Turbulent Flow Heat Transfer through a Circular Tube with Novel Hybrid Grooved Tape Inserts: Thermohydraulic Analysis and Prediction by Applying Machine Learning Model

Suvanjan Bhattacharyya, Devendra Kumar Vishwakarma, Shramona Chakraborty, Rahul Roy, Alibek Issakhov and Mohsen Sharifpur
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Suvanjan Bhattacharyya: Department of Mechanical Engineering, Birla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani 333 031, Rajasthan, India
Devendra Kumar Vishwakarma: Department of Mechanical Engineering, Birla Institute of Technology and Science, Pilani, Pilani Campus, Vidya Vihar, Pilani 333 031, Rajasthan, India
Shramona Chakraborty: Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur-721 302, West Bengal, India
Rahul Roy: Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur-721 302, West Bengal, India
Alibek Issakhov: Faculty of Mechanics and Mathematics, Department of Mathematical and Computer Modelling, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan
Mohsen Sharifpur: Clean Energy Research Group, Department of Mechanical and Aeronautical Engineering, Engineering III, University of Pretoria, Lynnwood Road, Pretoria 0002, South Africa

Sustainability, 2021, vol. 13, issue 6, 1-41

Abstract: The present experimental work is performed to investigate the convection heat transfer (HT), pressure drop (PD), irreversibility, exergy efficiency and thermal performance for turbulent flow inside a uniformly heated circular channel fitted with novel geometry of hybrid tape. Air is taken as the working fluid and the Reynolds number is varied from 10,000 to 80,000. Hybrid tape is made up of a combination of grooved spring tape and wavy tape. The results obtained with the novel hybrid tape show significantly better performance over individual tapes. A correlation has been developed for predicting the friction factor ( f ) and Nusselt number ( Nu ) with novel hybrid tape. The results of this investigation can be used in designing heat exchangers. This paper also presented a statistical analysis of the heat transfer and fluid flow by developing an artificial neural network (ANN)-based machine learning (ML) model. The model is trained based on the features of experimental data, which provide an estimation of experimental output based on user-defined input parameters. The model is evaluated to have an accuracy of 98.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce.

Keywords: heat transfer enhancement; tape inserts; heat exchanger; machine learning; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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