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I a CUMULATIVE DAMAGE APPROACH to MODELING A CUMULATIVE DAMAGE APPROACH TO MODELING ATMOSPHERIC CORROSION OF STEEL Dissertation Submitted to The School of Engineering of the UNIVERSITY OF DAYTON In Partial Fulfillment of the Requirements for The Degree of Doctor of Philosophy in Materials Engineering By David Harry Rose UNIVERSITY OF DAYTON Dayton, Ohio December, 2014 i A CUMULATIVE DAMAGE APPROACH TO MODELING ATMOSPHERIC CORROSION OF STEEL Name: Rose, David Harry APPROVED BY: __________________________ __________________________ Douglas C. Hansen, Ph.D. Charles E. Browning, Ph.D. Advisory Committee Chairman Advisory Committee Member Professor Torley Chair in Composite Materials Engineering Materials Chair, Department of Chemical and Materials Engineering __________________________ __________________________ P. Terrence Murray, Ph.D. Maher Qumsiyeh, Ph.D. Advisory Committee Member Advisory Committee Member Professor Professor Materials Engineering Mathematics __________________________ Peter Sjöblom, Ph.D. Advisory Committee Member President S&K Technologies LLC __________________________ __________________________ John G. Weber, Ph.D. Eddy M. Rojas, Ph.D., M.A., P.E. Associate Dean Dean School of Engineering School of Engineering ii © Copyright by David Harry Rose All rights reserved 2014 iii ABSTRACT A CUMULATIVE DAMAGE APPROACH TO MODELING ATMOSPHERIC CORROSION OF STEEL Name: Rose, David Harry University of Dayton Advisor: Dr. Douglas C. Hansen Past attempts to develop models that predict atmospheric corrosion rates used statistical regression, “power-law”, or other approaches that result in linear or simple nonlinear corrosion rate predictions. Such models were calibrated by statistically comparing corrosion test results to predictions based upon long-term (e.g., annual) deposition measurements of chloride aerosols and/or SO2. Relative humidity, if explicitly considered, was only used to define the amount of time during the year when conditions were thought to be favorable for corrosion. Most models ignored temperature effects but those that do only consider annual averages. A new approach was constructed to predict corrosion rates using the concept of cumulative damage. This new model is analogous to some types of fatigue models [1, 2] and is based upon the Eyring equation, which was originally developed to predict the dependence of chemical reaction rates on levels of the presumed acceleration factors. The model makes hourly weight loss predictions, which when added together makes longer-term “cumulative” predictions. Principal advantage of using hourly predictions is that the effects of diurnal and seasonal temperature cycles and related changes to relative humidity are explicitly considered. The iv stochastic nature of atmospheric contaminants is considered as well. An inverse modeling approach using Monte Carlo simulations was used to fit various candidate models to proxy environmental characterization data representing conditions at corrosion test sites. Proxy data (measured elsewhere and for other purposes) was used to infer the stochastic environmental severity at sites where corrosion tests were conducted. Such data included hourly SO2 and ozone data obtained from the Environmental Protection Agency’s Air Quality System database, longer-term chloride deposition data from the National Atmospheric Deposition Program’s on- line database, and hourly weather data from the U.S. Air Force’s 14th Weather Squadron. Proxy data was used so that if this current research proved successful, follow-on work could lead to a practical methodology that design engineers could employ to make realistic predictions without having to explicitly characterize the environment at a location of interest. Cumulative predictions made using such data were statistically compared to quarterly corrosion test results that came from a DoD Strategic Environmental Research and Development funded effort and other related programs that used the same testing protocols. Billions of simulations were conducted whereby coefficients employed by the candidate model were randomly varied and the individual predictions statistically compared to test measurements in order to identify the most accurate model. Each candidate model was calibrated by considering hourly data for an entire year at multiple locations in order to quantify interactions between acceleration factors. The degree of fit between the model results and test measurements at the calibration sites was very high (R2=~ 0.99). When the optimum model was applied to locations where corrosion tests were conducted but not used for calibration, the fit was not quite as good, but was still quite high (R2=~0.86). Analyses were conducted to identify ways to further improve accuracy, thus laying the framework for future efforts. v Dedicated to my wife, Fran, our sons David and Wesley, and our daughter Stephanie vi ACKNOWLEDGEMENTS I would like to offer special thanks to my advisor, Dr. Douglas Hansen, for agreeing to help me fulfill my long-time dream. If not for his technical advice, encouragement, support, and willingness to take on an older student, the research effort described in this dissertation would not have been possible. Special thanks are also offered to Dr. Daniel Eylon, Director of the Graduate Materials Engineering Program, for facilitating my return to the University of Dayton. I would also like to thank Dr. James Snide (Professor Emeritus, Materials Engineering) for the helpful advice he offered throughout my research program. Similarly, I offer my thanks to the members of my advisory committee including Dr. Charles Browning (Chair, Chemical and Materials Engineering), Dr. P. Terrence Murray (Materials Engineering), Dr. Maher Qumsiyeh (Mathematics), and Dr. Peter Sjöblom (President, S&K Technologies LLC). I would also like to express my deepest appreciation to my friend, Mr. Scott McCombie, from Booz Allen and Hamilton. Scott is a computer scientist and he realized the problem I was working on could be more effectively solved using massively parallel computing. He worked with me to convert my initial programming approach and algorithms into an application that ran on a massively parallel computer, which allowed me to conduct far more simulations than would have been otherwise possible. Going forward, the enhanced capabilities available using parallel computing provides the opportunity to evolve the proof-of-concept effort described here into the design analysis tool that has been my vision from the very start. I would also like to thank Mr. Paul Lein from Quanterion Solutions and Dr. Jorge Romeu, Research Professor of vii Industrial Statistics and Operations Research at Syracuse University, for the numerous mathematical discussions we had over the years. Their suggestions and advice at the early stages of my research proved quite helpful. Last but certainly not least, I want to thank my wife, Fran. Little did she know the inspiration she provided me starting from the very first day we met would lead to my long-term quest for learning. She has been supportive from the beginning, through now, when as a middle aged man, I decided to return to school full-time to pursue a Ph.D. For her love, inspiration, and support over these past 30 years, I am eternally grateful. viii TABLE OF CONTENTS ABSTRACT ........................................................................................................................................ iv DEDICATION .................................................................................................................................... vi ACKNOWLEDGEMENTS .................................................................................................................. vii LIST OF FIGURES ............................................................................................................................. xvi LIST OF TABLES ............................................................................................................................. xxix LIST OF ABBREVIATIONS AND NOTATIONS ................................................................................. xxxv CHAPTER 1 INTRODUCTION ........................................................................................................ 1 1.1 Past Modeling Effort .................................................................................................................. 3 1.2 Literature Search ....................................................................................................................... 4 1.2.1 Factors that Lead to Corrosion ............................................................................................. 4 1.2.2 Statistical Assessment of Corrosion Models ...................................................................... 13 1.2.3 Atmospheric Severity and its Importance to Modeling Efforts .......................................... 16 1.2.4 Legacy Modeling Efforts ..................................................................................................... 17 1.2.5 Environmental Parameter Characterization Methods ....................................................... 27 CHAPTER 2 DERIVATION OF THE CUMULATIVE DAMAGE MODEL ............................................ 30 ix 2.1 Construction of Notional Corrosion Model ............................................................................. 31 2.2 Derivation of Functions to Update the Notional Model ......................................................... 36 2.2.1 Temperature-Relative Humidity Functions .......................................................................
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