  Optimal control of turbulent mixed-convection flows has attracted
considerable attention from researchers. Numerical algorithms such as Genetic
Algorithms (GAs) are powerful tools that allow to perform global optimization.
These algorithms are particularly of great interest in complex optimization
problems where cost functionals may lack smoothness and regularity. In
turbulent flow optimization, the hybridization of GA with high fidelity
Computational Fluid Dynamics (CFD) is extremely demanding in terms of
computational time and memory storage. Thus, alternative approaches aiming to
alleviate these requirements are of great interest. Nowadays, data driven
approaches gained attention due to their potential in predicting flow solutions
based only on preexisting data. In the present paper, we propose a near-real
time data-driven genetic algorithm (DDGA) for inverse parameter identification
problems involving turbulent flows. In this optimization framework, the
parametrized flow data are used in their reduced form obtained by the POD
(Proper Orthogonal Decomposition) and solutions prediction is made by
interpolating the temporal and the spatial POD subspaces through a recently
developed Riemannian barycentric interpolation. The validation of the proposed
optimization approach is carried out in the parameter identification problem of
the turbulent mixed-convection flow in a cavity. The objective is to determine
the inflow temperature and inflow velocity corresponding to a given temperature
distribution in a restricted area of the spatial domain. The results show that
the proposed genetic programming optimization framework is able to deliver good
approximations of the optimal solutions within less than two minutes.
