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 ...

The paper investigates the properties of reduced order models obtained by projection of a high order system. It answers questions such as are any two models of different orders related by a projection? Is it possible to obtain the same reduced order model using different projections? Etc. It is shown that in cases where not all models of a certain reduced order can be obtained by a projection, ...