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tool for resonance neutron imaging

Project description

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Abstract

ImagingReso is an open-source Python library that simulates the neutron resonance signal for neutron imaging measurements. By defining the sample information such as density, thickness in the neutron path, and isotopic ratios of the elemental composition of the material, this package plots the expected resonance peaks for a selected neutron energy range. Various sample types such as layers of single elements (Ag, Co, etc. in solid form), chemical compounds (UO2, Gd2O3, etc.), or even multiple layers of both types can be plotted with this package. Major plotting features include display of the transmission/attenuation in wavelength, energy, and time scale, and show/hide elemental and isotopic contributions in the total resonance signal.

The energy dependent cross-section data used in this library are from National Nuclear Data Center, a published online database. Evaluated Nuclear Data File (ENDF/B-VII.1) [1] is currently supported and more evaluated databases will be added in future.

Python packages used are: SciPy [2], NumPy [3], Matplotlib [4], Pandas [5] and Periodictable [6].

Statement of need

Neutron imaging is a powerful tool to characterize material non-destructively. And based on the unique resonance features, it is feasible to identify elements and/or isotopes which resonance with incident neutrons. However, a dedicated tool for resonance imaging is missing, and ImagingReso we presented here could fill this gap.

Community guidelines

How to contribute

Clone the code to your own machine, make changes and do a pull request. We are looking forward to your contribution to this code!

How to report issues

Please use ‘Issues’ tab on Git to submit issue or bug.

Support

You can email authors for support.

Installation instructions

Python 3.x is required for installing this package.

Install ImagingReso by typing the following command in Terminal:

$ pip install ImagingReso

or

$ conda config --add channels conda-forge
$ conda install imagingreso

or by typing the following command under downloaded directory in Terminal:

$ python setup.py

Example usage

Example of usage is presented at http://imagingreso.readthedocs.io/ . Same content can also be found in tutorial.ipynb under /notebooks in this repository.

Calculation algorithm

The calculation algorithm of neutron transmission T(E), is base on Beer-Lambert law [7]-[9]:

Beer-lambert Law 1

where

Ni : number of atoms per unit volume of element i,

di : effective thickness along the neutron path of element i,

σij (E) : energy-dependent neutron total cross-section for the isotope j of element i,

Aij : abundance for the isotope j of element i.

For solid materials, the number of atoms per unit volume can be calculated from:

Beer-lambert law 2

where

NA : Avogadro’s number,

Ci : molar concentration of element i,

ρi : density of the element i,

mij : atomic mass values for the isotope j of element i.

References

[1] M. B. Chadwick et al., “ENDF/B-VII.1 Nuclear Data for Science and Technology: Cross Sections, Covariances, Fission Product Yields and Decay Data,” Nuclear Data Sheets, vol. 112, no. 12, pp. 2887–2996, Dec. 2011.

[2] T. E. Oliphant, “SciPy: Open Source Scientific Tools for Python,” Computing in Science and Engineering, vol. 9. pp. 10–20, 2007.

[3] S. van der Walt et al., “The NumPy Array: A Structure for Efficient Numerical Computation,” Computing in Science & Engineering, vol. 13, no. 2, pp. 22–30, Mar. 2011.

[4] J. D. Hunter, “Matplotlib: A 2D Graphics Environment,” Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, May 2007.

[5] W. McKinney, “Data Structures for Statistical Computing in Python,” in Proceedings of the 9th Python in Science Conference, 2010, pp. 51–56.

[6] P. A. Kienzle, “Periodictable V1.5.0,” Journal of Open Source Software, Jan. 2017.

[7] M. Ooi et al., “Neutron Resonance Imaging of a Au-In-Cd Alloy for the JSNS,” Physics Procedia, vol. 43, pp. 337–342, 2013.

[8] A. S. Tremsin et al., “Non-Contact Measurement of Partial Gas Pressure and Distribution of Elemental Composition Using Energy-Resolved Neutron Imaging,” AIP Advances, vol. 7, no. 1, p. 15315, 2017.

[9] Y. Zhang et al., “The Nature of Electrochemical Delithiation of Li-Mg Alloy Electrodes: Neutron Computed Tomography and Analytical Modeling of Li Diffusion and Delithiation Phenomenon,” Journal of the Electrochemical Society, vol. 164, no. 2, pp. A28–A38, 2017.

Meta

Yuxuan Zhang - zhangy6@ornl.gov

Jean Bilheux - bilheuxjm@ornl.gov

Distributed under the BSD license. See LICENSE.txt for more information

https://github.com/ornlneutronimaging/ImagingReso

Publication

Yuxuan Zhang and Jean Bilheux, “ImagingReso: A Tool for Neutron Resonance Imaging”, The Journal of Open Source Software, 2 (2017) 407, doi:10.21105/joss.00407

Acknowledgements

This work is sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle LLC, under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan(http://energy.gov/downloads/doe-public-access-plan).

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