Efficient Monte Carlo Methods For Characterization Of Safety Margins Of Nuclear Power Plants

Project: Research project

Project Details


The goal of the award is to devise efficient computational methods for performing risk and safety analyses of nuclear power plants. A recent international effort of the Nuclear Energy Agency formulated a new framework, known as risk-informed safety-margin characterization, to address several critical issues facing the nuclear industry today. The impacts of these developments, which include extending licenses for aging facilities and operating them at higher power levels, can profoundly affect safety margins, and there is an urgent need to better understand the risks in the changes. Indeed, the catastrophic 2011 accident at the Fukushima Daiichi plant in Japan clearly shows the utmost importance of accurately measuring risk. In addition to performing efficient nuclear safety analyses, the techniques may also be applied to attack problems in civil and mechanical engineering, catastrophe modeling, and finance.

This research combines theoretical and practical components, including designing new Monte Carlo algorithms, formally establishing their validity, and implementing them to analyze risks of existing nuclear plants. The core of the work will focus on variance-reduction techniques for efficient simulation for performing risk and safety analyses of nuclear facilities. Because current simulations can require enormous computational effort, applying variance reduction is essential for performing safety analyses. If successful, the new methods will profoundly reduce computation time, perhaps by orders of magnitude. To further account for the statistical error of the resulting estimators, the investigator also plans to produce asymptotically valid confidence intervals.
Effective start/end date9/1/158/31/18


  • National Science Foundation


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.