Reduced Order Models for Computational Fluid Dynamics

Dear Colleagues,

Despite the recent increase in the available computation power, there are still several cases in computational fluid dynamics where standard discretization techniques (finite elements, finite volumes, finite differences, spectral elements, etc.) become unaffordable. Such situations occur when a large number of system configurations are in need of being tested, or limited computational time is required. Typical examples of this type are shape optimization, uncertainty quantification, and real-time control. A viable approach to reduce the computational burden is given by reduced-order models. Many different types of reduced order have been developed over the years, and a possible distinction is between those that are intrusive and require the knowledge of the underlying full order model and those that are merely data-driven and therefore non-intrusive. In the first category fall the reduced-basis method, the POD-Galerkin approach, and the proper generalized decomposition. In the second category, one can find truncation-based methods, dynamic mode decomposition, neural networks, and in general all the models based on just input–output data. Possible applications include but are not limited to uncertainty quantification, inverse problems, real-time control, shape optimization, etc.

This Special Issue will publish original research, overviews, and applications on reduced-order models for computational fluid dynamics of both the intrusive and non-intrusive type.

Dr. Giovanni Stabile
Guest Editor

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