Authors : Karam Dhafer Abdullah

Volume/Issue : Volume 10 - 2025, Issue 7 - July

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Google Scholar : https://tinyurl.com/vtx7p3xd

DOI : https://doi.org/10.38124/ijisrt/25jul328

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This research presents a hybrid turbulence modeling framework that couples fractional-order Navier-Stokes equations with a machine learning-based subgrid-scale stress closure model. The objective is to enhance the accuracy of turbulent flow simulations by incorporating long-range memory and non-local effects via fractional calculus, alongside neural network-inspired closures. A simplified 1D fractional-order Burgers' equation is used with a synthetic ML-based stress term to illustrate the method. Results show improved flow representation, highlighting the model’s potential for broader applications in fluid mechanics.

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3. Duraisamy, K., et al. (2019). Turbulence Modeling in the Age of Data. Annual Review of Fluid Mechanics.
4. Li, C., & Zeng, F. (2015). Numerical Methods for Fractional Calculus. CRC Press.