JOURNAL ARTICLE

Thermodynamic overview and heat engine efficiency of Kerr–Sen–AdS black hole.

  • Published In: International Journal of Geometric Methods in Modern Physics, 2023, v. 20, n. 8. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Roy, Tanusree; Sardar, Alok; Debnath, Ujjal 3 of 3

Abstract

This paper reflects a study on the thermodynamic features of charged rotating Kerr–Sen–AdS black holes discussed with an extended phase space, where the negative cosmological constant is accounted for pressure. Thermal stability/instability and dependency of the phase transition points on the parameters of a black hole have been discussed further. The authors have systematically studied the throttling process of the black hole considering its mass is identified by its enthalpy. Moreover, the phenomenon of Joule–Thomson expansion has been explored, and inversion temperature for the black hole has been investigated using a numerical approach. Next, a heat engine is constructed by considering the black hole as a working object and subsequently, its efficiency is calculated by considering a rectangular heat cycle in the P – V plane. Then the effects of the black hole parameters on its efficiency and their respective roles are studied, followed by a careful comparison of the efficiency with that of a Carnot engine so that the second law of thermodynamics holds true. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Geometric Methods in Modern Physics. 2023/07, Vol. 20, Issue 8, p1
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:0219-8878
  • DOI:10.1142/S0219887823501360
  • Accession Number:163910154
  • Copyright Statement:Copyright of International Journal of Geometric Methods in Modern Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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