On the Thermal Conductivity Assessment of Oil-Based Hybrid Nanofluids using Extended Kalman Filter integrated with feed-forward neural network

Jamei, Mehdi; Olumegbon, Ismail Adewale; Karbasi, Masoud; Ahmadianfar, Iman; Asadi, Amin; Mosharaf-Dehkordi, Mehdi

doi:10.1016/j.ijheatmasstransfer.2021.121159

Cited by 61 publications

(13 citation statements)

References 102 publications

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“…Given this, understanding how to create an appropriate thermophysical characteristic model is quite beneficial and important. The analytical model-based method and data-driven machine learning approach are the two main approaches used to model and forecast the characteristics of nanofluids. , Compared to the analytical model-based method, the data-driven machine learning approaches represented by the ANN, , ANFIS, GEP, for example, have attracted a lot of interest in recent years because of their superior mapping, modeling, and forecast capabilities. According to the literature study, the predictive models’ accuracies are related to their structures, functions used, input variables used, and algorithms used. , Maleki et al conducted a comparison of prediction performances for three machine learning algorithms For model prediction of the TC of nanofluids containing ZnO nanoparticles, the multivariate adaptive regression splines (MARS), ANN, and group method of data handling (GMDH) were utilized.…”

Section: Machine Learning For Thermophysical Propertiesmentioning

confidence: 99%

“…The analytical model-based method and data-driven machine learning approach are the two main approaches used to model and forecast the characteristics of nanofluids. 62,126 Compared to the analytical model-based method, the data-driven machine learning approaches represented by the ANN, 42,127 ANFIS, 128 GEP, 129 for example, have attracted a lot of interest in recent years because of their superior mapping, modeling, and forecast capabilities. According to the literature study, the predictive models' accuracies are related to their structures, functions used, input variables used, and algorithms used.…”

Section: Machine Learning For Thermophysical Propertiesmentioning

confidence: 99%

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

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

222

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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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Section: Machine Learning For Thermophysical Propertiesmentioning

confidence: 99%

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

222

View full text Add to dashboard Cite

show abstract

“…Predicting the thermal conductivity of hybrid nanofluids using machine learning methods: (a) change of thermal conductivity with temperature and solid concentration; (b) variation of pumping power with temperature and solid concentration in laminar flow; (c),(d) The violin plots of distribution function of measured and predicted thermal conductivity of oil-based hybrid nanofluids …”

Section: Predicting the Thermal Conductivity Of Nanofluids Using Mach...mentioning

confidence: 99%

A Comprehensive Review of Predicting the Thermophysical Properties of Nanofluids Using Machine Learning Methods

Wang

Chen

2022

Ind. Eng. Chem. Res.

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Nanofluids are often used as heat transfer fluids due to their good thermal and flow properties. Nanofluids are widely used in energy systems such as solar collectors, heat exchangers, and heat pipes. The thermophysical properties of nanofluids can significantly affect their performance in engineering systems. According to current research in this field, machine learning is a very attractive method to predict the thermophysical properties of nanofluids. We believe that, compared with experiments, machine learning methods are more efficient, rapid, accurate, and practical. The accuracy of the model is affected by factors such as the input variables, the data set selected during the modeling process, and the applied algorithm. This is a comprehensive review that describes the use of different machine learning methods to predict the thermophysical properties of nanofluids (conventional and hybrid nanofluids). This is very useful for researchers, scientists, and engineers in the field of nanofluids.

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“…The coolant properties are important factors affecting the overall performance of a HE, such as when it is used as an intercooler [ 70 ]. Nanofluids that consist of nanoparticles suspended in base fluids can give superior thermal conductivity and heat transfer performance compared with conventional coolants, such as water and ethylene glycol, which have lower thermal conductivities [ 71 , 72 ]. The increase in total heat flow rate in the presence of nanoparticle concentrations is explained as due to an increased collision rate between nanoparticles and the walls of HE channels.…”

Section: Introducing Nanofluids In Heat Exchangersmentioning

confidence: 99%

Application of Nanofluids in Gas Turbine and Intercoolers—A Comprehensive Review

et al. 2022

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Today, the optimal use of non-renewable energy sources, reducing pollution, and increasing the efficiency of power-generating cycles are of particular importance. There are several ways to increase the efficiency of gas turbines; one that has recently attracted attention is to use an intercooler. However, the efficiency of the heat exchanger used in intercoolers depends on the type of heat exchanger, the characteristics of the operating fluid and the thermal boundary layers, and the pump speed. Improving the thermophysical properties of the working fluid is a passive method of increasing heat transfer, which has attracted the attention of those researching engineering applications. The current review addresses the latest methods of improving gas turbine efficiency using nanofluids and includes experimental and numerical studies. First, the general principles governing turbines are described, then the commonly used types of heat exchangers are introduced. Finally, studies on the use of nanofluids in heat exchangers are reviewed. The technology of producing nanoparticles that can be used in heat exchangers is also discussed. This review article can provide the reader with comprehensive information on making nanofluids and using them in heat exchangers used as intercoolers.

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On the Thermal Conductivity Assessment of Oil-Based Hybrid Nanofluids using Extended Kalman Filter integrated with feed-forward neural network

Cited by 61 publications

References 102 publications

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

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

A Comprehensive Review of Predicting the Thermophysical Properties of Nanofluids Using Machine Learning Methods

Application of Nanofluids in Gas Turbine and Intercoolers—A Comprehensive Review

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