Experimental investigation on thermal conductivity of fly ash nanofluid and fly ash-Cu hybrid nanofluid: prediction and optimization via ANN and MGGP model

Kanti, Praveen; Sharma, K.V.; Said, Zafar; Jamei, Mehdi; Yashawantha, Kyathanahalli Marigowda

doi:10.1080/02726351.2021.1929610

Cited by 35 publications

(10 citation statements)

References 46 publications

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“…The best architecture to build an ANN model is proposed to predict the dynamic viscosity. The use of various artificial intelligence techniques in predicting the thermos‐physical properties of fly ash nanofluid is demonstrated well by Kanti et al 14–17 …”

Section: Introductionmentioning

confidence: 91%

“…The best architecture to build an ANN model is proposed to predict the dynamic viscosity. The use of various artificial intelligence techniques in predicting the thermos-physical properties of fly ash nanofluid is demonstrated well by Kanti et al [14][15][16][17] The literature review helped identify that majority of the work was carried out on metal or metallic oxide nanoparticles compared to functionalized graphene-based nanoparticles. The majority of the work was carried out on various types of heat exchangers and cooling devices compared to the compact heat exchangers.…”

Section: Introductionmentioning

confidence: 99%

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Predicting and optimizing the heat transfer performance of radiator with functionalized graphene nanofluids

et al. 2022

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Accurately predicting the heat transfer characteristics of coolants used in thermal management of energy systems like heat exchangers, power electronics, and heating, ventilation, and air conditioning is indispensable in maintaining its operating conditions within safety limits. Apart from safety, factors such as power consumption and operating cost are the most important constraints to be considered in designing an energy‐efficient and cost‐effective cooling solution. In this study, the experimental data available from previous research on the use of functionalized graphene‐based nanofluids in compact heat exchangers such as the automotive radiator is used to optimize the heat transfer performance parameters like Nusselt number of the nanofluid, the friction factor, and effectiveness of the heat exchanger. A supervised machine learning technique like the artificial neural network is used to obtain the objective functions of the response variables in terms of input features such as Reynolds number, Prandtl number, the volume concentration of nanoparticles in the base fluid, number of transfer units, heat capacity, the density of nanofluid, pressure drop and velocity. On the current dataset, it is found that by using the Bayesian regularization training algorithm and tangent sigmoidal activation function in the neural network, the best accuracies in the prediction can be achieved. Well‐known nature‐inspired optimization algorithms like genetic algorithms and simulated annealing are used in optimizing the above‐mentioned response variables. Both algorithms converged to the same values of the objective functions. The optimum values of Nusselt number, effectiveness, and friction factor are 105.65, 0.506, and 0.0038, respectively, for the given composition of the nanofluid and radiator configuration.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Predicting and optimizing the heat transfer performance of radiator with functionalized graphene nanofluids

et al. 2022

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“…The thermal conductivity of a test nanofluid is determined by numerous parameters, including nanoparticle size, concentration, synthesis technique, and temperature. The majority of the literature revealed that nanofluids might increase heat conductivity to various degrees. , The basic characteristics of nanoparticles, base fluid, and their temperature, are macroscopic parameters that can impact the thermal conductivities of nanofluids …”

Section: Machine Learning For Thermophysical Propertiesmentioning

confidence: 99%

“…123,124 The basic characteristics of nanoparticles, base fluid, and their temperature, are macroscopic parameters that can impact the thermal conductivities of nanofluids. 125 One of the most significant factors for theoretical as well as numerical analyses of heat transport systems employing nanofluids as coolants is the selection of nanofluid's thermophysical characteristics. Given this, understanding how to create an appropriate thermophysical characteristic model is quite beneficial and important.…”

Section: Machine Learning For Thermophysical Propertiesmentioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

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223

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Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

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“…Choi and Eastman [6] was the pioneer one to use the term nanofluid to describe manufactured colloids made of nanoparticles scattered in a base fluid. In recent years, the improvement of the nanofluid system in the transmission of heat has piqued the interest of researchers and industry representatives from a wide range of disciplines including manufacturing, automotive, and electronics [7][8][9][10][11][12][13][14][15][16][17][18]. The thermal conductivity behavior of colloidal suspension has been studied by a large number of researchers, and the results have been published several times [19,20].…”

Section: Introductionmentioning

confidence: 99%

Using Micropolar Nanofluid under a Magnetic Field to Enhance Natural Convective Heat Transfer around a Spherical Body

Yaseen¹,

Alwawi

Swalmeh³

et al. 2022

ARFMTS

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An analysis is discussed of the heat and mass transfer for micropolar nanofluid in presence of natural convection from a spherical body with magneto-hydrodynamic (MHD) effects. The constant wall temperature boundary condition is also studied. By employing proper similarity transformations, the governing equations are converted into a set of partial differential equations (PDEs) with the used boundary conditions, which can then be solved numerically via the efficient Keller-box implicit numerical finite difference method. The numerical results of impacts of the controlling parameters on heat transfer physical quantities have been presented, tabular and graphically, by MATLAB symbolic software. Comparisons of the current study results to previously published results show good agreement, indicating that our numerical computations are legitimate and accurate. Increasing nanoparticle volume fraction is observed to depress local skin friction, Nusselt number, and angular velocity while the reverse effects are observed for velocity and temperature.

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Experimental investigation on thermal conductivity of fly ash nanofluid and fly ash-Cu hybrid nanofluid: prediction and optimization via ANN and MGGP model

Cited by 35 publications

References 46 publications

Predicting and optimizing the heat transfer performance of radiator with functionalized graphene nanofluids

Predicting and optimizing the heat transfer performance of radiator with functionalized graphene nanofluids

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Using Micropolar Nanofluid under a Magnetic Field to Enhance Natural Convective Heat Transfer around a Spherical Body

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