Distributed Dynamic Event Tree Generation for Reliability and Risk Assessment

1
Distributed Dynamic Event Tree Generation for
Reliability and Risk Assessment

• Benjamin Rutt, Umit Catalyurek
• Dept. of Biomedical Informatics, The Ohio State
  University
• Aram Hakobyan, Kyle Metzroth, Tunc Aldemir,
  Richard Denning
• Nuclear Engineering Program, The Ohio State
  University
• Sean Dunagan, David Kunsman
• Sandia National Laboratories
• CLADE06 Paris, France
• June 19, 2006

2
Outline

• Introduction
• Objectives
• System Overview
• Distributed Execution
• Distributed Database Support
• Experimental Results
• Conclusion and Future Work

3
Introduction

• Probabilistic Risk Assessment (PRA)
• Quantification of the risk and reliability
  associated with system operation
• Integral part of US Nuclear Regularity Commission
  for licensing, also used by NASA
• Level 2 PRA
• Analysis of radionuclide release from containment
  
• Little progress in improving the methods used in
  the Level 2 element of Probabilistic Risk
  Assessment (PRA) since the release of NUREG-1150
  in 1991
• Current PRA methodology uses static
  event-tree/fault-tree
• necessitates to either make assumptions in
  advance about the relative timing of events or to
  consider the occurrence of events (such as
  hydrogen combustion) at multiple stages of the
  scenario

4
Objectives

• Real plant Level 2 PRA consists of hundreds of
  manual simulation runs
• Currently organized and analyzed manually
• Approximate processing time one man year
• Exact timing and magnitude of system variables
  are critical in determining the risk
• augment the static methods with Dynamic PRA
• Create mechanized driver to generate dynamic
  accident progression event trees
• Use of distributed computing to perform dynamic
  analyses that characterize containment failure or
  bypass and source terms in a mechanistically
  consistent manner
• Treatment of uncertainties (epistemic and
  aleatory)
• Create software that easily organizes and stores
  event tree data
• Develop user-friendly interface for display and
  control of the above

5
Overview of Reliability and Risk Assessment
Framework
6
RRAF Architecture Overview
7
Distributed Execution System

• Execution of stand-alone or parallel simulator on
  a distributed environment
• Staging of input files and output files
• Branching and task migration
• Dynamic Workflow
• Application specific vs Generalized tools for
  computation steering and check-pointing
• Simulator agnostic Driver
• Requirements
• SIM reads its input from command-line and/or text
  file
• SIM has check-pointing feature
• SIM allows user-defined control-functions (e.g.
  stopping if certain condition is true)
• SIM output can be utilized to detect stopping
  condition

8
Driver

• Simulator Agnostic
• determines when branching is to occur
• initiates multiple restarts of system code
  analyses
• determines the probabilities of scenarios when
  to terminate

9
Distributed Database Support

• Metadata Management
• Store, access and visualize the Event-Tree
• Store, access metadata regarding each individual
  run (branch)
• Access to simulation data
• Single scenarios data is distributed on multiple
  machines due to branching
• Data is distributed on flat files (binary and
  text)
• Current Prototype uses
• MySQL for Metadata
• STORM for accessing to distributed simulation
  data

10
Data Virtualization with STORM

• Applications developers generally prefer storing
  data in files
• Support high level queries on multi-dimensional
  distributed datasets
• Many possible data abstractions, query interfaces
• Grid virtualized object relational database or
  XML database
• Grid virtualized objects with user defined
  methods invoked to access and process data

Virtual Tables
Data Virtualization
Data Service
Scientific Datasets
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STORM

• Support efficient selection of the data of
  interest from distributed scientific datasets and
  transfer of data from storage clusters to compute
  clusters
• Front-end
• Support a basic SQL Select query with a virtual
  relational table view or a virtual XML database
  view
• A lightweight layer on top of datasets
• STORM runtime middleware STORM carries out query
  execution, query planning

SELECT ltDataElementsgt FROM Dataset-1,
Dataset-2,, Dataset-n WHERE ltExpressiongt AND
ltFilter(ltDataElementgt)gt GROUP-BY-PROCESSOR
ComputeAttribute(ltDataElementgt)
12

• STORM Services
• Query
• Meta-data
• Indexing
• Data Source
• Filtering
• Partition Generation
• Data Mover

13
Case Study

• Zion station blackout accident with failure of
  Auxiliary Feedwater system
• Includes models of creep rupture of major RCS
  components (surge line, hot leg, and SG tubes)
• No pump seal leakage allowed
• MELCOR severe accident simulation code

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Creep Rupture of RCS Components

• Currently
• Larson-Miller correlation is used in MELCOR creep
  rupture modeling with
• Proposed
• Cumulative distribution function developed for R
  in the form of a lognormal distribution with a
  mean value of µ 1 and standard deviation of s
  0.4

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Branching Points for Creep Rupture Model

• CDF of Creep Rupture Parameter R represented as a
  failure probability or so called fragility curve
  for surge line, hot leg, and SG tubes (see next
  slide)
• Discretization of cumulative failure probability
  at 5, 25, 50, 75, 95 (as an example)
• Corresponding R values of 0.518, 0.764, 1.00,
  1.31, and 1.931 chosen as branching points (See
  next slide)
• Two outcomes at each branching point fails, and
  does not fail

16
Fragility Curve For Creep Rupture Model
Branching Points
17
Creep Rupture Modes
No uncertainty, R 1
Range where SG tubes may fail if uncertainty
introduced
18
Experimental Results

• Experiments performed on a Linux compute cluster
• 40 dual 2.4 GHz Opteron 250 processors, 8GB mem,
  2x250GB SATA RAID0
• Nodes connected with a gigabit switched network
• 3 configurations
• 20 processor, 10 node
• 40 processor, 20 node
• 80 processor, 40 node
• 1 initiating event 4 dynamic workflows
• Executed concurrently, 1316 total branches, i.e.
  300 branches per experiment

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Queue Wait and Execution Time
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Processor Utilization
21
Processor Utilization (contd)
22
Processor Utilization (contd)
23
Average execution time per branch
24
Scheduling approaches
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Conclusion and Future Work

• First generic dynamic event-tree generation
  infrastructure
• Effectively one run of driver with much shorter
  combined simulation time compared with existing
  PRA results
• Scenarios can be identified that are not
  accounted for in the conventional PRA Level-2
  analysis
• Much more descriptive graphical illustration of
  event tree results
• Was this a new workflow problem? Yes/No )
• Future Work
• Integrate with other plant simulators RELAP work
  starts this summer
• More generic metadata management system to
  accommodate different simulators
• ? Mobius
• Support for other distributed execution
  frameworks and or queuing systems such as Condor,
  PBS, etc.
• Clustering and classification of scenarios
• Off-line (post-processing) for visualization
• Online for both for visualization and branch
  elimination
• UI for entering branching and truncation rules

26
Thanks

• Questions/Comments?
• Contact
• umit_at_bmi.osu.edu
• For more information
• http//bmi.osu.edu and http//msc.osu.edu