PSAM 16 - Session Th25 Overview

PSAM 16 Conference Session Th25 Overview

Session Chair: Yong-Joon Choi (yong-joon.choi@inl.gov)

Paper 1 JU83

Lead Author: Jukka Koskenranta Co-author(s): Ilkka Paavola Ilkka.Paavola@fortum.com Rasmus Hotakainen Rasmus.Hotakainen@fortum.com

Loviisa nuclear power plant spent fuel storage risk analysis
Spent fuel of Loviisa NPP is stored submerged in water pools of spent fuel storage (SFS) at Loviisa site at least 10 years and up to many decades. First 1 – 2 years the spent fuel is cooled in refuelling pools in reactor buildings. After SFS the spent fuel will be moved to final repository at Olkiluoto. Loviisa NPP PRA covers level 1 and level 2 PRA for reactors and refuelling pools for both unit 1 and 2 and common spent fuel storage for both units. Loviisa NPP PRA covers also all operating and shutdown states and all types of initiating events. Current seismic PRA is outdated and under significant update. Loviisa SFS PRA models SFS with maximum expected heating capacity of current operating license from 2030. Mission times varies from no time to recover up to 2 months. Criteria for result evaluation is fuel exposure or mechanical break. Equipment failures have only minor impact to the risk because of low heating rate. Seismic initiating events cause over 50 % of fuel damage frequency (FDF) and large release frequency. Also early release frequency is considered. FDF 1,9E-7/a is about 1 % of Loviisa unit 1 and 2 core damage frequency.

Loviisa nuclear power plant spent fuel storage risk analysis

Spent fuel of Loviisa NPP is stored submerged in water pools of spent fuel storage (SFS) at Loviisa site at least 10 years and up to many decades. First 1 – 2 years the spent fuel is cooled in refuelling pools in reactor buildings. After SFS the spent fuel will be moved to final repository at Olkiluoto. Loviisa NPP PRA covers level 1 and level 2 PRA for reactors and refuelling pools for both unit 1 and 2 and common spent fuel storage for both units. Loviisa NPP PRA covers also all operating and shutdown states and all types of initiating events. Current seismic PRA is outdated and under significant update. Loviisa SFS PRA models SFS with maximum expected heating capacity of current operating license from 2030. Mission times varies from no time to recover up to 2 months. Criteria for result evaluation is fuel exposure or mechanical break. Equipment failures have only minor impact to the risk because of low heating rate. Seismic initiating events cause over 50 % of fuel damage frequency (FDF) and large release frequency. Also early release frequency is considered. FDF 1,9E-7/a is about 1 % of Loviisa unit 1 and 2 core damage frequency.

Paper JU83 | Download the paper file. | Download the presentation pdf file.

Name: Jukka Koskenranta (jukka.koskenranta@fortum.com)

Bio: Responsible for Loviisa NPP PRA external hazards. Few last years worked mainly with seismic PRA and seismic hazard. Experience also from many other PRA tasks including e.g. internal initiating events, internal hazards and estimating event probabilities and frequencies. I have mainly worked with level 1 PRA, but also some experience form level 2 PRA.

Country: FIN
Company: Fortum Power and Heat Oy
Job Title: Senior Engineer, Probabilistic Risk Assessment

Paper 2 YO298

Lead Author: Yong-Joon Choi Co-author(s): Chris Gosdin (cgosdin@fpolisolutions.com) Gabrielle Palamone (gabrielle.palamone@fpolisolutions.com) Cesare Frepoli (frepolc@fpolisolutions.com) Jason Hou (jason.hou@ncsu.edu)

Safety Analysis of Accident Tolerance Fuel (ATF) with Increased Enrichment and Extended Burnup: Simulation Tools Review
One of the main obstacles for deploying near-term accident-tolerant fuels (ATFs) with higher burnup is related to successfully passing regulatory-required fuel safety assessments. A phenomenon called fuel fragmentation, relocation, and dispersal (FFRD) that is observed during postulated accident events may cause fuel damage exceeding the postulated safety limits. There have been multiple research efforts dedicated to investigation of the FFRD phenomenon and its consequences. However, most of them have focused on conventional fuel (e.g., non-ATF). The recent study by the U.S. Nuclear Regulatory Commission [1] addresses that fuel fragmentation can be observed starting from 55GWd/MTU burnup (average burnup of US nuclear power plant is 45GWd/MTU) for standard UO2 fuel during a design basis loss of coolant accident (LOCA). Generally, ATFs have advantage of better mechanical strength under high-temperature accident conditions over the traditional fuel (i.e., zircalloy cladding). The increased fuel enrichment and associated increased burnup would allow extension of refueling cycle from 18 to 24 months, which is a significant economic benefit for operating nuclear reactors. However, safety analyses of ATF with HBU are still incomplete especially in terms of FFRD, a necessary step for fuel to be licensed . The recently-initiated project under the Light Water Reactor Sustainability Program focuses on the investigation of the FFRD effects on the ATF with higher enrichment and burnup during a LOCA. The research scope includes the development of optimized reactor core configuration with ATF specific to a 24-month fuel cycle, fuel analysis with respect to the FFRD phenomenon, source term evaluation, and consequence analysis. The analyses within the scope of the project require multiple simulation models and an investigation of available computer codes was conducted to determine code applicability and capability to meet project goals. This paper summarizes the overall research plan as well as presents the results of the review of select simulation tools in the area of core design, system and accident analysis, and fuel performance. [1] U.S. NRC, Interpretation of Research on Fuel Fragmentation, Relocation, and Dispersal at High Burnup, RIL 2021-13, 2021

Safety Analysis of Accident Tolerance Fuel (ATF) with Increased Enrichment and Extended Burnup: Simulation Tools Review

One of the main obstacles for deploying near-term accident-tolerant fuels (ATFs) with higher burnup is related to successfully passing regulatory-required fuel safety assessments. A phenomenon called fuel fragmentation, relocation, and dispersal (FFRD) that is observed during postulated accident events may cause fuel damage exceeding the postulated safety limits. There have been multiple research efforts dedicated to investigation of the FFRD phenomenon and its consequences. However, most of them have focused on conventional fuel (e.g., non-ATF). The recent study by the U.S. Nuclear Regulatory Commission [1] addresses that fuel fragmentation can be observed starting from 55GWd/MTU burnup (average burnup of US nuclear power plant is 45GWd/MTU) for standard UO2 fuel during a design basis loss of coolant accident (LOCA). Generally, ATFs have advantage of better mechanical strength under high-temperature accident conditions over the traditional fuel (i.e., zircalloy cladding). The increased fuel enrichment and associated increased burnup would allow extension of refueling cycle from 18 to 24 months, which is a significant economic benefit for operating nuclear reactors. However, safety analyses of ATF with HBU are still incomplete especially in terms of FFRD, a necessary step for fuel to be licensed . The recently-initiated project under the Light Water Reactor Sustainability Program focuses on the investigation of the FFRD effects on the ATF with higher enrichment and burnup during a LOCA. The research scope includes the development of optimized reactor core configuration with ATF specific to a 24-month fuel cycle, fuel analysis with respect to the FFRD phenomenon, source term evaluation, and consequence analysis. The analyses within the scope of the project require multiple simulation models and an investigation of available computer codes was conducted to determine code applicability and capability to meet project goals. This paper summarizes the overall research plan as well as presents the results of the review of select simulation tools in the area of core design, system and accident analysis, and fuel performance. [1] U.S. NRC, Interpretation of Research on Fuel Fragmentation, Relocation, and Dispersal at High Burnup, RIL 2021-13, 2021

Paper YO298 | Download the paper file. | Download the presentation pdf file.

Name: Yong-Joon Choi (yong-joon.choi@inl.gov)

Bio: Since 2012, Dr. Choi is a program manager and senior research scientist at Idaho National Laboratory. In his capacity, he leads various programs under DOE's Light Water Reactor Sustainability program. He is also a member of RELAP5-3D nuclear thermal-hydraulics code development team. Prior to INL, he worked at the OECD Nuclear Energy Agency for seven years as program manger for developing advanced nuclear fuel cycles and related strategy and policy. Dr. Choi received his Ph.D. and grand master degree on thermal system energy from the University of Marne-La-Vallee, France, M.S. and B.S in nuclear engineering in Kyunghee University, Korea.

Country: USA
Company: Idaho National Laboratory
Job Title: Program Manager / Senior Researcher

Paper 3 EU70

Lead Author: Eunseo So Co-author(s): Yunyeong Heo, Yunyeong.Heo@inl.gov Mohammad Abdo, Mohammad.Abdo@inl.gov Yong-Joon Choi, Yong-Joon.Choi@inl.gov

Development of Genetic Algorithms for Plant Reload Optimization for an Operating Pressurized Water Reactor
This paper summarizes the development, results, and enhancement activity of the Artificial Intelligence (AI) based automated nuclear power plant fuel reload optimization platform under the guidance of the United States Department of Energy, Light Water Reactor Sustainability Program, and Risk-Informed Systems Analysis Pathway. The research focuses on the optimization of the fuel arrangement to maximize the fuel cycle length. The AI-based Genetic Algorithm works with both convex and non convex, constrained or unconstrained problems. This can help explain the relationship between the fuel arrangement and fuel cycle length, in particular, the surrogate models used to reconstruct the Multiphysics problem maps the features/inputs of the problem to the fuel cycle length to provide such explanation. The Genetic Algorithm is composed of several evolutionary processes: fitness evaluation, parent selection, crossover, mutation, survivor selection, and termination. Crossover and mutation are the main steps responsible for injecting randomness/heuristics to prevent the algorithm from getting stuck in local minima. In this paper, roulette wheel parent selection, one-point crossover, swap mutation, and fitness-based survivor selection are used for demonstration to convert the fuel arrangement problem from the physical world (phenotype space) to the computational word (genotype space) via a user performed encoding/decoding step. Here, the search variables (genes) are the fuel locations in the core, whereas the values each variable takes, represent the fuel identification that will be placed in that specific location. The optimization process was demonstrated with a 1/4 core initial loading problem. In the core, 56 locations are loaded with five types of fuel assemblies, each type has different amount of enrichment and burnable poisons. As a result, the fuel cycle length increased to over 590 days, which is very close to the expected value. The results and enhancements in the optimization algorithm are also discussed in this paper.

Development of Genetic Algorithms for Plant Reload Optimization for an Operating Pressurized Water Reactor

This paper summarizes the development, results, and enhancement activity of the Artificial Intelligence (AI) based automated nuclear power plant fuel reload optimization platform under the guidance of the United States Department of Energy, Light Water Reactor Sustainability Program, and Risk-Informed Systems Analysis Pathway. The research focuses on the optimization of the fuel arrangement to maximize the fuel cycle length. The AI-based Genetic Algorithm works with both convex and non convex, constrained or unconstrained problems. This can help explain the relationship between the fuel arrangement and fuel cycle length, in particular, the surrogate models used to reconstruct the Multiphysics problem maps the features/inputs of the problem to the fuel cycle length to provide such explanation. The Genetic Algorithm is composed of several evolutionary processes: fitness evaluation, parent selection, crossover, mutation, survivor selection, and termination. Crossover and mutation are the main steps responsible for injecting randomness/heuristics to prevent the algorithm from getting stuck in local minima. In this paper, roulette wheel parent selection, one-point crossover, swap mutation, and fitness-based survivor selection are used for demonstration to convert the fuel arrangement problem from the physical world (phenotype space) to the computational word (genotype space) via a user performed encoding/decoding step. Here, the search variables (genes) are the fuel locations in the core, whereas the values each variable takes, represent the fuel identification that will be placed in that specific location. The optimization process was demonstrated with a 1/4 core initial loading problem. In the core, 56 locations are loaded with five types of fuel assemblies, each type has different amount of enrichment and burnable poisons. As a result, the fuel cycle length increased to over 590 days, which is very close to the expected value. The results and enhancements in the optimization algorithm are also discussed in this paper.

Paper EU70 | Download the paper file. | Download the presentation PowerPoint file.

A PSAM Profile is not yet available for this author.

Paper 4 FR99

Lead Author: Frida Olofsson Co-author(s): Anders Olsson, anders.olsson@vysusgroup.com

Challenges and lessons learned from a PSA on a spent fuel pool facility
Performing a PSA on a spent fuel pool storage facility provides new challenges but also new insights. Even though the nuclear fuel is the common subject for the risk to be evaluated, the differences compared to a traditional PSA for a nuclear power plant are several. In the spent fuel pool facility, the risk is evaluated both regarding the process of the transport containers and fuel elements handling, as well as for the long-term storage in spent fuel pools. The studied end-states and sequences depend on where in the facility the spent fuel is situated. Another of the most appearing differences from a traditional PSA is the long time windows. This issue pervades for example in the analysis of manual actions as well as when modelling mission times for systems and components. The facility has a low grade of automatization and, therefore, the importance of human actions is even more prominent. The system mission times originates from deterministic acceptance criteria. This assumption has proven to have an important impact on the analysis. Most methodologies used in the spent fuel pool PSA are developed with respect to nuclear power plants and related equipment, time windows and prevailing circumstances etc. Challenges are encountered in applying methodologies commonly used for nuclear power plants in the areas of e.g. HRA and area event analysis. The methods have consequently in some cases been modified to adapt them for this purpose. The PSA has contributed with important insights about the dominant risks at the spent fuel pool facility, for other (non-PSA) parts of the organization and has also led to actual measures regarding equipment, procedures and training.

Challenges and lessons learned from a PSA on a spent fuel pool facility

Performing a PSA on a spent fuel pool storage facility provides new challenges but also new insights. Even though the nuclear fuel is the common subject for the risk to be evaluated, the differences compared to a traditional PSA for a nuclear power plant are several. In the spent fuel pool facility, the risk is evaluated both regarding the process of the transport containers and fuel elements handling, as well as for the long-term storage in spent fuel pools. The studied end-states and sequences depend on where in the facility the spent fuel is situated. Another of the most appearing differences from a traditional PSA is the long time windows. This issue pervades for example in the analysis of manual actions as well as when modelling mission times for systems and components. The facility has a low grade of automatization and, therefore, the importance of human actions is even more prominent. The system mission times originates from deterministic acceptance criteria. This assumption has proven to have an important impact on the analysis. Most methodologies used in the spent fuel pool PSA are developed with respect to nuclear power plants and related equipment, time windows and prevailing circumstances etc. Challenges are encountered in applying methodologies commonly used for nuclear power plants in the areas of e.g. HRA and area event analysis. The methods have consequently in some cases been modified to adapt them for this purpose. The PSA has contributed with important insights about the dominant risks at the spent fuel pool facility, for other (non-PSA) parts of the organization and has also led to actual measures regarding equipment, procedures and training.

Paper FR99 | Download the paper file. | Download the presentation PowerPoint file.

A PSAM Profile is not yet available for this author.