Modeling of IFA-409 by Means of TRANSURANUS Code

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1 Modeling of IFA-49 by Means of TRANSURANUS Code Davide ROZZIA 1, Alessandro DEL NEVO 2, Alessandro ARDIZZONE 3, Pietro AGOSTINI 2 1-Dipartimento Ingegneria Meccanica Nucleare e della Produzione, UNIPI Largo Lucio Lazzerino, 1 561, Pisa (PI), Italy ABSTRACT 2-CR Brasimone, ENEA Località Brasimone 432 Camugnano (BO), Italy 3-Politecnico di Torino, POLITO Corso Duca degli Abruzzi, Torino (TO), Italy daviderozzia@libero.it, alessandro.delnevo@enea.it, alessandro_ardizzone@aol.com, pietro.agostini@enea.it Inert fission gas atoms have a very low solubility in the UO 2 matrix causing two important life-limiting phenomena in the fuel rod: either they remain in the pellets and contribute to the swelling, or they are released from the pellets to the pin free volume. Therefore, the correct prediction of Fission Gas Release () is an essential tool to assess the behavior of the fuel matrix under steady state and transient conditions. OECD/NEA sets up the public domain database on nuclear fuel performance experiments for the purpose of code development and validation International Fuel Performance Experiments database. This database includes the IFA-49 experiment. The objective of this experiment was to investigate the Fission Gas Release () phenomenon in BWR fuel rods irradiated at high burn-up during normal operation. The datasets have been distributed in the framework of the IAEA CRP FUMEX III (28-211). It includes 4 rods base irradiated in Halden Boiling Water Reactor in the upper cluster of IFA-49 from May 1973 until June The final burn-up ranges from 42 to 45 MWd/kgUO 2. After the base irradiation, and gap pressurization were measured and then the rods were reconstructed, re-instrumented and subjected to power ramps (IFA-535.5,6). The present paper focuses on IFA-49 base irradiation only. The aim of the activity is to summarize the main results obtained after the simulations of 4 BWR fuel rods included in the above mentioned database by means of TRANSURANUS code. Particular emphasis is given to the main variables which influence the phenomenon in normal operation. 1 INTRODUCTION Gaseous isotopes tend to diffuse inside the grain (intragranular processes) and to cumulate at grain boundaries in form of bubbles and structures of connected bubbles (intergranular processes)[1]. The first step is single gas atom diffusion in the lattice. This mechanism includes different contributions that may be classified as temperature depended and athermal. The thermal activated processes are assumed to dominate the diffusion above 14 C being an increasing function of the temperature itself. The fission fragments contribute to the diffusion process too by means of their associated irradiation damage 61.1

2 61.2 cascades. This last process dominates the diffusion process at temperatures below 1 C and is practically athermal. Either natural or radiation produced imperfections in the solid matrix reduce the amount of fission products available for diffusion by trapping the migrating atoms into bubbles. A fraction of the gas atoms trapped in bubbles can be re-dissolved in the surrounding matrix through the interaction of a fission fragment with the bubble. The trapping rate depends on the size of the intra-granular bubbles, hence on temperature, fission rate and burn-up. A second important effect of trapping occurs at grain boundaries (inter-granular process). When gas reaches grain boundaries, it precipitates on grain faces to form lenticular bubbles. When the saturation concentration is reached on the grain face, fission gas accumulates on the grain edge. If the gas at grain edges has also saturated, inter-linkage occurs forming a so-called tunnel network through which the gas can be released, especially in the case of transients when a sudden interconnection or opening of grain face bubbles due to micro-cracking along grain boundaries takes place. Cracking causes instantaneous venting of the gas cumulated in the structure. This process is connected to the burn-up (i.e. it occurs close to 14 C at 5 MWd/kgU and at 15 C at 6 MWd/kgU)[2]. The present paper aims to investigate the phenomenon by means of TRANSURANUS code version 212[3]. The activity is based on the assessment of a dataset included in the IFPE database[4] and released in the IAEA-CRP-FUMEX-III project[5]: the IFA-49 experiment[6]. 2 DESCRIPTION OF THE EXPERIEMENT The IFA-49 experiment was carried out to provide high burn-up BWR fuel rods to be ramp tested in the IFA and IFA experiments. It consists of a base irradiation up to an average burn-up of about 45 MWd/kgUO 2. Four rods fabricated by GE Company were irradiated in the upper cluster of IFA 49 from May 1973 until June After an initial period at 38 kw/m, the average linear heat rating was kept between 25 and 3 kw/m for most of the time during the irradiation. Pellet peak rating was about 2 % higher than rod average. The main design data of the test fuel rods is given in Table 1. At the end of the experiment, the rod pressurization and the rod free volume were measured. Table 1: IFA-49, main design data. IFA 49 fuel rods Quantity Uncertainty Number of fuel rods / label 4 BWR type: Fuel - clad radial gap 122 μm +27 / -12 μm (from ovality) Filling gas / pressure 98.4 %He %Ar /.1 MPa -- Fuel rod total free volume 9.8 cm 3 ± 1% (assumed) Fuel rod active length 466 mm ± 1mm (assumed) End pellet material / length 85% Hf 15%Y / 17 mm -- Fuel fabricant G.E. San Jose Fuel Laboratory+ -- Fuel material / enrichment UO 2 / 9.88 % -- Pellet density / O/M ratio 94.7 %TD / 2.4 ± 1% TD (assumed) / ±.2(assumed) Pellet OD / height 1.54 / 1.4 mm -- Cladding fabricant / material G.E. Wilmington / Zr-2 -- Cladding OD/ ID mm / 1.81 mm -- Cladding thickness.88 mm ± 1% (assumed) Cladding ovality max/min 25 μm /1 μm -- 3 MODEL DESCRIPTION The activity is performed using TRANSURANUS code, version v1m1j12, with the deterministic option, steady state thermal and mechanical analysis[3]. The models selected are

3 61.3 generally the ones standard for the transient to be simulated. Only the active part of the fuel is accounted in the simulation. The active part has been divided into 5 axial slices of equal length according to the experimental data available[6]. The nominal design values are used if available, the measured values are considered when nominal values are not specified. The fuel average grain size is not reported in the database, it has been assumed 1 μm (average grain diameter). The boundary conditions implemented for the analysis are: linear heat rate at 5 axial position, cladding waterside temperature histories (axially constant), fast neutron flux (3.8*1 13 n/cm 2 -s, >1 MeV) and coolant pressure (3.1 MPa). The rate of increase/decrease between different constant linear heat rate spans has been selected as 6 kw/ (m*h). 3.1 Modeling of The fission gas intragranular equation adopted in the code is according to Speight[7] (Eq.1). It assumes spherical grain of constant radius that contains immobile bubbles and equilibrium between trapping and resolution inside each bubble: 2 tot 2 C Cs Cb Deff [ C ] 2 s Cs r r r (1) b D eff D b g Where D(t) is the single gas atom diffusion coefficient; D eff C s (r,t) C b (r,t) g b β(t) is the effective diffusion coefficient is the local concentrations of gas in solution in the fuel matrix; is the local concentrations of gas in solution in bubbles; is the probability per unit time of gas atoms in solution being captured by a bubble; is the probability per unit time of gas atom in bubble being re-dissolved to the matrix; is the rate at which gas is produced. Eq.(1) is computed by means of the URGAS algorithm[8]. The effective diffusion coefficient is selected according to Matzke[9] (for the thermal component) and constant provided by I for the athermal part[3] in the form given in Eq.2. This option is labeled MOD6. D eff D thermal D athermal 5*1 8 * e 4262 T and D eff 1*1 The grain boundaries are treated according to the modified Koo model developed to account grain boundary cracking that occurs in case of power excursions[2]. In steady state conditions, it is based on the approach of Dowling White Tucker[1] that assumes the existence of a saturation concentration limit above which grain edge saturation is assumed and the excess of gas is released. This limit is set in the code as constant equal to 1*1-4 mol/m 2 (standard option)[3]. If transient conditions are met, the saturation concentration limit is set to zero and the gas stored at grain boundaries is instantaneously released. Transient conditions activates if both the following control flags are verified (Eq.3): LHR 3.5kW / m and T 15*(1 Where LHR T bu loc power excursion in a time step; is the local fuel temperature [K]; is the local burn-up [MWd/kgU]. This option is labeled 3. The code incorporates two additional models to treat the intra-granular diffusion coefficient, two additional models for grain boundary simulation and one additional algorithm to solve Eq.(1)[3]. bu loc 8 ) 25 (2) (3)

4 REFERENCE ANALYSIS The (accuracy ±1.8%) was computed from pressure measurements. Rod #89 is analyzed in Figure 1, similar considerations apply to the other rods. The figure contains: The experimental and inner pin pressure at the end of the irradiation (at 2 C). The simulated reference and inner pin pressure as function of time. The simulated assuming the standard inter-granular model 1. The simulated fuel centerline temperature in the peak axial position. The temperature thresholds for a-thermal (1 C) and thermal intra-granular diffusion (14 C), equal axial (14 C) and columnar (19 C) grain growth. The irradiation can be subdivided in three main zones. In the initial period, (up to about 8 hrs) the fuel pellet experiences relatively high temperature. In this zone, the rapidly increases up to about 15% and it is mainly due to thermal activated intra-granular diffusion processes. Between 8hrs and 3-35hrs the fuel temperature generally remains below 14 C and the fractional release of inert gases tends to decrease down to 1%. In this zone, both a-thermal and thermal activated intra-granular diffusion processes contribute to the. The third zone is characterized by high temperatures (slightly greater than the initial period) in which thermal intra-granular diffusion dominates again causing a final release of about 2%. The contribution of the inter-granular model of Koo appears crucial in the first and in the third zone. In particular, the standard model for steady state operation (1) underestimates the contribution in these zones. In agreement with the analysis, the inner rod pressurization is simulated according to the gas release trend. The main results are summarized in Table 2. Rods #81 and #811 are in very good agreement with the experimental trends. Rod #89 is under-predicted by about 2 % (inside the acceptability lower band). Rod #812 is over-predicted (by about 3 %, inside the acceptability upper band); the standard model 1 overestimates rod #812 too (about 2%). This seems connected to the simulation of the last 5 hrs in which the fuel temperature may be overestimated. Table 2: IFA-49, summary of the reference analysis. Rod label EXP calc. Reference Error EXP - Reference calc. 1 Error EXP Inner pressure - Inner pressure - Peak axial fuel centerline T COLUMNAR GRAIN GROWTH EQUAL -AXIAL GRAIN GROWTH DIFFUSION THERMAL + ATHRMAL DIFFUSION Temperature [ C] / Pressure [bar] THERMAL 1 ATHRMAL DIFFUSION Time[hrs] Figure 1: IFA-49, rod #89, and pin pressurization trends.

5 SENSITIVITY ANALYSIS Several sensitivity analyses were performed mainly addressing the influence of modelling options (i.e. fuel conductivity, fuel swelling, fuel relocation, fuel densification) and boundary and initial conditions (i.e. design tolerances, uncertainties on the power history). Here after are presented sample results of main interest. 5.1 models In addition to the reference models (red-square points in Figure 2), the intra-granular processes can be modeled with the following options: The thermal diffusion coefficient is that of Matzke, the athermal diffusion is according to White - Tucker[3]. Identified as MOD 4, (blue points in Figure 2). The single gas atom diffusion coefficient is given by Turnbull[3]. Identified as MOD 9 (black points in Figure 2). While the inter-granular processes can be modeled with the following options: The grain boundary saturation concentration is a constant that depends on the intragranular model. Identified as 1[3] (rhombus points in Figure 2). The grain boundary saturation concentration depends on the fuel temperature (constant/t) and on the intra-granular model. Identified as 2[3] (triangular points in Figure 2). The combinations among these options give rise to nine different calculations whose results are summarized in Figure 2. MOD9 bounds all the predictions. This is because of two reasons: from one hand, it is characterized by the highest intra-granular diffusion coefficient and, from the other hand, it is associated to the highest inter-granular saturation concentration[3]. Therefore, when the ramp release model activates ( 3), the grain boundary venting causes over-prediction whereas lower gas release is obtained with the other options. The intra-granular diffusion coefficient MOD4 is greater or equal to the reference model depending on the local temperature[3]. Since these models are associated to the same inter-granular saturation concentrations, giving any inter-granular model, MOD4 tends to predict higher gas release than the reference. Among these simulations, the reference case captures the experiment with the best agreement, Table Fuel conductivity correlations The fuel conductivity is modeled according to the new recommended correlation for UO x fuel, fitted to data from I[3]. It depends upon the local temperature, the local burn-up, the Gadolinia content, the local porosity, and High Burn-up Structure (HBS) formation. This last phenomenon occurs at pellet rim when the local burn-up exceeds 75 MWd/kgU and causes micro-structural changes that tend to degrade locally the fuel conductivity. Six correlations are available in the code, all of them consider the local temperature, the local burn-up, and the local porosity: COND 18 is the original correlation of Lucuta. It is recommended for fuel temperatures less than 15 C (therefore, due to the temperature trend reached in IFA- 49, it is expected that it over-estimates the conductivity). COND 19 is an extension of the original MATPRO-11. HBS is not treated. COND 2 is according to Harding and Martin. It was the old recommended. It does not consider HBS effect. COND 22 is given by Lassmann and Moreno. It doesn t treat HBS.

6 61.6 COND 23 is according to Wiesenack. It considers the HBS. COND 28 was developed according to Delette and Charles. This correlation does not consider HBS. The correlation of Lucuta underestimates the fuel temperature and, therefore, the. This is due to its applicability range. Excepts for the Wiesenack s correlation (COND 23), the remaining models do not consider the HBS effects. Since these rods experience HBS formation (the local burn-up at pellet periphery exceed 85 MWd/kgU), they tend to underestimate the, Table 4. The correlation of Wiesenack under-predicts the. Rod labe l EX P FG R Table 3: IFA-49, sensitivity analyses on modeling: models. calc. MOD6 calc. MOD4 calc. MOD9 Referenc e Rod Label Table 4: IFA-49, sensitivity analyses on modeling: fuel conductivity correlations. Exp Ref. COND18 COND19 COND2 COND22 COND23 COND CALC MOD6-1 - Reference (3) - MOD4-2 - MOD9-1 - MOD % MOD6-2 - MOD4-1 - MOD4-3 - MOD % EXP Figure 2: IFA-49, sensitivity analysis on models. CALC Reference - COND-18 - COND-19 - COND-2 - COND-22 - COND-23 - COND % EXP % Figure 3: IFA-49, sensitivity analysis on fuel conductivity correlations. 5.3 Design tolerances and uncertain parameters The is affected by a large variety of design parameters. The gap initial size, the pellet density and the average grain size are considered here. The range of the first two parameters is given in Table 1. The average grain size (not given in the database) was fixed to 1μm and is assessed in the range ±5%.

7 61.7 The simulations are sensitive to gap maximization and density minimization, Figure 4 and Table 5. These calculations tend to increase the by increasing the temperature drop across the gap region or by reducing the fuel conductivity. Opposite consideration applies to gap minimization and density maximization even if they highlight a lower impact on the. The grain size largely affects the simulations. An increase of the grain size reduces the and vice-versa. In particular, the effect is not symmetric since the impact of grain reduction is larger than those of grain increase. In general, grain size has two main effects. Firstly, the larger is the grain, the larger is the diffusion distance for the fission gas atoms created in the grains. This tends to reduce the release rate (as achieved in the calculations). Secondly, the larger is the grain, the lower is the capacity of the grain boundaries to store fission gas as their total surface-to-volume ratio is decreasing. In despite of this, for steady state conditions is low (1-2%) and dominated by the process to saturate the grain boundaries, and therefore it can be concluded that cannot be strongly dependent on the grain size. On the other hand, under power excursions (as those occurred in IFA-49), these contributions may impact on together with micro-cracking of the grain boundaries that is more or less a stochastic phenomenon. After 5 years of testing the contribution of these three mechanisms to during power ramp is still controversial: some of the experiments highlight largest release from small grains and some other highlight opposite results. Rod Label Table 5: IFA-49, sensitivity analyses on design tolerances and uncertain parameters. Exp Ref. MAX GAP MIN GAP MAX DENS MIN DENS MAX GRAIN MIN GRAIN % 3 +1 CALC Reference - MAX GAP 5 - MIN GAP - MAX DENS -5% - MIN DENS - MAX grain - MIN grain EXP Figure 4: IFA-49, sensitivity analysis on design tolerances and uncertain parameters. 6 CONCLUSIONS The phenomenon has been assessed against IFA-49 experiment. It includes four BWR rods base irradiated in the HBWR up to 45 MWd/kgU. The fuel rods have been subjected to measurement. Even if the experiment does not involve power ramping, the Koo model specifically developed to account for micro-cracking occurrence is applied since the linear heat rate evolution does not seem close to steady state conditions and the resulting measured (16-21%) largely overpasses steady state typical values (1-3%). The results achieved from the simulations bring to the conclusions hereafter summarized:

8 61.8 The selection of the models available in code has a large impact on the results. Nevertheless, the recommended options for power ramp conditions agree with the experimental measures. Fuel conductivity largely affect the. Among the correlations available in the code, the reference correlation according to the new standard model for UO x developed by I is closer to the experimental trends since it accounts for HBS effects. The gap size and pellet density design tolerances affect. In particular, the density minimization largely enhances the simulations. The grain size impacts on the simulation. The contribution of small grains is larger than those of large grains. In particular, it will be useful to know the local deviation of grain size with respect to its average value. REFERENCES [1] P. Van Uffelen, Modeling of Nuclear Fuel Behavior. Publications Office, JRC Publications, Report EUR EN, European Commission, 26. [2] Y.H. Koo, COSMOS: A computer code to analyze LWR UO 2 and MOX fuel up to high burnup. Annals Nucl. En. Vol 21 pp , [3] K. Lassmann, A. Schubert, P. Van Uffelen, J. van de Laar, TRANSURANUS Handbook version v1m1j12. JRC, I 212. [4] OECD/NEA, The Public Domain Database on Nuclear Fuel Performance Experiments for the Purpose of Code Development and Validation, International Fuel Performance Experiments (IFPE). [5] J. Killeen, Fuel Modeling at Extended burn-up, III. IAEA-OECD-JRC, 21. [6] V. Tosi, The effect of pressurization on in high burn-up BWR type fuel rods. Institut for energiteknikk, HRP, HWR-198, [7] M.V. Speight, A calculation on the migration of fission gas in material exhibiting precipitation and resolution of gas atoms under irradiation. Nucl. Science and Engineering 37 (1969) [8] P.T. Elton, K. Lassmann, Calculational methods for diffusional gas. J. Nucl. Mater. 11 (1987) [9] Hj. Matzke, Gas release mechanisms in UO 2 -a critical overview. Radiation Effects, 198, Vol. 53, pp [1] D.M. Dowling, R.J. White and M.O. Tucker, The effect of irradiation-induced resolution on fission gas release. J. Nucl. Mater. 11 (1982) ABBREVIATIONS CRP Coordinate Research Project HBWR Halden Boiling Water Reactor HRP Halden Reactor Project IAEA International Atomic Energy Agency ID Inner Diameter IFA Instrumented Fuel Assembly IFPE International Fuel Performance Experiment database I Institute for Trans-uranium Elements LHR Linear Heat Rate OD Outer Diameter Fission Gas Release FUMEX-III FUel Modeling at EXtended burn-up GE General Electric company NEA Nuclear Energy Agency OECD Org. for Economic Coop. and Dev. O/M Oxygen to Metal ratio PIE Post Irradiation Examinations TD Theoretical Density TRANSURANUS code