Studies on the detection of incipient coolant boiling in nuclear reactors using artificial neural networks

Ann. Nucl. EnergyVol. 22, No. 7, pp. 483-496, 1995

Pergamon

STUDIES

Copyright © 1995 ElsevierScienceLtd Printed in Great Britain.All rights reserved 0306-4549/95 $9.50+ 0.00

0306.4549(94)00060-3

ON THE DETECTION

IN NUCLEAR

REACTORS

OF INCIPIENT

USING ARTIFICIAL

COOLANT NEURAL

BOILING

NETWORKS

R. Kozma 1'2 and K. Nabeshima 3

1Tohoku University, Department of Nuclear Engineering Aramaki-Aza, Aoba, Sendal 980-77, Japan 2 Interfaculty Reactor Institute, T U Delft Mekelweg 15, 2629 JB Delft, Netherlands 3 Japan Atomic Energy Research Institute Tokai-mura, Naka-gun, Ibaraki-ken 319-11, Japan (Received 23 July 1994)

Abstract The sensitivity of coolant boiling monitoring based on the analysis of signals of neutron detectors in a nuclear reactor is studied. Thermal hydraulic processes related to coolant boiling have typical time constant in the order of a few seconds. An efficient coolant-state monitoring system should have a response time comparable with this value of the time constant in order to detect changes at an early stage. The proposed system described in this paper has the required fast response. The proposed monitoring system utilizes advanced signal processing methods based on artificial neural networks in order to achieve early detection of changes in the state of the coolant. The networks have been trained to identify small variations in the power spectral density functions of neutron detector signals. The boiling monitoring method has been tested by using in-core neutron detector signals measured at the N I O B E loop located in the Hoger Onderwijs Reactor (HOR) of Inteffaculty Reactor Institute, Delft, The Netherlands. It is shown that boiling detection can be accomplished within about 16 s after the onset of surface boiling in a coolant channel. Results obtained by artificial neural networks have been compared with the efficiency of anomaly detection based on the analysis of band-passed variance of neutronic fluctuations. It is shown that artificial neural nets detect the anomaly faster and more reliably than variance-based statistical methods.

I. I N T R O D U C T I O N

In this paper, we focus on problems related to boiling monitoring in light-water reactors (LWRs). The presence of coolant boiling has different implications on the reactor operation, depending on the type of L W R investigated. In research reactors, the onset of boiling is considered to be an accidental situation, for it can easily lead to burnout and consequent fuel melting Woodruff (1984). Coolant boiling has to be prevented by all means, and, in case of occurrence, it has to be detected as early as possible in order to avoid d a m a g e of the reactor fuel Behringer (1982), DeVries (1986). 483

484

R. Kozma and K. Nabcshima

Subcooled boiling is allowed in the regions with the highest heat flux densities in the cores of modern pressurized water reactors (PWRs). A reliable method of subcooled boiling monitoring is desirable in this case. Saturated boiling has been detected in P W R s operating under abnormal circumstances Rindelhardt (1985), Defloor and Baeyens(1988). In spite of significant efforts, however, no reliable method of subcooled monitoring has been established in P W R cores yet Bernard (1982), Katona (1985), Bauernfeind and Wach(1988), Por (1988). The main task of coolant monitoring in boiling water reactors (BWRs) is the determination of two-phase flow characteristics (e.g. flow regimes, velocity distribution and void fraction),and the detection of changes in these characteristics. A criticaloverview of parameter monitoring methods is given by Lubbesmeyer (1984). Various methods are used to detect anomalies in a system based on the analysis of measured time series. A widely used method is the sequential probability ratio test (SPRT) introduced by Wald (1947). Different advanced versions of the original S P R T algorithm are described, e.g., in Lin and Blostein (1992), Takahashi (1992). It is generally assumed that the analyzed time series is either Gaussian or it can be transformed to a Gaussian form. In this case, statisticaltests are performed which are based on the evaluation of the (band-passed) variance. An alternative method utilizes artificialneural networks (ANNs) to identify changes in the system. The relationship between the variation of the properties of the APSDs and the physical quantities which generate these changes may have non-linear character. An advantage of ANN-based methods is that no assumptions are made regarding the Gaussian nature or linearity of the analyzed processes. In number of actual experiments the measured time series are essentially non-Gaussian and of non-linear character. In this case, NNs provide a useful and effectivetool for monitoring the reactor. A N N s have been used successfully for state identification and parameter monitoring in-complex systems, e.g., in nuclear power plants Bartlett and Uhrig (1992), Parlos (1994), or in chemical and other plants Watanabe (1989), Himmelblau (1992), Werbos (1993). Auto power spectral densities (APSDs) of the neutron detector signals contain information about the state of the reactor. Frequency domain applications include signal validation and surveillance of reactor systems for internal structural degradation, Korsah and Uhrig (1991), Loskiewicz and Uhrig (1993), Racz and Kiss (1994). The first attempts to enhance the sensitivity of boiling detection by applying A N N models are described by Kozma (1992a) and Nabeshima (1994). The above works indicate the feasibilityof ANNbased boiling detection but they give no detailed quantitative evaluation of the applied methodology. This is given in the present work, where APSDs of incore neutron detectors are analyzed by the A N N in order to detect the onset of boiling. In the firstpart of the paper, the method used for anomaly monitoring is described. Special emphasis is put on developing a fast-responding method based on artificialneural networks. The second part of the paper contains the results obtained when actual boiling signals have been analyzed. A N N s and usual statisticalmethods are compared regarding their capability to detect the onset of boiling in a reactor.

II. N E U R A L II.A

NETWORK

MODEL

Methodological Background

Artificial neural networks find their applications in a wide range of identification and classification problems. An important reason of the success of A N N s is that they can approximate any well-behaved mapping with an arbitrary accuracy. Moreover, there are efficientalgorithms to obtain networks with the desired properties Hecht-Nielsen (1990). Perhaps, backpropagation is the most popular algorithm that generates an A N N with the prescribed mapping properties. Backpropagation is a typical example of realizing supervised learning. A number of unsupervised learning strategies also exist and gain

Incipient coolant boiling

485

popularity in ANN modeling; see, e.g., Kohonen (1990). In the forthcoming discussions, details of the applied three-layer, feedforward ANN are described. The network consists of processing elements (nodes) which are connected with each other. There are three types of nodes: input, hidden, and output nodes. External data enter the network through the input nodes. Hidden nodes receive outputs from several other nodes and, after a typically nonlinear transformation, transmit data to other nodes. Finally, output nodes receive data from certain nodes and generate a single output value. Following the formalism introduced by Werbos (1990), the mapping by the network is given as follows: xi = X i , l < i < m (1) i-1 neti = E W i j x j

,

m < i ~ g + n

(2)

j=l

xi = 1/(1 + e x p ( - n e t i ) ) , Yi = Xi+N,

m < i < N + n

1< i < n

(3) (4)

Here xi is the output value generated by the i-th node. There are totally N + n nodes, which include m input nodes denoted by X i , N - m hidden nodes, and n output nodes denoted by Yi. Wij stands for the weight of the connection between the i-th and j-th nodes. Eqs. (1) to (4) represents the important feedforward character of the network, i.e., there is a hierarchy (ranking) between the nodes and the information is propagating always from the lower ranking node to the higher ranking one (forward sweep). At the end of the forward sweep, the calculated output values Yi are compared to the target values Yi*, i = 1 , . . . , n. The difference between the actual output and the target value is often characterized by the mean-square error E(t): E(t)

=

:1

n

•

(t) - Y,(t))'

(5)

i=l

After E ( t ) is calculated, a new ANN is created with the following updated connection weights: W , j ( t + 1) = W i j ( t ) - ),F_W,i(t),

(6)

where ~ is the learning rate and F _ W i j ( t ) is the derivative of the error function with respect to the weight W~j. The parameter t indicates the number of iterations. In this backward sweep, the error of the output is propagating back in the network to modify the weights (backpropagation). There are various ways to determine F_W~j(t), see, Werbos (1990). In the case of layered architecture, there are groups of nodes, called layers. There are no connections between the nodes in a given layer, and the information is transferred from the (k - 1)-th layer to the k-th one. The significance of the layered neural networks is outlined by Koimogorov's classical result which states that a properly chosen three-layer feed-forward ANN is capable to approximate any continuous mapping; see, e.g., Hecht-Nielsen (1990). II.B Implementation of boiling detection For the purposes of the present study, a 3-layer, fully connected neural network architecture has been selected. We apply feedforward, hetero-associative network with standard backpropagation learning algorithm. The network utilizes sigmoidal transfer functions defined by F_~1. (3). Characteristic patterns representing the system state enter the network through the input layer. The number of input nodes correspond to the resolution of the input patterns. The output nodes indicate the system state identified by the ANN.

486

R. Kozma and K. Nabeshima

The structure of the applied network is shown in Fig. 1. The input layer consists of 128 nodes, which corresponds to discrete frequency points of the APSD, ranging from 0.125 Hz to 16 Hz. The number of nodes in the hidden layer is fixed at 8. The output layer has 2 nodes. Both output nodes take values between 0 and 1. The first node is used to obtain general information about the state of boiling. Its value varies from 0 (no boiling), through 0.5 (partial boiling), to 1 (developed boiling). The second node is used to emphasize partial boiling state. This node has a value of 1 in the case of partial boiling and 0 in all other cases. The correspondence between boiling states and output node values is summarized in Table I. Table I Output Node Values

Input Layer (128) - Frequency points

Boiling Pattern

Output node

#1

#2

Absence of Boiling

0.0

0.0

Partial Boiling

0.5

1.0

Developed Boiling

1.0

0.0

Figure 1. Structure of the three-layer neural network The practical implementation of the method includes the following major steps: 1. calculating the time series of APSDs; each APSD has been evaluated over a given time interval by exponential averaging; 2. selecting the structure and learning algorithms for the NN; determining those sections from the time series data which are typical of various boiling patterns; 3. training the NN on the selected set of APSDs and using the trained NN to analyze the actual APSDs in order to extract information about the state of the coolant in the reactor. Exponential averaging means that a time window is applied, in which the most recent data block has the largest weight (unity), while the weights of the preceding blocks decrease exponentially with increasing time lags. Each data block contains 128 sample points. The sampling frequency is 32 Hz, i.e., a new data block arrives in every 4 s. In order to analyze how the length of the applied time window influences the effectiveness of boiling detection, APSDs have been determined by using various coefficients of exponential averaging. The applied data windows have characteristic size of 3, 10, 20, and 100 data blocks, which correspond to time constants of 12 s, 40 s, 80 s, and 400 s, respectively.

III. E X P E R I M E N T A L

III.A NIOBE Set-Up Boiling experiments have been performed at the NIOBE facility in the HOR reactor of Inteffaculty Reactor Institute, Delft University of Technology, Delft, The Netherlands. NIOBE is a simulated MTR-type fuel assembly, in which coolant boiling can be investigated under various thermal hydraulic

Incipient coolant boiling

487

circumstances in a nuclear reactor environment. The main characteristics of the experiments are summarized below. Details of the boiling experiment are given by Kozma (1992b). NIOBE consists of an electrically heated, simulated fuel assembly and an autonomous coolant loop with a circulation pump. The simulated assembly in located in the reflector of the reactor, at the bottom of the 7 m deep reactor pool. The coolant circuit is divided into two loops after the circulation pump. Each loop has its own turbine flowmeter and an electrically controlled valve which is located above the water level of the reactor basin. They are connected to the simulated assembly with the help of a set of 7 m long tubes. Coolant boiling can be initiated inside the simulated fuel assembly by reducing the coolant flow rate and/or increasing the electrical heating power. FUEL ASSLV.BL¥

~]

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Figure 2. Map of the HOR core; NIOBE is located in the reflector. The experimental assembly is equipped with 4 strings of self-powered neutron detectors (SPNDs) located in instrument tubes next to the three fuel plates. The applied cadmium SPNDs are sensitive to neutron flux fluctuations up to high frequencies (,,~ 50 Hz or more) due to their prompt responding character Klelss and Van Dam (1981). The temperature of the fuel plates is measured by chromel-alumel thermocouples (TCs) of 0.5 nun diameter which are built into the plates with an explosive welding technique to assure excellent thermal contact between the TCs and the plate material. There is a TC at the exit of both channels which measures the coolant temperature. The heating power of the plates can be adjusted continuously or step-wise with a maximum of 7 kW per plate. This value is close to the 7.2 kW per plate maximum at HOR operating at nominal power of 2 MW. By changing the heating power or the flow rate, different thermal hydraulic conditions can be generated in NIOBE.

III.B Boiling Induced Neutron Flux Perturbations at NIOBE Changes in the thermal hydraulic state of the coolant in the channels of NIOBE lead to changes in the neutron flux of HOR. In this section, stationary neutronic effects will be calculated. In the analysis, multi-group neutron diffusion equations are solved for HOR with the help of the CITATION diffusion code Fowler (1971) in two-dimensional slab geometry. Calculations have been performed with different amounts of a homogeneously distributed void fraction in the coolant channels of NIOBE, which correspond to different levels of boiling. In the calculations, the assembly was assumed to be in the reflector at the actual position of the NIOBE, as it is shown in Fig. 2. The calculated fast and thermal neutron fluxes are shown in Figs. 3a-b. The thermal flux has peaks in the reflector region. There are 4 peaks inside the core region as

488

R. Kozma and K. Nabeshima

well, caused by the water gaps at the positions of the withdrawn control assemblies. The location of NIOBE is marked by a black square. It is seen that NIOBE is found in the reflector peak. ~q

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Figure 3. Calculated fast (a) and thermal (b) neutron flux distribution at NIOBE; calculations performed by CITATION in x-y slab geometry. Position of NIOBE is marked by black square. In Fig. 4, the calculated thermal neutron flux is shown across the NIOBE as a function of the average void fraction. The coolant channels are located between positions 23.1 cm and 23.6 cm, and 24.0 cm and 24.5 cm. In the present study, we analyze signals of SPNDs in the instrument tube located between 25.5 cm and 26.5 cm. It is seen that the thermal neutron flux decreases by about 2 % at the detector position in the case of 50 % void fraction in the channels. 45

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The uncertainty of the neutron flux measurement varies between + 0.3 % and + 1 % . This error is due to the error of measuring the detector current and to the fluctuations of the neutron flux itself. Therefore, no measurable neutron flux level variations are expected in experiments with void fractions below ,,~ 5 to 10 %, when the average boiling-induced change in the neutron flux is less than 1%. These are the experiments with the incipience of boiling and low-void subcooled boiling. Let us consider the reactivity effects caused by the operation of NIOBE. The calculated void reactivity coei~cient is -0.03 pcm per cm 3 of void in NIOBE. This means that 50 % voidage in the 2 coolant channels of NIOBE yields -5.9 peru reactivity variation. Based on the control rod importance curves

Incipient coolant boiling

489

of HOR, the maximum rod movement needed to compensate 5.9 pcm reactivity decrease is less than 3 mm when the control rods are close to the top of the core Stigter (1991). At lower void fraction values, the corresponding control rod movement is smaller and it is difficult to identify by visual inspection of the operation data file. Changes in the dc signal level of an SPND during an experiment with various boiling transients are shown in Fig. 5. In the first part of the experiment, till about 2000 s, no boiling took place and a stationary situation is maintained. The dc signal level fluctuates between about -4.76 V and -4.72 V. This change represents a relative peak-to-peak variation of about 1%. During the time period of 2000 s to 5000 s, different transients with boiling have been introduced in NIOBE. After 5000 s, a thermal hydraulic state with intensive boiling in the coolant channels have been maintained.

i ..................

0

1000

2000

3000

4000

5000

..................

6000

..................

7000

................

8000

9000

Time fs) Figure 5. Changes in the dc signal level of an in-core neutron detector in the NIOBE; experiment with various stationary and transient conditions. The dc signal follows the general trend of the variation of the boiling conditions in the NIOBE. The signal fluctuates intensively and it is not possible to obtain clear information about the onset of boiling. Detectable variations of the average signal level occur only in experiments with intensive boiling (highest heating powers). It is concluded that changes in the average neutron flux caused by boiling at NIOBE cannot serve as a tool for boiling indication during experiments with low void fraction. IV. E V A L U A T I N G T H E P E R F O R M A N C E

OF BOILING DETECTION

IV.A The response time of the A N N toward boiling anomaly The layered, feedforward NN described by Eqs. (1) to (4) has been used for analyzing actual boiling data measured at the NIOBE facility. The present study concentrates on effects related to the onset of boiling. Accordingly, those parts of the experiments will be analyzed, in which the originally singlephase coolant flow changes to two-phase flow. 3 x 50 learning patterns (APSDs) have been selected which represent non-boiling, partial boiling, and developed boiling states, respectively. APSDs have have been evaluated in every 4 s. Results of boiling detection are shown in Figs. 6a-c and 7a-c for two events of onset of boiling. In Figs. 6a and 7a, wall temperature measured by a thermocouple inside a fuel plate at medium axial elevation is shown by solid line. Dashed lines in Figs. 6a and 7a indicate the threshold temperature above which boiling is initiated on the wall. Boiling occurs at time instant of 2580 s in the experiment shown in Fig. 6a and at 4260 s in the case of Fig. 7a. Boiling has been initiated in both cases by a stepwise increase of the heat flux density at the fuel plates of the experimental fuel assembly. In

490

R. Kozma and K. Nabeshima

Figs. 6b and 7b, the boiling indicator values determined by output node #1 are shown. Stars, plus signs, 'x'-marks and circles denote results obtained by spectrum averaging with time constants (ATC) corresponding to 3, 10, 20, and 100 blocks, respectively. The values of output node # 2 are given in Figs. 6c and 7c; the notations are the same as in Figs. 6b and 7b. See Table I for the interpretation of the indications of the output nodes.

0~570

2580

2590

2600 Time (s)

2610

620

2630

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Figure 6. Analysis of the first onset of boiling event (t~,e~ = 2580 s) (a) Wall temperature (solid line), boiling threshold temperature (dashed line) (b) Boiling indicator value vs. time curves determined by output node ~1 for different averaging time constant (ATC) values; notations : o : ATC -- 100 blocks, x : ArC -- 20 blocks, + : ATC = 10 blocks, * : ArC = 3 blocks. (c) Partial boiling indicator calculated by output node ~2; same notations as in Fig. 6b. Experiments with time constants corresponding to 100 blocks are not very sensitive to changes in the coolant state. They have values significantly exceeding 0 in the absence of boiling as well. This is caused by earlier boiling anomalies which influence the APSDs for a long time due to the large time constant value of 400 s. Such a large time constant is not suitable for early boiling detection. Experiments with the shortest time constant (3 blocks -- 12 s) show the best performance. Before the onset of boiling, indicator # 1 varies around 0. It starts to deviate from zero with a 8 s delay following the onset of boiling in Fig. 6b. The same delay is 4 s in Fig. 7b. The value of the indicator increases rapidly in time. At the 3rd block (12 s) after the beginning of boiling, indicator # 1 exceeds the value of 0.25 which is considered to be a reliable indication of boiling. Indicator # 2 shows somewhat poorer performance and it reaches a reliable boiling indication level with a delay of 20 s and 16 s, respectively; see Fig. 6c and Fig. 7c. Experiments with time constants of 10 blocks and 20 blocks follow a trend similar to the one we have observed in the case of 3 blocks. The main difference is that the output nodes indicate thermal

Incipient coolant boiling

491

hydraulic changes slower in the case of larger time constants. This effect is clear in Figs. 6b and 6c.

(a)

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Figure 7. Analysis of the first onset of boiling event (tanset = 4260 s) (a) Wall temperature (solid line), boiling threshold temperature (dashed line) (b) Boiling indicator value vs. time curves determined by output node ~1 for different averaging time constant (ATC) values; notations : o : ATC = 100 blocks, x : ATC = 20 blocks, + : ATC = 10 blocks, * : ATC = 3 blocks. (c) Partial boiling indicator calculated by output node ~2; same notations as in Figure 7 (b). In order to analyze the spectral distribution of boiling effects, additional studies have been performed by selecting various frequency ranges between 0 Hz and 16 Hz. Neural networks have been constructed to study the following frequencies bands: 2 Hz to 12 Hz, 2 Hz to 6 Hz, and 4 Hz to 16 Hz. In the case of 2 Hz to 12 Hz, the NN had 80 input nodes, 7 hidden nodes and 2 output nodes. The structures of the other two NN are as follows: 40 input-, 6 hidden-, 2 output nodes, and 97 input-, 7 hidden-, 2 output nodes, respectively. Results show that the frequency range 2 Hz to 6 Hz is the most important for the detection of partial boiling, while developed boiling manifests itself mainly at frequencies above 6 Hz. IV.B Comparison of Various Boiling Detection Methods The discussions to be conducted in this section utilize the results of boiling detection by ANNs trained over APSDs calculated with averaging time constant (ATC) value of 12 s, which corresponds to 3 blocks. This ATC value assures a reliable indication of boiling within 12-16 s, as it has been demonstrated in Figs. 6 to 7. Details of the statistical method which will be used for the comparison with the ANN-based monitoring are given below. The method is based on the evaluation of the intensity of the fluctuations of the detector signals. The intensity of the fluctuations is characterized by the variance (a 2) and it can

492

R. Kozma and K. Nabeshima

be determined as follows:

1 [f-a2 -----~N JO APSDNN(f)df

(7)

The factor in front of the integral on right-hand side of the above equation represents normalization, where 12 is the square of the average signal level. APSDNN(f) is the autospectum of the fluctuating component of the neutron detector signal, while f denotes frequency. Variance analysis is often an efficient way of detecting changes in the state of the monitored system, even if the average signal level changes only slightly or does not changes at all. The normalized root-mean-square (NRMS) method is the refinement of the above variance evaluation. In the case of some practically important anomalies, the change of the noise signature is concentrated at a limited frequency band. By monitoring that specific frequency band, it is possible to derive a sensitive anomaly indicator. The NRMS over frequencies If1, f2] is defined as follows: NRMS =

(8)

APSDN~(I)dl.

The NRMS over the total frequency range is equal to standard deviation of the fluctuations (a). The error of NRMS consists two parts: the errors of the NRMS of the spectra and of the measured dc signals, respectively. The relative error of the NRMS is given by the relationship Jenkins and Watts

(1968):

a~rRMs/NRMS = 0.5~f'~/(Nn),

(9)

where N and n are the number of records and the number of spectrum-points-within the considered frequency region, respectively. Wb is the window parameter; its value is 0.75 in the present measurements. I

~"

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Time (s) Figure 8. Normalized Root-Mean-Square (NRMS) noise intensity over frequencies 1 Hz to 7 Hz. In the case of a single APSD without averaging (N=I), the relative NRMS over n = 128 spectrum points is 4 %. The relative magnitude of the fluctuation of the dc component is less than 1%, therefore, it is a second order effect compared to the statistical uncertainty given by Eq. (9). The actual value depends on the experimental circumstances. The error of the NRMS increases when narrowing the frequency band in Eq. (8). Anomaly detection based on NRMS monitoring works as follows. First one declares a certain threshold value of the change of the NRMS. If the NRMS varies beyond the threshold, the presence of anomaly is confirmed. It is often assumed that the fluctuation of the NRMS has a Gaussian distribution. Under this assumption, analytical expressions can be derived between the value of the threshold and the

Incipient coolant boiling

493

reliability of the anomaly detection Jenkins (1968). Typical values of the threshold lie between 2 to 3 times the determined error of the NRMS. (a) Median filter - 3 b l o c k s

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2500

2550

Time (s) Figure 9. Boiling indicators based on filtered Normalized Root-Mean-Square (NtLMS) noise and neural networks; (1) - NtLMS over frequencies 1 Hz to 7 Hz, (2) - NRMS over frequencies 2 Hz to 6 Hz, (3) boiling indicator # 2 by the neural network, (4) - boiling indicator ~1 by the neural network. (a) - filter width 3 blocks (12 s); (b) - filter width 5 blocks (20 s); (c) - filter width is 10 blocks (40 s). In Fig. 8, the NRMS is shown for frequencies [1 Hz, 7 Hz]. Some trends resembling the change of the boiling state of the coolant can be seen, but the fluctuation of the NRMS are greatly mask the changes; compare Fig. 5 and Fig. 8. The effect of the large fluctuations can be reduced by using median filter. Median filter means averaging the value of a certain number of consecutive data points. The effect of the median filter shown in Fig. 9a-c for frequency bands of [1 Hz, 7 Hz] and [2 Hz, 6 Hz]. The length of the filter is m = 3, 5, and 10 in Fig. 9a, Fig. 9b and Fig. 9c, respectively. For comparison, the boiling indicators generated by the ANN are given as well in Fig. 9a-c. In the case of m = 3 in Fig. 9a, the filtered NRMS signals fluctuate very much. The fluctuations are smaller in Fig. 9b. A significant increase of NRMS can be identified starting from about 2620 s in the case of curve corresponding [2 Hz, 6 Hz] and from about 2650 s for frequencies [1 Hz, 7 Hz]. The delay time of boiling detection decreases for m = 10; see Fig. 9c. For the narrow frequency band of [2 Hz,

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6 Hz], boiling is detected at about t = 2610 s, i.e.,the performance of the NILMS method approaches that of the ANN. V. D I S C U S S I O N S It is clear that a better boiling detection is obtained by focusing on a narrow frequency band. The reason of success is the presence of a boiling-induced resonance peak in the APSDs of the neutron detectors. Monitoring peaks in the neutron noise APSDs is one of the most successful field of neutron noise analysis. It is widely used in NPP surveillance systems to detect abnormal behavior of different mechanical/structural components of the plant; see, e.g., Trenty (1991).

w0 2.5 .....................................................................

Figure 10. Contour plot of the neutron noise autospectrum during a transient; time axis: 0 s to 800 s, frequency axis: 2 Hz to 5 Hz. A peak is present from 240 s to 480 s and after about 700 s. The location of the peak shifts from 4 Hz to 3 Hz.

3.75

5.13

400

800

Time (s) One has to be careful, however, with concentrating the monitoring to a given narrow frequency band, due the following two problems: (1) the dominant frequency of the anomaly can shift during the measurement and, eventually, it can disappear from the monitored frequencies; (2) the onset of a new type of anomaly or the co-existence of different anomalies. The first problem is very important in the case of boiling monitoring, when small changes in the thermal-hydraulic state of the coolant can lead to shift in the dominant frequency. This conclusion is illustrated in Fig. 10, where the boiling-induced peak in the neutron noise APSD is shown. It is seen that the frequency of the peak shifted from 4 Hz to about 3 Hz during a 240 s long boiling period. This shift did not influence the etficiency of the boiling monitoring in the actual experiment because of the sufficiently widely chosen [2 Hz, 6 Hz] frequency window. 1.5

................ !............

i..........

i

i

i

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~ 7000

7200

7400

7600 7800 Time (s)

8000

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Figure 11. Detection of ~tll-sccle boiling by different methods. The size of the median filter is 10 blocks in the case of NRMS-based indicators. (a) - NtLMS method over [1 Hz, 7 Hz]; (b) - NRMS method over [2 Hz, 6 Hz]; (c) - ANN output node #1 (boiling indicator); (d) - ANN output node #2 (partial boiling indicator).

Incipient coolant boiling

495

Fig. 11 shows an example of a new boiling anomaly. This type of boiling has a rather wide-band effect from about 4 Hz to 16 Hz. The NRMS indicators over frequencies [1 Hz, 7 Hz] and [2 Hz, 6 Hz] have significant problems in detecting this type of boiling, although, they showed quite good performance in the case of boiling analyzed in Fig. 9. For an efficient boiling detection, the NRMS-based indicators must exceed the value of 1.1 - 1.15. As it is seen in Fig. 11 this criteria is not fulfilled between 8000 s and 8200 s, although, fully developed boiling took place in the experiment shown in Fig. 11. At the same time, the ANN clearly indicates the presence of boiling over the whole time range. By careful inspection of the APSDs, it is possible to identify the special features of the different spectra and indicate the dominant frequency range of different anomalies. Also, one could use sliding frequency window for the evaluation of the APSDs and to infer the necessary diagnostic information. The nice thing about the ANN is that it does this job without the interference of human experts. In fact, some ANNs are not only able to complete the task of reliable state indentification, but also can extract information on the anomaly; see, e.g., Ishikawa (1992). Finally, it has to be emphasized that human expertise is not useless even for an artificially intelligent computing system, and significant efforts are made to integrate various knowledge resources in the framework of the diversification paradigm Kitamura (1993). VI. C O N C L U D I N G R E M A R K S A N D R E C O M M E N D A T I O N S By applying neural network models, coolant boiling has been detected within about 12 to 16 s following the onset of boiling anomaly by making use of in-core neutron detectors. It has been found that partial coolant boiling has a dominant effect at frequencies between 2 Hz and 6 Hz. This is caused by boiling induced vibrations Kozma (1992b). On the other hand, the broad-frequency l~ubbling noise is the cause of the APSD change at higher frequencies in the case of developed boiling. These results have been obtained by applying various ANN input layer architectures. By reducing the analyzed frequency range, however, the overall performance of the boiling detection becomes worse. Therefore, it is suggested to use the maximum available frequency information for boiling detection. The performance of ANN models is compared with the results obtained with statistical analysis of the band-passed variance of the neutron detector signal. By applying a narrow-band frequency window centered around the dominant frequency of the anomaly, the performance of the rms-based method can approach that of the ANN. This fine-tuned rms-based monitoring method, however, has clear disadvantages in the case of spectral shifts and/or multiple anomalies. A properly trained ANN does not suffer from these problems. It is concluded that ANNs can serve as important building blocks of an efficient boiling monitoring system in nuclear reactors. Preliminary studies indicate that boiling detection can be realized by using signals of ex-core ionisation chambers too. Moreover, research is underway to develop a boiling detection method based on instant neutron noise APSDs (without averaging) which yield a boiling detection within about 4 s following the onset of incipient boiling. To reach this goal, further methodological improvements are needed; see Kozma (1994).

Acknowledgments The authors wish to express their gratitude to the working collectives of DSA Group, ECN, Netherlands and Department of Reactor Physics, IRI, Netherlands for their assistance in the experiments. Special thanks are due to dr. E. Tiirkcan, ECN, Netherlands, for organizing the on-line boiling detection project, and to Prof. H. Van Dam and dr. J.E. Hoogenboom, IRI, Delft, Netherlands for their continuous support. Useful advices of Prof. M. Kitamura, Tohoku University, Japan are greatly acknowledged.

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