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Computers and Fluids - Elsevier | 2024 Impact Factor:3.0 | Cite Score:5.6 | Q1

Computers and Fluids Journal

Impact Factor and Journal Rank of Computers and Fluids

  • About: Computers & Fluids is a multidisciplinary journal that interprets the term fluid broadly, encompassing hydro- and aerodynamics, high-speed and physical gas dynamics, turbulence and flow stability, multiphase flow, rheology, tribology, aeroacoustics, and fluid-structure interaction, provided that computer techniques play a significant role in associated studies or design methodologies.
  • Editorial Focus:
    Computers & Fluids publishes research that advances understanding and application of fluid dynamics through computational techniques. The journal encourages papers that innovate in numerical methods, explore novel applications, and incorporate machine learning to enhance traditional approaches.
  • Impact and Contribution:
    As a leading journal in its field, Computers & Fluids facilitates the dissemination of cutting-edge research across diverse disciplines. It provides a platform for researchers and practitioners to exchange ideas, address challenges, and contribute to advancements in fluid dynamics and computational methodologies.

  • Editor-in-Chief:  P. Cinnella

  • Scope: Topics covered include air, sea, and land vehicle motion; flow physics; energy conversion and power; chemical reactors and transport processes; ocean and atmospheric effects; pollution; biomedicine; noise and acoustics; and magnetohydrodynamics.
  • The journal focuses on the following areas:
    Development of numerical methods relevant to fluid flow computations.
  • Computational analysis of flow physics and fluid interactions.
  • Novel applications of computational fluid dynamics to flow systems and design.
  • Uncertainty quantification in fluid flow simulations.
  • Reduced-order and surrogate models for fluid flows.
  • Optimization and control of fluid systems.
  • Machine learning approaches applied to fluid flow modeling, with a focus on scientific rigor and comparison with traditional numerical methods.
  • Theoretical analysis and physical consistency of machine learning models, including discussions on limitations and merits.
  • Latest Research Topics for PhD in Computer Science

  • Print ISSN:  0045-7930

    Electronic ISSN:  1879-0747

  • Abstracting and Indexing:  Science Citation Index Expanded, Scopus.

  • Imapct Factor 2024:  3.0

  • Subject Area and Category:  Computer Sciences, Industrial Engineering

  • Publication Frequency:  Monthly

  • H Index:  136

  • Best Quartile:

    Q1:  Computer Science (miscellaneous)

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  • Cite Score:  5.6

  • SNIP:  1.290

  • Journal Rank(SJR):  0.878