کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5477418 | 1521562 | 2017 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Automatic plume episode identification and cloud shine reconstruction method for ambient gamma dose rates during nuclear accidents
ترجمه فارسی عنوان
شناسایی اپیزود اتوماتیک و روش بازسازی ابری برای میزان دوز گاز طبقاتی در حوادث هسته ای
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی هسته ای و مهندسی
چکیده انگلیسی
Ambient gamma dose rate (GDR) is the primary observation quantity for nuclear emergency management due to its high acquisition frequency and dense spatial deployment. However, ambient GDR is the sum of both cloud and ground shine, which hinders its effective utilization. In this study, an automatic method is proposed to identify the radioactive plume passage and to separate the cloud and ground shine in the total GDR. The new method is evaluated against a synthetic GDR dataset generated by JRODOS (Real Time On-line Decision Support) System and compared with another method (Hirayama, H. et al., 2014. Estimation of I-131 concentration using time history of pulse height distribution at monitoring post and detector response for radionuclide in plume. Transactions of the Atomic Energy Society of Japan 13:119-126, in Japanese (with English abstract)). The reconstructed cloud shine agrees well with the actual values for the whole synthetic dataset (1451 data points), with a very small absolute fractional bias (FB = 0.02) and normalized mean square error (NMSE = 2.04) as well as a large correlation coefficient (r = 0.95). The new method significantly outperforms the existing one (more than 95% reduction of FB and NMSE, and 61% improvement of the correlation coefficient), mainly due to the modification for high deposition events. The code of the proposed methodology and all the test data are available for academic and non-commercial use. The new approach with the detailed interpretation of the in-situ environment data will help improving the ability of off-site source term inverse estimation for nuclear accidents.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Environmental Radioactivity - Volumes 178â179, November 2017, Pages 36-47
Journal: Journal of Environmental Radioactivity - Volumes 178â179, November 2017, Pages 36-47
نویسندگان
Xiaole Zhang, Wolfgang Raskob, Claudia Landman, Dmytro Trybushnyi, Christoph Haller, Hongyong Yuan,