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Difference between revisions of "Modified Gyroscope"

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[[Category:benchmark]]
 
[[Category:benchmark]]
[[Category:linear system]]
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[[Category:ODE]]
[[Category:parametric system]]
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[[Category:linear]]
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[[Category:Parametric]]
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[[Category:affine parameter representation]]
 
[[Category:time invariant]]
 
[[Category:time invariant]]
[[Category:geometric parameters]]
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[[Category:second differential order]]
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[[Category:two parameters]]
 
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{{Infobox
[[Category:second order system]]
 
  +
|Title = Modified Gyroscope
  +
|Benchmark ID = modifiedGyroscope_n17931m1q1
  +
|Category = misc
  +
|System-Class = AP-LTI-SOS
  +
|nstates = 17931
  +
|ninputs = 1
  +
|noutputs = 1
  +
|nparameters = 3
  +
|components = B, C, E, K, M
  +
|License = NA
  +
|Creator = [[User:Feng]]
  +
|Editor =
  +
* [[User:Feng]]
  +
* [[User:Himpe]]
  +
* [[User:Will]]
  +
* [[User:Saak]]
  +
* [[User:Lund]]
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|Zenodo-link = NA
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}}
   
 
==Description==
 
==Description==
  +
<figure id="fig:gyro">[[File:Gyroscope.jpg|300px|thumb|right|<caption>Schematic representation of the gyroscope.</caption>]]</figure>
The device is a MEMS gyroscope based on the butterfly gyroscope [1] developed at the Imego institute in Gothenburg, Sweden (see also: [http://simulation.uni-freiburg.de/downloads/benchmark/The%20Butterfly%20Gyro%20%2835889%29/ http://simulation.uni-freiburg.de/downloads/benchmark/The Butterfly Gyro (35889)], where a non-parametrized model for the device is given ). A gyroscope is a device used to measure angular rates in up to three axes.
 
  +
  +
The device is a [[wikipedia:Vibrating_structure_gyroscope#MEMS_gyroscopes|MEMS gyroscope]] based on the butterfly gyroscope <ref name="lienemann2004"/> developed at the [http://www.imego.com/ Imego institute] in Gothenburg,
  +
Sweden (see also: [[Butterfly_Gyroscope|Butterfly Gyroscope]], where a non-parametrized model for the device is given).
  +
A [[wikipedia:Gyroscope|gyroscope]] is a device used to measure angular rates in up to three axes.
   
The basic working principle of the gyroscope can be described as follows, see also [2]. Without applied external rotation, the paddles vibrate in phase
+
The basic working principle of the '''gyroscope''' can be described as follows, see also <ref name="Moo07"/>.
with the function <math>z(t),</math> see Fig. 1 below. Under the influence of an external rotation about the <math>x</math>-axis (drawn
+
Without applied external rotation, the paddles vibrate in phase with the function <math>z(t),</math> see Fig.&nbsp;1.
  +
Under the influence of an external rotation about the <math>x</math>-axis (drawn in red),
in red), an additional force due to the Coriolis acceleration acts upon the
 
  +
an additional force due to the Coriolis acceleration acts upon the paddles.
paddles. This force leads to an additional small out-of-phase vibration
 
between two paddles on the same side of the bearing. This
+
This force leads to an additional small out-of-phase vibration between two paddles on the same side of the bearing.
out-of phase vibration is measured as the difference of the z-displacement
+
This out-of phase vibration is measured as the difference of the <math>z</math>-displacement of the nodes with the red dots.
  +
Thus, measuring the displacement of two adjacent paddles, the rotation velocity can be ascertained.
of the nodes with the red dots. Thus, measuring the displacement of two
 
adjacent paddles, the rotation velocity can be ascertained.
 
   
 
==Motivation==
 
==Motivation==
   
When planning for and making decisions on future improvements of the Butterfly Gyroscope, it is of importance to improve the efficiency of the gyro simulations. Repeated analysis of the sensor structure have to be conducted with respect to a number of important issues. Examples of such are sensitivity to shock, linear and angular vibration sensitivity, reaction to large rates and/or acceleration, different types of excitation load cases and the effect of force-feedback. The use of model order reduction indeed decreases run time for repeated simulations.
+
When planning for and making decisions on future improvements of the butterfly gyroscope, it is of importance to improve the efficiency of the gyro simulations. Repeated analysis of the sensor structure have to be conducted with respect to a number of important issues.
  +
Examples of such are sensitivity to shock, linear and angular vibration sensitivity, reaction to large rates and/or acceleration,
 
  +
different types of excitation load cases and the effect of force-feedback.
 
  +
The use of model order reduction indeed decreases run time for repeated simulations.
   
 
==The Parametrized Model==
 
==The Parametrized Model==
   
Two parameters are of special interest for the model. The first one is the
+
Two parameters are of special interest for the model.
quantity that is to be sensed, the rotation velocity <math>\theta</math> around the <math>x</math>-axes.
+
The first one is the quantity that is to be sensed, the rotation velocity <math>\theta</math> around the <math>x</math>-axes.
 
The second parameter is the width of the bearing, <math>d</math>.
 
The second parameter is the width of the bearing, <math>d</math>.
The parametrized system below is obtained by
+
The parametrized system below is obtained by finite element discretization of the parametrized model (in the form of partial differential equations) for the '''gyroscope'''.
finite element discretization of the parametrized model (in the form of partial differential equations) for the gyroscope. The details of constructing the parametrized system can be found in [2]. The system is of the following
+
The details of constructing the parametrized system can be found in <ref name="Moo07"/>.
  +
The system is of the following form:
form:
 
   
<math>
+
:<math>
  +
\begin{align}
M(d)\ddot{x}+D(\theta)\dot{x}+T(d)x=B
 
  +
M(d)\ddot{x}(t) +D(d,\theta)\dot{x}(t) +T(d)x(t) &= B, \\
  +
y(t) &=Cx(t).
  +
\end{align}
 
</math>
 
</math>
   
  +
Here,
<math>
 
y=Cx.
 
</math>
 
   
  +
* <math>M(d)=M_1 + dM_2\in \mathbb R^{n\times n}</math> is the mass matrix,
Here,
 
<math>M(d)=(M_1+dM_2)\in \mathbb R^{n\times n}</math> is the mass matrix,
+
* <math>D(d,\theta)=\theta(D_1 + d D_2)\in \mathbb R^{n\times n}</math> is the damping matrix,
  +
* <math>T(d)=T_1+(1/d)T_2+dT_3\in \mathbb R^{n\times n}</math> is the stiffness matrix,
  +
* <math>B \in \mathbb R^{n \times 1}</math> is the load vector,
  +
* <math>C \in \mathbb R^{1 \times n}</math> is the output matrix,
  +
* <math>x \in \mathbb R^{n}</math> is the state vector,
  +
* and <math>y \in \mathbb R </math> is the output response.
   
  +
The quantity of interest <math>y</math> of the system is <math>\delta z(t)</math>, which is the difference of the displacement <math>z(t)</math> between the two red markings on the ''east'' side of the bearing (see <xr id="fig:gyro"/>).
<math>D(\theta)=\theta(D_1+dD_2)\in \mathbb R^{n\times n}</math> is the damping matrix,
 
   
  +
The parameters of the system, <math>d</math> and <math>\theta</math>, represent the width of the bearing(<math>d</math>) and the rotation velocity along the <math>x</math>-axis (<math>\theta</math>), with the ranges: <math>\theta\in [10^{-7}, 10^{-5}]</math> and <math>d\in [1,2]</math>.
<math>T(d)=T_1+(1/d)T_2+dT_3\in \mathbb R^{n\times n}</math> is the stiffness matrix,
 
   
<math>B \in \mathbb R^{n}</math> is the load vector, <math>C \in \mathbb R^{1 \times n}</math> is the output matrix, <math>x \in \mathbb R^{n}</math> is the state vector, and <math>y \in \mathbb R </math> is the output response.
+
The device works in the frequency range <math>f \in [0.025, 0.25]</math>MHz and the degrees of freedom are <math>n = 17913</math>.
   
  +
==Data==
The variables <math>d</math> and <math>\theta</math> are the parameters of the system, where
 
<math>d</math> is the width of the
 
bearing and <math>\theta</math> is the rotation velocity along the <math>x</math>-axis.
 
   
  +
The model is generated in ANSYS.
The interesting output <math>y</math> of the system is <math>\delta z(t)</math> which is the
 
  +
The system matrices <math>M_1, \, M_2, \, D_1, \, D_2, \, T_1, \, T_2, \, T_3</math>, and <math> B</math> are in the [http://math.nist.gov/MatrixMarket/ MatrixMarket format], and can be downloaded here: [[Media: Gyroscope_modi.tgz|Gyroscope_modi.tgz]]. The matrix <math>C</math> defines the output, which has zeros on all the entries, except on the 2315th entry, where the value is <math>-1</math>, and on the 5806th entry, the value is <math>1</math>;
difference of the displacement <math>z(t)</math> between the two red dots on
 
  +
in MATLAB notation, it is <tt>C(1,2315) = -1</tt> and <tt>C(1,5806) = 1</tt>.
the same side of the bearing (see Fig.1). The number of degrees of freedom is <math>n=17913</math>.
 
   
The interesting range for the parameters are: <math>\theta\in [10^{-7}, 10^{-5}]</math> and <math>d\in [1,2]</math>. The device works in the frequency range <math>f \in [0.025, 0.25]</math>MHz.
+
The matrices have been reindexed for [[MORB]], as shown in the next section. In particular, <math>D_i</math> is denoted instead by <math>E_i</math>, and <math>T_i</math> by <math>K_i</math>.
   
==Data==
+
==Dimensions==
   
  +
System structure:
The model is generated in ANSYS. The system matrices <math>M_1, \, M_2, \, D_1, \, D_2, \, T_1, \, T_2, \,T_3</math>, and <math> B</math> are in the MatrixMarket format (http://math.nist.gov/MatrixMarket/), and can be downloaded here [[Media: Gyroscope_modi.tgz|Gyroscope_modi.tgz]]. The matrix C defines the output, who has zeros at all the entries, except that on the 2315th entry, the value is -1, and on the 5806th entry, the value is 1. In MATLAB notation, it is <math>C(:, 2315)=-1</math> and <math>C(:, 5806)=1</math>.
 
   
  +
:<math>
==References==
 
  +
\begin{array}{rcl}
  +
(M_1 + d M_2)\ddot{x}(t) + \theta(E_1 + d E_2) \dot{x}(t) + (K_1 + d^{-1} K_2 + d K_3)x(t) &=& B \\
  +
y(t) &=& Cx(t)
  +
\end{array}
  +
</math>
   
  +
System dimensions:
[1] J. Lienemann, D. Billger, E. B. Rudnyi, A. Greiner, J. G. Korvink, "MEMS Compact Modeling Meets Model Order Reduction: Examples of the
 
Application of Arnoldi Methods to Microsystem Devices," Nanotech, 2004, pp. 303–306.
 
   
  +
<math>M_{1,2}, E_{1,2}, K_{1,2,3} \in \mathbb{R}^{17931 \times 17931}</math>,
[2] C. Moosmann, "ParaMOR
 
  +
<math>B \in \mathbb{R}^{17931 \times 1}</math>,
Model Order Reduction for parameterized MEMS applications," PhD thesis, Department of Microsystems Engineering,
 
  +
<math>C \in \mathbb{R}^{1 \times 17931}</math>.
University of Freiburg, Freiburg, Germany 2007.
 
  +
  +
==Citation==
  +
  +
To cite this benchmark, use the following references:
  +
  +
* For the benchmark itself and its data:
  +
::The MORwiki Community, '''Modified Gyroscope'''. MORwiki - Model Order Reduction Wiki, 2018. https://modelreduction.org/morwiki/Modified_Gyroscope
  +
  +
@MISC{morwiki_modgyro,
  +
author = <nowiki>{{The MORwiki Community}}</nowiki>,
  +
title = {Modified Gyroscope},
  +
howpublished = {{MORwiki} -- Model Order Reduction Wiki},
  +
url = <nowiki>{https://modelreduction.org/morwiki/Modified_Gyroscope}</nowiki>,
  +
year = 2018
  +
}
  +
  +
* For the background on the benchmark: <span class="plainlinks">[https://morwiki.mpi-magdeburg.mpg.de/BibTeX/#morMoo07 morMoo07]</span> (<span class="plainlinks">[https://morwiki.mpi-magdeburg.mpg.de/BibTeX/html/mor_bib.html#morMoo07 BibTeX]</span>)
  +
  +
==References==
   
  +
<references>
[3] B. Salimbahrami, R. Eid, B. Lohmann, "Model Reduction by Second Order
 
Krylov Subspaces: Extensions, Stability and Proportional Damping,"
 
IEEE International Symposium on Intelligent Control, 2006, pp. 2997–3002.
 
   
  +
<ref name="lienemann2004">J. Lienemann, D. Billger, E. B. Rudnyi, A. Greiner, J. G. Korvink, "<span class="plainlinks">[http://www.nsti.org/procs/Nanotech2004v2/6/W58.01 MEMS Compact Modeling Meets Model Order Reduction: Examples of the Application of Arnoldi Methods to Microsystem Devices]</span>", TechConnect Briefs (Technical Proceedings of the 2004 NSTI Nanotechnology Conference and Trade Show, Volume 2): 303--306, 2004.</ref>
[4] L. Feng, P. Benner, J.G Korvink, "Subspace recycling accelerates the parametric macromodeling of MEMS" International Journal for Numerical Methods in Engineering, to appear.
 
   
  +
<ref name="Moo07">C. Moosmann, "<span class="plainlinks">[http://www.freidok.uni-freiburg.de/volltexte/3971/ ParaMOR Model Order Reduction for parameterized MEMS applications]</span>", PhD thesis, Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany 2007.</ref>
Fig.1
 
[[File:Gyroscope.jpg]]
 
   
  +
</references>
   
 
==Contact==
 
==Contact==

Latest revision as of 07:35, 17 June 2025


Modified Gyroscope
Background
Benchmark ID

modifiedGyroscope_n17931m1q1

Category

misc

System-Class

AP-LTI-SOS

Parameters
nstates
17931
ninputs

1

noutputs

1

nparameters

3

components

B, C, E, K, M

Copyright
License

NA

Creator

Lihong Feng

Editor
Location

NA


Description

Figure 1: Schematic representation of the gyroscope.

The device is a MEMS gyroscope based on the butterfly gyroscope [1] developed at the Imego institute in Gothenburg, Sweden (see also: Butterfly Gyroscope, where a non-parametrized model for the device is given). A gyroscope is a device used to measure angular rates in up to three axes.

The basic working principle of the gyroscope can be described as follows, see also [2]. Without applied external rotation, the paddles vibrate in phase with the function z(t), see Fig. 1. Under the influence of an external rotation about the x-axis (drawn in red), an additional force due to the Coriolis acceleration acts upon the paddles. This force leads to an additional small out-of-phase vibration between two paddles on the same side of the bearing. This out-of phase vibration is measured as the difference of the z-displacement of the nodes with the red dots. Thus, measuring the displacement of two adjacent paddles, the rotation velocity can be ascertained.

Motivation

When planning for and making decisions on future improvements of the butterfly gyroscope, it is of importance to improve the efficiency of the gyro simulations. Repeated analysis of the sensor structure have to be conducted with respect to a number of important issues. Examples of such are sensitivity to shock, linear and angular vibration sensitivity, reaction to large rates and/or acceleration, different types of excitation load cases and the effect of force-feedback. The use of model order reduction indeed decreases run time for repeated simulations.

The Parametrized Model

Two parameters are of special interest for the model. The first one is the quantity that is to be sensed, the rotation velocity \theta around the x-axes. The second parameter is the width of the bearing, d. The parametrized system below is obtained by finite element discretization of the parametrized model (in the form of partial differential equations) for the gyroscope. The details of constructing the parametrized system can be found in [2]. The system is of the following form:


\begin{align}
M(d)\ddot{x}(t) +D(d,\theta)\dot{x}(t) +T(d)x(t) &= B, \\
y(t) &=Cx(t).
\end{align}

Here,

  • M(d)=M_1 + dM_2\in \mathbb R^{n\times n} is the mass matrix,
  • D(d,\theta)=\theta(D_1 + d D_2)\in \mathbb R^{n\times n} is the damping matrix,
  • T(d)=T_1+(1/d)T_2+dT_3\in \mathbb R^{n\times n} is the stiffness matrix,
  • B \in \mathbb R^{n \times 1} is the load vector,
  • C \in \mathbb R^{1 \times n} is the output matrix,
  • x \in \mathbb R^{n} is the state vector,
  • and y \in \mathbb R is the output response.

The quantity of interest y of the system is \delta z(t), which is the difference of the displacement z(t) between the two red markings on the east side of the bearing (see xx--CrossReference--dft--fig:gyro--xx).

The parameters of the system, d and \theta, represent the width of the bearing(d) and the rotation velocity along the x-axis (\theta), with the ranges: \theta\in [10^{-7}, 10^{-5}] and d\in [1,2].

The device works in the frequency range f \in [0.025, 0.25]MHz and the degrees of freedom are n = 17913.

Data

The model is generated in ANSYS. The system matrices M_1, \, M_2, \, D_1, \, D_2, \, T_1, \, T_2, \, T_3, and  B are in the MatrixMarket format, and can be downloaded here: Gyroscope_modi.tgz. The matrix C defines the output, which has zeros on all the entries, except on the 2315th entry, where the value is -1, and on the 5806th entry, the value is 1; in MATLAB notation, it is C(1,2315) = -1 and C(1,5806) = 1.

The matrices have been reindexed for MORB, as shown in the next section. In particular, D_i is denoted instead by E_i, and T_i by K_i.

Dimensions

System structure:


\begin{array}{rcl}
(M_1 + d M_2)\ddot{x}(t) + \theta(E_1 + d E_2) \dot{x}(t) + (K_1 + d^{-1} K_2 + d K_3)x(t) &=& B \\
y(t) &=& Cx(t)
\end{array}

System dimensions:

M_{1,2}, E_{1,2}, K_{1,2,3} \in \mathbb{R}^{17931 \times 17931}, B \in \mathbb{R}^{17931 \times 1}, C \in \mathbb{R}^{1 \times 17931}.

Citation

To cite this benchmark, use the following references:

  • For the benchmark itself and its data:
The MORwiki Community, Modified Gyroscope. MORwiki - Model Order Reduction Wiki, 2018. https://modelreduction.org/morwiki/Modified_Gyroscope
@MISC{morwiki_modgyro,
  author =       {{The MORwiki Community}},
  title =        {Modified Gyroscope},
  howpublished = {{MORwiki} -- Model Order Reduction Wiki},
  url =          {https://modelreduction.org/morwiki/Modified_Gyroscope},
  year =         2018
}

References

  1. J. Lienemann, D. Billger, E. B. Rudnyi, A. Greiner, J. G. Korvink, "MEMS Compact Modeling Meets Model Order Reduction: Examples of the Application of Arnoldi Methods to Microsystem Devices", TechConnect Briefs (Technical Proceedings of the 2004 NSTI Nanotechnology Conference and Trade Show, Volume 2): 303--306, 2004.
  2. 2.0 2.1 C. Moosmann, "ParaMOR Model Order Reduction for parameterized MEMS applications", PhD thesis, Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany 2007.

Contact

Lihong Feng