7 research outputs found
Identification of Port-Hamiltonian Systems from Frequency Response Data
In this paper, we study the identification problem of a passive system from
tangential interpolation data. We present a simple construction approach based
on the Mayo-Antoulas generalized realization theory that automatically yields a
port-Hamiltonian realization for every strictly passive system with simple
spectral zeros. Furthermore, we discuss the construction of a frequency-limited
port-Hamiltonian realization. We illustrate the proposed method by means of
several examples
An Operator Inference Oriented Approach for Mechanical Systems
Model-order reduction techniques allow the construction of low-dimensional
surrogate models that can accelerate engineering design processes. Often, these
techniques are intrusive, meaning that they require direct access to underlying
high-fidelity models. Accessing these models is laborious or may not even be
possible in some cases. Therefore, there is an interest in developing
non-intrusive model reduction techniques to construct low-dimensional models
directly from simulated or experimental data. In this work, we focus on a
recent data-driven methodology, namely operator inference, that aims at
inferring the reduced operators using only trajectories of high-fidelity
models. We present an extension of operator inference for mechanical systems,
preserving the second-order structure. We also study a particular case in which
complete information about the external forces is available. In this
formulation, the reduced operators having certain properties inspired by the
original system matrices are enforced by adding constraints to the optimization
problem. We illustrate the presented methodology using three numerical
examples
Structure-Preserving Model Reduction of Physical Network Systems
This paper considers physical network systems where the energy storage is naturally associated to the nodes of the graph, while the edges of the graph correspond to static couplings. The first sections deal with the linear case, covering examples such as mass-damper and hydraulic systems, which have a structure that is similar to symmetric consensus dynamics. The last section is concerned with a specific class of nonlinear physical network systems; namely detailed-balanced chemical reaction networks governed by mass action kinetics. In both cases, linear and nonlinear, the structure of the dynamics is similar, and is based on a weighted Laplacian matrix, together with an energy function capturing the energy storage at the nodes. We discuss two methods for structure-preserving model reduction. The first one is clustering; aggregating the nodes of the underlying graph to obtain a reduced graph. The second approach is based on neglecting the energy storage at some of the nodes, and subsequently eliminating those nodes (called Kron reduction).</p