Eur. Phys. J. Spec. Top.
https://doi.org/10.1140/epjs/s11734-025-02031-x

Editorial

Advancement in computational fluid mechanics: bridging theory and application

¹ Department of Aerospace Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, India
² Department of Mechanical Engineering, Indian Institute of Technology Delhi, 110016, New Delhi, India
³ Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, 247667, Roorkee, India
⁴ College of Engineering, The University of Iowa, 52242, Iowa City, USA

^a ashoke@iitk.ac.in

Abstract

This special issue, Advancement in Computational Fluid Mechanics: Bridging Theory and Application, presents a curated set of studies that exemplify the continuing evolution of Computational Fluid Mechanics (CFM) as both a scientific discipline and an engineering tool. The contributions cover an extensive range of flow phenomena and modeling scales, encompassing magnetohydrodynamic and nanofluid convection, multiphase and interfacial dynamics, turbulent reacting flows, hemodynamic transport in biological systems, and flow–structure interactions in complex geometries. Together, these works demonstrate how advanced numerical frameworks such as lattice Boltzmann and immersed boundary formulations, hybrid RANS–LES turbulence models, AI-assisted and optimization-driven simulations, and data-integrated reduced-order modeling are enabling unprecedented predictive capability across domains from propulsion and energy systems to biomedical and microfluidic devices. Several studies highlight the growing integration of machine learning and high-performance computing to accelerate model calibration, enhance physical fidelity, and uncover new parametric insights. Collectively, the articles reinforce the central vision of this collection: to bridge rigorous theoretical development with application-driven problem solving, thereby transforming CFM from a traditional analysis tool into a unifying framework for innovation in science, technology, and design.