IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/7594496.html
   My bibliography  Save this article

An Efficient Mathematical Approach for the Fraction Order Differentiation Based on Future Applications of Chaotic Parameter

Author

Listed:
  • Ghulam Bary
  • Waqar Ahmed
  • Muhammad Sajid
  • Riaz Ahmad
  • Muhammad Farooq Saleem Khan
  • Md Fayz-Al-Asad
  • Nawaf N. Hamadneh
  • Ilyas Khan

Abstract

Normalized chaotic parameters examine the characterization of the particle production fluids produced at unusual energies and investigate a remarkable behavior in quantum measurement. The analogous characterization can be analyzed to probe the chaotic systems of boson particles creating sources of extraordinary energy. We observe that the bosons appear to be the appropriate aspirants of chaos fractions, and the normalized chaotic parameters evaluate the presence of such conglomerate phases significantly. The core point of this manuscript is that we calculate and examine the normalized chaotic parameters by differential equations to analyze the characteristics of the chaotic systems and their applications in thermal as well as in mechanical engineering. With such an efficient and distinctive approach, we perceive significant consequences for the correlator at higher temperature regimes.

Suggested Citation

  • Ghulam Bary & Waqar Ahmed & Muhammad Sajid & Riaz Ahmad & Muhammad Farooq Saleem Khan & Md Fayz-Al-Asad & Nawaf N. Hamadneh & Ilyas Khan, 2021. "An Efficient Mathematical Approach for the Fraction Order Differentiation Based on Future Applications of Chaotic Parameter," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, October.
    • Handle: RePEc:hin:jnlmpe:7594496
    DOI: 10.1155/2021/7594496
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/7594496.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/7594496.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/7594496?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Bary, Ghulam & Ahmed, Waqar & Sajid, Muhammad & Ahmad, Riaz & Alshammari, Nawa & Khan, Ilyas, 2022. "A remarkable chaotic analysis for coherence fraction order with its applications," Chaos, Solitons & Fractals, Elsevier, vol. 154(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:hin:jnlmpe:7594496. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    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.