IDEAS home Printed from https://ideas.repec.org/a/oup/ijlctc/v11y2016i3p431-439..html
   My bibliography  Save this article

Design and optimization of a thermoacoustic heat engine using reinforcement learning

Author

Listed:
  • Jurriath-Azmathi Mumith
  • Tassos Karayiannis
  • Charalampos Makatsoris

Abstract

The thermoacoustic heat engine (TAHE) is a type of prime mover that converts thermal power to acoustic power. It is composed of two heat exchangers (the devices heat source and sink), some kind of porous medium where the conversion of power takes place and a tube that houses the acoustic wave produced. Its simple design and the fact that it is one of a few prime movers that do not require moving parts make such a device an attractive alternative for many practical applications. The acoustic power produced by the TAHE can be used to generate electricity, drive a heat pump or a refrigeration system. Although the geometry of the TAHE is simple, the behavior of the engine is complex with 30+ design parameters that affect the performance of the device; therefore, designing such a device remains a significant challenge. In this work, a radical design methodology using reinforcement learning (RL) is employed for the design and optimization of a TAHE for the first time. Reinforcement learning is a machine learning technique that allows optimization by specifying ‘good’ and ‘bad’ behavior using a simple reward scheme r. Although its framework is simple, it has proved to be a very powerful tool in solving a wide range of complex decision-making/optimization problems. The RL technique employed by the agent in this work is known as Q-learning. Preliminary results have shown the potential of the RL technique to solve this type of complex design problem, as the RL agent was able to figure out the correct configuration of components that would create positive acoustic power output. The learning agent was able to create a design that yielded an acoustic power output of 643.31 W with a thermal efficiency of 3.29%. It is eventually hoped that with increased understanding of the design problem, in terms of the RL framework, it will be possible to ultimately create an autonomous RL agent for the design and optimization of complex TAHEs with minimal predefined conditions/restrictions.

Suggested Citation

  • Jurriath-Azmathi Mumith & Tassos Karayiannis & Charalampos Makatsoris, 2016. "Design and optimization of a thermoacoustic heat engine using reinforcement learning," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 11(3), pages 431-439.
  • Handle: RePEc:oup:ijlctc:v:11:y:2016:i:3:p:431-439.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ijlct/ctv023
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Geng & Tang, Lihua & Mace, Brian & Yu, Zhibin, 2021. "Multi-physics coupling in thermoacoustic devices: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    2. Mosa Machesa & Lagouge Tartibu & Modestus Okwu, 2021. "Prediction of the Oscillatory Heat Transfer Coefficient in Thermoacoustic Refrigerators," Sustainability, MDPI, vol. 13(17), pages 1-17, August.
    3. Zhang, Yutao & Shi, Xueqiang & Li, Yaqing & Zhang, Yuanbo & Liu, Yurui, 2020. "Characteristics of thermoacoustic conversion and coupling effect at different temperature gradients," Energy, Elsevier, vol. 197(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:oup:ijlctc:v:11:y:2016:i:3:p:431-439.. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/ijlct .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.