A Comprehensive Characterization of Environmental Severity Across 25 Sites in Nine Climactic Zones. Part 2: Corrosion Sensors Free

Corrosion of military assets has been identified as a significant factor in material readiness and cost of operation. In 2017, corrosion costs across naval aviation assets were estimated at approximately 3.7 billion dollars, and the presence of corrosion was responsible for 25.3% of all hours that aircraft were unavailable.¹  The issue is severe enough that corrosion prevention and control is a legal requirement for all active U.S. military programs.²  The risks of corrosion can be addressed through many different tools and techniques, including the application of organic primers and topcoats, inorganic surface treatments, temporary operational chemicals, and routine maintenance inspections.³  Further evaluation of historical corrosion maintenance data demonstrated that risks of corrosion can stem from five primary modes of failure.⁴  Four of these (material availability, maintainer training, maintainer compliance, and equipment design) can be mitigated using various techniques, but the last mode of failure, environmentally-driven corrosion, is more difficult to address at scale.

Historically, characterizing the risk of corrosion at a specific location has been accomplished by deploying sacrificial witness coupons and assessing the amount of corrosion damage.^5-8  Use of this technique has led to updates in military standards regarding location-specific aircraft maintenance intervals.^3,9  However, this process is time- and material-intensive and, because of that, does not happen frequently. This means that changes in local climate are often over-simplified and erroneously estimated. The use of sensors, especially for sites that have already deployed witness coupons, can be a valuable tool in reducing the overall workload to the researcher, while also providing temporally dense information. In addition, the time-dependent data is anticipated to be critical in developing more effective environmental severity models. An evaluation of current modeling products using experimental data collected from recent exposures has demonstrated that current modeling toolkits do not accurately predict corrosion.^10-11  Additionally, a review of recent projects and publications has demonstrated the desire to improve the current capabilities for corrosion modeling.^12-14 

Early on, aircraft corrosion sensing technology was described in the late 1980s and featured certain aspects of technology that are still in use today.¹⁵  Iterative sensor development since then has produced sensors capable of providing in situ measurement capabilities by assessing galvanic corrosion or contamination from conductive species.^6,16-17  These improvements, along with miniaturization of sensing elements, have begun to yield new corrosion technologies capable of being deployed in the field as stand-alone systems to monitor environmental conditions over a year-long period.⁴ 

In recent history, the application of corrosion sensors has helped redefine long-held beliefs in several fields of corrosion. Sensors were used to highlight the concern that corrosion in natural environments occurs frequently at relative humidity values less than the 80% criteria outlined in ISO 9223.^18-19  Additionally, sensors have been used to study environmental conditions in conjunction with multiple witness coupon materials.^4,15,20  This approach has the potential to reduce the need for more complex measurement techniques like chloride wet candle, a time-intensive process susceptible to data collection issues.²¹  The on-going maturation of corrosion sensor technologies has resulted in the publication of two industry standards to ensure comparability between different measurement techniques.^22-23 

As stated in Part 1 of this series,¹¹  an on-going desire to optimize aviation maintenance practices against local environmental risk resulted in a new study to characterize the local environmental severity at military air stations across the United States and abroad. The findings discussed here will focus on determining how corrosion sensors can describe environmental severity, and assess if the corrosion sensors can provide results that can be related to traditional exposure methods such as witness coupons assessed via mass loss. Additionally, the relationship between corrosion sensor responses and basic weather-driven features will be assessed to determine how certain environmental factors impact sensor performance.

A LunaLabs Acuity LS^† corrosion sensor was selected for deployment where the sensor is comprised of five discrete monitoring units.²⁴  Along with temperature and relative humidity, a gold interdigitated electrode (IDE) collects impedance measurements at 10 Hz and 25 kHz to track the conductivity between the two gold wires. As this sensor is not treated with paint, primer, or water-displacing chemicals only the fast frequency 25 kHz data set will be used. This measurement set has previously been used as a tool for monitoring the amount of contamination from conductive species.^4,8,11  The free corrosion sensor consists of thin AA-7075 (UNS A97075⁽¹⁾) plates sandwiched together. Like the gold IDE, an impedance measurement tracks conductance between two aluminum plates and in this instance, the presence of conductance indicates that free corrosion is occurring. The galvanic sensor utilized a common aerospace alloy combination of CRES A286 (UNS S66286) and AA-7075. For this sensor, thin plates of alternating alloys were sandwiched together, and a zero-resistance ammeter (ZRA) measurement tracked the amount of current passed between the two alloys while corrosion was occurring. The sampling interval was set to collect a data point every 30 min for every site.

The data obtained from witness coupons are discussed in detail in part one of this series.¹¹  In summary, triplicate witness coupons of AA7075-T6, AA2024-T3 (UNS A92024), 1010 carbon steel (UNS G10100), and Aermet 100 (UNS K92580) were deployed for 6- and 12-month intervals to determine the alloy-specific corrosion damage. In addition, triplicate 99.999% silver (UNS P07020) coupons were used to semiquantitatively evaluate atmospheric levels of aerosolized chloride and sulfide chemistries at the same 6- and 12-month intervals. This was accomplished via a silver reduction process described in Part 1 of this with units of coulombs of charge measured during the reduction process.

All witness coupons and sensors were mounted on acrylic panels using 5.08 cm length nylon bolts and nuts. Three witness coupons were mounted at a standoff height of 2.54 cm relative to the acrylic panel. The corrosion sensors were secured flush to the acrylic panel resulting in the height of the sensing elements being equal to the height of the witness coupons relative to the acrylic base. Each panel was designed to be hung on a chain link fence with zip ties, as illustrated in Figure 1. 5.08 cm (inner-diameter) schedule-40 PVC pipe was installed at the bottom of each panel to produce a 15° offset from vertical for all sensors and coupons.

This study involved deployments across 25 sites. The sites were a mix of United States Naval Air Stations (NAS), United States Marine Corps Air Stations (MCAS), United States Joint Reserve Bases (JRB), and one United Kingdom Royal Air Force (RAF) base. Figure 2 depicts the locations of deployment in the U.S. (not including Japan, United Kingdom, or Italy-based sites).

Local representatives at the sites depicted in Figure 2 were shipped a set of panels. About 2/3 of the sites were able to install within a 5 d window. Shipping issues resulted in the remaining sites being installed up to a month after the target date. It should be noted that the installation photos for sites with shipping delays showed no signs of flash rusting or deleterious features, and the included corrosion sensors recorded no corrosive events while in shipment. After installing the panels, local representatives took photos to document the installation and provided the research team with a GPS pin to mark its location.

When recovering corrosion sensors from each site, the local representative would bring panels indoors for 24 h to dry before shipping. To ease requirements for sample collection, sites were allowed a window of 7 d before or after the target date to pull the panels inside. The corrosion sensors were left exposed for approximately 1 y before being shipped back for follow-on analysis. Once the sensors were received by the authors, the data set was immediately downloaded. The time stamp was adjusted from coordinated universal time (UTC) to the time zone where the sensor was deployed to facilitate comparisons against open-source weather data.

Historical weather data for U.S.-based sites were collected from the National Centers for Environmental Information, an organization that is part of the United States National Oceanographic and Atmospheric Administration (NOAA).²⁵  Within the web-based tool, the dates of exposure and the zip code associated with the sensor’s deployed location were used to locate the closest weather station. Hourly data reports including temperature, relative humidity, wind direction and speed, and precipitation were collected for each site. At all but one of the sites, the NOAA station was less than 4.5 km away from the sensor’s location. The exception was at Kennedy Space Center, where the closest public weather station was 23.3 km miles away.

During the deployment, there were two sites that experienced a compromised data set. For NAS Mayport, the conductance sensor was observed to be nonresponsive after approximately four months and the galvanic sensor from NAS Oceana was corrupted early in the deployment. For both sites, the remaining sensing units continued to function for the full year as designed so the small data loss was manageable. Aside from that, there were no observed data collection failures across all of the sensors.

As discussed in the Experimental Procedures section, the sensor recorded the temperature and relative humidity every 30 min while deployed. Figures 3 and 4 demonstrate the distribution of temperature and relative humidity for each site over the entire exposure period. The sites with the highest mass loss values for aluminum and steel are listed on the left and progress to the right until the sites with the lowest mass loss are on the right end. The relative humidity distribution does provide early indications of correlations against witness coupon damage, especially for milder sites where the relative humidity distributions had median values (indicated by white circles) below 60% RH. A red line was drawn in Figure 4 to indicate the deliquescence point of sodium chloride to help highlight a critical value when evaluating the data. A Spearman rank correlation coefficient analysis was executed to understand the linkage between %RH and steel and aluminum mass loss values. The RH data sets for each site were evaluated against five criteria which included the % of total data points >80% RH, % of total data points between 80% RH and 55% RH, % of data points below 55% RH, the mean humidity value over the entire year, and the most common RH value (mode). The analysis demonstrated that the percentage of data points below 55% RH had the strongest correlation against the four mass loss values with R² values between −0.70 and −0.78. In contrast, the coefficients for values > 80% RH were between 0.41 and 0.46, indicating a weaker correlation and calling into question the feasibility of using 80% RH as an industry benchmark. While the correlation between the amount of time the humidity is below 55% RH and mass loss is technically considered a strong correlation, an evaluation of the rank orders had some strong disagreements which would hinder the ability to accurately assess site severity by using humidity. For example, New Orleans would have been ranked the fourth most severe site using the <55% RH criteria but a rank order using mass loss would list New Orleans as the 15th most severe site. Because of these findings, relative humidity as a single data point for site severity characterization is not sufficient to accurately assess local site severity.

When evaluating conductance sensor data associated with the gold IDE, a clear distinction must be addressed. This sensor does not directly indicate when corrosion is occurring. Rather, it is used to understand when environmental conditions have reached a critical threshold where the risk is high for corrosion to occur. The data collection process creates two data points for every measurement. First is the amount of conductance (in μS) at the moment of sensor interrogation and the second is the integrated value of all the conductance data points collected up to the most recent data point. For assessing site severity, the integrated value, referred to as cumulative conductance (in units of C/V) was recorded at the dates when the 6- and 12-month witness coupon panels were removed. These are displayed in Figure 5. An additional insert with the data replotted on a log scale was presented to provide better clarity into sites with lower values. Unlike the temperature and relative humidity data sets, which had a weak relationship to site severity, the trend in environmental severity was evident through the cumulative conductance measurements. The data at Mayport was corrupted for unknown reasons and was not available for evaluation. As an added benefit, an analysis of the individual conductance readings at each site, such as the ones shown for NRL Key West (Figure 6), are recognized to play an important role in understanding the relationship between weather conditions and sensor response but the scope of time-dependent data analysis is too broad to include in this publication.

In the same way as the conductance sensor, the galvanic and free corrosion sensors were evaluated based on the cumulative charge observed after 6 and 12 month. For clarity, the galvanic corrosion sensor functions by conducting a ZRA measurement between the aluminum and steel plates. Individual data points are described in units of μA while the integrated values are described in terms of coulombs. The free corrosion sensor functions via an impedance measurement like the conductance sensor but in this case the units of measurement are μA for individual data points while the integrated values are described in terms of coulombs. For the galvanic sensor data in Figure 7, the sensor at Oceana was nonfunctional but no failures were observed in the free corrosion data in Figure 8.

The discussion section will focus on two areas of analysis. First, correlations between individual sensor elements and the previously discussed witness coupon data will be evaluated. Second, data from local weather stations will be compared to the sensor data to better understand the relationship between certified weather station data and sensor records.

One of the key factors in assessing sensor data for future applications is understanding how the data sets correlated against historical severity assessment techniques. In this case, the witness coupon mass loss and silver contamination data were presented in the early publication.¹¹  The first analysis in this publication executed a Pearson’s correlation assessment to assess the relationships between the previously published mass loss and silver data combined with the sensor data described in this publication. As seen in Table 1, most correlations between witness coupons and sensor elements would be considered strongly correlated, with values greater than 0.70. The only exception to this statement is the correlations between the steel alloys and the AA7075 free corrosion sensor, but that trend was also observed when assessing the correlation between aluminum and steel witness coupons. At the start of this study, it was anticipated that the conductance data (a measure of contaminant loading on the sensor) would correlate strongly to the silver chloride data, however, this was not the case. The mechanisms of contamination accumulation are different, where silver oxidation requires a chemical reaction in the presence of the correct ion while the conductance sensor assesses the total deposition of multiple conductive contaminants.²⁶  As an alternative assessment, it is interesting to note the moderate relationship between the galvanic sensor and the silver chloride data. Lastly, it was confirmed that there was a strong correlation between the three discrete sensing elements on the sensor.

Additionally, the relationship between the galvanic and free corrosion sensors compared against the conductance sensor was evaluated. In past research, the authors have treated the conductance sensor as a tool to assess when the environment is conducive for corrosion to occur. It was therefore expected that most of the galvanic and free corrosion sensor data points would have an accompanying nonzero conductance data point. However, this assumption proved to be false as 31% of the galvanic sensor and 20% of the free corrosion sensor data points occurred when the conductance sensor did not record an active data point. The exact reason for this disparity is still under investigation. A potential theory for this could be that as the galvanic and free corrosion sensors corrode, the surface roughens, allowing for better water retention on the surface. The gold conductance sensor would be immune to this effect and would dry out faster. This disparity would lead the corrosion sensors to record additional data points after a contamination event, resulting in the findings above. However, these trailing responses were small in magnitude and did not significantly affect the cumulative data observed after 12 month.

The overall goal of this project was to determine the location-specific risk of environmentally driven corrosion. Instead of using the Pearson correlation to study how different data sets were related, a Spearman rank correlation was executed to determine if corrosion sensor inputs delivered the same corrosion severity rank order as the co-located witness coupons. The silver data sets were removed from this assessment due to the weak correlations observed in Table 1 and the acknowledged semiquantitative nature of silver reduction data. As seen in Table 2, the use of the galvanic sensor to develop a rank order of site severity correlated the most strongly to any single witness coupon alloy. The conductance sensor was found to have near-equivalent correlation while the free corrosion sensor had the weakest correlation of the three. These are promising results as they speak to the future applicability of adopting monitoring techniques that reduce or eliminate the need for accompanying witness coupons, significantly simplifying the overall assessment process, especially when re-evaluating sites with historical data. However, the authors determined that when assessing a site for the first time, it is more effective to combine the findings from both witness coupons and sensor data to develop a more comprehensive assessment of corrosion risk.

While sensors provide an important tool in assessing corrosion risk, use of corrosion sensors, even as a standalone technology, would still require time to complete a deployment, significantly slowing the rate of analysis. There is a general recognition within the corrosion community that modeling corrosion severity using historical weather data would be ideal forproviding the fastest assessment of localized severity.^27-30  In Part I, the authors highlighted that current corrosion models require refinement to more accurately predict corrosion. The application of temporally dense data sets from the corrosion sensor means that researchers can now match discrete weather events to sensor responses, significantly improving the ability to predict the aggregate risk of corrosion severity at a specific location.

That said, there are significant differences in how weather station data is collected relative to corrosion sensors. The dry bulb temperature recorded by NOAA weather stations employs a design to eliminate factors such as solar irradiance or cooling winds from influencing the temperature measurement.³¹  In contrast, the temperature sensor on the corrosion sensor is affected by those conditions when boldly exposed outdoors. To assess this, the difference between hourly dry bulb temperature data and the corrosion sensor was assessed throughout the entire exposure period for each site. The hour-by-hour site-specific differences were averaged into a single hourly value to determine if there was a significant difference between temperature readings. Figure 9 demonstrates that when sunlight is present, the sensor’s recorded temperature will be higher than the associated NOAA weather data and reaches a peak difference of approximately 10°C. As an accompanying analysis, these same environmental factors were expected to produce different results when comparing the RH data. This was confirmed in Figure 10 but it was noted that the relationship was inverted relative to temperature. As sunlight warmed the sensor unit, the %RH value would decrease leading to lower values relative to the closest weather station. This is not necessarily a negative finding for corrosion sensors as the sensor may more accurately portray the conditions observed by structures or equipment stored outside. For both Figures 9 and 10, individual NOAA/sensor relationships were plotted to demonstrate how individual sites can vary from the overall curve but still maintain the previously described trends.

Additionally, as models look to incorporate data from more advanced corrosion assessment tools, it is important to understand what conditions induce an event that is detectible by the different corrosion sensors. The role that relative humidity plays in inducing corrosion has been well studied and is used as a key indicator of severity in several corrosion assessment tools.^19,32-33  As such, an analysis of the distribution of corrosion sensor data points with respect to RH was completed, where RH values were divided into 10% RH bins for both the sensor and NOAA data. The site-specific conductance data distribution in each humidity range was converted to a percent of the site’s total number of conductance data points. In Figure 11, there are a few observations to be made. First, both NOAA and sensor RH comparisons show that the most common humidity range to have detectable conductance is between 90% and 100%. However, the differences between NOAA and sensor relationships become evident when looking at the distribution of data. The corrosion sensor data had a higher number of conductance readings occur when the humidity was recorded at 100% (saturation) and the number of readings in lower humidity ranges (RH < 80%) decreased rapidly. On the other hand, the conductance responses related to the NOAA humidity data have more active sensing events in lower humidity bins. This agrees more closely with previous research which stated that corrosion is still likely to occur when the humidity is below 80%.¹⁸  While not shown here, an analysis of the relationships between the remaining sensing elements (galvanic and free corrosion) and humidity data revealed the same trends.

Evaluation of the time-dependent temperature and RH data series for all of the sites reveals a pronounced cyclic pattern consistent with a diurnal cycle. This pattern, where RH values are the highest in the early morning hours, was expected to induce a higher number of corrosion sensor events. An analysis of these relationships, represented in Figure 12, was seen for all three of the sensing elements. This relationship will be important to understand for future research efforts as it likely indicates that contamination episodes thatoccur during the day, when sunlight is present to minimize wetting of the surface, may not register as a corrosive event until the early morning of the next day, once the RH is above the deliquescence point or condensing dew has formed on the sensing elements.

Lastly, understanding how major weather events can drive corrosion was evaluated. Several of the sites studied in this effort were evaluated during an early project that also used the same sensor configuration.⁴  For this specific topic, Joint Reserve Base New Orleans was selected as the monitoring station as it was in the path of Hurricane Ida in 2021 but had no significant weather events during the 2023 deployment. An evaluation of the time-dependent data in Figure 13 demonstrates a marked increase in the galvanic corrosion sensor data directly related to the impact of the hurricane. A comparison of the 12-month cumulative corrosion sensor data sets revealed that the hurricane caused the conductance data to be 33% higher and the galvanic sensor to be 20% higher for the exposure period impacted by the storm. However, the impact of the storm was not observed on the concurrently deployed witness coupons, where the 12-month mass loss values for steel coupons changed by 3% for 1010 and 4% for Aermet 100 between the two exposure periods. The aluminum coupons could not be compared for this analysis due to a change in coupon geometry between studies. This behavior of the corrosion sensors is especially impactful when considering the condition of assets such as aircraft or ground vehicles which can move between locations easily and are more at risk of individual events driving material condition.

• Corrosion sensors were successfully deployed at 25 sites for a 1 y period. The sensor data sets were found to be well-correlated with co-deployed witness coupons and described how the local environment fluctuated in severity over a year.

• Comparison with standard weather data indicated that sensors are influenced by environmental factors that do not influence weather station data. This can result in very different temperature and relative humidity values for the same point in time, depending on the position of the sun.

• The data collected is expected to provide very important information to improve current corrosion modeling toolkits due to the high sampling rate of the sensors allowing for more accurate comparisons against individual weather events.

The authors would like to recognize the NAVAIR Corrosion Management Board for their funding support (Project No. FY22-003). Additionally, the authors would like to thank the Naval Research Lab for their support of shipping logistics, material analysis, and advice during deployment planning stages. Lastly, the project would not have succeeded without the assistance of each of the local site representatives who volunteered for this effort. NAVAIR Public Release 2024-0867. Distribution Statement A—“Approved for public release; distribution is unlimited”.