  Reduced order modeling (ROM) techniques are numerical methods that
approximate the solution of parametric partial differential equation (PDE) by
properly combining the high-fidelity solutions of the problem obtained for
several configurations, i.e. for several properly chosen values of the
physical/geometrical parameters characterizing the problem. In this
contribution, we propose an efficient non-intrusive data-driven framework
involving ROM techniques in computational fluid dynamics (CFD) for hemodynamics
applications. By starting from a database of high-fidelity solutions related to
a certain values of the parameters, we apply the proper orthogonal
decomposition with interpolation (PODI) and then reconstruct the variables of
interest for new values of the parameters, i.e. different values from the ones
included in the database. Furthermore, we present a preliminary web application
through which one can run the ROM with a very user-friendly approach, without
the need of having expertise in the numerical analysis and scientific computing
field. The case study we have chosen to test the efficiency of our algorithm is
represented by the aortic blood flow pattern in presence of a Left Ventricular
Assist Device (LVAD) when varying the pump flow rate.
