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<big>COSSAN TRAININ COURSES</big>
  
 
== 8th Cossan Training Wuhan University ==
 
== 8th Cossan Training Wuhan University ==
 
30 November - 1 December 2019
 
30 November - 1 December 2019
  
{{important|Licence server| 192.168.5.23}}
+
{{important|Licence server| 192.168.5.219}}
  
== Lecture Notes ==
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Requirements: MS Windows 10 or Linux distribution, Matlab Run Time 2018b, JavaOpenJDK
 +
 
 +
=== Lecture Notes ===
 
* {{pdf|01_IntroWuhan.pdf|01. Intro}}
 
* {{pdf|01_IntroWuhan.pdf|01. Intro}}
* {{pdf|03_CossanSoftware.pdf|02. Cossan Software}}
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* {{pdf|02_CossanSoftwareWuhan.pdf|02. Cossan Software}}
* {{pdf|04_UQ.pdf|03. Uncertainty Quantification}}
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* {{pdf|03_UQWuhan.pdf|03. Uncertainty Quantification}}
 +
* {{pdf|04_MonteCarloWuhan.pdf|04. Monte Carlo}}
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* {{pdf|05_AdvancedMonteCarlo.pdf|05. Advanced Monte Carlo}}
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* {{pdf|06_OpenCossanWuhan.pdf|06. OpenCossan (introduction)}}
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* {{pdf|07_ConnectorWuhan.pdf|07. Link external solver (Connector)}}
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* {{pdf|08_SensitivityWuhan.pdf|08. Sensitivity Analysis}}
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* {{pdf|09_MetaModelsWuhan.pdf|09. Meta-models}}
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* {{pdf|10_OptimisationWuhan.pdf|10. Optimisation}}
  
''UNCERTAINTY QUANTIFICATION AND MANAGEMENT USING COSSAN SOFTWARE'''
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=== Examples ===
  
== 7th Cossan Training Wuhan University ==
+
==== Model 1 ====
'''8TH - 10TH APRIL 2019'''
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Let assume we want to compute the output of a model defined as:
 +
y=x*a;
  
'''UNIVERSITY OF LIVERPOOL'''
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where x is a random variable and a is a parameter.
  
----
 
  
 +
'''Define Evaluator'''
  
The materials for this training course are stored on the university's COSSAN drive.
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Structure Input/Output
  
Kindly follow the instructions on this document to map this drive to your machine.
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Toutput(n).y=Tinput(n).x*Tinput(n).a;
  
[[Media:Access_To_Drive.pdf‎]]
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Matrix Input/Output
  
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  Moutput=Minput(:,1).*Minput(:,2);
  
== Lecture Notes ==
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See also: [[Mio_(editor)]]
 +
 
 +
 
 +
==== Cantilever Beam ====
 +
See Tutorial  [[Cantilever Beam]]
 +
 
 +
==== Buffon example ====
 +
* Define 2 random variables u1 and u2 (uniform distribution between 0 and 1) and 2 functions d=u1*D and theta=u2*pi/2
 +
* Define parameter t=10
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* Define paremeter L=8
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*  Define 2 parameters (t and L) 
 +
*  Define a matlab connector [[matlab connector (editor)]] to compute <math>L cos \phi</math>. Script script:
 +
  <&L&>*cos(<%phi%>)
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* Define a performance function in the evaluator -> Performance Function as Demand (d) Capacity (t). See [[Performance Functions (editor)]] for more details
 +
* Perform Monte Carlo simulation (from Reliability Analysis)
 +
 
 +
=== Ishigami Function===
 +
Click on the links for detailed explanation of the [[Ishigami Function]] and the definition of the [[Ishigami_Function_(Connector)]].
 +
 
 +
== 8th Cossan Training Wuhan University ==
 +
'''30TH NOVEMBER - 1ST DECEMBER 2019'''
 +
 
 +
'''WUHAN UNIVERSITY '''
 +
 
 +
----
 +
 
 +
 
 +
 
 +
=== Lecture Notes ===
 
* {{pdf|01_Intro.pdf|01. Intro}}
 
* {{pdf|01_Intro.pdf|01. Intro}}
 
* {{pdf|02_LinuxCommand.pdf|02. Useful commands}}
 
* {{pdf|02_LinuxCommand.pdf|02. Useful commands}}
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* {{pdf|13_Optimisation.pdf|13. Optimisation}}
 
* {{pdf|13_Optimisation.pdf|13. Optimisation}}
  
== Videos ==
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=== Videos ===
  
 
* [https://stream.liv.ac.uk/s/5pwr8983  The Cossan software]
 
* [https://stream.liv.ac.uk/s/5pwr8983  The Cossan software]
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* [https://stream.liv.ac.uk/s/hqnffkgv Metamodel in COSSAN-X]
 
* [https://stream.liv.ac.uk/s/hqnffkgv Metamodel in COSSAN-X]
 
* [https://stream.liv.ac.uk/s/a8rsdpuq Optimization (overview)]
 
* [https://stream.liv.ac.uk/s/a8rsdpuq Optimization (overview)]
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 +
 +
 +
 +
[[Media:TutorialCantileverBeamOptimization.zip‎]]
  
 
=== Useful links ===
 
=== Useful links ===
* [[Launch_COSSAN-X_with_MobaXTerm| Lunch COSSAN-X from the RISK Cluster]]
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* [[Launch_COSSAN-X_with_MobaXTerm| Launch COSSAN-X from the RISK Cluster]]
 
* [[:Category:Tutorials| Tutorials]]
 
* [[:Category:Tutorials| Tutorials]]
  
 
== See Also ==
 
== See Also ==
 
[[Category:Training]]
 
[[Category:Training]]

Revision as of 06:38, 1 December 2019

COSSAN TRAININ COURSES

8th Cossan Training Wuhan University

30 November - 1 December 2019

Important.png Licence server
192.168.5.219


Requirements: MS Windows 10 or Linux distribution, Matlab Run Time 2018b, JavaOpenJDK

Lecture Notes

Examples

Model 1

Let assume we want to compute the output of a model defined as: y=x*a;

where x is a random variable and a is a parameter.


Define Evaluator

Structure Input/Output

Toutput(n).y=Tinput(n).x*Tinput(n).a;

Matrix Input/Output

 Moutput=Minput(:,1).*Minput(:,2);

See also: Mio_(editor)


Cantilever Beam

See Tutorial Cantilever Beam

Buffon example

  • Define 2 random variables u1 and u2 (uniform distribution between 0 and 1) and 2 functions d=u1*D and theta=u2*pi/2
  • Define parameter t=10
  • Define paremeter L=8
  • Define 2 parameters (t and L)
  • Define a matlab connector matlab connector (editor) to compute L cos \phi. Script script:
 <&L&>*cos(<%phi%>)
  • Define a performance function in the evaluator -> Performance Function as Demand (d) Capacity (t). See Performance Functions (editor) for more details
  • Perform Monte Carlo simulation (from Reliability Analysis)

Ishigami Function

Click on the links for detailed explanation of the Ishigami Function and the definition of the Ishigami_Function_(Connector).

8th Cossan Training Wuhan University

30TH NOVEMBER - 1ST DECEMBER 2019

WUHAN UNIVERSITY



Lecture Notes

Videos



Media:TutorialCantileverBeamOptimization.zip‎

Useful links

See Also