Recent Developments in Computational Fluid Dynamics and Turbulence Modeling

Dear Colleagues,

Computational Fluid Dynamics (CFD) and turbulence modeling have become invaluable tools in further enhancing our understanding and predictions of complex fluid flow phenomena across a wide range of scientific and engineering fields. Their application can be found in areas spanning aerospace and automotive engineering to environmental studies and biomedical flows. The ability to accurately simulate and analyze turbulent flows in the abovementioned fields has revolutionized design, optimization, and decision-making processes. This Special Issue highlights recent advancements in CFD methodologies and turbulence modeling techniques, showcasing cutting-edge research that addresses the challenges of accuracy, efficiency, and scalability in simulating turbulent flows.

The rapid evolution of high-performance computing (HPC), machine learning (ML), and advanced numerical algorithms has expanded the frontiers of CFD even further. Researchers are now able to tackle previously intractable problems, such as high-Reynolds-number flows, multiphase interactions, and reactive flows, with higher precision than ever. Additionally, the integration of data-driven approaches with traditional turbulence models is creating new hybrid methodologies that combine the strengths of physics-based and data-centric methods.

This Special Issue brings together contributions from leading experts, covering topics such as large-eddy simulation (LES), Reynolds-averaged Navier–Stokes (RANS) modeling, direct numerical simulation (DNS), and emerging machine learning-based turbulence models. By exploring both theoretical innovations and practical applications, this collection aims to provide a comprehensive overview of the state of the art in CFD and turbulence modeling, while inspiring future research directions in this dynamic field.

Recommended topics include, but are not limited to, the following:

1. Development and validation of Reynolds-averaged Navier–Stokes (RANS) models.
2. Direct numerical simulation (DNS) of turbulent flows at high Reynolds numbers.
3. Hybrid turbulence modeling approaches combining RANS, LES, and DNS.
4. Machine learning and artificial intelligence applications in turbulence modeling.
5. Multiphase and reactive turbulent flow simulations.
6. Applications of CFD in aerospace, automotive, energy, and environmental systems.
7. Data-driven turbulence modeling and reduced-order modeling techniques.
8. Wall-modeled LES and near-wall turbulence modeling challenges.
9. Non-Newtonian and compressible turbulent flow simulations.
10. Emerging trends in turbulence modeling for climate and geophysical flows.

Dr. Georgios Sofiadis
Prof. Dr. Ioannis Sarris
Guest Editors

Manuscript Submission Information

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• computational fluid dynamics (CFD)
• turbulence modeling
• turbulence models
• data-driven turbulence modeling
• high-performance computing (HPC)
• RANS
• LES
• DNS