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Development of Computational and Data Processing Tools for ADAPT to Assist Dynamic Probabilistic Risk Assessment

Jankovsky, Zachary Kyle
http://rave.ohiolink.edu/etdc/view?acc_num=osu1524194454292866

Abstract Details

2018, Doctor of Philosophy, Ohio State University, Nuclear Engineering.
Dynamic Probabilistic Risk Assessment (DPRA) methodologies are those that explicitly account for time when modeling the interactions between elements of a system. DPRA methodologies and the Dynamic Event Tree (DET) methodology specifically can offer significant benefits over traditional Probabilistic Risk Assessment (PRA) for certain systems and transients. The introduction of time-dependence allows uncertainties to be resolved in the ordering of events and in the difference in impact between an event occurring earlier versus later in the transient. The generation of a DET may be performed in a more mechanistic way than a traditional event tree as events are added according to system conditions represented by the system simulator rather than by the judgment of the analyst. However, challenges remain both in adequately addressing the relevant physical phenomena and in the analysis of results. The goal of this work is to advance the maturity of the ADAPT DET driver by introducing new computational and data processing tools which will allow more advanced insights to be gleaned from DPRA. The first new tool is a platform for combining multiple simulators to generate a single DET in ADAPT. The platform allows for more detailed handling of complex phenomena since a more general code can be run for the entire transient or for more temporal phases of a transient to be handled under a single DET. While DETs have been produced using multiple simulators before, this new approach allows for any number of simulators to be arranged in a desired transition scheme. In a case study, an overall pressurized water reactor Interfacing Systems Loss of Coolant Accident (ISLOCA) accident progression was tracked using MELCOR while specific uncertainties of radiation dose to operators taking action within a building were resolved using RADTRAD. The second tool addresses the current lack of tools to gather insights from DETs. Traditional Importance Measures (IMs) are not ideal for dynamic analysis as there is no explicit treatment of the timing effects that DPRA may capture. A platform has been added to ADAPT to determine dynamic IM s, dubbed DYnamic Importances (DYIs). Three sample DYIs were developed and tested against the ISLOCA case study as well as a study of a Transient Overpower (TOP) at a Sodium-cooled Fast Reactor (SFR). The DYIs allow any input parameter to be examined against any simulator output for a variety of values: the maximum or minimum values across all branches in the sequence, the final value, or all values. The third and final new tool addresses the potentially unwieldy size of DET by allowing the analyst to take a slice of the DET that meets a desired set of rules. Rules can be based on time-dependent conditions, e.g., "temperature above 750K at any time after 3 hours", and may be combined to return specific sections of the overall DET. DYIs may be applied against the sliced DET in the same way as the overall set and the results may be compared to examine the impact of desired conditions. This was demonstrated on the TOP case study where it was found that certain postulated operator actions may be more or less effective given the conditions of the plant relatively early in the transient. The case studies and their results are not meant to be comprehensive analyses of the given transients but to serve as demonstrations of the new computational and post-processing capabilities of ADAPT as developed in this dissertation. These tools have proved to be powerful additions to the existing DET tool kit in the areas of mechanistic exploration of uncertainties and analysis of results.
Tunc Aldemir, PhD (Advisor)
Carol Smidts, PhD (Committee Member)
Marat Khafizov, PhD (Committee Member)
Matthew Denman, PhD (Committee Member)
242 p.
Nuclear Engineering
Probabilistic Risk Assessment; Dynamic PRA; Dynamic Event Trees; Dynamic Importance Measures; ADAPT; MELCOR; RADTRAD; SAS4A

Recommended Citations

Citations

  • Jankovsky, Z. K. (2018). Development of Computational and Data Processing Tools for ADAPT to Assist Dynamic Probabilistic Risk Assessment [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524194454292866

    APA Style (7th edition)

  • Jankovsky, Zachary. Development of Computational and Data Processing Tools for ADAPT to Assist Dynamic Probabilistic Risk Assessment. 2018. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1524194454292866.

    MLA Style (8th edition)

  • Jankovsky, Zachary. "Development of Computational and Data Processing Tools for ADAPT to Assist Dynamic Probabilistic Risk Assessment." Doctoral dissertation, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524194454292866

    Chicago Manual of Style (17th edition)

osu1524194454292866
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