Experimental investigation of thermal conductivity and its ANN modeling for glycol-based Ag/ZnO hybrid nanofluids with low concentration

Barewar, Surendra D.; Tawri, Shravan; Chougule, Sandesh S.

doi:10.1007/s10973-019-08618-6

Cited by 32 publications

(6 citation statements)

References 37 publications

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“…The procedure of finding optimal ANN is illustrated in Figure 2. Many researchers have used a similar method to find the best network 34‐55 …”

Section: Resultsmentioning

confidence: 99%

“…An average error of −0.41% and −0.02% was obtained for ANN and correlation, respectively. Barewar et al 51 investigated thermophysical properties of Ag/ZnO‐EG nanofluids with volume fractions between 0.05 and 0.2 vol% compared with ZnO‐EG nanofluids. They developed an empirical correlation by curve‐fitting to estimate the TC enhancement at various temperatures and volume fractions compared to the ANN developed model.…”

Section: Introductionmentioning

confidence: 99%

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Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Motahar

Sadri

2021

Intl J of Energy Research

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The weak thermal conductance of a phase change material (PCM) can be intensified by dispersing nanostructured materials called nano-PCM. Accurate thermal conductivity (TC) prediction of nano-PCM is essential to evaluate heat transport during phase change processes, namely, melting and solidification. The present study develops an artificial neural network (ANN) to forecast the TC of n-octadecane as a PCM with dispersed oxide nanoparticles. A total of 122 experimental datasets from existing literature with a wide range of temperatures (5-60 C), nanoparticles (CuO, Al 2 O 3 , TiO 2 , and mesoporous SiO 2 ), nanoparticle mass fractions (0.5-12 wt%) are compiled to train a multi-layered feed-forward ANN with Levenberg-Marquardt back-propagation algorithm.An optimal architecture of the neural network is acquired by varying the number of network hidden layers, the number of neurons in each layer, and the transfer function of layers. The minimum mean square error (MSE) of 1.3512 Â 10 À5 is obtained for the best developed ANN. Results show that average absolute deviation (AAD) of 0.002458, mean absolute percentage error (MAPE) of 0.8260%, and correlation coefficient (R) of 0.999964948 are achieved for training data. Moreover, MAPE, AAD, and R values are, respectively, 0.9478, 0.002167, and 0.9999715861 for testing data. The maximum percentage errors of ANN computed values are 2.31%, and 0.812% for liquid and solid phases, respectively. This indicates that the ANN model accurately predicts the enhanced TC of nano-PCM across various oxide nanoparticles, temperatures, and nanoparticle loadings.

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“…The procedure of finding optimal ANN is illustrated in Figure 2. Many researchers have used a similar method to find the best network 34‐55 …”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Motahar

Sadri

2021

Intl J of Energy Research

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“…Next, mean square error (MSE) for output of the network and experimental values which is also introduced to the model, will be calculated. This MSE will be calculated for all three data sets including train, validation, and test data sets [54].…”

Section: Figure 2: Schematic Of Developed Ann Modelmentioning

confidence: 99%

Latent Heat Prediction of Nano Enhanced Phase Change Material by ANN Method

Jaliliantabar¹,

Mamat²,

Sudhakar³

2022

Energy Engineering

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Thermal characteristics of phase change material (PCM) are important in design and utilization of thermal energy storage or other applications. PCMs have great latent heat but suffer from low thermal conductivity. Then, in recent years, nano particles have been added to PCM to improve their thermophysical properties such as thermal conductivity. Effect of this nano particles on thermophysical properties of PCM has been a question and many experimental and numerical studies have been done to investigate them. Artificial intelligence-based approach can be a good candidate to predict thermophysical properties of nano enhance PCM (NEPCM). Then, in this study an artificial neural network (ANN) has been developed to predict the latent heat of the NEPCM. A comprehensive literature search was conducted to acquire thermal characteristics data from various NEPCM to train and test this artificial neural network model. Twenty different types of Nano particle and paraffin based PCMs were used in ANN development. The most important properties which are used as the input for the developed ANN model are NP size, density of NP, latent heat of PCM, density of PCM, concentration and latent heat of NEPCM in the range of 1-60 nm, 100-8960 kg/m 3 , 89.69-311 kJ/kg, 760 to 1520 kg/m 3 , 0.02-20 wt% and 60.72-338.6 kJ/kg, respectively. The output variable was latent heat of NEPCM. The result indicates that the ANN model can be applied to predict the latent heat of nano enhanced PCM satisfactory. The correlation coefficient of the created model was 0.97. This result shows ability of ANN to predict the latent heat of NEPCM.

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“…Maximum collector efficiency found 62.87% for 3.0% concentration and 0.045kg/s mass flow rate. Barewar et al [35] synthesized Ag/ZnO-glycol hybrid nanofluid to investigate thermophysical properties experimentally. Experimental results compared with a model developed by the artificial neural network (ANN).…”

Section: Zink Oxide (Zno)mentioning

confidence: 99%

A review on convective heat augmentation techniques in solar thermal collector using nanofluid

Deshmukh

Karmare

2021

Journal of Thermal Engineering

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Solar water heating system is convincing technology to convert solar energy into thermal energy. According to the survey, approximately 42% of refined crude oil is used in industries and commercial applications for heating processes. Fossil fuel is the main energy source that is depleting continuously. Solar energy is an environment-friendly energy source, which can fulfill energy demand. Solar thermal collectors are most popular in domestic as well as industrial sectors for water heating due to their ease of operation and simple maintenance. Extensive work is going on to improve the thermal performance of solar thermal collectors using passive techniques. Passive techniques include the use of nanofluid, twisted tape, Phase Changing Materials. Active and passive techniques have a significant contribution to solar thermal collector thermal performance enhancement. This paper reviews the work carried out and current progress to enhance the thermal efficiency of solar water heaters using nanofluid. In addition to this, a detailed discussion and limitations of existing research have made from this discussion, research gaps are identified and possible future modifications are suggested.

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Experimental investigation of thermal conductivity and its ANN modeling for glycol-based Ag/ZnO hybrid nanofluids with low concentration

Cited by 32 publications

References 37 publications

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Latent Heat Prediction of Nano Enhanced Phase Change Material by ANN Method

A review on convective heat augmentation techniques in solar thermal collector using nanofluid

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