer devices

An updated review of nanofluids in various heat transfer devices

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid

An artificial neural network (ANN) solution to the prediction of age-hardening and corrosion behavior of an Al/TiC functional gradient material (FGM)

Contact Info

Product

Resources

About

Application of artificial neural network for performance prediction of a nanofluid-based direct absorption solar collector

Cited by 30 publications

References 33 publications

An updated review of nanofluids in various heat transfer devices

An updated review of nanofluids in various heat transfer devices

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO 2 nanofluid

An artificial neural network (ANN) solution to the prediction of age-hardening and corrosion behavior of an Al/TiC functional gradient material (FGM)

Contact Info

Product

Resources

About

A novel comparative investigation of the effect of the number of neurons on the predictive performance of the artificial neural network: An experimental study on the thermal conductivity of ZrO ₂ nanofluid