Aircraft Corrosion Maintenance and Sustainment Luna Innovations Incorporated  friedersdorf@lunainc.com Office 1.434.220.0148 www.lunainc.com Fritz Friedersdorf 5 October 2017

Issue of Corrosion for DoD Aviation Corrosion increases costs and reduces availability of DoD aviation systems $2.6 billion cost of corrosion for Navy and Marine Corps aviation (LMI 2011) 25% of maintenance costs / 25 days of corrosion-related non-availability per year $4.5 billion cost of corrosion for Air Force aircraft and missiles (LMI 2012) 24% of maintenance costs / 15.9 days of corrosion-related non-availability per year Corrosion management and sustainment for DoD aircraft is schedule based Wash cycles are set by corrosion severity zones (TO 1-1-691) Severe (30 days) / Moderate (90 days) / Mild (180 days) There is a desire within DoD to implement CBM for improved corrosion prevention and control

Aircraft Corrosion Health Monitoring Individual Aircraft Tracking Measure corrosion severity Detect upset conditions Predict time to next inspection Schedule preventive maintenance Establish long term trends Manage inspections and maintenance processes Perform load leveling among aircraft Examine fleet wide trends for life cycle sustainment Validate usage assumptions Optimize availability and life cycle costs Fleet Management

Aircraft Environment and Corrosivity Measurements Corrosion prediction requires inputs, models, and actionable outputs that can be used by maintainers Aircraft corrosion monitoring systems quantify: Environmental parameters (environmental severity) Corrosion rate of surrogate materials (corrosivity) Sensors for use in environment and corrosivity measurements have been standardized Electrochemical sensors are compliant with ANSI/NACE Standard TM0416-2016 Currently a new work item within ISO

Corrosion Prediction Historically, environmental severity and corrosivity are measured by average environmental conditions and/or mass loss coupons ISO 9223 - Corrosivity of atmospheres - Classification, determination and estimation Environmental parameters of significance Relative humidity Time of wetness Amounts of corrosive contaminants Temperature Location based average conditions have limited utility for individual aircraft tracking and fleet management Recent efforts have focused on continuous, dynamic measurements and models to predict corrosion rate

Aircraft Monitoring Systems Aircraft sensor nodes include environmental and corrosivity sensors RH and air temperature sensor Combined sensing elements with digital output Surface temperature sensor RTD embedded within node enclosure material Conductance (salt contaminants) Gold IDE Aluminum free corrosion rate Aluminum alloy interdigitated electrode (IDE)

Monitoring System Correlation to Mass Loss Corrosion rate sensor output is strongly correlated to mass loss coupon measurements AA7075-T6 mass loss coupons with / A286 SS fasteners 45-day GMW14872 accelerated test Both sensor and mass loss coupon response exhibit power law behavior

Predictive Modeling ANN Instantaneous corrosion rate is dependent on environmental conditions Non-linear / Interaction effects / Hysteresis Environmental parameters can be used to predict corrosion rate ANN

Corrosion monitoring system data within HH-60, Patrick AFB, FL Upset Conditions Deviation of aircraft environment from predicted conditions can be an indication of operational or maintenance issues Isolated instances where aircraft humidity is much higher than expected humidity based on local NOAA data Corrosion monitoring system data within HH-60, Patrick AFB, FL

Aircraft Operation High altitude flight cold soaks the structure, and upon landing in a high humidity climate, condensation causes high corrosion

Summary The is a significant need for improved aircraft corrosion maintenance and sustainment Standardized measurement methods for atmospheric corrosion monitoring are available Machine leaning techniques may be useful for corrosion prediction Individual aircraft monitoring can be used to quantify the effect of upset conditions and operations Successful CBM will depend on the integration of monitoring systems, predictive models, and network systems Output must be informative to maintainers