An Artificial Intelligence Approach to Predict the Thermophysical Properties of MWCNT Nanofluids

Bakthavatchalam, Balaji; Shaik, Nagoor Basha; Hussain, Patthi

doi:10.3390/pr8060693

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…In terms of properties prediction, it was shown previously that both effective thermal conductivity and effective viscosity lacks universal correlations and can only be determined through experimental means. However, artificial neural networking that is based on data mining has started to show good accuracy in predicting these properties, but further research is still required in this area [ 489 , 490 , 491 , 492 ].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

Carbon-Based Nanofluids and Their Advances towards Heat Transfer Applications—A Review

et al. 2021

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Nanofluids have opened the doors towards the enhancement of many of today’s existing thermal applications performance. This is because these advanced working fluids exhibit exceptional thermophysical properties, and thus making them excellent candidates for replacing conventional working fluids. On the other hand, nanomaterials of carbon-base were proven throughout the literature to have the highest thermal conductivity among all other types of nanoscaled materials. Therefore, when these materials are homogeneously dispersed in a base fluid, the resulting suspension will theoretically attain orders of magnitude higher effective thermal conductivity than its counterpart. Despite this fact, there are still some challenges that are associated with these types of fluids. The main obstacle is the dispersion stability of the nanomaterials, which can lead the attractive properties of the nanofluid to degrade with time, up to the point where they lose their effectiveness. For such reason, this work has been devoted towards providing a systematic review on nanofluids of carbon-base, precisely; carbon nanotubes, graphene, and nanodiamonds, and their employment in thermal systems commonly used in the energy sectors. Firstly, this work reviews the synthesis approaches of the carbon-based feedstock. Then, it explains the different nanofluids fabrication methods. The dispersion stability is also discussed in terms of measuring techniques, enhancement methods, and its effect on the suspension thermophysical properties. The study summarizes the development in the correlations used to predict the thermophysical properties of the dispersion. Furthermore, it assesses the influence of these advanced working fluids on parabolic trough solar collectors, nuclear reactor systems, and air conditioning and refrigeration systems. Lastly, the current gap in scientific knowledge is provided to set up future research directions.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Carbon-Based Nanofluids and Their Advances towards Heat Transfer Applications—A Review

et al. 2021

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“…The weights values are multiplied with input values and added with a bias to obtain the least possible error as per Eq. 1 [26]. Furthermore, the calculated total value will be added with a bias value.…”

Section: Methodsmentioning

confidence: 99%

Corrosion Behavior of LENS Deposited CoCrMo Alloy Using Bayesian Regularization-Based Artificial Neural Network (BRANN)

Shaik¹,

Mantrala²,

Bakthavatchalam³

et al. 2021

J Bio Tribo Corros

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The well-known fact of metallurgy is that the lifetime of a metal structure depends on the material's corrosion rate. Therefore, applying an appropriate prediction of corrosion process for the manufactured metals or alloys trigger an extended life of the product. At present, the current prediction models for additive manufactured alloys are either complicated or built on a restricted basis towards corrosion depletion. This paper presents a novel approach to estimate the corrosion rate and corrosion potential prediction by considering significant major parameters such as solution time, aging time, aging temperature, and corrosion test time. The Laser Engineered Net Shaping (LENS), which is an additive manufacturing process used in the manufacturing of health care equipment, was investigated in the present research. All the accumulated information used to manufacture the LENS-based Cobalt-Chromium-Molybdenum (CoCrMo) alloy was considered from previous literature. They enabled to create a robust Bayesian Regularization (BR)-based Artificial Neural Network (ANN) in order to predict with accuracy the material best corrosion properties. The achieved data were validated by investigating its experimental behavior. It was found a very good agreement between the predicted values generated with the BRANN model and experimental values. The robustness of the proposed approach allows to implement the manufactured materials successfully in the biomedical implants.

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“…Each weight is weighted according to how much it contributed to the inaccuracy. The error is used to modify the weight of the ANN's unit connections to account for the discrepancy between the predicted and actual outcomes 33 . The ANN will learn how to lower the chance of errors and undesired outcomes over time.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Experimental investigation and ANN modelling on CO2 hydrate kinetics in multiphase pipeline systems

Shaik

Sayani

Benjapolakul

et al. 2022

Sci Rep

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Gas hydrates are progressively becoming a key concern when determining the economics of a reservoir due to flow interruptions, as offshore reserves are produced in ever deeper and colder waters. The creation of a hydrate plug poses equipment and safety risks. No current existing models have the feature of accurately predicting the kinetics of gas hydrates when a multiphase system is encountered. In this work, Artificial Neural Networks (ANN) are developed to study and predict the effect of the multiphase system on the kinetics of gas hydrates formation. Primarily, a pure system and multiphase system containing crude oil are used to conduct experiments. The details of the rate of formation for both systems are found. Then, these results are used to develop an A.I. model that can be helpful in predicting the rate of hydrate formation in both pure and multiphase systems. To forecast the kinetics of gas hydrate formation, two ANN models with single layer perceptron are presented for the two combinations of gas hydrates. The results indicated that the prediction models developed are satisfactory as R2 values are close to 1 and M.S.E. values are close to 0. This study serves as a framework to examine hydrate formation in multiphase systems.

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An Artificial Intelligence Approach to Predict the Thermophysical Properties of MWCNT Nanofluids

Cited by 12 publications

References 27 publications

Carbon-Based Nanofluids and Their Advances towards Heat Transfer Applications—A Review

Carbon-Based Nanofluids and Their Advances towards Heat Transfer Applications—A Review

Corrosion Behavior of LENS Deposited CoCrMo Alloy Using Bayesian Regularization-Based Artificial Neural Network (BRANN)

Experimental investigation and ANN modelling on CO2 hydrate kinetics in multiphase pipeline systems

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