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

Rationale, Computational Platform and Development Plan

Nam Dinh, Vince Mousseau and Robert Nourgaliev

Content

• Rationale

• Computational Platform

• Development Plan

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

CapacityLoad

CL

UQ

Surprise

Safety Margin

Power Uprate,

Higher Burnup

Variability of Input

System

TransientMulti-physics

ComputationalExpenses

Modeling uncertainty

Engineering Analysis (large # calculations)

Confidence (error bar) in calculated results

(Dimensions, Components,

Heterogeneity)

Physics Modeling (Simplification)

“First principles”

Computing Expenses Validation Adequacy

[CFD-RANS] System codes

Separate Effects

Simplified Plant, “Detail” Processes

System Analysis

Real-timeSimulators

System Complexity

CGM- and AMR-based

System Analysis

Vehicle

Length and time scales resolved (Dx and Dt)

Physics, modelingClosures

PDE

DNS

Mega-Coarse Graining

DM

CFD-LES

CG-LES

RANS

Experiments (SET, IET)

Computational Platform

0D

1D

3DCG

3D

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

Eulerian-Average Navier-Stokes (LES, RANS)

Lagrangian-Average Navier-Stokes (LANS/-NS)

Discrete Modeling

Database CGM

Adaptive Model Refinement

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

Structural Mechanics

CoreNeutronics

Multi-physics,Multi-scaleAlgorithms

Fuel Performance

ThermalHydraulics

SensitivityAnalysis,

UncertaintyQuantification

AdvancedSolutionMethods(Solvers)

ComputableMeshing

HPC: HighPerformanceComputing

PlantI&C

ActiveComponents

PassiveComponents

Fluids, MaterialsProperties

Governing Physics●●●●●

Models

Computational Infrastructure

Heterogeneous System●●●●●

ComponentsPump, Valve, etc.

Pipe, Tanks, etc.

●●●●●●

●●●●●●e.g., Coolant Chemistry, FPT

PC clusters and up

Nuclear SystemsSafety Analysis

Transient/AccidentScenario

Multi-physicsPlant Model

Thermal-HydraulicsSystem Model

Coarse-Grain (SGS)Closure Laws

Core NeutronicsModel

Single-Physics

Plant Discrete Modeling (Meshing)

●●●

DatabasesData Management

Correlations HPC-GeneratedHigh-Fidelity

IE and SE “Data”

AdvancedDiagnostics

IE and SEExperiments

Local Parameters

Data Mining

Uncertainty Quantification

Safety Margin(with UQ)

Adequacy of plant discrete model, model fidelity level, and closure data support

UncertaintyAcceptable?

Model Fidelity Selection

Yes

No

Identify weakness ? Discrete Model ? Model Fidelity ? Closure DataUse Sensitivity Analysis (SA)

●●

●

V&V

DemonstrationDevelopment

Research R7 Project Work Domain

Multiple Physics

Heterogeneous System ( Multiple Components)

Computational Methods

Complexity

Investigate selected topics (in the Development’s three dimensions; see above), which present major obstacles to achieving the R7 code operability and intended functionality.

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.

R7 Code V&V Planning (System Safety Objectives)

Requirements for database content and management

Consistency

Forecast of future data availability and quality

Acquisition of the R7’s key support data

Development Plan

V&V

DemonstrationDevelopment

Research

CAML and HPC support

RELAP users

Training

R7 “activists”

Contributors Modules

INL and non-INL

Aligned Projects Methods, Models

Testing

ETFD Database

CFD Database

New ExperimentsAdvanced Diagnostics

Data Management

R7 Project Leverage Domain

R7 Project Work Domain

Formation Phase (I) Maturation Phase (II)

R7 RD&D and V&V Project

Broaden scope Refinement

Applications

Expansion (III)

Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7

Extend V&V

V&VV&V

V&V V&V V&V V&V V&V

RD&DRD&D

RD&D RD&D RD&D RD&D RD&D

EngineeringAnalysis

Modeling (Subgrid Closure)

Database (SE, IE )

V&V

Higher FidelityModels

Advanced Diagnostics, DNS

Labs,HPC

Coarse-GrainClosure Models

System-ScaleModels

PlantDynamics

Multi-Scale Treatment

Separate-Effect(SE) Benchmarks

Integral-Effect(IE) Benchmarks

Multi-physics(MP) Benchmarks

Multi-Physics Treatment

Industry-WideDatabases

(CFD, ETFD, Plant Data)

Verification

Validation

Validation

Validation ReactorMeasurements

AdvancedDiagnostics

IE and SEExperiments

HPC-GeneratedHigh-Fidelity

IE and SE “Data”

MP Verification

Multi-Tier Diagnostics & Computer-Aided V&V Strategy for R7 Code

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.