Heat and Fluid Flow Analysis and ANN-Based Prediction of A Novel Spring Corrugated Tape

Basma Souayeh, Suvanjan Bhattacharyya, Najib Hdhiri and Mir Waqas Alam
Additional contact information
Basma Souayeh: Department of Physics, College of Science, King Faisal University, PO Box 400, Al-Ahsa 31982, Saudi Arabia
Suvanjan Bhattacharyya: Department of Mechanical Engineering, Birla Institute of Technology and Science Pilani, Pilani Campus, Vidhya Vihar, Pilani 333031, India
Najib Hdhiri: Department of Physics, Laboratory of Fluid Mechanics, Faculty of Sciences of Tunis, Tunis 2092, Tunisia
Mir Waqas Alam: Department of Physics, College of Science, King Faisal University, PO Box 400, Al-Ahsa 31982, Saudi Arabia

Sustainability, 2021, vol. 13, issue 6, 1-24

Abstract: A circular tube fitted with novel corrugated spring tape inserts has been investigated. Air was used as the working fluid. A thorough literature review has been done and this geometry has not been studied previously, neither experimentally nor theoretically. A novel experimental investigation of this enhanced geometry can, therefore, be treated as a new substantial contribution in the open literature. Three different spring ratio and depth ratio has been used in this study. Increase in thermal energy transport coefficient is noticed with increase in depth ratio. Corrugated spring tape shows promising results towards heat transfer enhancement. This geometry performs significantly better (60% to 75% increase in heat duty at constant pumping power and 20% to 31% reduction in pumping power at constant heat duty) than simple spring tape. 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 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. The results of this investigation can be used in designing heat exchangers.

Keywords: Heat transfer; tape inserts; corrugation; 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 (2)

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