Computers and Concrete

  • 제32권4호
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  • Pages.399-410
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  • 2023
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  • 1598-8198(pISSN)
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  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

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Design of intelligent computing networks for a two-phase fluid flow with dusty particles hanging above a stretched cylinder

  • Tayyab Zamir (Department of Mathematics, COMSATS University Islamabad, Attock Campus) ;
  • Farooq Ahmed Shah (Department of Mathematics, COMSATS University Islamabad, Attock Campus) ;
  • Muhammad Shoaib (Department of Mathematics, COMSATS University Islamabad, Attock Campus) ;
  • Atta Ullah (Department of Mathematics, COMSATS University Islamabad, Attock Campus)
  • 투고 : 2023.04.01
  • 심사 : 2023.06.14
  • 발행 : 2023.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study proposes a novel use of backpropagated Levenberg-Marquardt neural networks based on computational intelligence heuristics to comprehend the examination of hybrid nanoparticles on the flow of dusty liquid via stretched cylinder. A two-phase model is employed in the present work to describe the fluid flow. The use of desulphated nanoparticles of silver and molybdenum suspended in water as base fluid. The mathematical model represented in terms of partial differential equations, Implementing similarity transformationsis model is converted to ordinary differential equations for the analysis . By adjusting the particle mass concentration and curvature parameter, a unique technique is utilized to generate a dataset for the proposed Levenberg-Marquardt neural networks in various nanoparticle circumstances on the flow of dusty liquid via stretched cylinder. The intelligent solver Levenberg-Marquardt neural networks is trained, tested and verified to identify the nanoparticles on the flow of dusty liquid solution for various situations. The Levenberg-Marquardt neural networks approach is applied for the solution of the hybrid nanoparticles on the flow of dusty liquid via stretched cylinder model. It is validated by comparison with the standard solution, regression analysis, histograms, and absolute error analysis. Strong agreement between proposed results and reference solutions as well as accuracy provide an evidence of the framework's validity.

키워드

참고문헌

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