Abstract We investigate the use of reduced-order modelling to run discrete element simulations at higher speeds. Taking a data-driven approach, we many offline in advance and train model predict velocity field from mass distribution system control signals. Rapid inference particle velocities replaces intense process computing contact forces updates. In coupled DEM multibody simulation, predicto...

Abstract To counter the volatile nature of renewable energy sources, gas networks take a vital role. But, to ensure fulfillment contracts under these circumstances, vast number possible scenarios, incorporating uncertain supply and demand, has be simulated ahead time. This many-query network simulation task can accelerated by model reduction, yet, large-scale, nonlinear, parametric, hyperbolic ...

Greater numbers of power electronics (PEs) converters are being connected to energy systems due the development renewable sources, high-voltage transmission, and PE-interfaced loads. Given that electronics-based devices synchronous machines have very different dynamic behaviours, some modelling approximations, which may commonly be applied run transient simulations transmission systems, not opt...

Partial order reduction is a very succesful technique for avoid ing the state explosion problem that is inherent to explicit state model checking of asynchronous concurrent systems It exploits the commu tativity of concurrently executed transitions in interleaved system runs in order to reduce the size of the explored state space Directed model checking on the other hand addresses the state exp...

We apply the proper orthogonal decomposition (POD) to the nonlinear Schrödinger (NLS) equation to derive a reduced order model. The NLS equation is discretized in space by finite differences and is solved in time by structure preserving symplectic midpoint rule. A priori error estimates are derived for the POD reduced dynamical system. Numerical results for one and two dimensional NLS equations...

Model order reduction is a mathematical technique to transform nonlinear dynamical models into smaller ones, that are easier to analyze. In this paper we demonstrate how model order reduction can be applied to nonlinear electronic circuits. First we give an introduction to this important topic. For linear time-invariant systems there exist already some well-known techniques, like Truncated Bala...

where the matrix M ∈ RN×N is assumed to be invertible. Models of mechanical systems are often of this type since (1.1) then represents the equation of motion of the system. For such a system M = MT , C = CT and K = KT are respectively the mass, damping and stiffness matrices, f(t) ∈ RN×1 is the vector of external forces, and x(t) ∈ RN×1 is the vector of internal generalized coordinates (see [4]...

Krylov subspace methods have become very popular, not only for solving large scale linear systems, but also in the area of model order reduction. It is also well known that the performance of iterative solution techniques depends crucially on the choice of preconditioning matrix. Within the area of model order reduction, the concept of preconditioning has not yet been introduced. However, recen...