Performance analysis of a two-stage travelling-wave thermo-acoustic engine using Artificial Neural Network

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 approa...

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Autores principales:	Ngcukayitobi Miniyenkosi, Sibutha Sphumelele, Tartibu Lagouge K, Bannwart Flavio C
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/9bbe90619c8c45b98ef8243f605aa18a
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id	oai:doaj.org-article:9bbe90619c8c45b98ef8243f605aa18a
record_format	dspace
spelling	oai:doaj.org-article:9bbe90619c8c45b98ef8243f605aa18a2021-12-02T17:13:35ZPerformance analysis of a two-stage travelling-wave thermo-acoustic engine using Artificial Neural Network2261-236X10.1051/matecconf/202134700023https://doaj.org/article/9bbe90619c8c45b98ef8243f605aa18a2021-01-01T00:00:00Zhttps://www.matec-conferences.org/articles/matecconf/pdf/2021/16/matecconf_sacam21_00023.pdfhttps://doaj.org/toc/2261-236XThermo-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.Ngcukayitobi MiniyenkosiSibutha SphumeleleTartibu Lagouge KBannwart Flavio CEDP SciencesarticleEngineering (General). Civil engineering (General)TA1-2040ENFRMATEC Web of Conferences, Vol 347, p 00023 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Ngcukayitobi Miniyenkosi Sibutha Sphumelele Tartibu Lagouge K Bannwart Flavio C Performance analysis of a two-stage travelling-wave thermo-acoustic engine using Artificial Neural Network
description	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.
format	article
author	Ngcukayitobi Miniyenkosi Sibutha Sphumelele Tartibu Lagouge K Bannwart Flavio C
author_facet	Ngcukayitobi Miniyenkosi Sibutha Sphumelele Tartibu Lagouge K Bannwart Flavio C
author_sort	Ngcukayitobi Miniyenkosi
title	Performance analysis of a two-stage travelling-wave thermo-acoustic engine using Artificial Neural Network
title_short	Performance analysis of a two-stage travelling-wave thermo-acoustic engine using Artificial Neural Network
title_full	Performance analysis of a two-stage travelling-wave thermo-acoustic engine using Artificial Neural Network
title_fullStr	Performance analysis of a two-stage travelling-wave thermo-acoustic engine using Artificial Neural Network
title_full_unstemmed	Performance analysis of a two-stage travelling-wave thermo-acoustic engine using Artificial Neural Network
title_sort	performance analysis of a two-stage travelling-wave thermo-acoustic engine using artificial neural network
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/9bbe90619c8c45b98ef8243f605aa18a
work_keys_str_mv	AT ngcukayitobiminiyenkosi performanceanalysisofatwostagetravellingwavethermoacousticengineusingartificialneuralnetwork AT sibuthasphumelele performanceanalysisofatwostagetravellingwavethermoacousticengineusingartificialneuralnetwork AT tartibulagougek performanceanalysisofatwostagetravellingwavethermoacousticengineusingartificialneuralnetwork AT bannwartflavioc performanceanalysisofatwostagetravellingwavethermoacousticengineusingartificialneuralnetwork
_version_	1718381334508863488

Performance analysis of a two-stage travelling-wave thermo-acoustic engine using Artificial Neural Network

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