Data from experiments and direct simulations of turbulence have historically been used to
calibrate simple engineering models such as those based on the Reynolds-averaged Navier …

In computational fluid dynamics simulations of industrial flows, models based on the
Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important …

Reynolds-averaged Navier–Stokes simulations represent a cost-effective option for practical
engineering applications, but are facing ever-growing demands for more accurate …

In ecology, the true causal structure for a given problem is often not known, and several
plausible models and thus model predictions exist. It has been claimed that using weighted …

This paper presents a novel and promising approach to turbulence model formulation, rather
than putting forward a particular new model. Evolutionary computation has brought symbolic …

Data-driven turbulence modeling has been considered an effective method for improving the
prediction accuracy of Reynolds-averaged Navier–Stokes equations. Related studies aimed …

Epidemiological modelling has assisted in identifying interventions that reduce the impact of
COVID-19. The UK government relied, in part, on the CovidSim model to guide its policy to …

This work presents developments to a novel evolutionary framework that symbolically
regresses algebraic forms of the Reynolds stress anisotropy tensor. This work contributes to …

Accurate simulation of transition from the laminar to the turbulent flow is of great importance
in industrial applications. In the present work, the framework of field inversion and machine …

The possible ill conditioning of the Reynolds average Navier–Stokes (RANS) equations
when an explicit data-driven Reynolds stress tensor closure is employed is a discussion of …