Spatial and model-order based reactor signal analysis methodology for BWR core stability evaluation

Dokhane, A.; Ferroukhi, H.; Zimmermann, Martin; Aguirre, C.

doi:10.1016/j.anucene.2006.08.010

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…The second data set to be analyzed in this paper corresponds to low power (18.8%) and low coolant ow rate (33.5%) conditions, during the generator synchronization of the KKL core. The systematic analysis of the plant data by using the PSI stability methodology 22 (not shown here), revealed high decay ratios (i.e., DR = 0.6 -0.9) at the very low resonance frequency of 0.27 Hz for most of the in-core neutron detectors. It is well known in stability analysis, however, that at such low core power/ ow levels, the reactor core is expected to be very stable, and therefore, the source of such high decay ratios is intended to be estimated via connectivity analysis.…”

Section: B High Decay Ratio During Start-up Of Kkl Cycle 33mentioning

confidence: 78%

“…It is well known in stability analysis, however, that at such low core power/ ow levels, the reactor core is expected to be very stable, and therefore, the source of such high decay ratios is intended to be estimated via connectivity analysis. The extended statistical and postprocessing analysis of the measured data in both the time domain and the frequency domain, which is based on the consolidated PSI methodology, 22 revealed which signals are exhibiting a distinct oscillatory behavior during this measurement. Figure 9 presents the time-and frequency-dependent behavior of these reac- For this example, the model order is estimated by the Bayesian information criterion as p = 6, and then a MVAR( 6) is tted to the measured data.…”

Section: B High Decay Ratio During Start-up Of Kkl Cycle 33mentioning

confidence: 99%

See 1 more Smart Citation

Application of causality analysis on nuclear reactor systems

Chionis

Dokhane

Ferroukhi

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Causality analysis is a substantial tool for identifying cause-and-e ect links between di erent components of a system and has been extensively used in various areas of science such as neuroscience, climatology, and econometrics. This analysis is carried out in terms of the renormalized partial directed coherence and the directed transfer function connectivity measures. Applying such analysis in the nuclear reactor eld is of paramount importance since it can help in inferring cause-and-e ect relationships between highly coupled processes, and consequently, it can assist on the safe and reliable operation of a nuclear power plant during the occurrence of possible disturbances or malfunctions. The e ectiveness of the connectivity analysis is demonstrated through several simulated and measured test cases. Results show that the connectivity analysis is able to identify accurately the importance and central role of the activation signal when it is applied on a simple analytical model and a simulated nuclear reactor system. In addition, the application on more realistic and complex measured data sets of a Swiss boiling water reactor illustrates the capability of this analysis to indicate possible causes behind the observed anomalies or trends observed at certain conditions and, more importantly, allows a better understanding of the underlying interactions among di erent neutronic and thermal-hydraulic processes.

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Section: B High Decay Ratio During Start-up Of Kkl Cycle 33mentioning

confidence: 78%

Section: B High Decay Ratio During Start-up Of Kkl Cycle 33mentioning

confidence: 99%

Application of causality analysis on nuclear reactor systems

Chionis

Dokhane

Ferroukhi

et al. 2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…They were evaluated using time-series-analysis methodology developed over the years at Paul Scherrer Institute, using HPTSAC code, based on the combination of Akaike information criterion, Minimum description length principle, and the plateau method (Dokhane et al, 2006). According to the HPT-SAC estimations, the dynamics of the objective processes are well represented by VARMA(4,3) model.…”

Section: Application Of Csarma For Causality Analysis Of Kkl Plant Inmentioning

confidence: 99%

“…To evaluate the core stability properties, system identification methods based on univariate Autoregressive Moving-Average (ARMA) modeling of neutron flux signals are usually applied (Rotander et al, 1999). This is for instance the case in the PSI methodology developed over the years for applications to the Swiss reactors (Dokhane et al, 2006). Typically, the ARMA models are estimated based on the assumption that process noise (e.g., pressure and temperatures) excites the core dynamics and that the driving noise is white.…”

Section: Introductionmentioning

confidence: 99%

Application of Continuous and Structural ARMA modeling for noise analysis of a BWR coupled core and plant instability event

Demeshko

Dokhane

Washio

et al. 2015

Annals of Nuclear Energy

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Discrete and continuous time signals Reactor noise and causality analysis BWR stability and dynamics a b s t r a c t This paper presents a first application of a novel Continuous and Structural Autoregressive Moving Average (CSARMA) modeling approach to BWR noise analysis. The CSARMA approach derives a unique representation of the system dynamics by more robust and reliable canonical models as basis for signal analysis in general and for reactor diagnostics in particular. In this paper, a stability event that occurred in a Swiss BWR plant during power ascension phase is analyzed as well as the time periods that preceded and followed the event. Focusing only on qualitative trends at this stage, the obtained results clearly indicate a different dynamical state during the unstable event compared to the two other stable periods. Also, they could be interpreted as pointing out a disturbance in the pressure control system as primary cause for the event. To benchmark these findings, the frequency-domain based signal transmission-path (STP) method is also applied. And with the STP method, we obtained similar relationships as mentioned above. This consistency between both methods can be considered as being a confirmation that the event was caused by a pressure control system disturbance and not induced by the core. Also, it is worth noting that the STP analysis failed to catch the relations among the processes during the stable periods, that were clearly indicated by the CSARMA method, since the last uses more precise models as basis.

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“…For nonparametric methods, DR is evaluated from the autocorrelation function of the signal. For parametric methods, it is evaluated from the impulse response of the system or from its effective transfer function [17]. Different parametric models are actually used, being that the autoregressive moving average (ARMA), the autoregressive (AR) or the moving-average (MA) are the most common ones.…”

Section: Mathematical Modelsmentioning

confidence: 99%

Analyses of Instability Events in the Peach Bottom-2 BWR Using Thermal-Hydraulic and 3D Neutron Kinetic Coupled Codes Technique

Costa

Petruzzi

D'Auria

et al. 2008

Science and Technology of Nuclear Installations

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Boiling water reactor (BWR) instabilities may occur when, starting from a stable operating condition, changes in system parameters bring the reactor towards an unstable region. In order to design more stable and safer core configurations, experimental and theoretical studies about BWR stability have been performed to characterise the phenomenon and to predict the conditions for its occurrence. In this work, contributions to the study of BWR instability phenomena are presented. The RELAP5/MOD3.3 thermalhydraulic (TH) system code and the PARCS-2.4 3D neutron kinetic (NK) code were coupled to simulate BWR transients. Different algorithms were used to calculate the decay ratio (DR) and the natural frequency (NF) from the power oscillation predicted by the transient calculations as two typical parameters used to provide a quantitative description of instabilities. The validation of the code model set up for the Peach Bottom Unit 2 BWR plant is performed against low-flow stability tests (LFSTs). The four series of LFST have been performed during the first quarter of 1977 at the end of cycle 2 in Pennsylvania. The tests were intended to measure the reactor core stability margins at the limiting conditions used in design and safety analyses.

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Spatial and model-order based reactor signal analysis methodology for BWR core stability evaluation

Cited by 14 publications

References 8 publications

Application of causality analysis on nuclear reactor systems

Application of causality analysis on nuclear reactor systems

Application of Continuous and Structural ARMA modeling for noise analysis of a BWR coupled core and plant instability event

Analyses of Instability Events in the Peach Bottom-2 BWR Using Thermal-Hydraulic and 3D Neutron Kinetic Coupled Codes Technique

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