Reduced Order Models (ROMs) represent a powerful tool to capture the most important features of a flow field by using a small number of degrees of freedom. Recently, the interest in the use of ROMs as low cost surrogate models for design and optimisation purposes has increased. These applications introduce several challenges related to the training of the model and the estimation of the error in the predicted field. In particular, the different ROM procedures share the need of a training stage in which several high-fidelity simulations are performed in order to get a set of snapshots and to build a reference database. The choice of the test configurations in the space of the design parameters is crucial since it influences directly the ability of the model to predict a wide range of configurations. In order to optimise this sampling a possible approach is represented by the use of a recursive Voronoi algorithm which explores the design space and focuses the attention on the regions which require further explorations [1]. When a ROM is used to predict a field which corresponds to a set of parameters not included in the database particular care must be taken. A possible approach is based on an optimal transport method which is used to enrich the database by creating additional artificial snapshots related to the new set of parameters. The optimal transport method allows to perform this operation by identifying the movement of the coherent structures between the original snapshots already present in the database [1]. Even if the previous strategy can reduce the number of required high fidelity simulations, the cost of the training stage can become significant when a ROM is used to describe complex flow configurations. Furthermore, the non linear nature of the fluid dynamics equations and the high sensitivity of the flow field to some design parameters (geometry of the body, Reynolds and Mach number,…) make the direct use of ROMs particularly difficult for general purpose and industrial applications. In order to avoid this shortcoming, an hybrid approach can be efficiently used to deal with complex flows [2]. In particular, the computational domain can be split in two regions: a region close to the body in which the effects of the body are directly taken into account by CFD and a far-field region in which the flow is described by the ROM. The coupling between the CFD solver and the ROM requires the definition of an overlapping region on which the ROM solution is projected. As a result, the extension of the domain on which the expensive CFD simulation has to be performed is strongly reduced. In this work, the previously described hybrid ROM/CFD approach is used to study the effects of a gust on an airfoil. The gust profile and its translation in the far-field can be easily described by a ROM defined in a frame of reference which moves following the gust. In this way, the gust can be introduced in the CFD domain as a forcing term in the boundary conditions. However, the interaction between the gust and the airfoil can alter significantly the shape and the speed of the gust. This requires the introduction of a dynamical feedback between the boundary forcing term and the field computed inside the CFD domain. In this way, the forcing term is calibrated at each time step in order to follow the current actual position and shape of the gust inside the domain. In our presentation we will describe a possible path to the introduction of the gust effects in the hybrid ROM/CFD approach.[1] Bergmann, M., Colin, T., Iollo, A., Lombardi, D., Saut, O., & Telib, H. (2013). Reduced Order Models at work. Modeling, Simulation and Applications, 9.[2] Buffoni, M., Telib, H., & Iollo, A. (2009). Boundary conditions by low-order modelling. In Computational Fluid Dynamics 2006 (pp. 747-752). Springer Berlin Heidelberg.< Réduire