Numerical simulation and Artificial neural network illustration of phase-change material integrated into lattice structures printed in 3D

Open Access

Year : 2024 | Volume : | : | Page : –
By

Deepak Kumar Yadav

Rajeev Kumar Singh

Arvind Kumar Gupta

Pushpendra Kumar Singh Rathore

Basant Singh Sikarwar

  1. Assistant Professor Department of Mechanical Engineering, Amity University Uttar Pradesh, Noida Uttar Pradesh India
  2. Assistant Professor Department of Mechanical Engineering, Amity University Uttar Pradesh, Noida Uttar Pradesh India
  3. Assistant Professor 2Department of Mechanical Engineering, J.C. Bose University of Science and Technology, YMCA, Faridabad Haryana Haryana India
  4. Assistant Professor 1Department of Mechanical Engineering, Amity University Uttar Pradesh, Noida Uttar Pradesh India
  5. Assistant Professor 1Department of Mechanical Engineering, Amity University Uttar Pradesh, Noida Uttar Pradesh India

Abstract

This work examines the phase change material (P.C.M.) deposited in various lattice formations—such as “S.C., B.C.C., and F.C.C”.—at varied characteristics. The test concentrates on comprehending heat transport properties and thermal activity throughout the “melting and solidification processes”. The heater’s maximum temperature, P.C.M. “melting and solidification”, and Nusselt number are among the essential factors examined. According to the findings, the heater’s maximum temperature drops as porosity increases. Although the Nusselt values for the various lattice forms are similar, the S.C. lattice has a slightly higher Nusselt number. As porosity increases, so are the periods required for melting and solidification. Three essential parameters are predicted using an artificial neural network trained using the Bayesian Regularisation approach. The network’s input parameters are set to porosity and time. When assessing the performance of P.C.M. with three lattice structures, the optimum structure of the ANN demonstrates excellent accuracy. 0.00003601 is the minimal mean square error, while 0.9998 is the most significant correlation coefficient. The trained artificial neural network (ANN) forecasts P.C.M. behaviour with 82% “S.C., B.C.C., and F.C.C”. An exact match between the simulation and the ANN predictions for P.C.M. with 82% porosity lattice structures is found.

Keywords: solidification, resistance

How to cite this article: Deepak Kumar Yadav, Rajeev Kumar Singh, Arvind Kumar Gupta, Pushpendra Kumar Singh Rathore, Basant Singh Sikarwar. Numerical simulation and Artificial neural network illustration of phase-change material integrated into lattice structures printed in 3D. Journal of Polymer and Composites. 2024; ():-.
How to cite this URL: Deepak Kumar Yadav, Rajeev Kumar Singh, Arvind Kumar Gupta, Pushpendra Kumar Singh Rathore, Basant Singh Sikarwar. Numerical simulation and Artificial neural network illustration of phase-change material integrated into lattice structures printed in 3D. Journal of Polymer and Composites. 2024; ():-. Available from: https://journals.stmjournals.com/jopc/article=2024/view=152376

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Ahead of Print Open Access Review Article
Volume
Received March 22, 2024
Accepted May 11, 2024
Published June 28, 2024
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