Computing Surrogates for Gas Network Simulation Using Model Order Reduction

Sara Grundel (), Nils Hornung (), Bernhard Klaassen, Peter Benner and Tanja Clees
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Sara Grundel: Max Planck Institute
Nils Hornung: Fraunhofer SCAI
Bernhard Klaassen: Fraunhofer SCAI
Peter Benner: Max Planck Institute
Tanja Clees: Fraunhofer SCAI

A chapter in Surrogate-Based Modeling and Optimization, 2013, pp 189-212 from Springer

Abstract: Abstract 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 range of applications. Model order reduction, on the other hand, is an approach that takes the principal structure of the equations into account to construct low-dimensional approximations to the problem. We give an introductory survey of both methods, discuss their application to gas transport problems and compare both methods by means of a simple test case from industrial practice.

Keywords: Reproducing kernels; Radial basis functions; Model order reduction; Proper orthogonal decomposition; Gas transport; Networks; Differential algebraic equations (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-7551-4_9

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DOI: 10.1007/978-1-4614-7551-4_9

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