کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6759551 | 1431395 | 2018 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Deconvolution-based real-time neutron flux reconstruction for Self-Powered Neutron Detector
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
مهندسی انرژی و فناوری های برق
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چکیده انگلیسی
Self-Powered Neutron Detector (SPND) is useful for in-core neutron flux measurement in nuclear reactors due to its tiny size, simple structure, ruggedness and self-powered feature. One type of SPNDs with delayed current from instable intermediate nuclides cannot directly represent the real-time in-core neutron flux Φ(t) by their current I(t), which should be avoided during reactor control and protection. In this paper, we proposed a deconvolution-based method to reconstruct real-time neutron flux for SPND. Following the establishment of dynamic model, the unit-impulse response function h(t) was easily obtained when neutron flux was unit-impulse. Then, the iterative compensation relations were established for delay compensation according to the convolution relationship I(t)â¯=â¯Î¦(t)â¯*â¯h(t). In the meanwhile, determination methods for initial values were also proposed and the compensation performance for jump neutron flux was demonstrated to be only 0.3â¯s. Furthermore, the dependences on initial conditions and sampling time interval were studied systematically, indicating our method is effective and robust. Finally, our method has been compared with a typical compensation method and validated with measured current, showing its advantages. This method is very attractive due to its obvious simplicity, high intuitiveness and general applicability.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Nuclear Engineering and Design - Volume 326, January 2018, Pages 261-267
Journal: Nuclear Engineering and Design - Volume 326, January 2018, Pages 261-267
نویسندگان
Qingmin Zhang, Bangjie Deng, Xinxin Liu, Chengyuan Li, Yaodong Sang, Liangzhi Cao, Guangwen Bi, Chuntao Tang, Peng Zhang, Dayin Tong, Yang Li,