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Reduced order models allow quickly predict fluid behaviour and to better understand flow phenomena. They are the key enablers of closed-loop flow control. In this paper, reduced-order model (ROM) of an incompressible flow around a wall-mounted cylinder is constructed, by means of Galerkin projection of Navier–Stokes equations onto space spanned by the most dominant eigenmodes of dynamic mode decomposition (DMD). Additionally, genetic algorithm-based calibration is applied to improve the predictive performance of the model. The resulting low-dimensional model of the flow consists of six degrees of freedom and precisely reproduces the dynamics of limit cycle oscillations.