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Computing Surrogates for Gas Network Simulation Using Model Order Reduction

In: Surrogate-Based Modeling and Optimization

Author

Listed:
  • Sara Grundel

    (Max Planck Institute)

  • Nils Hornung

    (Fraunhofer SCAI)

  • Bernhard Klaassen

    (Fraunhofer SCAI)

  • Peter Benner

    (Max Planck Institute)

  • Tanja Clees

    (Fraunhofer SCAI)

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.

Suggested Citation

  • Sara Grundel & Nils Hornung & Bernhard Klaassen & Peter Benner & Tanja Clees, 2013. "Computing Surrogates for Gas Network Simulation Using Model Order Reduction," Springer Books, in: Slawomir Koziel & Leifur Leifsson (ed.), Surrogate-Based Modeling and Optimization, edition 127, pages 189-212, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-7551-4_9
    DOI: 10.1007/978-1-4614-7551-4_9
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