methods for evaluating the accuracy of fission product concentration measurements in nuclear reactor primary heat transport systems

Transcription

1 FULL FULL ARTICLE ARTICLE Fission product concentration in reactor primary heat transport systems is a common diagnostic indicator for assessing reactor core condition and determining the presence, size, power, location, residence time, burnup, etc., of defected fuel. Typically, diagnostic assessment assumes a priori that measured data (activity concentration measurements and reactor parameters) are accurate; however, this is not always a valid assumption. A set of novel methods has been developed for detecting minor discrepancies in fission product concentration measurements and reactor parameters (such as issues with transit times, purification, and spectral analysis). A variety of techniques are discussed and applied to a variety of reactor types (mainly commercial power plant designs); these techniques and concepts can be modified and applied for research and (or) commercial applications. methods for evaluating the accuracy of fission product concentration measurements in nuclear reactor primary heat transport systems Steve Livingstone Atomic Energy of Canada Limited, Chalk River, ON K0J 1J0, Canada Article Info Keywords: defected fuel; nuclear; nuclear fuel; fission product; heat transport system Article History: Received 16 January 2014, Accepted 9 April 2014, Available online 12 June DOI: Corresponding author: livingss@aecl.ca Nomenclature BWR Boiling water reactor CANDU Canadian deuterium uranium COLDD CANDU online defect diagnostic FP Fission products GFP Gaseous FP monitoring system GS Grab samples IX Ion exchange NRU National research universal reactor PHTS Primary heat transport system PHWR Pressurized heavy water reactor PWR Pressurized water reactor STAR Steady-state and transient activity release WWER Water-cooled water-moderated energy reactor Key Symbols β p (t) Ion purification/degassing rate coefficient (s 1 ) κ(t) Other losses (+) or gains ( ) (s 1 ) λ Decay constant (s 1 ) λ* Effective decay constant (s 1 ) R c (t) Total FP release into the PHTS from all sources (atoms 1 ) t Time (s) y c Cumulative yield (atom fission 1 ) 1. Introduction Fission products (FP) released from fuel defects into reactor primary heat transport systems (PHTS) are a common diagnostic indicator for assessing reactor core condition and determining the presence, size, power, location, residence time, burnup, etc., of defected fuel. A fuel defect occurs when there is a breach in the fuel cladding of a reactor fuel element that allows FP and fuel contamination to escape into the PHTS and reactor coolant to enter into the defected element. Although relatively rare in modern reactors, fuel defects have always been an important concern for reactor fuel operation and behaviour and play a critical role in health (e.g., impact on PHTS radiation fields), safety (e.g., plants may have limits imposed on activity inventories), and economics A correction was made to the e-first version of this paper on 19 June 2014 prior to final issue publication. The current online and print versions are identical and both contain the correction. AECL NUCLEAR REVIEW 29

2 (e.g., defects can lead to lost revenue and various expenses). As a result, extensive effort has been spent to eliminate defects and improve defect detection and diagnostic capability. Defect detection and diagnostics is a global endeavour in the nuclear industry, and numerous codes, techniques, and reactor types are involved (e.g., [1 5]). Defect diagnostics is akin to detective work, as all available information is compiled and analyzed in an attempt to infer as much detail as possible about any possible defect or fuel contamination in the PHTS and reactor core. Typically, diagnostic assessment assumes a priori that measured data (activity concentration measurements and reactor parameters) are accurate; however, this is not always a valid assumption. A set of novel techniques are developed for detecting minor discrepancies in FP concentration measurements and reactor parameters (such as issues with transit times, purification, and spectral analysis). This paper focuses on techniques using PHTS activity measurements from gamma spectrometry that are performed online directly on the PHTS or off-line in grab samples (GS) of the PHTS. The techniques assume a common fuel design of ceramic oxide pellets in metallic cladding with water cooling (e.g., Canada deuterium uranium (CANDU), pressurized water reactor (PWR), boiling water reactor (BWR), water-cooled watermoderated energy reactor (WWER, also known by its Russian name Vodo-Vodyanoi Energetichesky reactor)); exotic reactor designs may require different approaches (e.g., molten salts, metallic fuel, high temperature gas cooled, etc.). The intent of the techniques is to verify that no large or subtle inconsistencies or biases are present in nuclide activity data or reactor parameters required for defect diagnostics. The paper expands on concepts first introduced by Livingstone [6], describes new techniques, expands concepts to different reactor types, and applies methods to new data. The majority of the approaches described in this paper can be understood by a basic understanding of mass balance in the PHTS. Mass balance dictates that the rate of change of the nuclide inventory in the PHTS must match the production and loss terms. Several defect diagnostic techniques are based fundamentally on this balance as shown in Equation (1). Accumulation ¼ Release from fuel þ Release from fuel contamination Decay Purification Leakage Unknown losses=gains dn C ðtþ dt ¼ R f ðtþþr tr ðtþ kn C ðtþ b p ðtþn C ðtþ eðtþ M C ðtþ N CðtÞ jðtþn C ðtþ ð1þ Equation (1) can be rearranged to show: ) R C ðtþ ¼ M C k dcðtþ þ k CðtÞ dt R C ðtþ ¼R f ðtþþr tr ðtþ k ðtþ ¼k þ b p ðtþþ eðtþ M C ðtþ þ jðtþ ð2aþ ð2bþ where β p (t) is ion purification/degassing rate coefficient 1 (s 1 ), C(t) is PHTS activity concentration λn c /M c (Bq kg 1 ), eðtþ is PHTS leakage rate (kg s 1 ), κ(t) is other losses (+) or gains ( ) term (s 1 ), λ is radioactive decay constant (s 1 ), λ* is effective radioactive decay constant (s 1 ), M c is PHTS mass (kg), N c is FP inventory in the PHTS (atom), R tr (t) is total FP release rate from fuel contamination (atom s 1 ), R f (t) is total FP release rate from all defected elements (atom s 1 ), R c (t) is total FP release into the PHTS from all sources (atom s 1 ), and t is time (s). Equation (1) is applicable to each nuclide of interest, invokes no assumptions on release mechanism or equilibrium state, and includes a new additional term κ(t) (s 1 ) introduced to represent unknown and other losses of any form (e.g., neutron absorption losses/gains, deposition/release from PHTS surfaces, etc.). Positive κ represents losses, negative κ represents gains, and several terms may be used (i.e., κ 1 (t), κ 2 (t), κ i (t)); if the loss/gain is not proportional to N c (e.g., parent production) additional modifications maybe required, outside of κ(t), to Equations (1) and (2). The effective decay constant λ* is the apparent decay constant of the nuclide as it would be measured in the PHTS. As shown in Equation (2b), the size of κ relative to the other components of λ* dictates the impact of the other or unknown losses. κ may also be used to represent bias and uncertainty in transit times or purification systems, as these would appear as changes to λ*. A common method of analyzing defected fuel involves examining R c /y c (the cumulative yield corrected release rate) or R c /B (the fractional release to birthrate ratio (where B (atom s 1 ) is the FP birthrate in the fuel element)), and then comparing measurements against models and (or) predictions (e.g., [1, 4, 6, 7]). Normalizing the release against the yield or birthrate enables direct comparison of the relative release of different FP as a function of their respective decay constants. Section 2 describes how this methodology (typically used for defect diagnostics) can be exploited to examine nuclide and reactor data accuracy. Nuclides with high neutron absorption (e.g., 135 Xe) or long lived parents (e.g., 132 I) are typically neglected in this paper, as these introduce uncertainty into the error checking methods. However, detailed 1 For ionic species β p (t) is the flow rate (kg s 1 ) through the ion exchange (IX) columns, divided by the coolant mass (kg), and multiplied by the IX removal efficiency (%). For gaseous species β p (t)is based on the degassing system flow rate and removal efficiency. 30

3 modelling and analysis can be performed, if required, to include any nuclide of interest. 2. Spectral Analysis Assuming accurate reactor parameters, the following four sub-sections describe methods for checking the gamma spectral analysis. Subsequently, Section 3 describes methods for examining reactor parameters assuming accurate gamma spectral analysis Checking spectral analysis The first step in analyzing PHTS FP data is to examine a plot of R c /y c versus λ during defect-free operation. In this instance, the only source of FP was from fuel contamination that was present from previous defects or surface contamination on fresh fuel and PHTS surfaces. As the fuel contamination particle size is usually smaller than the fission fragment recoil distance, fission products are released directly into the PHTS from fission events [8]. Therefore, R c /y c should be a constant value independent of λ and should be proportional to the mass of fuel contamination in the PHTS and in-core surfaces. Figure 1 shows R c /y c versus λ plotted during a period of defect-free operation, based on data provided by Swann [9], and it provides a wealth of information about spectral analysis of this specific dataset. R c values were determined using Equation (2) (assuming steady-state conditions) and averaged during the period; 1 standard deviation was used to represent the variation, and each average was normalized against 138 Xe (ideally the normalized R c /y c should equal 1 for each nuclide). As y c is burnup dependent, and the source burnup was not known, R c was normalized against y c at a burnup of 0 and 500 MWh/kgU for each nuclide (careful examination of Figure 1 suggests a high burnup source). Close examination of Figure 1 shows activated corrosion products, depositing FP, soluble FP, and gaseous FP that should all be self-consistent assuming accurate data acquisition and analysis. 125 Sb is a known activation corrosion product in CANDU plants [10], and therefore its location on Figure 1 is dependent on antimony sources in the PHTS. 99 Mo is a depositing fission product in CANDU PHTS chemistry [11], and therefore its low value in Figure 1 is expected as the unknown deposition losses, κ(t), were not included in the R c calculations. Examining the noble gases and radioiodines closely in Figure 1 elucidates possible uncertainties. 131 I appears slightly above 133 Xe, suggesting a possible bias in the purification data used to calculate R c ; however, it is close to the measurement uncertainty. 133 I shows significant variation and should be neglected for defect diagnostics; issues are possibly due to the use of a low intensity gamma ray (345 kev at 0.104%) for identification, as the highest intensity gamma ray (529 kev at 87%) that is a factor 800 more prevalent, is beyond the range of the planar detector used for these measurements [9, 12]. 85m Kr shows an unusually low R c /y c ; the cause of this discrepancy is unknown and suggests a bias in the 85m Kr values. The close similarity between 134 I and 138 Xe gives confidence in a well-known transit time (Section 3.1). Every plot of R c /y c versus λ for any reactor type must be understood and analyzed to provide confidence in the use of the data for further analysis or to highlight possible subtle corrections to modelling efforts. The example provided is unique to a particular reactor and measurement system design; other datasets may show different results. FIGURE 1. Data analyzed during a period of defect-free operation. Vertical error bars are one standard deviation of the data during the period. Each nuclide is normalized against 138 Xe. Data are from a CANDU Reactor (June September 2005 from Figures in Swann [9]). AECL NUCLEAR REVIEW 31

4 2.2. Comparing iodine and noble gas R c /y c values The next step is to analyze R c /y c versus λ when a defect is present in the PHTS, which impacts the relative behaviour of the FP. When fission occurs inside a reactor fuel element, there is a multitude of phenomena and processes that determine the FP movement, and in the case of defected elements, release to the PHTS [8, 13]. Detailed description of these processes is outside the scope of this paper; however, key concepts are that it takes a finite period for FP to migrate into the PHTS from a defected element and that reactive and soluble radioiodines typically experience slower transport than inert noble gases. Therefore, during transport, shorter lived species will decay relatively more than longer lived species, and typically inert gases will escape after a shorter period than iodines (as iodines are held up by interactions during transport). This means that iodine R c /y c must be similar or less than noble gases (for comparable λ), and shorter lived species must have lower R c /y c than longer lived istopes (of the iodines or noble gases), the exception is fuel contamination or extreme defects when FP release directly from fission into the PHTS, as discussed in Section 1. When examining a plot of R c /y c versus λ with a defect present, there are a few patterns that emerge (Figures 2 and 3). For data that contain both noble gases and iodines, nuclides of similar λ should have R c /y c values of noble gases higher than R c /y c of the iodines, or the gases and iodines should have similar values (i.e., large defects or fuel contamination). Furthermore, longer lived species should have similar or higher R c /y c values than shorter lived species. These general trends neglect neutron absorption and parent production effects that may need to be considered. In Figure 2, R c /y c of GS iodines is incorrectly above the online noble gas values, but both iodines and gases show the expected decreasing R c /y c with increasing λ, consistent with a defect present in core. Further investigation and knowledge of the system status is required to determine the root cause of the discrepancy between the iodine and gas R c /y c before the data are used for analysis (in this instance the online measurement system was still under development). The correct trends in each nuclide family provide confidence in the measurements and suggest a possible off-set error (calibration issue, transit time error, incorrect reactor parameters, etc.,) in the gaseous FP monitoring system (GFP) or GS system. PHTS systems are very complex (multiple loops, side-streams, balancing systems, etc.,) and subtle design nuances may impact the FP measurements Checking fits to known models After the above preliminary checks are complete, another test of data accuracy is the ability to fit more complicated models to the data. Various reactor designs and operators have developed numerous models and codes for performing defect diagnostics. The intent of this test is to confirm measurements match established models; outliers or data bias should be automatically detected and rectified by the defect diagnostic code. For example, in Figure 3 CANDU online defect diagnostic (COLDD) [6] was used to fit different R c /y c models to BWR data 2. Visually, the data appear to follow the model predictions very well (neutron absorption was not included for 135 Xe and may explain its slight deviation), providing confidence in the measurements and models. For a more FIGURE 2. R c /y c versus λ analysis with a defect present at Darlington-1 in 2011 (Data courtesy of Ontario Power Generation). The relatively low gas values require investigation. Combined loop gases are sampled downstream of the IX system that is fed from both loops (i.e., gases communicate between loops but iodines do not). 2 Although COLDD is a CANDU defect diagnostic code, the equations and theory are very similar to those employed by CHIRON [4] for PWR/BWR analysis [15, 16]. 32

5 parameters are determined for one FP type (soluble FP or gaseous FP), the same parameters should apply to all the other nuclides of that nuclide type; if not, that would suggest an unknown issue. Therefore, in Figure 4, once 131 I was properly fit, STAR and the data were consistent for the remaining iodines. However, caution is required when considering 132 I (owing to parent production from 132 Te) or a gas analysis including 135 Xe (owing to the strong neutron absorption loss term); these factors make 135 Xe and 132 I modelling difficult. FIGURE 3. Hatch-1 BWR noble off-gas data from Zavadowski et al. [17] analyzed using COLDD [6]. Note: 135 Xe neutron absorption is not included in the models or measurement. FIGURE 4. STAR prediction versus coolant activity behaviour during IX flow change (no IX flow on days 3 4.3) at CANDU Reactor Darlington-1 in 2011 (data courtesy of Ontario Power Generation). quantitative analysis, it is possible to calculate the goodness of fit, fit coefficient uncertainties, and ensure coefficients are within a known range [6]. An algorithm can then be developed to handle outliers and unexpected results as they appear. Another example in Figure 4 shows the application of steadystate and rransient activity release (STAR) [1, 7] to FP data from a CANDU plant (the spike during days 3 5 is discussed in Section 3.2). Modelling FP data with STAR involves determining defect parameters that match FP measurements. The power of STAR for error checking is that once input 2.4. Comparison of different measurement techniques Another different and robust method of checking activity measurements for their accuracy is to compare activities measured by two or more independent systems. One example of this method is the simultaneous comparison of gamma spectrometry performed online against off-line grab sampling of the PHTS. Grab sampling typically involves extracting a depressurized and cooled sample of the PHTS, whereas the online system often monitors a side-stream of pressurized PHTS in real time, although possibly at a lower temperature. Depressurization of the GS impacts gaseous measurements, but soluble radioiodines are not affected. Therefore, a direct comparison of radioiodine measurements between GS and online values should be consistent within measurement uncertainty. The complete independence of the two systems increases confidence in the comparison. Typically, online and GS measurements should be in agreement; however, Figure 5 [7] and Figure 6, [14] are two datasets that show a factor 2 difference between 131 I measured by grab sampling compared with online systems in two different reactors. Figure 5 is an example of online data exceeding GS data, and Figure 6 shows the exact opposite. A factor 2 discrepancy in 131 I measurements could impact defect analysis, as 131 I is the longest lived radioiodine commonly used for defect diagnostics and plays a major role in several techniques (for example determining defect numbers in WWER reactors [3]). Furthermore, a discrepancy in 131 I lowers the overall confidence in the online and GS measurements until the source of the error can be found. Although this example is very specific to a particular reactor type and specific measurement systems, any reactor design that monitors coolant activity with multiple independent systems has the ability to compare and contrast the systems to ensure precise measurements. 3. Reactor Parameter Analysis The next two sections describe methods for examining potential errors in transit times and purification data. Earlier work has shown that errors and biases in these two parameters can impact defected fuel diagnostics [6]. AECL NUCLEAR REVIEW 33

6 FIGURE 5. Factor 2 discrepancy between grab sample (low values) and GFP (high values) measurements of 131 I concentration (CANDU Reactor data modified from Figure 9-6 of El-Jaby [7]). FIGURE 6. Factor 2.5 discrepancy between grab sample (high values) and GFP (low values) measurements of 131 I concentration. CANDU Reactor Wolsong [14] Checking transit times The transit time refers to the time between FP release into the PHTS and measurement. This duration can be seconds (online system), minutes, or hours (grab sampling) depending on the reactor and measurement system design. To account for the transit time, the measured data must be corrected to include decay and parent production during transit. Transit time can be measured and (or) verified by analyzing the R c /y c values during defect-free operation of the shortest lived species; ideally 89 Kr (t 1/2 = 3.15 min), 138 Xe (t 1/2 = min), and 134 I(t 1/2 = 52.5 min). 89 Kr and 138 Xe are the best candidates as they are noble gases; however, they are not always available and therefore 134 I may be required. Short-lived species are used as their half-lives are typically comparable with the transit time, they reach equilibrium quickly, and direct recoil is their dominant release mechanism (independent of λ). Figure 1 [9] and Figure 3 [6, 17] are CANDU and BWR data, respectively, that illustrate systems with known and correct transit times. The similar values of the shortest lived species during defect-free operation confirm the correct transit time 34

7 correction has been applied. If an incorrect transit time is used, the short-lived species will have varying R c /y c values. This methodology can also be used in reverse; if the transit time is unknown, the value can be determined by optimizing the transit time correction to minimize the difference in R c /y c values for the short-lived FP. Errors in transit time appear as κ(t) and will impact short-lived FP with minimal impact on long-lived FP; this would typically impact analysis of fuel contamination Checking purification flow rates and efficiencies Purification systems in power reactors are often used to remove soluble (e.g., radioiodines in ion exchange (IX) columns) and gaseous (e.g., degassing systems) FP. Performance metrics (e.g., flow rates and removal efficiencies to calculate β p (t)) for these FP removal systems are typically measured directly by monitoring and sampling the inlet and outlet flows. However, it is also possible to indirectly examine purification flow rate and efficiency changes by monitoring FP data to confirm correct understanding and behaviour of the reactor purification system. For example, Livingstone [6] describes measuring the effective half-life of 131 I in the National research universal reactor (NRU) as a means of indirectly monitoring the IX removal efficiency over time. These measurements were then compared and contrasted against direct measurements; agreement between the independent systems provided confidence in both measurements. Unlike the NRU reactor, in the case of LWR/ PHWR/WWER commercial reactors, shutdown iodine spiking requires a more detailed model and higher frequency sampling (effective half-life is much shorter) to measure effective halflives and examine purification capabilities [3, 18, 19]. Errors in purification flow rates and efficiencies appear as κ(t), and will impact long-lived FP (small λ) more readily, with minimal impact on short-lived FP. A further example is shown in Figure 4 where the STAR code [1] is used to model nuclide inventory during a transient change in purification flows. The STAR model predicts expected behaviour following a purification flow shutdown, but the PHTS nuclides do not change. As per Equations (1) and (2), if the purification term (β p s 1 ) is removed (as occurred during days in Figure 4), then the longer lived isotopes must increase as β p > λ 131I, λ 133I. Therefore, this method showed an inconsistency between IX flow rates and radioiodine behaviour that needs to be understood. 4. Impact of Errors on Analysis Large errors and biases will be detected during measurement and analysis; however, slight errors and biases may persist that inadvertently impact defect diagnostics. Measurement errors that impact short-lived FP (e.g., transit time uncertainties or measurement issues) will have the greatest impact on uranium contamination calculations. Errors impacting long-lived FP (e.g., purification uncertainties or measurement issues) may have a significant impact on most techniques using the data to infer defect information. The nature and extent of the impact will depend on the uncertainty, reactor type, and analysis technique. Figure 1 shows a slight discrepancy in 85m Kr, which is not discussed in several publications that use this data source [7, 9, 20]. However, the inconsistency was only present during a short period and only applied to one nuclide in the noble gas family. Therefore, when analysis is performed on several nuclides, and only one is slightly biased, it will not have significant effects on the results and may not warrant further investigation. For example, during the development of CHIRON [4] it was found that adding or removing 132 Te effects on 132 I had a minimal affect when using a model that examined five iodines ( 131 I, 132 I, 133 I, 134 I, and 135 I) [4], yet when STAR is used to model 132 I specifically, 132 Te effects are required [1]. 5. Conclusions Fission product concentration in reactor coolant is a key parameter for defect diagnostics, and personnel involved must be confident that the data is valid and there are no inaccuracies. Several techniques and examples have been provided for testing FP data and reactor parameters to ensure data accuracy. Although the examples considered in this paper are for specific reactor and measurement designs, the general concepts can be adapted and applied to any reactor type. Continuous examination and understanding of FP data will ensure accurate defect diagnostics and indirectly monitor independent reactor systems (e.g., purification systems). Depending on the application, subtle errors and biases may not impact final results; personnel using and analyzing data need to decide on the level of understanding, accuracy, and precision required for their application. Acknowledgements The author would like to thank Bruce Power and Ontario Power Generation for providing invaluable data used in this work and Dr. Ali El-Jaby (CNSC) for his input. references [1] A. El-Jaby, B.J. Lewis, W.T. Thompson, F. Iglesias and M. Ip, 2010, A General Model for Predicting Coolant Activity Behaviour for Fuel-Failure Monitoring Analysis, Journal of Nuclear Materials, 399, pp doi: /j.jnucmat [2] P. Slavyagin, L. Lusanova and V. Miglo, 2002, Regulation of the Fission Product Activity in the Primary Coolant and Assessment of Defective Fuel Rod Characteristics in Steady-state WWER-type Reactor Operation, IAEA International Technical Meeting on Fuel Failure in Water Reactors: Causes and Mitigation, June 2002, Slovakia, pp [3] V.V. Likhanskii, I.A. 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