Kyle Metzroth

The Mass of the Central Black Hole in the Seyfert Galaxy NGC 4151

In order to improve the reverberation-mapping-based estimate of the mass of the central supermassive black hole in the Seyfert 1 galaxy NGC 4151, we have reanalyzed archival ultraviolet monitoring spectra from two campaigns undertaken with the International Ultraviolet Explorer. We measure emission-line time delays for four lines, C IV λ1549, He II λ1640, C III] λ1909, and Mg II λ2798, from both campaigns. We combine these measurements with the dispersion of the variable part of each respective emission line to obtain the mass of the central object. Despite the problematic nature of some of the data, we are able to measure a mass of (4.14 ± 0.73) × 107 M☉, although this, like all reverberation-based masses, is probably systematically uncertain by a factor of 3-4.

Development of a code-agnostic computational infrastructure for the dynamic generation of accident progression event trees

Analysis of dynamic accident progression trees (ADAPT) is a mechanized procedure for the generation of accident progression event trees. Use of ADAPT substantially reduces the manual and computational effort for Level 2 probabilistic risk assessment (PRA) of nuclear power plants; reduces the likelihood of input errors; determines the order of events dynamically; and treats accidents in a phenomenology consistent manner. ADAPT is based on the concept of dynamic event trees which use explicit modeling of the deterministic dynamic processes that take place within the plant (through system simulation codes such as MELCOR, RELAP) for the modeling of stochastic system evolution. The computational infrastructure of ADAPT is presented, along with a prototype implementation of ADAPT using MELCOR for the PRA modeling of a station blackout in a pressurized water reactor. The computational infrastructure allows for flexibility in linking with different simulation codes, parallel processing of the scenarios under consideration, on-line scenario management (initiation as well as termination) and user-friendly graphical capabilities.

SCENARIO CLUSTERING AND DYNAMIC PROBABILISTIC RISK ASSESSMENT

A challenging aspect of dynamic methodologies for probabilistic risk assessment (PRA), such as the Dynamic Event Tree (DET) methodology, is the large number of scenarios generated for a single initiating event. Such large amounts of information can be difficult to organize for extracting useful information. Furthermore, it is not often sufficient to merely calculate a quantitative value for the risk and its associated uncertainties. The development of risk insights that can increase system safety and improve system performance requires the interpretation of scenario evolutions and the principal characteristics of the events that contribute to the risk. For a given scenario dataset, it can be useful to identify the scenarios that have similar behaviors (i.e., identify the most evident classes), and decide for each event sequence, to which class it belongs (i.e., classification). It is shown how it is possible to accomplish these two objectives using the Mean-Shift Methodology (MSM). The MSM is a kernel-based, non-parametric density estimation technique that is used to find the modes of an unknown data distribution. The algorithm developed finds the modes of the data distribution in the state space corresponding to regions with highest data density as well as grouping the scenarios generated into clusters based on scenario temporal similarities. The MSM is illustrated using the data generated by a DET algorithm for the analysis of a simple level/temperature controller and reactor vessel auxiliary cooling system.

Distributed dynamic event tree generation for reliability and risk assessment

Level 2 probabilistic risk assessments of nuclear plants (analysis of radionuclide release from containment) may require hundreds of runs of severe accident analysis codes such as MELCOR or RELAP/SCDAP to analyze possible sequences of events (scenarios) that may follow given initiating events. With the advances in computer architectures and ubiquitous networking, it is now possible to utilize multiple computing and storage resources for such computational experiments. This paper presents a system software infrastructure that supports execution and analysis of multiple dynamic event-tree simulations on distributed environments. The infrastructure allow for 1) the testing of event tree completeness, and, 2) the assessment and propagation of uncertainty on the plant state in the quantification of event trees

Development and application of the dynamic system doctor to nuclear reactor probabilistic risk assessments.

This LDRD project has produced a tool that makes probabilistic risk assessments (PRAs) of nuclear reactors - analyses which are very resource intensive - more efficient. PRAs of nuclear reactors are being increasingly relied on by the United States Nuclear Regulatory Commission (U.S.N.R.C.) for licensing decisions for current and advanced reactors. Yet, PRAs are produced much as they were 20 years ago. The work here applied a modern systems analysis technique to the accident progression analysis portion of the PRA; the technique was a system-independent multi-task computer driver routine. Initially, the objective of the work was to fuse the accident progression event tree (APET) portion of a PRA to the dynamic system doctor (DSD) created by Ohio State University. Instead, during the initial efforts, it was found that the DSD could be linked directly to a detailed accident progression phenomenological simulation code - the type on which APET construction and analysis relies, albeit indirectly - and thereby directly create and analyze the APET. The expanded DSD computational architecture and infrastructure that was created during this effort is called ADAPT (Analysis of Dynamic Accident Progression Trees). ADAPT is a system software infrastructure that supports execution and analysis of multiple dynamic event-tree simulations on distributed environments. A simulator abstraction layer was developed, and a generic driver was implemented for executing simulators on a distributed environment. As a demonstration of the use of the methodological tool, ADAPT was applied to quantify the likelihood of competing accident progression pathways occurring for a particular accident scenario in a particular reactor type using MELCOR, an integrated severe accident analysis code developed at Sandia. (ADAPT was intentionally created with flexibility, however, and is not limited to interacting with only one code. With minor coding changes to input files, ADAPT can be linked to other such codes.) The results of this demonstration indicate that the approach can significantly reduce the resources required for Level 2 PRAs. From the phenomenological viewpoint, ADAPT can also treat the associated epistemic and aleatory uncertainties. This methodology can also be used for analyses of other complex systems. Any complex system can be analyzed using ADAPT if the workings of that system can be displayed as an event tree, there is a computer code that simulates how those events could progress, and that simulator code has switches to turn on and off system events, phenomena, etc. Using and applying ADAPT to particular problems is not human independent. While the human resources for the creation and analysis of the accident progression are significantly decreased, knowledgeable analysts are still necessary for a given project to apply ADAPT successfully. This research and development effort has met its original goals and then exceeded them.