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README.txt
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README.txt
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Greensinversion is a package for model-based inversion of
flash thermography measurement data.
PREREQUISITES
-------------
* Fast computer with moderately high-end GPU (suggest at least
1GB video ram), multiple cores, and lots of memory (128GB minimum)
* greensconvolution package and all its prerequisites, including:
Python -- Tested with Python 2.7; should work with Python 3.x
but might need minor compatibility bugfixes
Numpy -- Any recent version should be fine
Scipy -- Any recent version should be fine
Cython -- Any recent version should be fine. Cython will need to be
configured with a suitable C compiler. On Linux this is
usually handled by your package manager. On Windows, see
https://github.com/cython/cython/wiki/installingonwindows and
https://github.com/cython/cython/wiki/CythonExtensionsOnWindows
OpenCL -- You will also to have the OpenCL installable client driver
available. On Linux this is usually as simple as
"dnf install ocd-icd-devel". On Windows make sure you have
the OpenCL drivers provided by your GPU card vendor installed
PyOpenCL -- From https://mathema.tician.de/software/pyopencl/
On Linux this may be available with your package manager,
e.g. "dnf install python2-pyopencl".
NetCDF4 Python bindings -- http://unidata.github.io/netcdf4-python/. On
Linux this may be as simple as
"dnf install python2-netcdf4"
INSTALLATION
------------
To build greensinversion:
python setup.py build
To install into site-packages (may need to be root or Administrator)
python setup.py install
TESTING/VERIFICATION
--------------------
Run the demos/verification.py script and make sure
that none of the assertions fail.
RUNNING MODEL-BASED INVERSION
-----------------------------
Most people will want to start from the commented example:
demos/greensinversion_inverse_demo_TWIRAW.py
* Be sure to program in suitable values for density (rho)
and specific heat (c) for your material, as well as
suitable values for the through-thickenss and in-plane
diffusivities (alphaz and alphaxy, respectively)
* Be sure to evaluate the spatial pixel size of your thermal
images and assign dx and dy (in meters)
* Be sure to select spatial downsampling if your thermal
resolution is very high (much higher than 1 pixel/mm).
Otherwise the compute workload might be extreme.
* Be sure to select which OpenCL device to use. You can
use the "clinfo" command to list available devices.
If you don't specify the example will pick the first GPU
it sees.
* If analyzing data from a composite,
adjust nominal_lamina_thickness
* Set the layer z positions and reflector densities
(reflectors variable)
* May need to increase numplotrows and numplotcols if
the number of reflecting layers is increased.
* Adjust frames_to_discard from looking at your raw
data and finding how many frames (including the frame
marked as the flash) are contaminated by saturation.
These will be ignored in the inversion process.
* Include appropriate code to load in your thermal
image sequence, and determine the time of the first
frame, the timestep, the frame index (0-based) of the
frame with the flash, and a data array, with time,
y, and x indices. The example reads uncalibrated
data from a Thermal Wave Imaging .RAW file.
It is best if your data is calibrated in degrees Kelvin.
That way the power fields plotted will be in units of J/m^2.
Otherwise your output power will not be in meaningful units.
* Mask out regions in the thermal image data beyond
the boundaries of the sample as NaN. This will
reduce edge effects and accelerate the computation.
* You will need to run it once to determine a suitable
Tikhonov parameter for your setup. The Tikhonov parameter
is dependent on your camera noise level and on the
scaling of the measured temperatures.
If you have a cooled InSb camera and calibrated temperature
measurements (in deg. K) then our value of 7.5e-11 would
be a good starting point. Otherwise the first plot generated
when running the example is a diagnostic plot for the
regularization process. You will want to select the Tikhonov
parameter to match the y value on that plot above where it
transitions from vertical to horizontal.
* Set the plot_min_power_per_area and plot_max_power_per_area
to see a reasonable color scaling of the plots. For reasonable
flash lamps and calibrated temperatures in deg. K, we have
found that -10000 to +30000 J/m^2 is a good range.
ACKNOWLEDGMENTS
---------------
If using or building on this software please be sure to cite the authors!
S.D. Holland and B. Schiefelbein, Model-based inversion for pulse thermography, J. Exp. Mech (under review, 2018)
This material is based own work supported by NASA through Early
Stage Innovation grant #NNX15AD75G.
Copyright (C) 2015-2018 Iowa State University Research Foundation, Inc.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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