Online Archive of University of Virginia Scholarship
Empirical Model Relating Chloride Loading Density and Conductance for Prediction of Galvanic Corrosion; Safety, Secrets, and Settlements: How U.S. Military Contractors Manage Public Image185 views
Author
Barbieri, Matthew, School of Engineering and Applied Science, University of Virginia
Advisors
Martukanitz, Richard, EN-Mat Sci & Engr Dept, University of Virginia
Norton, Peter, EN-Engineering and Society, University of Virginia
Abstract
Military contracting is a $420 billion industry in the United States. Companies invest resources to compete for lucrative contracts. Corrosion protection of military equipment also advances U.S. Military contractors' business agenda by reducing cost of repair. Chloride loading density can predict corrosion in real, complex environments. By correlating sensor parameters to this environmental condition, lab data can help researchers predict long-term corrosion in less controlled conditions. To win contracts, military contractors rely on a positive public image to gain support in Congress and the Department of Defense. To manage public perceptions, U.S. military contractors engage in a unique form of advertising, hide controversy behind legal ambiguity, and withhold information from the public through their protections as a private company handling sensitive secrets.
Degree
BS (Bachelor of Science)
Keywords
galvanic corrosion; chloride loading density; military contracting; defense contracting; Lockheed Martin; Blackwater USA; public perceptions
Notes
School of Engineering and Applied Science
Bachelor of Science in Materials Science and Engineering
Technical Advisor: Richard Martukanitz
STS Advisor: Peter Norton
Technical Team Members: Trevor Eggleston, Spencer Blankenship, John Emery
Barbieri, Matthew. Empirical Model Relating Chloride Loading Density and Conductance for Prediction of Galvanic Corrosion; Safety, Secrets, and Settlements: How U.S. Military Contractors Manage Public Image. University of Virginia, School of Engineering and Applied Science, BS (Bachelor of Science), 2023-05-12, https://doi.org/10.18130/k6hn-f545.
