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Shirley, Rachel BenishAn assessment of the scope of a full validation of the Technique for Human Error Rate Prediction (THERP) in a digital nuclear power plant simulator
Master of Science, The Ohio State University, 2015, Nuclear Engineering
Science-based Human Reliability Analysis (HRA) seeks to experimentally validate HRA methods in simulator studies. Emphasis is on validating the internal components of the HRA method, rather than the validity and consistency of the final results of the method. This thesis is an assessment of the requirements for a simulator study validation of the Technique for Human Error Rate Prediction (THERP), a foundational HRA method. The aspects requiring validation include the tables of Human Error Probabilities (HEPs), the treatment of stress, and the treatment of dependence between tasks. We estimate the sample size, n, required to obtain statistically significant error rates for validating HEP values, and the number of observations, m, that constitute one observed error rate for each HEP value. As m tends to be very high (due to the low error rates), two methods are introduced for estimating the mean error rate using fewer observations. The first method uses the median error rate, and the second method is a Bayesian estimator of the error rate based on the observed errors and the number of observations. Both methods are tested using computer-generated data. A pilot experiment conducted in The Ohio State University’s Nuclear Power Plant Simulator Facility is discussed. In the experiment, student operators perform a maintenance task in a BWR simulator. Errors are recorded, and error rates are compared to the THERP-predicted error rates. While the observed error rates are generally consistent with the THERP HEPs, further study is needed to provide confidence in these results as the pilot study sample size is small. Sample size calculations indicate that a full-scope THERP validation study would be a substantial but potentially feasible undertaking; forty hours of observation would provide sufficient data for a preliminary study, and observing 101 operators for twenty hours each would provide data for a full validation experiment.

Committee:

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

Subjects:

Nuclear Engineering

Keywords:

Human Reliability Analysis, SImulator, Nuclear Power, THERP, Validation

Shirley, Rachel BScience Based Human Reliability Analysis: Using Digital Nuclear Power Plant Simulators for Human Reliability Research
Doctor of Philosophy, The Ohio State University, 2017, Nuclear Engineering
Nuclear power plant (NPP) simulators are proliferating in academic research institutions and national laboratories in response to the availability of affordable, digital simulator platforms. Accompanying the new research facilities is a renewed interest in using data collected in NPP simulators for Human Reliability Analysis (HRA) research. An experiment conducted in The Ohio State University (OSU) NPP Simulator Facility develops data collection methods and analytical tools to improve use of simulator data in HRA. In the pilot experiment, student operators respond to design basis accidents in the OSU NPP Simulator Facility. Thirty-three undergraduate and graduate engineering students participated in the research. Following each accident scenario, student operators completed a survey about perceived simulator biases and watched a video of the scenario. During the video, they periodically recorded their perceived strength of significant Performance Shaping Factors (PSFs) such as Stress. This dissertation reviews three aspects of simulator-based research using the data collected in the OSU NPP Simulator Facility: First, a qualitative comparison of student operator performance to computer simulations of expected operator performance generated by the Information Decision Action Crew (IDAC) HRA method. Areas of comparison include procedure steps, timing of operator actions, and PSFs. Second, development of a quantitative model of the simulator bias introduced by the simulator environment. Two types of bias are defined: Environmental Bias and Motivational Bias. This research examines Motivational Bias— that is, the effect of the simulator environment on an operator’s motivations, goals, and priorities. A bias causal map is introduced to model motivational bias interactions in the OSU experiment. Data collected in the OSU NPP Simulator Facility are analyzed using Structural Equation Modeling (SEM). Data include crew characteristics, operator surveys, and time to recognize and diagnose the accident in the scenario. These models estimate how the effects of the scenario conditions are mediated by simulator bias, and demonstrate how to quantify the strength of the simulator bias. Third, development of a quantitative model of subjective PSFs based on objective data (plant parameters, alarms, etc.) and PSF values reported by student operators. The objective PSF model is based on the PSF network in the IDAC HRA method. The final model is a mixed effects Bayesian hierarchical linear regression model. The subjective PSF model includes three factors: The Environmental PSF, the simulator Bias, and the Context. The Environmental Bias is mediated by an operator sensitivity coefficient that captures the variation in operator reactions to plant conditions. The data collected in the pilot experiments are not expected to reflect professional NPP operator performance, because the students are still novice operators. However, the models used in this research and the methods developed to analyze them demonstrate how to consider simulator bias in experiment design and how to use simulator data to enhance the technical basis of a complex HRA method. The contributions of the research include a framework for discussing simulator bias, a quantitative method for estimating simulator bias, a method for obtaining operator-reported PSF values, and a quantitative method for incorporating the variability in operator perception into PSF models. The research demonstrates applications of Structural Equation Modeling and hierarchical Bayesian linear regression models in HRA. Finally, the research demonstrates the benefits of using student operators as a test platform for HRA research.

Committee:

Carol Smidts, PhD (Advisor); Ronald Boring, PhD (Committee Member); Tunc Aldemir, PhD (Committee Member); Catherine Calder, PhD (Committee Member)

Subjects:

Nuclear Engineering

Keywords:

Human Reliability Analysis, Bayesian Analysis, Simulator Bias, Performance Shaping Factor, Nuclear Power, Structural Equation Modeling, Student Operators, Digital Nuclear Power Plant Simulator, Risk Assessment

Miller, Ian TimothyProbabilistic finite element modeling of aerospace engine components incorporating time-dependent inelastic properties for ceramic matrix composite (CMC) materials
Master of Science, University of Akron, 2006, Applied Mathematics
The research included in this abstract pertains to probabilistic finite-element creep analysis of a composite combustor liner. A composite combustor liner is an aerospace engine component that is subjected to very high temperatures, ranging between 1500 - 2100 degrees Fahrenheit. A creep analysis of this component is essential for rational design as creep (a slow time-dependent information under constant load) is prevalent at high temperatures. In a probabilistic analysis, many, if not all, of the state variables are represented by random variables with appropriate probability distributions incorporating relevant parameters. This formalism is much more realistic, as it more accurately describes the variability in properties and loadings that are inherent in the composition of aerospace materials and loadings encountered by aerospace components.

Committee:

Ali Hajjafar (Advisor)

Keywords:

Creep Analysis; Reliability Analysis; Aerospace Engine Components; Ceramic Matrix Composite Materials; Finite Element Analysis

Gupta, AtulDevelopment of Boiling Water Reactor Nuclear Power Plant Simulator for Human Reliability Analysis Education and Research
Master of Science, The Ohio State University, 2013, Mechanical Engineering
This thesis discusses the development of Full scope BWR Simulator developed for human reliability course initiated at The Ohio State University. Human System Interface (HSI) guidelines followed to set-up the simulator room and steps undertaken to create the generic simulator from the plant based simulator have also been discussed. The setup of simulator is followed by the course introduction. The developed course with its five components: human factors, human reliability analysis models, integration of HRA into PRA, open research issues in HRA, and hands on experiment in NPP simulator; has been explained in detail. The hands on experiment consists of training, HRA analysis, simulator session, students’ comparison of theoretical HEP value with practical value, and assessment of learning using preliminary pre-experiment and post-experiment questionnaires. The detailed questionnaire analysis is also included in this thesis. We also propose ideas to develop a better questionnaire to assess students learning, and to develop a better human factors and human reliability analysis course.

Committee:

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

Subjects:

Mechanical Engineering

Keywords:

Human reliability analysis; human factors; BWR simulator

Pilla, SrikanthIntegration of Micromechanical and Probabilistic Analysis Models of Nanocomposites
Master of Science in Mechanical Engineering, University of Toledo, 2005, Mechanical Engineering
Carbon nanofiber/nanotube reinforced composites show great potential as a replacement for conventional composite materials because of their exceptional properties. Experimental results demonstrate that substantial improvements in the mechanical behavior of a nano-structured composite can be attained using small amounts of carbon nanofibers/nanotubes as reinforcing phase. To date many researchers have theoretically predicted the effective behavior of conventional composites and nanocomposites using traditional methods (for example using Mori-Tanaka or Halpin-Tsai models). The effect of the interface between the nanofibers/nanotubes and the matrix has also been investigated. There is uncertainty in the value of the modulus of the reinforcement in nanocomposites because it is difficult to measure the modulus. Moreover there is variability in the matrix and interface moduli. Therefore, it is important to study the effect of uncertainty and variability in the properties of the phase materials on the properties of nanocomposite. A large amount of work has been done on modeling uncertainty and variability in conventional materials (e.g., aluminum, steel or long fiber composites) and on predicting the probability distribution of the performance characteristics of structures made of these materials. However, an integrated tool is needed for probabilistic analysis of structures made of carbon nanofiber/nanotube composites. In this thesis, existing models for stiffness analysis of conventional composites and nanocomposites have been modified and integrated with tools for deterministic and probabilistic analysis of structures. A two-step model has been developed for determining deterministically the stiffness of nanocomposite materials considering the effect of the interface between the reinforcement and the matrix. A methodology consisting of the above two-step model, deterministic analysis of plates and probabilistic analysis of structures has also been developed and demonstrated. It is shown that it is important to consider the interface between the reinforcement and polymer matrix and the variability and uncertainty in the properties of the phase materials of a nanocomposite.

Committee:

Efstratios Nikolaidis (Advisor)

Subjects:

Engineering, Mechanical

Keywords:

nanocomposites; micromechanical modeling; probabilistic analysis; reliability analysis; interface modeling

Miran, Seyedeh AzadehRELIABILITY-BASED MANAGEMENT OF BURIED PIPELINES CONSIDERING EXTERNAL CORROSION DEFECTS
Master of Science in Engineering, University of Akron, 2016, Engineering
Corrosion is one of the main deteriorating mechanisms that degrade the energy pipeline integrity, due to transferring corrosive fluid or gas and interacting with corrosive environment. Corrosion defects are usually detected by periodical inspections using in-line inspection (ILI) methods. In order to ensure pipeline safety, this study develops a cost-effective maintenance strategy that consists of three aspects: corrosion growth model development using ILI data, time-dependent performance evaluation, and optimal inspection interval determination. In particular, the proposed study is applied to a cathodic protected buried steel pipeline located in Mexico. First, time-dependent power-law formulation is adopted to probabilistically characterize growth of the maximum depth and length of the external corrosion defects. Dependency between defect depth and length are considered in the model development and generation of the corrosion defects over time is characterized by the homogenous Poisson process. The growth models unknown parameters are evaluated based on the ILI data through the Bayesian updating method with Markov Chain Monte Carlo (MCMC) simulation technique. The proposed corrosion growth models can be used when either matched or non-matched defects are available, and have ability to consider newly generated defects since last inspection. Results of this part of study show that both depth and length growth models can predict damage quantities reasonably well and a strong correlation between defect depth and length is found. Next, time-dependent system failure probabilities are evaluated using developed corrosion growth models considering prevailing uncertainties where three failure modes, namely small leak, large leak and rupture are considered. Performance of the pipeline is evaluated through failure probability per km (or called a sub-system) where each sub-system is considered as a series system of detected and newly generated defects within that sub-system. Sensitivity analysis is also performed to determine to which incorporated parameter(s) in the growth models reliability of the studied pipeline is most sensitive. The reliability analysis results suggest that newly generated defects should be considered in calculating failure probability, especially for prediction of long-term performance of the pipeline and also, impact of the statistical uncertainty in the model parameters is significant that should be considered in the reliability analysis. Finally, with the evaluated time-dependent failure probabilities, a life cycle-cost analysis is conducted to determine optimal inspection interval of studied pipeline. The expected total life-cycle costs consists construction cost and expected costs of inspections, repair, and failure. The repair is conducted when failure probability from any described failure mode exceeds pre-defined probability threshold after each inspection. Moreover, this study also investigates impact of repair threshold values and unit costs of inspection and failure on the expected total life-cycle cost and optimal inspection interval through a parametric study. The analysis suggests that a smaller inspection interval leads to higher inspection costs, but can lower failure cost and also repair cost is less significant compared to inspection and failure costs.

Committee:

Qindan Huang, Dr (Advisor); Qindan Huang, Dr (Committee Chair); Ping Yi, Dr (Committee Member); Shengyong Wang, Dr (Committee Member)

Subjects:

Civil Engineering; Engineering; Industrial Engineering

Keywords:

Corrosion; Pipeline; Reliability analysis; Failure mode; Reliability-based repair criteria; Optimal inspection interval; Life-cycle cost

Yang, LuoRELIABILITY-BASED DESIGN AND QUALITY CONTROL OF DRIVEN PILES
Doctor of Philosophy, University of Akron, 2006, Civil Engineering
Driven piles are widely used as foundations for buildings, bridges, and other structures. Since 1994, AASHTO (American Association of State Highway and Transportation Officials) has been in process to change from ASD (Allowable Stress Design) method to LRFD (Load and Resistance Factor Design) method for foundation design. The adoption of LRFD approach makes possible the application of reliability analysis to quantify uncertainties associated with various load and resistance components, respectively. Although there exist some recommendations for incorporation of set-up into ASD and quality control methods for driven piles, most of these recommendations were developed purely based on the engineering experience with no attendant database and reliability analysis. A successful application of probability approach will definitely result in significant improvements on the design and quality control of driven piles. Therefore, there is a need to develop the quality control criterion and to improve the LRFD of driven piles in the framework of reliability-based analysis. In this study, the new reliability-based quality control criteria on driven piles are developed based on acceptance-sampling analysis for various pile test methods with lognormal statistical characteristics. An optimum approach is recommended for the selection of the number of load tests and the required measured capacities for quality control of various load test methods of driven piles. The databases containing a large number of pile testing data are compiled for piles driven into clay and into sand, respectively. Based on the compiled databases, a new methodology is developed to incorporate set-up into the LRFD of drive piles using FORM (First Order Reliability Method) where the separate resistance factors for measured reference capacity and predicted set-up capacity are derived to account for different degrees of uncertainties associated with these two capacity components. Based on Bayesian theory, a new methodology is developed to optimize the LRFD of driven piles by combining the results from static calculation and dynamic pile testing. Specifically, the results from dynamic pile tests are incorporated to reduce the uncertainties associated with static analysis methods by updating the resistance factors in LRFD. Finally, a new one-dimensional wave equation based algorithm to interpret High Strain Testing data for estimation of resistances of driven piles is proposed.

Committee:

Robert Liang (Advisor)

Keywords:

Driven Piles; Reliability analysis; Load and Resistance Design; Dynamic pile tests; Quality Control; Set-up

Cheng, NanBayesian Nonparametric Reliability Analysis Using Dirichlet Process Mixture Model
Master of Science (MS), Ohio University, 2011, Industrial and Systems Engineering (Engineering and Technology)
This thesis develops a Bayesian nonparametric method based on Dirichlet Process Mixture Model (DPMM) and Markov chain Monte Carlo (MCMC) simulation algorithms to analyze non-repairable reliability lifetime data. Kernel distributions of the model will be implemented with Weibull, Lognormal and Exponential. The influence of prior distribution on the model parameters is studied. Both simulated and experimental data are used to test the proposed models. Our data analysis results indicate that the Dirichlet Process Lognormal Mixture (DPLNM) model is more flexible than the Dirichlet Process Exponential Mixture (DPEM) model and the Dirichlet Process Weibull Mixture (DPWM) model in terms of capturing different shapes of the life time distribution functions. Typically, when handling the practical data generated from devices with embedded nano-crystals, only the DPLNM model can produce a good fit towards the data. Although the lognormal distribution does not have closed form reliability function, censored data can still be easily handled using modern sampling techniques, such as Slice Sampling.

Committee:

Tao Yuan (Committee Chair)

Subjects:

Engineering

Keywords:

Dirichlet Process Mixture Model (DPMM);Bayesian Nonparametric Reliability Analysis;Dirichlet Process Lognormal Mixture (DPLNM) model