2017 Water Reactor Fuel Performance Meeting September 10 (Sun) ~ 14 (Thu), 2017 Ramada Plaza Jeju Jeju Island, Korea

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1 Plant and Cycle Specific Fuel Assembly Bow Evolution Assessment Yuriy Aleshin 1, Jorge Muñoz Cardador 2 1 Westinghouse Electric Company LLC, PWR Fuel Technology: 5801 Bluff Road, Hopkins, SC USA aleshiy@westinghouse.com 2 ENUSA Industrias Avanzadas, S.A. (Grupo SEPI), Fuel Technology: C/ Santiago Rusiñol, 12, Madrid Spain jmu@enusa.es ABSTRACT: Fuel assembly distortion (growth and bow) is an important fuel performance characteristic. A systematic methodology has been developed to explicitly address fuel assembly distortion stability and reduce its associated risks (handling damage potential, water gap variation effect, incomplete control rod insertion, etc.). This methodology has been applied to complete a prediction of bow evolution for each fuel assembly in-core during a selected operational cycle. The bow measurements come from campaigns performed on EDF NPP fleet to provide out-of-core bow for each fuel assembly incore at the beginning and at the end of the selected cycles. The out-of-core bow measurements obtained at the beginning of the selected cycle have been used as an input to the methodology together with specific fuel assembly model, appropriate operational parameters and representative loading and boundary conditions. The full core simulation generates a fuel assembly bow evolution throughout the cycle and out-of-core bow shapes at the end of the cycle. The predicted fuel assembly bow magnitudes and shapes have been compared with the available out-of-core bow measurements at the end of the cycle. A comparison between measured and predicted out-of-core bow magnitude and shape for each fuel assembly in-core has been used to assess the bow evolution prediction capability of the developed methodology. It has been concluded that the developed fuel assembly bow prediction methodology can reasonably assess the bow evolution of a fuel assembly in-core. Some potential improvements associated with the in-core loading conditions have been identified. It is expected that the reliable in-core bow and water gap distribution generated using the developed methodology could be used in the downstream safety analyses assessment as required. KEYWORDS: Fuel Assembly, Distortion, Growth and Bow. I. BACKGROUND Fuel assembly dimensional stability (growth and distortion) is important fuel performance characteristic. Excessive growth leads to extreme axial force which can provoke significant fuel assembly distortion and, ultimately, fuel assembly component structural failure. Fuel assembly distortion may adversely affect fuel handling, RCCA insertability, plant operation, etc. A systematic methodology has been developed to explicitly address fuel assembly distortion stability and to reduce its associated risks (handling damage potential, water gap variation effect, incomplete control rod insertion, etc.). Fuel assembly distortion throughout a plant specific cycle is assessed based on the fuel inventory, fuel management and operational conditions using the fuel design specific model, available fuel assembly distortion measurements, representative in-core loading and boundary conditions. This methodology has been applied to complete a prediction of bow evolution for each fuel assembly in-core during a selected operational cycle. The predicted fuel assembly bow magnitudes and shapes have been compared with the available out-of-core bow measurements at the end of the cycle. A comparison between measured and predicted out-of-core bow 1

2 magnitude and shape for each fuel assembly in-core has been used to assess the bow evolution prediction capability of the developed methodology. II. METHODOLOGY OVERVIEW The systematic methodology consists of the following steps which are used in consecutive order to assess fuel assembly distortion at various cycle times: 1. Develop a representative fuel assembly model and verify its ability to simulate out-of-core and in-core behaviors of a fuel assembly; 2. Prepare in-core simulation input based on cycle specific information and available out-of-core fuel assembly distortion measurements; 3. Simulate fuel assembly bow evolution during the selected cycle; 4. Compare measured and predicted out-of-core fuel assembly bow. Details about each part of methodology are provided below. II.A. Fuel Assembly Model Development and Verification Fuel assembly model development is the first step in the fuel assembly in-core behavior simulation process (Ref. 1). The fuel assembly finite element model consists of the skeleton, fuel rod bundle and their interface. This model supports fuel assembly behavior during testing (skeleton lateral and fuel assembly lateral and axial stiffness) and operation (single and row/column of fuel assemblies at in-core conditions). A generic fuel assembly model which describes all of the relevant fuel assembly components and their interactions is developed first. After that, the generic model parameters are updated based on the physical design information and test results. Some of the model parameters are tuned to match the available test results. The fuel assembly model is then verified using skeleton and fuel assembly lateral stiffness test data and fuel assembly growth post-irradiation inspection results. II.A.1. Skeleton Model Development and Verification A typical fuel assembly skeleton consists of guide thimbles, instrument tubes, spacer grids, top and bottom nozzles and hold-down springs as presented in Fig. 1 (Ref. 2, 3). The skeleton model includes two vertical beams representing the guide thimble and instrument tubes. The skeleton model includes models of spacer grids in order to describe the spacer grid structure in more detail (number of straps, strap thickness, etc.). The top nozzle hold-down spring packs are simulated by two equivalent spring elements. The skeleton model uses realistic material properties in order to describe skeleton component behavior during testing and operation. In order to simulate skeleton component behavior during operation, the model requires guide thimble growth and creep to be specified. The skeleton model is verified with data from the skeleton lateral stiffness test (Ref. 1). A comparison of the experimental stiffness data to the computed stiffness values from the skeleton model is shown in Fig. 2. 2

3 Fig. 1. Typical Skeleton Model Fig. 2. Comparison of Measured and Predicted Skeleton Lateral Stiffness II.A.2. Fuel Assembly Model Development and Verification The fuel assembly model consists of a skeleton, a fuel rod bundle, and their interfaces (Ref. 2, 3). The fuel assembly model is presented in Fig. 3. The fuel rod bundle is simulated by two effective beams, where each of the two beams represents one half of the fuel rods. The appropriate material properties used to describe fuel rod behavior during testing and irradiation. The fuel rod growth needs to be specified to simulate fuel assembly behavior during operation. The fuel rod bundle to skeleton interface includes fuel rod supports at spacer grid elevations and a gap between the top and bottom of the fuel rod and the top and bottom nozzles, respectively. The spacer grid model includes the spring and dimple supports. In the fuel assembly model, the contact force is fluence and temperature dependent. The friction factor is adjusted to include the out of plane spring and dimple effect on fuel rod breakaway force. The fuel rod to the nozzle gaps are simulated using gap elements. The fuel assembly model is verified with data from the fuel assembly lateral stiffness test (Ref. 2). A comparison of the experimental stiffness data to the computed stiffness values from the fuel assembly model is shown in Fig. 4. Fig. 3. Typical Fuel Assembly Model Fig. 4. Comparison of Measured and Predicted Fuel Assembly Stiffness 3

4 The fuel assembly growth simulation is an integral test of the developed fuel assembly model and operation dependent material properties (Ref. 2). Typically, the growth simulations are conducted for fuel assemblies with a known operational history and known growth obtained during post irradiation inspection results. The expected growth prediction is presented in Fig. 5. The developed model can then be used in the downstream analyses to simulate in-core bow evolution during selected cycle. II.B. Simulation Input Fig. 5. Comparison of Measured and Predicted Fuel Assembly Growth The in-core simulation requires a complete set of inputs to describe boundary and loading conditions including core operational parameters. The inputs include gravitational, buoyancy, hydraulic lift and lateral forces (Ref. 4) as well as holddown forces together with forces from fuel assembly-to-fuel assembly and fuel assembly-to-baffle interactions. The beginning of each cycle requires the irradiated fuel assembly distorted shapes in order to initiate the selected cycle simulations. Typically, the first step of the simulation is to assemble the core at beginning of cycle cold conditions based on the core loading pattern and core inventory for the selected cycle. A quality prediction of the in-core bow and gap distribution is strongly dependent on the availability of the fuel assembly distortion measurements. The cycle specific fluence and temperature distributions at span and spacer grid elevations are used to calculate guide thimble growth and creep, spacer grid growth, spacer grid spring force relaxation and fuel rod growth. II.C. Bow Simulation The next step in the methodology is to simulate the mechanical behavior of the fuel assembly during operation (Ref. 1). The purpose of in-core simulation is to determine fuel assembly distorted shape by taking into account the realistic fuel assembly conditions as well as the representative loading and boundary conditions for a specific cycle of the selected plant. The developed and validated fuel assembly model is a basis for multi-assembly simulation. This model is updated to the irradiated state of each fuel assembly in the core for a selected cycle as discussed in sections above. Thus, a core fuel assembly inventory is assembled from the specific single fuel assembly models. A typical in-core simulation is completed for a selected cycle of the specific nuclear power plant. The fuel assembly inventory and irradiated history of the fuel assembly should be available to create the simulation input. The burnup, neutron fluence, temperature, resident time, etc. are required to update the fuel assembly model characteristics to be consistent with the irradiated state at the beginning of the selected cycle. The gained growth, distortion (shape and bow) and spacer grid growth for each fuel assembly for the selected cycle are required to create a quality input and increase simulation accuracy. 4

5 The nuclear physics and thermal hydraulic characteristics are required to conduct a cycle specific simulation. This standard information is typically available as a part of the core design activities for the cycle of interest and for the previous cycles of the selected core. The lateral hydraulic forces may be considered for the cycle simulation (if available) (Ref. 4). Based on the current methodology, the core is represented by rows and columns of the fuel assemblies. A typical core layout is shown in Fig. 6. Each core row/column model consists of a combination of the single fuel assembly models as shown in Fig. 7. For the selected plant and operation cycle, a set of fuel assembly models is created to represent the core inventory. Fig. 6. Typical PWR Core Layout Fig. 7. Core Row/Column Representation For a selected row/column, the single fuel assembly models are positioned at the bottom core plate based on the fuel assembly-to-fuel assembly pitch (nominal). Sets of contact elements are introduced at the spacer grid elevations to describe fuel assembly- to-fuel assembly and fuel assembly-to-core barrel interfaces. The initial gaps are set based on a fuel assemblyto-fuel assembly nominal gap and relative fuel assembly bow. This core model has been found to be efficient for introducing required fuel assembly geometry and boundary conditions. The simulation is completed using a finite element analyses code together with pre- and post-processors to facilitate input data preparation and result reduction (Ref. 1). III. METHODOLOGY APPLICATION The developed methodology has been applied to complete a prediction of bow evolution for each fuel assembly in-core during a selected operational cycle. The bow measurements come from campaigns performed on the EDF NPP fleet in order to provide out-of-core bow for each fuel assembly in-core at the beginning and at the end of the selected cycle. The out-ofcore bow measurements obtained at the beginning of the selected cycle have been used as methodology inputs together with specific fuel assembly models, appropriate operational parameters and representative loading and boundary conditions. The full core simulation generates a fuel assembly bow evolution throughout the cycle and out-of-core bow shapes at the end of the cycle. The predicted fuel assembly bow magnitudes and shapes have been compared with the available out-of-core bow measurements at the end of the cycle. A comparison between measured and predicted out-of-core bow magnitude and shape for each fuel assembly in-core has been used to assess the bow evolution prediction capability of the developed methodology. The overall fuel assembly distorted shape at selected spacer grid elevation based on the out-of-core bow measurement and simulation is presented in Fig. 8. The arrow represents fuel assembly bow vector and color assigned according with bow magnitude. 5

6 BOC Out-of-Core Measurements (simulation input) EOC Out-of-Core Bow Measurements EOC Out-of-Core Bow (simulation output) Fig. 8. Out-of-Core Bow Distribution at Selected Spacer Grid (Note: Color indicates a range of the absolute bow magnitude from minimum to maximum ) The out-of-core bow distribution at the beginning of the selected cycle is random. However, the bow distribution at end of cycle is more regular. The simulation results are consistent with the end of cycle observation. In some cases, the BOC fuel assembly shape (order and/or direction) changes throughout the cycle. However, the methodology was able to simulate this behavior in majority of cases. The measured vs. predicted bow amplitude is presented in Fig. 9 for the overall population and the reduced population (~80% of overall) where the distorted shape is qualitatively consistent between the measured and calculated shapes. Note that the presented plots include the bow directions. Overall Data Population Reduced Data Population (based on qualitative distorted shape prediction) Fig. 9. Measured vs. Predicted Bow 6

7 IV. CONCLUSIONS The developed fuel assembly bow prediction methodology can reasonably assess the bow evolution of a fuel assembly in-core. The methodology predicts out-of-core distorted shape at ~80% of the core locations and bow magnitude with standard error of ~ 1.7 mm in case when the mesured and predicted distorted shapes are consistent. Some potential improvements associated with the in-core loading conditions have been identified. It is expected that the reliable in-core bow and water gap distribution generated using the developed methodology could be used in the downstream safety and performace analyses assessment as required. ACKNOWLEDGMENTS Westinghouse, ENUSA and KNF have agreed to work together and collaborate in the technical area of fuel assembly growth and bow assessment methodology development. As a part of this collaboration, a systematic methodology has been developed to explicitly address fuel assembly dimensional stability and reduce risk associated with implementing a new fuel assembly design. This methodology has been applied to simulate the in-core fuel assembly behaviour as a part of the presented work. The bow measurements used in this work come from campaigns performed on the EDF NPP fleet to provide out-of-core bow for each fuel assembly in-core at the beginning and at the end of the selected cycles. These unique measurements were a basis that enabled the presented work. REFERENCES 1. Y. Aleshin, M. Aullo, A. Cerracin, Methodology to Assess Fuel Assembly Dimensional Stability on Design Stage, WRFPM 2009, France, September E. Gutierrez, A. Cerracin, M. Morales, Y. Aleshin, 17x17 RFA Fuel Assembly Dimensional Stability, WRFPM 2011, China, September E. Gutierrez, J. Munoz, Y. Aleshin, D. Hubert, R. Fernandes, Benchmarking of SAVAN2D and MAC3S2 (Stage 1): Simulation of Fuel Assembly Mechanical Performance, WRFPM 2013, USA, September A. Petrarca, Y. Aleshin, Y. Xu, R. Corpa, J. Gomez, Effect of Lateral Hydraulic Forces on Fuel Assembly Bow, WRFPM 2015, Switzerland, September