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Convection Reaction: Difference between revisions

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{{preliminary}} <!-- Do not remove -->
[[Category:benchmark]]
[[Category:benchmark]]
[[Category:SLICOT]]
[[Category:SLICOT]]
[[Category:linear]]
[[Category:time invariant]]
[[Category:first differential order]]
[[Category:Sparse]]
[[Category:Sparse]]
[[Category:SISO]]
[[Category:SISO]]


'''This is a stub. Please expand.'''
{{Infobox
|Title          = Convection Reaction
|Benchmark ID    = convectionReaction_n84m1q1
|Category        = slicot
|System-Class    = LTI-FOS
|nstates        = 84
|ninputs        = 1
|noutputs        = 1
|nparameters    = 0
|components      = A, B, C
|License        = NA
|Creator        = [[User:Himpe]]
|Editor          =
* [[User:Himpe]]
* [[User:Mlinaric]]
|Zenodo-link    = NA
}}


==Description==
==Description==


This benchmark models a chemical reaction by a [[wikipedia:Convection-diffusion_equation|convection]]-[[wikipedia:Reaction-diffusion|reaction]] partial differential equation.
This benchmark models a chemical reaction by a [[wikipedia:Convection-diffusion_equation|convection]]-[[wikipedia:Reaction-diffusion|reaction]] partial differential equation on the unit square,
given by:
 
:<math>
\frac{\partial x}{\partial t} = \frac{\partial^2 x}{\partial y^2} + \frac{\partial^2 x}{\partial z^2} + 20 \frac{\partial x}{\partial z} - 180 x + f(y,z) x(t),
</math>
 
with Dirichlet boundary conditions and discretized with centered difference approximation.
 
The input vector <math>B</math> is composed of random elements and the output vector equals the input vector <math>C = B^T</math>.
 
More details can be found in <ref name="raschman80"/>, <ref name="saad88"/>, <ref name="grimme97"/> and <ref name="chahlaoui02"/>, <ref name="chahlaoui05"/>.
More details can be found in <ref name="raschman80"/>, <ref name="saad88"/>, <ref name="grimme97"/> and <ref name="chahlaoui02"/>, <ref name="chahlaoui05"/>.


==Origin==
==Origin==
Line 18: Line 44:
This benchmark is part of the '''SLICOT Benchmark Examples for Model Reduction'''<ref name="chahlaoui05"/>.
This benchmark is part of the '''SLICOT Benchmark Examples for Model Reduction'''<ref name="chahlaoui05"/>.


==Data==
The system matrices <math>A</math>, <math>B</math>, <math>C</math> are available from the [https://www.slicot.org/20-site/126-benchmark-examples-for-model-reduction SLICOT benchmarks] page: [https://www.slicot.org/objects/software/shared/bench-data/pde.zip pde.zip] and are stored as MATLAB [https://www.mathworks.com/help/matlab/import_export/mat-file-versions.html .mat] file.


==Data==
Here is [https://www.python.org Python] code for loading the matrices (<math>A</math> is a sparse matrix of 16-bit integers and <math>B</math> and <math>C</math> are full matrices stored as sparse matrices):


The system matrices <math>A</math>, <math>B</math>, <math>C</math> are available from the [http://slicot.org/20-site/126-benchmark-examples-for-model-reduction SLICOT benchmarks] page: [http://slicot.org/objects/software/shared/bench-data/pde.zip pde.zip] and are stored as MATLAB [https://www.mathworks.com/help/matlab/import_export/mat-file-versions.html .mat] file.
:<syntaxhighlight lang="python">
import numpy as np
from scipy.io import loadmat


mat = loadmat('build.mat')
A = mat['A'].astype(np.float64)
B = mat['B'].toarray()
C = mat['C'].toarray()
</syntaxhighlight>


==Dimensions==
==Dimensions==
Line 29: Line 65:


:<math>
:<math>
\begin{array}{rcl}
\begin{align}
\dot{x}(t) &=& Ax(t) + Bu(t) \\
  \dot{x}(t) &= A x(t) + B u(t) \\
y(t) &=& Cx(t)
  y(t) &= C x(t)
\end{array}
\end{align}
</math>
</math>


Line 40: Line 76:
<math>B \in \mathbb{R}^{84 \times 1}</math>,
<math>B \in \mathbb{R}^{84 \times 1}</math>,
<math>C \in \mathbb{R}^{1 \times 84}</math>.
<math>C \in \mathbb{R}^{1 \times 84}</math>.


==Citation==
==Citation==
Line 77: Line 112:
<ref name="saad88"> Y. Saad.  <span class="plainlinks">[https://doi.org/10.1109/9.406 Projection and deflation method for partial pole assignment in linear state feedback]</span>, IEEE Transactions on Automatic Control, 33(3): 290--297, 1988.</ref>
<ref name="saad88"> Y. Saad.  <span class="plainlinks">[https://doi.org/10.1109/9.406 Projection and deflation method for partial pole assignment in linear state feedback]</span>, IEEE Transactions on Automatic Control, 33(3): 290--297, 1988.</ref>


<ref name="grimme97"> E.J. Grimme. <span class="plainlinks">[http://hdl.handle.net/2142/81180 Krylov Projection Methods for Model Reduction]</span>. PhD Thesis, University of Illinois at Urbana-Champaign, 1998.</ref>
<ref name="grimme97"> E.J. Grimme. <span class="plainlinks">[https://www.proquest.com/dissertations-theses/krylov-projection-methods-model-reduction/docview/304361372/se-2?accountid=14597 Krylov Projection Methods for Model Reduction]</span>. PhD Thesis, University of Illinois at Urbana-Champaign, 1998.</ref>


<ref name="chahlaoui02"> Y. Chahlaoui, P. Van Dooren, <span class="plainlinks">[http://eprints.maths.manchester.ac.uk/1040/1/ChahlaouiV02a.pdf A collection of Benchmark examples for model reduction of linear time invariant dynamical systems]</span>, Working Note 2002-2: 2002.</ref>
<ref name="chahlaoui02"> Y. Chahlaoui, P. Van Dooren, <span class="plainlinks">[http://eprints.maths.manchester.ac.uk/1040/1/ChahlaouiV02a.pdf A collection of Benchmark examples for model reduction of linear time invariant dynamical systems]</span>, Working Note 2002-2: 2002.</ref>

Latest revision as of 09:28, 30 November 2023


Convection Reaction
Background
Benchmark ID

convectionReaction_n84m1q1

Category

slicot

System-Class

LTI-FOS

Parameters
nstates
84
ninputs

1

noutputs

1

nparameters

0

components

A, B, C

Copyright
License

NA

Creator

User:Himpe

Editor
Location

NA


Description

This benchmark models a chemical reaction by a convection-reaction partial differential equation on the unit square, given by:

xt=2xy2+2xz2+20xz180x+f(y,z)x(t),

with Dirichlet boundary conditions and discretized with centered difference approximation.

The input vector B is composed of random elements and the output vector equals the input vector C=BT.

More details can be found in [1], [2], [3] and [4], [5].

Origin

This benchmark is part of the SLICOT Benchmark Examples for Model Reduction[5].

Data

The system matrices A, B, C are available from the SLICOT benchmarks page: pde.zip and are stored as MATLAB .mat file.

Here is Python code for loading the matrices (A is a sparse matrix of 16-bit integers and B and C are full matrices stored as sparse matrices):

import numpy as np
from scipy.io import loadmat

mat = loadmat('build.mat')
A = mat['A'].astype(np.float64)
B = mat['B'].toarray()
C = mat['C'].toarray()

Dimensions

System structure:

x˙(t)=Ax(t)+Bu(t)y(t)=Cx(t)

System dimensions:

A84×84, B84×1, C1×84.

Citation

To cite this benchmark, use the following references:

  • For the benchmark itself and its data:
Niconet e.V., SLICOT - Subroutine Library in Systems and Control Theory, http://www.slicot.org
@MANUAL{slicot_pde,
 title =        {{SLICOT} - Subroutine Library in Systems and Control Theory},
 organization = {Niconet e.V.},
 address =      {\url{http://www.slicot.org}},
 key =          {SLICOT}
}
  • For the background on the benchmark:
@ARTICLE{Saa88,
 author =       {Y. Saad},
 title =        {Projection and deflation method for partial pole assignment in linear state feedback},
 journal =      {IEEE Transactions on Automatic Control},
 volume =       {33},
 number =       {3},
 pages =        {290--297},
 year =         {1988},
 doi =          {10.1109/9.406}
}

References

  1. P. Raschman, M. Kuhicek, M. Maros. Waves in distributed chemical systems: Experiments and computations. In: New Approaches to Nonlinear Problems in Dynamics - Proceedings of the Asilomar Conference Ground: 271--288, SIAM, 1980.
  2. Y. Saad. Projection and deflation method for partial pole assignment in linear state feedback, IEEE Transactions on Automatic Control, 33(3): 290--297, 1988.
  3. E.J. Grimme. Krylov Projection Methods for Model Reduction. PhD Thesis, University of Illinois at Urbana-Champaign, 1998.
  4. Y. Chahlaoui, P. Van Dooren, A collection of Benchmark examples for model reduction of linear time invariant dynamical systems, Working Note 2002-2: 2002.
  5. 5.0 5.1 Y. Chahlaoui, P. Van Dooren, Benchmark Examples for Model Reduction of Linear Time-Invariant Dynamical Systems, Dimension Reduction of Large-Scale Systems, Lecture Notes in Computational Science and Engineering, vol 45: 379--392, 2005.