Model order reduction for gas and energy networks

Computing Surrogates for Gas Network Simulation using Model Order Reduction

CPU-intensive engineering problems such as networks of gas pipelines can be modelled as dynamical or quasi-static systems. These dynamical systems represent a map, depending on a set of control parameters, from an input signal to an output signal. In order to reduce the computational cost, surrogates based on linear combinations of translates of radial functions are a popular choice for a wide ...

Model Order Reduction for Networks of ODE and PDE Systems

We propose a model order reduction (MOR) approach for networks containing simple and complex components. Simple components are modeled by linear ODE (and/or DAE) systems, while complex components are modeled by nonlinear PDE (and/or PDAE) systems. These systems are coupled through the network topology using the Kirchhoff laws. As application we consider MOR for electrical networks, where semico...

A New Model for Designing Gas Distribution Networks

A new model for the design of the city gas distribution network has been presented. On the basis of mass flow rates and pressure balances a nonlinear system of equation is obtained. A method for solving the latter is outlined which gives accurate results. Also, this method, when compared to the other accessible ones, takes less computer time. Finally in this method the actual length of netw...

Energy preserving model order reduction of the nonlinear Schrödinger equation

An energy preserving reduced order model is developed for the nonlinear Schrödinger equation (NLSE). The NLSE is discretized in space by the symmetric interior penalty discontinuous Galerkin (SIPG) method. The resulting system of Hamiltonian ordinary differential equations are integrated in time by the energy preserving average vector field (AVF) method. Preservation of the semi-discrete energy...

Developing an Artificial Neural Networks Model for Predicting Output Energy and GHG Emission of Strawberry Production