ML24313A166
| ML24313A166 | |
| Person / Time | |
|---|---|
| Issue date: | 11/13/2024 |
| From: | Jeffrey Poehler NRC/RES/DE |
| To: | |
| Jeff Poehler 301-415-8353 | |
| References | |
| Download: ML24313A166 (16) | |
Text
2024 US DOE-NE Light Water Reactor Sustainability Program Materials Research Pathway (MRP)
Hybrid Stakeholder Engagement Meeting, November 13, 2024 Jeff Poehler, Nuclear Regulatory Commission NRC Metals Research Supporting Long-Term Operation - Update
2 Materials and Aging Research Research objectives
- Improve timeliness of regulatory decision-making on the use of new materials, manufacturing technologies, and inservice inspection techniques through independent and confirmatory research.
- Address materials degradation during long-term plant operation.
- Inform and enhance the use of risk information in regulatory decision making.
Strategic Focus Areas
- Support resolution of safety-significant technical issues
- Maintain core capabilities to support emerging technical needs related to corrosion, metallurgy, component integrity assessment, and non-destructive examination
- Enhance modeling/analytical tools to support efficient regulatory decision-making
- Foster collaborations with domestic and international counterparts to stimulate information sharing and cooperative research approaches More information contained in U.S.NRCs Research Prospectus for Fiscal Years 2022 - 2024 (ML22235A651)
LTO Harvesting LTO Research Strategy Knowledge Management IAD SMILE FIDES PWSCC CGR Validation Crack Initiation Long-term aging Piping Integrity Flaw Evaluation Software HELB Thermal Aging NDE Advanced UT AI/ML Non-metallic repairs Steam Generator Independent Evaluation of ET POD Assessment Probabilistic Integrity Analysis xLPR FAVPRO NRC Metals Research Areas for LTO
4 LTO Workshop NRC recently held a workshop, NRC Hybrid Workshop on Structural Materials, Research for 80 Years and Beyond.-October 1-3, 2024
~100 in-person attendees, 20 countries First two days covered metallic materials Each technical session had 3 presentations and 45-minute panel discussion NRC will publish a summary report in 2025.
Hope to use discussions at the workshop as the seed for creating a strategic plan for research to support operation beyond 80 years.
More information
- Agenda
- Presentations
5 LTO-Related Metallic Materials 2024 Technical Reports Probabilistic Integrity Analysis Released FAVPRO v1.0 on June 12, 2024 with updated User and Theory Manuals and Software Quality Assurance documentation. (more detail in spotlight)
TLR-RES/DE/REB-2024 FAVPRO Software Verification and Validation Plan and Results Report (ML24102A185)
TLR-RES/DE/REB-2024 FAVPRO Software Quality Assurance Plan (SQAP) (ML24095A318)
TLR-RES/DE/REB-2024 FAVPRO Configuration Management and Maintenance Plan (CMMP) (ML24095A319)
TLR-RES/DE/REB-2024 FAVPRO v1.0 User Manual (ML24113A237)
TLR-RES/DE/REB-2024-08, "Probabilistic Fracture Mechanics Analysis of French Stress Corrosion Cracking Operating Experience Applied to the US Fleet using the Extremely Low Probability of Rupture Code" Steam Generator Integrity Issued technical letter report - Evaluation of Eddy Current Sizing Capability for PWSCC at the Expansion Transition Regions of Steam Generator Tubes (ML24093A097)
Piping integrity Issued technical letter report - Assessment of Thermal Aging Embrittlement of Austenitic Stainless Steel Weld Metals (ML24180A123)
6 Spotlight - Probabilistic Integrity Analysis -
FAVPRO 1.0 Release One of two applications of probabilistic integrity computer codes developed and maintained by NRC
- xLPR - Piping integrity
- FAVOR/FAVPRO-Reactor Pressure Vessel Integrity These codes support LTO by providing tools for assessing structural integrity of components.
For RPVs, FAVPRO can be used to evaluate many different scenarios, such as higher levels of embrittlement, effect of different embrittlement trend curves, use of direct fracture toughness (future),
More information: FAVPRO Contacts: Chris Nellis Christopher.Nellis@nrc.gov; Chris Ulmer Christopher.ulmer@nrc.gov
7 What is FAVPRO?
Probabilistic Fracture Mechanics tool for RPV integrity assessment Focus on cylindrical beltline 1D finite element axisymmetric solver
- Stresses and temperatures from any TH transient
- Stress intensity factors (ID, OD, embedded flaws)
Deterministic run modes
- Through-wall profiles (T,, SIFs)
- Time histories
- Critical RTNDT (embrittlement) for crack growth Probabilistic run mode
- Conditional probabilities of crack growth initiation (CPI) and vessel fracture (CPF)
Combination of conditional probabilities and transient frequencies to generate frequencies of crack growth initiation (FCI) and through-wall crack failure (TWCF) 7
FAVPRO Validated Capabilities June 12, 2024 8
- Heatup and cooldown transients
- 1D finite element solution for temperatures and stresses
- User specified material properties
- Weld residual stress option
- Crack-face pressure option
- Stress-free temperature model for cladding residual stress
- Flaw populations
- Semi-elliptical internal or external surface flaws
- Elliptical embedded flaws within base metal
- Cannot model semi-elliptical sub-cladding flaws
- As-found flaw population or sampled population from specified distributions
- Stress intensity factor influence coefficients approach for K calculations
- ASME solutions for base metal
- Custom solutions for cladding (ID surface flaws)
- Warm prestress options
- Several embrittlement trend curves
- Ductile tearing and crack arrest options
- Vessel chemistry and fluence sampling
- Resampling option for crack growth
9 Vision and Goals for FAVPRO Completely refactor FAVOR to create an improved tool with equivalent capabilities, written in modern Fortran GOALS
- Maintainability
- SQA and V&V improvements Testing Documentation
- Modularity, adaptability, easier feature development
- Modern programming Object-oriented code Parallel code
- Maximize automation for testing and documentation
- Program integration: 3 FAVOR into 1 FAVPRO
- Use State-of-Practice tools and libraries GitHub: source control State-of-practice build system State-of-practice unit testing framework Standardized I/O via Java Script Object Notation (JSON) 9
10 FAVPRO Features: New Embrittlement Trend Curves (ETC) 10
- Currently available ETC
- RG-1.99 Rev. 2
- EONY 2000 and 2006
- Added newer EONY 2013 model
- Kirk 2007, Radamo 2007, and Kirk+Radamo 2007
- Early versions of ASTM model
- Replaced by ASTM E-900
- Future: add non-US mainstream embrittlement trend curves to the FAVPRO options?
- Japanese model (update to JEAC4201, recently presented at FONTEVRAUD-10)
- French model (2011: FONTEVRAUD-7, or more recent if available)
Embrittlement Trend Curves FAVOR FAVPRO RG-1.99 Rev. 2 RG-1.99 Rev. 2 EONY 2000 EONY 2000 EONY 2006 EONY 2006 EONY 2013 Kirk 2007 ASTM E900 Radamo 2007 Kirk + Radamo 2007
11 Spotlight-Nondestructive Evaluation What are we doing? Evaluating effectiveness and reliability of NDE techniques. Looking at application of machine learning (ML) to NDE.
Motivation: Confirm adequacy of industry procedures and practices Regulatory Application: Support reviews of ASME Code modifications and proposed revisions of current requirements Collaboration: EPRI, IRSN, and PIONIC Supports LTO by enhancing NRC understanding of advanced NDE techniques, which will see more use as plants age.
Enhancing efficiency and effectiveness of NDE will help plants operate more safely and reduce operating costs.
More information: NRC NDE Research
Contact:
Carol Nove carol.nove@nrc.gov
12 Dual approach:
Evaluating machine learning (ML) for Ultrasonic Examinations (UT) - Oak Ridge National Laboratory (ORNL)
Evaluate commercially available automated data analysis platforms including rule-based and ML-based systems - Pacific Northwest National Laboratory (PNNL)
Objectives:
Assess current capabilities of ADA for UT NDE applications Automated Data Analysis (ADA)
Flaw inside the weldment Longitudinal 45 Shear 45 12 Provide technical basis to support regulatory decisions and Code actions related to ADA for NDE Variation in Data (probe/mode)
13 Machine Learning (ML) for UT NDE ML, if used with care, can be used for NDE data classification Capable of high true positive (TPR), low false positive (FPR) and false negative metrics (FNR)
May be able to learn key signatures using data from simple flaws (e.g., saw cuts) and generalize well to other flaw types (e.g. TFC)
Transfer learning techniques (retraining) may be useful for improving accuracy with new data sets Training data should be representative of the types of data expected during testing High accuracy possible if test data is in distribution relative to training data 13
ML for UT NDE H. Sun, P.Ramuhalli, and R. Jacob, Machine Learning for Ultrasonic Nondestructive Examination of Welding Defects: A Systematic Review, Ultrasonics, Vol. 127 Issue 1, Jan 2023, Pages 106854 (ML22284A071)
H. Sun, R. Jacob, and P. Ramuhalli, Classification of Ultrasonic B-Scan Images from Welding Defects Using a Convolutional Neural Network, Proc. 13th NPIC&HMIT 2023, Pages 272 - 281. ISBN 978-0-89448-791-0 (ML23241A961)
- Upcoming activities:
- Comparison of ML results with manual analysis performed by a qualified analyst
- Detection and sizing of degradation that the ML system has not been trained on
- Qualification of ML
- Methods for establishing confidence in ML results ML24046A150 14
15 Assessment of NDE for Carbon Fiber Reinforced Plastic(CFRP) Repairs 15 Objective: Evaluate the capabilities and limitations of NDE methods for examining CFRP repairs in NPPs Identify guidance and best practices for qualification mockup fabrication Assess various commercially available NDE methods to evaluate capabilities and limitations for detecting and characterizing flaws Assess the reliability of tap testing vs. other, more sophisticated techniques to provide a permanent record
Summary 14 NRC Office of Nuclear Regulatory Research conducts confirmatory research to establish technical bases that support regulatory decisions and development of regulatory guidance documents NRC staff exchanges information with domestic and international counterparts on materials performance and aging management of nuclear power plant structures and components and conducts independent analyses
- Research results
- Operating experience Research activities are prioritized to address potential safety-significant technical issues.
Long-lead-time confirmatory research is an important consideration in proactive aging management.