Recent Trends in Fluid Mechanics

This study proposes a novel Physics-Adaptive Digital Twin with Neural-Operator Reduced-Order Modelling (PADT-NO) framework for predictive modelling of complex, nonlinear, and multiscale fluid flows. The proposed mathematical framework integrates fundamental conservation laws, Navier–Stokes dynamics, physics-constrained neural operators, adaptive reduced-order modelling, and uncertainty-aware state estimation within a unified computational architecture.

The growth of the wind energy sector has increased the need for enhanced techniques with the goal of improving the efficiency and reliability of wind turbines.

The present work describes a numerical analysis of the steady, laminar flow over a nonlinear stretching sheet in a nanofluid medium.

Particle-based fluid simulation has emerged as a powerful computational framework for modeling complex multiphase flows involving large deformations, moving interfaces, and nonlinear fluid interactions that challenge conventional mesh-based approaches.

This study presents a comprehensive investigation of magneto-thermal hybrid nanofluid flow over stretching and shrinking surfaces using a fractional calculus framework to capture the memory and hereditary characteristics of complex fluid transport phenomena.

Fluid mechanics investigates the behavior of liquids and gases in motion and at rest, revealing the principles that control natural occurrences and engineering systems alike.