Development of radionuclide dispersion modeling software based on Gaussian Plume model
It has come to attention that Malaysia have been aiming to build its own nuclear power plant (NPP) for electricity generation in 2030 to diversify the national energy supply and resources. As part of the regulation to build a NPP, environmental risk assessment analysis which includes the atmospheric...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTM Press
2017
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/80701/1/KohMengHock2017_DevelopmentofRadionuclideDispersionModeling.pdf http://eprints.utm.my/id/eprint/80701/ https://dx.doi.org/10.11113/matematika.v33.n2.1003 |
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| Summary: | It has come to attention that Malaysia have been aiming to build its own nuclear power plant (NPP) for electricity generation in 2030 to diversify the national energy supply and resources. As part of the regulation to build a NPP, environmental risk assessment analysis which includes the atmospheric dispersion assessment has to be performed as required by the Malaysian Atomic Energy Licensing Board (AELB) prior to the commissioning process. The assessment is to investigate the dispersion of radioactive effluent from the NPP in the event of nuclear accident. This article will fo- cus on current development of locally developed atmospheric dispersion modeling code based on Gaussian Plume model. The code is written in Fortran computer language and has been benchmarked to a readily available HotSpot software. The radionuclide release rate entering the Gaussian equation is approximated to the value found in the Fukushima NPP accident in 2011. Meteorological data of Mersing District, Johor of year 2013 is utilized for the calculations. The results show that the dispersion of ra- dionuclide effluent can potentially affect areas around Johor Bahru district, Singapore and some parts of Riau when the wind direction blows from the North-northeast di- rection. The results from our code was found to be in good agreement with the one obtained from HotSpot, with less than 1% discrepancy between the two. |
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