Application of Adaptive Filter for Criticality Surveillance Systems
Project/Area Number  05680424 
Research Category 
GrantinAid for Scientific Research (C).

Research Field 
エネルギー学一般・原子力学

Research Institution  Setsunan University 
Principal Investigator 
YAMADA Sumasu Setsunan University, Faculty of Engineering, Professor, 工学部, 教授 (00029133)

CoInvestigator(Kenkyūbuntansha) 
KISHIDA Kuniharu Gifu University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (90115402)

Project Fiscal Year 
1993 – 1994

Project Status 
Completed(Fiscal Year 1994)

Budget Amount *help 
¥2,100,000 (Direct Cost : ¥2,100,000)
Fiscal Year 1994 : ¥500,000 (Direct Cost : ¥500,000)
Fiscal Year 1993 : ¥1,600,000 (Direct Cost : ¥1,600,000)

Keywords  system identification / subcriticality estimation / ARMA model / adaptive filter / recursive ARMA model identification / criticality surveillance / inverse problem / identifiability / システム同定 / 未臨界度推定 / ARMAモデル / 適応フィルタ / 逐次型ARMAモデル同定 / 臨界安全監視 / 逆問題 / 可同定性 / ゼロ出力炉 / 未臨界度 / 計算機シミュレーション 
Research Abstract 
Numerical analysis In the framework of the point reactor kinetics approximation, we have proposed a method for online subcriticality monitoring by recursive AutoRegressive Moving Average (ARMA) Model identification algorithms for the time series of neutron signal fluctuation, however, the transient characteristics for estimating timevarying subcriticality was not satisfactory and also there were problems of overand/or underestimations in some cases. To mitigate these problems, we proposed the application of ADF (Adaptive Filter) algorithms. The research was focused on the basic analysis of applicability of ADF algorithms for timevarying subcriticality estimation and we obtained the following conclusions. Estimated parameters and subcriticalities with ADF algorithms have larger stochastic fluctuation than by the one based on recursive ARMA model identification, however, the ADF algorithms have fairly better transient characteristics and no problems of overand/or underestimations.
… More
Hence, ADF algorithms can be applicable for estimating subcriticality cahnge in $ units, even though there exist stochastic fluctuations. Theoretical analysis We first theoretically confirmed that the ADF algorithm can identify the ARMA model of a stochastic system driven by a random white noise as an inverse model of the original system. Generally speaking, this is an inverse problem. Hence, identifiability of the system is the essential in the system identification. Hence, this problems have been studied in the framework of discretetime multiple inputoutput feedback system. Then, we reached the conclusion that the sufficient conditions for identifying the true system from the observed signatures are ; (1) the feedback system must be of the minimum phase, (2) the equivalent noise sources of the model assumed for the stochastic system have to be mutually independent. This indicates that a feedback system of three variables with two observation variables does not satisfy the second condition even if the noise sources of the true system are mutually independent. Less

Report
(4results)
Research Output
(6results)