TY - GEN
T1 - Performance analysis of a two-stage travelling-wave thermo-acoustic engine using Artificial Neural Network
AU - Ngcukayitobi, Miniyenkosi
AU - Sibutha, Sphumelele
AU - Tartibu, Lagouge K.
AU - Bannwart, Flávio C.
N1 - Publisher Copyright:
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
PY - 2020
Y1 - 2020
N2 - Thermo-acoustic systems can convert thermal energy into acoustic waves and vice-versa. This conversion is due to the thermo-viscous interaction between the acoustically oscillating gas fluid within a porous medium, referred to as a regenerator, and the pore internal walls. The thermo-acoustic approach is proposed in this study as an alternative sustainable solution for addressing the issue of electricity in remote areas of developing countries. This approach is environmentally friendly as it utilises air as the working medium and therefore does not generate harmful emissions. In this study, a two-stage travelling-wave thermo-acoustic engine has been modelled using DeltaEC. The simulation was performed by considering various input heat for both of the engine stages. The heat input for the first stage was set within the range of 359.48 to 455.75W, while in the second stage was within the range of 1307.99 to 1656.35W. Hundred (100) data were generated. This dataset was used to build an Artificial Neural Network (ANN) model. The ANN model was validated using the data extracted from DeltaEC. A good agreement between DeltaEC simulation results and ANN predictions was observed. This study shows that the ANN approach is capable of analysing intricate nonlinear thermoacoustic issues.
AB - Thermo-acoustic systems can convert thermal energy into acoustic waves and vice-versa. This conversion is due to the thermo-viscous interaction between the acoustically oscillating gas fluid within a porous medium, referred to as a regenerator, and the pore internal walls. The thermo-acoustic approach is proposed in this study as an alternative sustainable solution for addressing the issue of electricity in remote areas of developing countries. This approach is environmentally friendly as it utilises air as the working medium and therefore does not generate harmful emissions. In this study, a two-stage travelling-wave thermo-acoustic engine has been modelled using DeltaEC. The simulation was performed by considering various input heat for both of the engine stages. The heat input for the first stage was set within the range of 359.48 to 455.75W, while in the second stage was within the range of 1307.99 to 1656.35W. Hundred (100) data were generated. This dataset was used to build an Artificial Neural Network (ANN) model. The ANN model was validated using the data extracted from DeltaEC. A good agreement between DeltaEC simulation results and ANN predictions was observed. This study shows that the ANN approach is capable of analysing intricate nonlinear thermoacoustic issues.
UR - http://www.scopus.com/inward/record.url?scp=85127561997&partnerID=8YFLogxK
U2 - 10.1051/matecconf/202134700023
DO - 10.1051/matecconf/202134700023
M3 - Conference contribution
AN - SCOPUS:85127561997
T3 - 12th South African Conference on Computational and Applied Mechanics, SACAM 2020
BT - 12th South African Conference on Computational and Applied Mechanics, SACAM 2020
A2 - Skatulla, Sebastian
PB - EDP Sciences
T2 - 12th South African Conference on Computational and Applied Mechanics, SACAM 2020
Y2 - 29 November 2021 through 1 December 2021
ER -
