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Difference between revisions of "Windscreen"

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\{0.5j,j=1,\ldots,m\}</math> with <math>m=400</math>.
 
\{0.5j,j=1,\ldots,m\}</math> with <math>m=400</math>.
   
<xr id="fig1"/> shows the mesh of the car windscreen and <xr id="fig2"/> the frequency response <math>\vert \Re(y(\omega)) \vert</math>.
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Fig.&nbsp;1 shows the mesh of the car windscreen and Fig.&nbsp;2 the frequency response <math>\vert \Re(y(\omega)) \vert</math>.
   
 
==Origin==
 
==Origin==

Revision as of 09:53, 27 August 2021


Description

Figure 1
Figure 2

This is an example for a model in the frequency domain of the form


\begin{align}
  K_d x - \omega^2 M x & = f \\
  y & = f^* x
\end{align}

where f represents a unit point load in one unknown of the state vector. M is a symmetric positive-definite matrix and K_d = (1+i\gamma) K where K is symmetric positive semi-definite.

The test problem is a structural model of a car windscreen. [1] This is a 3D problem discretized with 7564 nodes and 5400 linear hexahedral elements (3 layers of 60 \times 30 elements). The mesh is shown in xx--CrossReference--dft--fig1--xx. The material is glass with the following properties: The Young modulus is 7\times10^{10}\mathrm{N}/\mathrm{m}^2, the density is 2490 \mathrm{kg}/\mathrm{m}^3, and the Poisson ratio is 0.23. The natural damping is 10\%, i.e. \gamma=0.1. The structural boundaries are free (free-free boundary conditions). The windscreen is subjected to a point force applied on a corner. The goal of the model reduction is the fast evaluation of y. Model reduction is used as a fast linear solver for a sequence of parametrized linear systems.

The discretized problem has dimension n=22692. The goal is to estimate x(\omega) for \omega\in[0.5,200]. In order to generate the plots, the frequency range was discretized as \{\omega_1,\ldots,\omega_m\} =
\{0.5j,j=1,\ldots,m\} with m=400.

Fig. 1 shows the mesh of the car windscreen and Fig. 2 the frequency response \vert \Re(y(\omega)) \vert.

Origin

This benchmark is part of the Oberwolfach Benchmark Collection[2]; No. 38886.

Data

Download matrices in the Matrix Market format:

The archive contains files windscreen.K, windscreen.M and windscreen.B representing K_d, -M and f accordingly.

Dimensions

System structure:


\begin{align}
  (K + \omega^2 M) x & = B \\
  y & = B^{\mathrm{T}} x
\end{align}

with \omega \in [0.5, 200].

System dimensions:

K \in \mathbb{C}^{22692 \times 22692}, M \in \mathbb{R}^{22692 \times 22692}, B \in \mathbb{R}^{22692 \times 1}.

Citation

To cite this benchmark, use the following references:

  • For the benchmark itself and its data:
Oberwolfach Benchmark Collection, Windscreen. hosted at MORwiki - Model Order Reduction Wiki, 2018. http://modelreduction.org/index.php/Windscreen
@MISC{morwiki_windscreen,
  author =       {{Oberwolfach Benchmark Collection}},
  title =        {Windscreen},
  howpublished = {hosted at {MORwiki} -- Model Order Reduction Wiki},
  url =          {http://modelreduction.org/index.php/Windscreen},
  year =         20XX
}
  • For the background on the benchmark:
@article{Mee07,
  author =       {K. Meerbergen},
  title =        {Fast frequency response computation for {R}ayleigh damping},
  journal =      {International Journal for Numerical Methods in Engineering},
  volume =       {73},
  number =       {1},
  pages =        {96--106},
  year =         {2007},
  doi =          {10.1002/nme.2058},
}

References

  1. K. Meerbergen, Fast frequency response computation for Rayleigh damping, International Journal for Numerical Methods in Engineering, 73(1): 96--106, 2007.
  2. J.G. Korvink, E.B. Rudnyi, Oberwolfach Benchmark Collection, In: Dimension Reduction of Large-Scale Systems, Lecture Notes in Computational Science and Engineering, vol 45: 311--315, 2005.