Numerical analysis of the flow pathlines in thermo-acoustic couples

L. K. Tartibu, T. Kunene

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

Thermo-acoustic systems use a high amplitude sound-wave for refrigeration or electricity generation without the drawbacks of expensive construction, adverse environmental impact or high maintenance cost. The effective conversion of energy occurs within the “stack” considered as the heart of the system. The time-averaged rate of heat transfer across the edges of the stack is a good indicator of an effective performance. Hence, studying the effect of the geometry of the stack edges together with their locations is useful. Furthermore, current manufacturing practices make it possible to develop diverse stack edges, resulting in an improved efficiency of the heat transfer. For effective modelling of the heat transfer rate, a second-order, double-precision discretization of state variables and a laminar viscous model was used. A numerical model was developed using the commercial code FLUENT. The evolution of the flow vortices at different drive ratio was analyzed. Two edges shapes were considered namely rectangular and rounded edges. Using numerical analysis, this study has pointed out that stack edge profiles has a significant effect on the overall performance of thermo-acoustic systems. Rounding the stack edge profile appears to be beneficial for the system performance. This study points out the link between the non-linearity observed in thermo-acoustic systems, the flow streaming and the mean vorticity at the stack edges.

Original languageEnglish
Pages (from-to)246-251
Number of pages6
JournalProcedia Manufacturing
Volume35
DOIs
Publication statusPublished - 2019
Event2nd International Conference on Sustainable Materials Processing and Manufacturing, SMPM 2019 - Sun City, South Africa
Duration: 8 Mar 201910 Mar 2019

Keywords

  • Flow streaming
  • Heat transfer
  • Stack
  • Thermo-acoustic

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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