www.inl.gov Performance Materials Models Materials Performance Gas for the ofDevelopment Fuel Multiscale Investigation of Fission 2 Novascone BISON Team: Millett MARMOT Team: Peterson John MOOSE Team:
1 Los Alamos National Laboratory National Alamos Los Idaho National Laboratory National Idaho 1 ,Yongfeng Zhang ,Yongfeng 1 , Spencer Ben 1 Richard Williamson Richard
Derek Gaston Derek Bulent 1 Biner , David Andersson , David
1 , Giovanni Pastore , Giovanni
1 1 , Michael Tonks , Michael , Cody ,Permann Cody 1 , Jason ,Hales Jason 2 , Chris ,Stanek Chris 1 1 , Paul , Paul 1 , David Andrs , David 1 , Steve 2
1 , Presentation Summary: • The US Department of Energy Nuclear Energy Advanced Modeling and Simulation Program (NEAMS) seeks to rapidly create and deploy “science-based” verified and validated modeling and simulation capabilities essential for the design, implementation, and operation of future nuclear energy systems. • In this talk, I will summarize NEAMS-funded efforts to develop advanced materials models for fuel performance using multiscale modeling and simulation. • Outline: 1. MOOSE-BISON-MARMOT 2. MOOSE summary 3. BISON summary 4. MARMOT summary 5. Example of multiscale model development LWR Fuel Behavior Modeling – U.S. State of the Art • Fuel performance codes are used today for determination of operational margins by calculating property evolution. • However, current US industry standard codes (e.g. FRAPCON and FALCON) have limitations in three main areas:
Numerical Capabilities Geometry representation Materials models • Serial • 1.5 or 2-D • Empirical • Inefficient Solvers • Smeared Pellets • Models only valid in • Loosely Coupled • Restricted to LWR Fuel limited conditions • High Software • Limited applicability in Complexity accident scenarios
FALCON model to investigate clad failure due to defect MOOSE-BISON-MARMOT • The MOOSE-BISON-MARMOT codes address current limitations in numerical capabilities, geometry representation and materials models.
Advanced 3D Fuel Performance Atomistic/Mesoscale Material Code Model Development • Models LWR, TRISO and metal • Predicts microstructure fuels in 2D and 3D evolution in fuel • Steady and transient reactor • Used with atomistic methods operations to develop multiscale materials models Multiphysics Object-Oriented Simulation Environment • Simulation framework allowing rapid development of FEM-based applications Multiphysics Object-Oriented Simulation Environment
• A finite element based framework for solving systems of partial differential equations • Employs an object-oriented approach to allow for rapid development of new simulation tools (Meets NQA-1 requirements) • Code development focuses on implementing physics rather than numerical issues • Comes with the Extended Library of Kernels (ELK) with various physics modules.
• Utilizes the Jacobian-free Newton–Krylov (JFNK) method to enable the implicit solution of large nonlinear systems of coupled physics equations • Ecosystem of 27 applications (including MARMOT) that have been used to model a wide range of problems Licensed Laboratories - Industry Partners - Universities
Fuel Performance Code
Solution method: Implicit finite element solution of the coupled thermomechanics and species diffusion equations using the MOOSE framework Multiphysics constitutive models: large deformation mechanics (plasticity and creep), cracking, thermal expansion, densification, radiation effects (swelling, thermal conductivity, etc.)
1400 431r1 LTC 431r1 UTC 431r2 LTC + 431r2 UTC 1200 431r3 LTC x 431r3 UTC 432r1 LTC 432r1 UTC Massively 432r2 LTC 432r3 LTC + 1000 +++++ 432r3 UTC + + Measured = Predicted + + + M=P+10% + parallel, has M=P-10% + + ++ + + 800 ++ + + + + been run on x + xxxx + x+ x x + x + x + x+ 600 x +x + x xxx 1 - 12,000 +x Substantial experimental +x +x + x xx +x +x
PredictedTemperature (C) + xx x + +x 400 + xx + xx validation is underway cpus. x +x +xx x+x +x x+x xx+ xxx x 200 200 400 600 800 1000 1200 1400 Measured Temperature (C)
R. L. Williamson, J. D. Hales, S. R. Novascone, M. R. Tonks, D. R. Gaston, C. J. Permann, D. Andrs and R. C. Martineau, “Multidimensional Multiphysics Simulation of Nuclear Fuel Behavior,” Journal of Nuclear Materials, 423, 149 (2012)
7 Mesoscale Multiphysics Simulation Tool • MARMOT predicts coevolution of microstructure and physical properties due to applied load, temperature, and radiation damage
Technique: Phase field coupled with large deformation solid mechanics and heat conduction solved with implicit finite elements using INL’s MOOSE framework
1 All models implemented in 10 MARMOT are: −1 10 • 1D, 2D or 3D dt −3 • Massively parallel, from 1 to 10 −3 −1 1 3 10 10 10 10 1000’s of processors Time • Able to employ mesh and time step adaptivity • Easily coupled to additional physics from the Extended MARMOT is being used at various labs and Universities: Library of Kernels (ELK)
Physical models include: § GB migration/grain growth § Species redistribution, phase separation § Void/Bubble growth and coalescence § Coupling between phase field and plasticity
8
Multiscale Materials Modeling Approach Modeling Approach: Physics-based materials models are developed using a hierarchical multiscale approach ranging from the atomistic to the macroscale
1st Principles Simulations Fuel • Identify important Performance bulk mechanisms Code (BISON) • Determine bulk Mesoscale Simulations • Predict fuel (MARMOT) material parameter performance • Predict and define values during microstructure state operation and variable evolution accident • Determine effect of Molecular Dynamics Simulations conditions • Investigate role of idealized evolution on material interfaces properties • Determine interface properties 7/20/09 Effects of Gaseous Fission Products
• We demonstrate our multiscale approach by focusing on fission gas behavior, due to its large impact on the fuel performance. • Gaseous fission products and point defects are constantly generated within the fuel during reactor operation
Fission gas effects fuel performance by: • Fission product swelling • Degradation of thermal conductivity • Fission gas release (increases plenum pressure and decreases gap thermal conductivity)
Nucleation Growth Percolation
10 Fission Gas Evolution Model • BISON Fission gas evolution model predicts the migration of the fission gas to GBs and its eventual release into the gap between the fuel and the cladding. GB coverage • Model approximates the gas diffusion Dispersed using a 1D spherical domain (grain) fission gas • Gas is divided into three types o Dispersed intragranular gas migrating towards GB. o Trapped gas in small intragranular Intragranular bubbles. bubbles o Gas in GB bubbles that can be released.
• Gas in GB bubble begins to release once the GB coverage reaches a set percolation threshold of 0.5.
11 Validation: RISO-3 AN3 • Model has been validated against a series of assessment cases, including the RISO-3 AN3 test: – Fuel pin CB8 was base irradiated in the Biblis A PWR for four cycles – Re-fabricated fuel pin was shortened and instrumented with a fuel centerline thermocouple and pressure transducer. – CB8-2R was ramp tested at the Riso DR3 water-cooled HP1 rig – Assumed short rod length through base irradiation for simulation – Modeled as 2D-RZ axisymmetric with smeared pellets
24000 45000
22000 40000
35000 20000 30000
18000 25000
16000 20000
15000 14000 Aspect ratio 10000 scaled 10x Average Linear Heat Rate (W/m) Rate Heat Linear Average 12000 (W/m) Rate Heat Linear Average 5000
10000 0 0 2E+07 4E+07 6E+07 8E+07 1E+08 1.2E+08 1.035E+08 1.036E+08 1.037E+08 1.038E+08 Time (seconds) Time (seconds) Comparisons During Power Ramp – Riso AN3
1600 45 RISO 1400 40 BISON TRANSURANUS-1 1200 35 ENIGMA-B
30 1000
25 800
RISO 20 600 BISON
Temperature(C) 15 TRANSURANUS-1 400 ENIGMA-B Fission Gas Release (%) 10
200 5
0 0 0 20 40 60 80 100 0 20 40 60 80 Time (hrs) Time (hrs) Fuel Centerline Temperature Fission Gas Release
We will now demonstrate how we are improving this model through the use of multiscale modeling and simulation. 13 Fission Gas Diffusivity Investigation
• Density functional theory (DFT) is being used to investigate the diffusion constants of fission gas (Xe) in UO2.
DFT calculations were conducted at LANL Mechanisms and values of diffusivity are calculated for intrinsic and radiation RISÖ AN3 60 60 diffusion mechanisms. FGR calculated (D experimental - Turnbull) !'" FGR calculated (D from modeling - low b.) ()*+,-./0+1/02/34 FGR calculated (D from modeling - high b.) !'% 50 FGR experimental (on-line) 50 "E&F-D ()*+,-.156/5+/704 FGR experimental (PIE) 8769:-./0+1/02/34 Linear power !'$ 8769:-.156,;-:7<-=,4 40 40 8769:-.156,;->/?>-=,4 B244
" !'#
30 30 !"& :[email protected]
!"" 20 20
'E"&-D Linear power [kW/m] Fission gas release [%] !"% 10 10 !"$
!"# 0 0 " % $ # '& '" '% '$ 1.0350E+08 1.0360E+08 1.0370E+08 1.0380E+08 Time [s] '&%BC-.D4 BISON simulation of FG release in fuel rod irradiation 14 Comparison to Turnbull experiment from Risö 3 project Impact of Grain Size Distribution on Fission Gas • Typical gas release models assume all grains have the same grain size. • 3D simulations were used to investigate the impact of the grain size distribution on the fission gas release. Single grain size Polycrystal average Grain structure created using normal grain growth simulation, giving a more realistic grain size distribution
Fission gas migration simulation was conducted in 3D grain structure and the total gas on the GB was compared to the spherical grain model. 15 Fission Gas Release
• Existing fission gas release model assumes no fission gas release until the GB fractional coverage reaches 50%. In reality, small amounts of gas are released at much smaller fractional coverages. • We are using phase field simulations to develop a more realistic description of fission gas release.
0.24x&+&26.54x11.10& Traditional Percola2on&frac2on&
Grain&boundary&coverage& 16 Impact on Model Behavior • We modified the fission gas model to consider the mesoscale-informed percolation model and repeated the RISO3 AN3 simulation:
RISÖ AN3 50 50 FGR experimental (PIE) FGR experimental (on-line) FGR calculated (saturation principle) FGR calculated (percolation model) 40 Linear power 40
30 30
20 20 Linear power [kW/m] power Linear Fission gasrelease Fission [%] 10 10
0 0 1.0350E+08 1.0360E+08 1.0370E+08 1.0380E+08 Time [s]
17 Fission Gas Thermal Conductivity Model
• Accounts for dispersed gas, as well as intra- and intergranular bubbles MD calculations were conducted at LANL
Dispersed fission gas Intergranular (GB) bubbles MD simulations are used to quantify impact of dispersed fission gas on thermal conductivity.
16 0.0% 14 0.303% 0.610% 12 0.927% Intragranular bubbles 10 Maxwell-Euken equation used to 8 −7 k (W/mK) 10 6 account for intragranular bubbles: 4 MD and
400 600 800 1000 1200 1400 mesoscale heat Temperature (K) conduction are 6 K/W) −8 2 10 used to 5 (m r = 0.1 µm k b 4 R parameterize s r = 0.2 µm
The three equations are combined b 3 r = 0.5 m constitutive according to: b µ −9 2 Data 10 model. Fit 0 0.2 0.4 1 0 2 4 6 8 GB coverage −3 Xe concentration x 10
18! Impact of Fission Gas Model Behavior
• RISO3 simulation was repeated using our thermal conductivity model coupled to the fission gas release model
2000
1500
1000 T (K) Data 500 NFIR Lucuta FG 0 3.284 3.286 3.288 3.29 Time (yrs) Fuel Centerline Temperature
19 Summary
• MOOSE-BISON-MARMOT provides an advanced fuel performance modeling capability, using multiscale modeling and simulation to develop
20 The National Nuclear Laboratory
This work was funded by the US Nuclear Energy Advanced Modeling and Simulation Program For more information, contact Michael Tonks at [email protected] 21