Flow reconstruction using Bayesian inference with model reduction techniques

RANS equations are nowadays widely used in industry because of their affordability in terms of computational costs. They reached a high level of complexity, as they involve systems of non-linear partial differential equations. However they still lack of generality as they are based on closure coefficients determined from fundamental real flow...

Approximate basis and a priori closure models for energy-conserving reduced order modeling of fluid flows

Model order reduction (MOR) has been a field of active research in the past twenty years, more recently also in fluid dynamics. The main advantage of MOR is computational cost reduction, which, along with equally important accuracy, constitute main objective in the MOR community.<br/>A main ongoing issue is that data volumes in fluid flow...

Development of a CFD data-driven surrogate model using the neural network approach for prediction of aircraft performance characteristics

Evaluation of aircraft performance for design, certification, and maintenance purposes requires aerodynamic knowledge for the entire flight envelope of an aircraft. Simplified models that relate geometric properties and flight conditions of an aircraft to its aerodynamic properties are simply not sufficient anymore, as non-linear aerodynamic...