A System Analysis Code to Support Risk-Informed Safety Margin Characterization:

1. A System Analysis Code to Support Risk-Informed Safety Margin Characterization: Rationale, Computational Platform and Development Plan Nam Dinh, Vince Mousseau and Robert Nourgaliev

2. Content • Rationale • Computational Platform • Development Plan

3. Rationale • Risk-Informed safety margin characterization (RISMC) • LWR Sustainability (safety margins) • Support for PRA • Increased role of non-DBA sequences • Multi-physics (TH, NK, CC, FP, SM) coupling • Sensitivity analysis, uncertainty quantification • Integrated safety assessment – Search for vulnerability • Passive safety design • Natural circulation • Reactor-to-containment connectivity • Multidimensional behavior • Long transients

4. Load Capacity Safety Margin UQ Surprise L C Power Uprate, Higher Burnup

5. System Computational Expenses Variability of Input Engineering Analysis (large # calculations) Modeling uncertainty Confidence (error bar) in calculated results Multi-physics Transient

6. CGM- and AMR-based System Analysis Vehicle System Complexity (Dimensions, Components, Heterogeneity) Real-time Simulators System Analysis Simplified Plant, “Detail” Processes Separate Effects “First principles” System codes Physics Modeling(Simplification) [CFD-RANS] Computing Expenses Validation Adequacy

7. Computational Platform Experiments (SET, IET)

8. 0D 1D 3D 3DCG Adaptive Model Refinement • Coarse-grain simulations effectively capture large-scale flow patterns • CFD flow solver transports SGS (EANS, LANS, DM) • Under-resolved flow structures is effectively represented by subgrid-scale (SGS) closure • Exploring Three CGM Theoretical Concepts • Lagrangian-Average Navier-Stokes (LANS/-NS) • CGM • Database • Eulerian-Average Navier-Stokes (LES, RANS) • Discrete Modeling

9. Attributes under Consideration • Fully implicit, nonlinearly (tightly) coupled multi-physics (neutronics, thermal hydraulics, structural mechanics, fuels) • High order accurate in time and in space, robust numerics • Parallel, high-performance computing • Adaptive Model Refinement (0D, 1D, 3D based on Error Estimation) • Built-in sensitivity analysis (Uncertainty Quantification, Quantitative PIRT) • CFD-based Coarse-Grain Modeling

10. Sensitivity Analysis, Uncertainty Quantification Governing Physics ●●●●● Models Structural Mechanics Core Neutronics Multi-physics, Multi-scale Algorithms Fuel Performance Thermal Hydraulics ●●● Advanced Solution Methods (Solvers) ●●● e.g., Coolant Chemistry, FPT Heterogeneous System ●●●●● Components Plant I&C Active Components Computable Meshing Passive Components Fluids, Materials Properties Pump, Valve, etc. Pipe, Tanks, etc. ●●● HPC: High Performance Computing ●●● PC clusters and up Computational Infrastructure

11. Adequacy of plant discrete model, model fidelity level, and closure data support Nuclear Systems Safety Analysis Transient/Accident Scenario Yes Plant Discrete Modeling (Meshing) Multi-physics Plant Model Safety Margin (with UQ) Uncertainty Acceptable? Single-Physics No ●●● Core Neutronics Model Thermal-Hydraulics System Model Identify weakness ? Discrete Model ? Model Fidelity ? Closure Data Use Sensitivity Analysis (SA) Model Fidelity Selection ●●● Coarse-Grain (SGS) Closure Laws Local Parameters Uncertainty Quantification Databases Data Management HPC-Generated High-Fidelity IE and SE “Data” Advanced Diagnostics IE and SE Experiments Correlations Data Mining

12. Development Plan Development Demonstration Heterogeneous System ( Multiple Components) Early applications of the R7 code to a selected set of plant transient and accident scenarios, to examine and demonstrate the code operability, intended features and V&V strategy. Complexity Multiple Physics Computational Methods Consistency Research V&V R7 Code V&V Planning (System Safety Objectives) Investigate selected topics (in the Development’s three dimensions; see above), which present major obstacles to achieving the R7 code operability and intended functionality. Forecast of future data availability and quality Requirements for database content and management Acquisition of the R7’s key support data R7 Project Work Domain

13. R7 Project Leverage Domain Development Demonstration RELAP users R7 “activists” CAML and HPC support ContributorsModules Training Testing Research V&V CFD Database R7 Project Work Domain Data Management ETFD Database Aligned ProjectsMethods, Models New Experiments Advanced Diagnostics INL and non-INL

14. Formation Phase (I) Maturation Phase (II) Expansion (III) Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 RD&D RD&D RD&D RD&D RD&D RD&D RD&D V&V V&V V&V V&V V&V V&V V&V Applications R7 RD&D and V&V Project • Extend V&V • Refinement • Broaden scope

15. Modeling (Subgrid Closure) Higher Fidelity Models Engineering Analysis V&V Labs, HPC Database (SE, IE ) Advanced Diagnostics, DNS

16. Multi-Physics Treatment Validation Industry-Wide Databases (CFD, ETFD, Plant Data) Multi-physics (MP) Benchmarks Plant Dynamics Reactor Measurements MP Verification Validation Integral-Effect (IE) Benchmarks System-Scale Models Verification Multi-Scale Treatment Advanced Diagnostics IE and SE Experiments Validation Separate-Effect (SE) Benchmarks Coarse-Grain Closure Models Multi-Tier Diagnostics & Computer-Aided V&V Strategy for R7 Code HPC-Generated High-Fidelity IE and SE “Data”

17. Concluding Remarks • The project aims to develop a next-generation system safety code that enables the nuclear power industry to meet requirements in future engineering analysis of plant transients and accidents. • The project’s (Phase I) technical objectives are (i) to develop the code’s computational frameworks and basic methods/models/components, (ii) to establish the code’s V&V methodology (requirements and feasibility), and (iii) to demonstrate the code’s intended capabilities through investigation of selected safety-significant transients in advanced reactor systems. • The guiding principle and major challenges in the development are selecting an appropriate level of fidelity and ensuring consistency between the level of detail in mathematical modeling, numerical solution methods and the evolving state-of-the-art capabilities in experimental diagnostics.