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Entry  Thu Jul 11 19:16:12 2013, Deep Chatterjee, General, General, TE noise problem - Matlab script TE_model.malmost_infinite_2D_axisym_TE_4.m
    Reply  Fri Jul 12 19:43:32 2013, Deep Chatterjee, General, General, TE noise problem - Matlab script codes_TE_calc.zip
       Reply  Sun Jul 14 15:13:46 2013, Deep Chatterjee, General, General, TE noise problem - Matlab script codes_TE_calc_(2).zip
          Reply  Mon Jul 15 16:05:29 2013, Deep Chatterjee, General, General, TE noise problem - Matlab script codes_TE_calc.zipJul_15.bmp
Message ID: 60     Entry time: Mon Jul 15 16:05:29 2013     In reply to: 58
Author: Deep Chatterjee 
Type: General 
Category: General 
Subject: TE noise problem - Matlab script 

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The present aim is the calculation of the TE noise like Liu and Thorne. Although, the results of Liu and Thorne are for the case of an infinite mirror, in our case, we try
to model a test mass with large dimensions in an attempt to get closer to the result due to Liu and Thorne

Following my previous post - The presents results given by the COMSOL simulations gave the same profile as Liu and Thorne's result which has a frequency dependence
of 1/omega^2 but was displaced from the analytic result by a constant factor which was dependent on the applied pressure on the face of the test mass which should not be
the case.

Following the steps followed Liu and Thorne, we had constructed the test mass as a cylinder, large compared to the beam spot size. An oscillating pressure was applied
to one of the faces and the from the temperature gradient generated in the process due to strains, one can calculate the work dissipated. The process involves integrating
the gradient of temperature over the entire geometry and taking the time avrage. However, this did not give the correct results, so it was decide to extract the Fourier coefficient
of the signal and perform the integration on it, as done in the case of TR niose by Koji Arai.

I mention the steps it was done -

> The data extracted was stored in a 3D array in matlab. using mphinterp - each dimension for r, z, t

> The number of radial slices and z slices is defined by the user previously

> For each r,z value there was a time signal.

> The fourier coefficient corresponding to the pressure oscillation frequency was extracted from the last three cycles
(In theory only one cycle should suffice)

> The above step was done so that random data( which is small in magnitude) generated by COMSOL is avoided.

> The fourier coefficients are stored in a 2D array corresponding to r and z values.

> The integration was performed using trapz twice once on each dimension to get the total volume integral

> The rest of the calculation is same as the previous script - plugging in the prefactors in the formulae and plotting

 

**The primary problem with this script is that the data extraction takes significant amount of time - For a mirror with 200 times the radius of the beam spot and 200 radial slices, it takes close to 6 minutes
to evaluate work dissipated for each frequency. The script solves for 16 frequency values. The results for small number of radial slices does not follow the straight line profile. For larger number of slices
the program takes a longer time. The results have not been checked yet.

 I am replying to report of the modification made to the script from the last one. The problem of too many radial and z slices have been avoided by using a slicing based on a gaussian
distribution both along the r and z directions - i.e. the slicing is heavy in the region around the surface is applied and much thinner towards the edges of the cylinder. The slicing has
been separately handled by a function named giveSlices() which does the slicing and returns the values as two arrays corresponding to r and z.
The extent to which the fineness of slicing is controlled in the geometry is by controlling the parameter SD in the code which is the standard deviation of the gaussian according to which
the slicing is controlled as being fine throughout or fine in the centre and thin with increasing r.

This method reduces the number of slices by almost 2 orders for significant large values of the dimensions of the test mass while it is expected to give similar results since slicing the
test mass finely at the edge is not required as the gradient of temperature almost falls to zero.

It is suspected that the long time of simulation is attributed to calling mphinterp() command a large number of times. In my next modification, it will be tried to use the command just once
followed by the proper data extraction from the output given by the command.

 

 Following the previous reply regarding the use of mphinterp(), the appropriate changes in code were made. The overhead time due to the call to COMSOL was responsible for the longer
simulation time. Right now, for relatively large no. of total slices (about 27000) just the extraction of data happens within a few seconds. However, for the larger number of slices, the codes 
will probably still take some time to complete. The codes are attached.

 The dependence of the Power Spectral Density on the parameters F0, which is the pressure amplitude, has been somewhat corrected for in the codes attached. The mistake lay in the fact that
extraction of the Fourier coefficients was being done for the ht.gradTmag, the Fourier coefficients of which are not the correct ones to be used in this case since ht.gradTmag is not the correct time signal.
In the present case the Fourier coefficients are extracted from the r and z components, squared and added to carry out the calculation. However, the results are still off from the L&T results by order of 5.
The codes and the plots are attached.

Jul_15.bmp

The red values are the L&T result while the blue sketch is the simulation

Attachment 1: codes_TE_calc.zip  7 kB
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