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Sundaramurthi, RanjitprakashHuman Reliability Modeling for Next Generation System Codes
Master of Science, The Ohio State University, 2011, Mechanical Engineering
This thesis derives the human reliability model requirements for the Next Generation System Code (NGSC) which will be utilized to determine risk-informed safety margins for Nuclear Power Plants (NPP) through dynamic probabilistic risk analysis. The proposed model is flexible, with the facility to apply a coarse-grain or a fine-grain structure based on the desired resolution level. The varying resolution is achieved by employing the procedure-based human reliability analysis methods (THERP or SPAR-H) for the coarse-grain structure and the advanced cognitive IDA method for the fine-grain structure. The thesis proposes improvements to the existing IDA model to incorporate functionalities demanded by the NGSC. The improvements are derived for four modules of IDA/IDAC. A Bayesian belief network is constructed for the performance-shaping factors and the conditional probability for existence of each factor is computed from data collected from aviation and nuclear accidents. The influence of the performance-shaping factors on the strategy-selection process of the operator is also depicted. A foundation is laid for the development of mental models with focus on NPP operation. The research lists the modifications/additions that are needed to the IDA method to enable the incorporation of Human Reliability Analysis (HRA) into NGSC codes.

Committee:

Carol Smidts (Advisor); Tunc Aldemir (Committee Member)

Subjects:

Cognitive Psychology; Mechanical Engineering

Keywords:

HRA modeling; IDA/IDAC; Causal Map; BBN; Mental Model; NGSC

Anderson, Jerone S.A Study of Nutrient Dynamics in Old Woman Creek Using Artificial Neural Networks and Bayesian Belief Networks
Master of Science (MS), Ohio University, 2009, Industrial and Systems Engineering (Engineering and Technology)
The Old Woman Creek National Estuary is studied in this project to evaluate effective modelling techniques for predicting Net Ecosystem Metabolism (NEM). NEM is modelled using artificial neural networks, Bayesian belief networks, and a hybrid model. A variety of data preprocessing techniques are considered prior to model development. The effects of discretization on model development are considered and discrete data is ultimately used to produce models which classify NEM into three ranges based on inputs with information significance. Artificial neural networks are found to be the most accurate for classification while Bayesian belief networks are found to provide a better framework for dynamically predicting NEM as inputs are changed.

Committee:

Gary R. Weckman, PhD (Advisor); David Millie, PhD (Committee Member); Kevin Berisso, PhD (Committee Member); Diana Schwerha, PhD (Committee Member)

Subjects:

Ecology; Engineering; Environmental Engineering; Industrial Engineering

Keywords:

BBN; ANN; ecology; NEM; Bayesian Belief Networks; Artificial Neural Networks; computer modelling