In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various DNN architectures which numerically predict ...

High fidelity simulations of flow might be quite demanding, because they involve up to O(106 – 109) degrees of freedom and several hours (or even days) of computational time, also on powerful hardware parallel architectures. ...

The mass, momentum and energy fluxes between the atmosphere and ocean surface depend on the state of the ocean surface. The fluxes in turn can significantly alter the nature of the marine boundary layer and the state of ...

Offshore wind energy is one of the most competitive renewable energy resources available to us, which until now been under- exploited. Most of the problems associated with wind farm installation like land acquisition, low ...

We present a nonintrusive approach for combining high-fidelity simulations using Finite-Volume (FV) methods with Proper Orthogonal Decomposition (POD) and Galerkin Reduced-Order Modeling (ROM) methodology. By nonintrusive ...

Implementation of three different Navier–Stokes solvers in an isogeometric finite element framework is presented in this paper. The three solvers Chorin projection method and Coupled formulation , both with the Spalart–Allmaras ...

Dominant flow structures in the wake region behind the turbine employed in the Blind Test campaign [1], [2] is investigated numerically. The effect on the wake configuration at variable operating conditions are studied. ...

Coupled fluid-structure interaction simulations of wind turbines have traditionally been considered computationally too expensive to carry out. However, more powerful computers and better solution techniques based on ...

Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two, ...