This article presents a comprehensive overview of the digital twin technology and its
capability levels, with a specific focus on its applications in the wind energy industry. It …

Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …

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 …

A digital twin can be defined as an adaptive model of a complex physical system. Recent
advances in computational pipelines, multiphysics solvers, artificial intelligence, big data …

In recent years, there has been growing interest in digital twin technology in both industry
and academia. This versatile technology has found applications across various industries …

The reliance on Computational Fluid Dynamics (CFD) simulations has drastically increased
over time to evaluate the aerodynamic performance of small-scale wind turbines. With the …

Recent attempts to discover energy–efficient and cost–effective power generation systems
unleash tremendous capabilities of urban rooftops vertical–axis wind turbines (VAWT). Their …

A wide range of previously designed methods for faster parametrization of partial differential
equations requires them to be solved using existing finite volume, finite element, and finite …

Producing high-fidelity real-time simulations of neutron diffusion in a reactor is
computationally extremely challenging, due, in part, to multiscale behaviour in energy and …

Wind energy in the atmospheric boundary layer serves as the primary source for energy
absorption and structural load on wind turbines. However, the impact of turbulent coherent …