Complete List of Authors: Fang, Fangxin; Imperial College London, Earth Science and Engineering Pain, ChristopherC; Imperial College London, Department of Earth Science and Engineering Navon, Michael; Florida State University, School of Computational Science Piggott, MD; Imperial College London, Earth Science and Engineering Gorman, Gerard; Imperial College London, Earth Science and Engineering...

Abstract: This paper considers a stabilizing reduced order model predictive control for constrained linear discrete-time systems. By employing a system decomposition on the inputoutput function space, a reduced order model predictive control law, which guarantees closedloop stability and feasibility, is obtained from a low dimensional on-line optimization problem. Numerical examples are provide...

Due to the increasing penetration of wind energy, it is necessary to possess design tools that are able to simulate the impact of these installations in utility grids. In order to provide a net contribution to this issue a detailed wind park model has been developed and is briefly presented. However, the computational costs associated with the performance of such a detailed model in describing ...

A reduced-order method based on Approximated Lax Pairs (ALP) is applied to the integration of electrophysiology models. These are often high-dimensional parametric equation systems, challenging from a model reduction standpoint. The method is tested on two and three dimensional test-cases, of increasing complexity. The solutions are compared to the ones obtained by a finite element. The reduced...

The use of finite element or finite difference techniques to discretize nonlinear smart material system models can yield full-order numerical models that accurately characterize the system dynamics but do so at significant computational cost. This can preclude the use of these full-order models for uncertainty analysis, sensitivity analysis, system design, or real-time control implementation. I...

In this article, an improved reduced order modelling approach, based on the proper orthogonal decomposition (POD) method, is presented. After projecting the governing equations of flow dynamics along the POD modes, a dynamical system was obtained. Normally, the classical reduced order models do not predict accurate time variations of flow variables due to some reasons. The response of the dynam...