Carl Sunde

Wavelet techniques for the determination of the decay ratio in boiling water reactors

Abstract The usefulness of wavelet transform and wavelet filtering techniques for the improvement of the estimation of the decay ratio (DR), characterising the stability of BWRs, is discussed. There are two distinct areas investigated. The first concerns the improvement of the quality of the traditionally used auto-correlation function (ACF) for the estimation of the DR, by trend elimination and denoising. The subsequent estimation of the DR itself is made by traditional methods such as the peak-to-peak method or curve fitting. The second area is the estimation of the DR by the use of continuous wavelet transform. The possibility of estimating two different DRs in case of dual oscillations, and in particular the finding of the higher DR, is also investigated. It was found that wavelet pre-processing does not always improve the estimation of the ACF of a non-ideal signal, compared to other methods; but for signals containing various trends and data scatter in the ACF, it brings a noticeable improvement. As an extension of the discrete wavelet methods, the continuous wavelet transform appears to be a promising candidate to determine the critical DR even in the case of two oscillations being co-existent with different stability properties. The methods investigated or developed here were also tested on measured data from Swedish BWRs.

Calculation of the neutron noise induced by shell-mode core-barrel vibrations in a 1-D, two-group, two-region slab reactor model

The subject of this paper is the calculation of the in-core neutron noise induced by the shell-mode vibrations of the core barrel. The original motivation was to investigate whether an out-of-phase behavior can exist between the in-core and ex-core (ex-vessel) detectors lying at the same azimuthal position. To this end, a two-region two-group diffusion model was used in one dimension. The noise was calculated by representing the vibrations of the core barrel by a model developed earlier to describe control rod vibrations. It was found that such an out-of-phase behavior indeed exists, although only for in-core detector positions close to the core boundary. This behavior is due to the local component of the noise, which is accounted for in a two-group treatment. The finding is in accordance with the experiment whose result prompted the present work. In addition to its effect on the phase, the local component also manifests itself by a large amplitude of the noise around the vibrating core boundary, i.e., in both the core and the reflector. The appearance and the properties of the local component of the neutron noise for core-barrel vibrations is the main finding of this paper. The results suggest that the efficiency of core-barrel vibrations can be enhanced if in addition to the ex-core detectors, the in-core detectors in the outermost fuel assemblies are used.

Investigation of detector tube impacting in the Ringhals-1 BWR

Neutron noise measurements were made in two consecutive fuel cycles in the Swedish BWR Ringhals-1 with the purpose of diagnostics of vibrations and impacting of detector strings. Two diagnostic tools were used, first a traditional spectral analysis and second a wavelet-based method. Both types of methods have been used in the past, but not simultaneously during one fuel cycle. In addition, a new method, wavelet-based coherence, was tested with success. Based on the results of the analysis, with emphasis on the traditional method, the detector tubes were divided into three groups with respect to the severity and likelihood of impacting. For the first series of measurements, these conclusions could be checked against visual inspection of the fuel assemblies during refuelling after the cycle, in order to find impacting damage. A good correlation between the prediction of the analysis and the inspection results was found.

Classification of Two-Phase Flow Regimes via Image Analysis by a Neuro-Wavelet Approach

A non-intrusive method of two-phase flow identification is investigated in this paper. It is based on image processing of data obtained from dynamic neutron radiography recordings. Classification of the flow regime types is performed by an artificial neural network (ANN) algorithm. The input data to the ANN are some statistical functions (mean and variance) of the wavelet transform coefficients of the pixel intensity data. The investigations show that bubbly and annular flows can be identified with a high confidence, but slug and churn-turbulent flows are more often mixed up in between themselves.