• NACE equips society to protect people, assets and
    the environment from the adverse effects of corrosion.
Atmospheric Corrosion Measurements to Improve Understanding of Galvanic Corrosion of Aircraft
Presenting Author:
Full Name: Fritz Friedersdorf
Parent Company:
Location: Phoenix Convention Center
100 North 3rd St.
Presentation Time: 4/16/2018 @ 10:00 AM
Secondary Authors:
  • Name:Robert Adey
  • Name:Thomas Curtin
  • Name:Matthew Merrill
  • Name:Mark Kim
  • Name:Patrick Kramer
Atmospheric Corrosion Measurements to Improve Understanding of Galvanic Corrosion of Aircraft

Atmospheric corrosion represents an annual multi-billion dollar cost burden for the aerospace and defense sectors.  For many aircraft, particularly those operating in marine environments, up to ninety percent of corrosion is due to galvanic interactions at dissimilar metal couples.  As new materials are introduced with the acquisition of more advanced aircraft, galvanic corrosion is likely to remain a concern.  The ability to model galvanic corrosion accurately holds the promise of being able to both predict the performance of new material combinations to guide material selection and predict corrosion damage for maintenance planning.  Such models often utilize data collected under immersion test conditions that are not representative of the thin-film electrolytes that are relevant to atmospheric corrosion and may diminish model accuracy and utility.  In this work, an atmospheric cell is presented that allows for measurements of corrosion kinetics using thin-film electrolytes.  It is observed that the limiting oxygen reduction current density on various alloys is increased several orders of magnitude over immersion results.  A segmented, galvanic sensor is presented that enables the experimental quantification of spatial distributions of galvanic current under thin film conditions that is compared to model predictions for verification of the suitability of immersion and thin-film electrolyte polarization data inputs.  The study also provides data to support the use of predictive modeling of corrosion under thin film conditions.

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