THERMAL SCIENCE

International Scientific Journal

Sathish Thanikodi

,

Dinesh Kumar Singaravelu

,

Chandramohan Devarajan

,

Vijayan Venkatraman

,

Venkatesh Rathinavelu

TEACHING LEARNING OPTIMIZATION AND NEURAL NETWORK FOR THE EFFECTIVE PREDICTION OF HEAT TRANSFER RATES IN TUBE HEAT EXCHANGERS

ABSTRACT
Heat exchangers are widely used in many field for the purpose of heat from one medium to another. In heat exchanger one or more fluids are used, and which are various types based on its flow and construction. Design of heat exchanger is one of the important field, in the research due to its application. In recent decade the simulation is used in most of the engineering application. A proper simulation technique can effectively analysis the functionality and behavior of any machine before its construction or production. In this sense the machine learning techniques are used in some simulation analysis to model the machine or engine. In this work we used a hybrid neural network for the modeling of shell and tube type heat exchanger and its heat transfer rate is predicted effectively. The computational performance of the proposed technique is compared with the conventional technique and it is proved the effectiveness of the hybrid machine learning technique.
KEYWORDS
heat exchanger, Artificial Neural Network, heat transfer rate, teaching learning optimization, shell and tube heat exchanger, hybrid machine learning
PAPER SUBMITTED: 2019-04-14
PAPER REVISED: 2019-05-14
PAPER ACCEPTED: 2019-06-05
PUBLISHED ONLINE: 2019-11-17
DOI REFERENCE: https://doi.org/10.2298/TSCI190714438T
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2020, VOLUME 24, ISSUE Issue 1, PAGES [575 - 581]
REFERENCES
  1. Jin Chen, et al., Vertically aligned and interconnected boron nitride nanosheets for advanced flexible nanocomposite thermal interface materials, ACS applied materials & interfaces 9 (2017), 36, pp. 30909-30917.
  2. T. Sathish, BCCS Approach for the Parametric Optimization in Machining of Nimonic-263 alloy using RSM, Materials Today Proceedings, Elsevier Publisher 5 (2018), 6, pp. 14416-14422.
  3. Saravankumar.P.T, et al., Ecological effect of corn oil biofuel with Si, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, doi.org/10.1080/15567036.2019.1576079 (2019).
  4. Venkatesh, R., et al., Comparison of Different Tool path in Pocket Milling, International Journal of Mechanical Engineering and Technology 9 (2018), 12, pp. 922-927.
  5. H. Bikas et al., Additive manufacturing methods and modelling approaches: a critical review,The International Journal of Advanced Manufacturing Technology 83 (2016), 1-4, pp. 389-405.
  6. Sathish, T., et al., An Extensive Review of Reverse Logistics and its Benefits in supply chain Management, International Journal of Mechanical and Production Engineering Research and Development, (2018), Special Issue, pp. 165-178.
  7. Vivekanandan. M et. al, Pressure Vessel Design using PV-ELITE Software with Manual Calculations and Validation by FEM, Journal of Engineering Technology, 8 (2019),1, pp.425-433.
  8. Sathish, T., Prediction of springback effect by the hybridisation of ANN with PSO in wipe bending process of sheet metal, Progress in Industrial Ecology 12 (2018), 1-2, pp. 112-119.
  9. Pradeep Mohan Kumar.K., et al., Computational Analysis and Optimization of Spiral Plate Heat Exchanger, J. of Applied Fluid Mechanics, Volume 11 (2018), Special Issue, pp.no, 121-128.
  10. Sathish, T., et al., Optimizing Supply Chain in Reverse Logistics, International Journal of Mechanical and Production Engineering Research and Development 7 (2017), pp. 551-560.
  11. Avudaiappan.T, et al., Potential Flow Simulation through Lagrangian Interpolation Meshless Method Coding, J. of Applied Fluid Mechanics, 11 (2018), Special Issue, pp. 129 -134.
  12. Godwin Antony, A., et al., Experimental investigation of I.C Engine using various Diesel Blends,Asian Journal of Research in Social Sciences and Humanities 6 (12) (2016), pp.221-235.
  13. Karthick, S., TDP: A novel secure and energy aware routing protocol for Wireless Sensor Networks, International Journal of Intelligent Engineering and Systems 11 (2018), 2, pp. 76-84,.
  14. Dinesh, S, et al., Analysis and optimization of Machining parameters in through Feed centerless grinding of high Carbon steel, Journal of Mechanical Engineering and Technology 9 (13), pp. 431-441.
  15. Qiuwang Wang, et al., Prediction of heat transfer rates for shell-and-tube heat exchangers by artificial neural networks approach,Journal of Thermal Science 15 (2006), 3, pp. 257-262.
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Teaching learning optimization and neural network for the effective prediction of heat transfer rates in tube heat exchangers

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence