Best Practices for Fuel System Contamination Detection and Remediation
[Technical Report, Final Report]
AIR FORCE PETROLEUM OFFICE FORT BELVOIR VA
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Military personnel are tasked with providing a clean, on-spec supply of jet fuel for a large variety of aircraft and ground powered vehicles. Occasionally, fuel filters become fouled and or fuel samples appear contaminated with particles. Fuel handlers must make several decisions based on the apparent severity of the condition. In the majority of cases the only remediation necessary is filtration and water separation. However, if the contamination is the result of a failing valve, pump, other component, or a microbial infestation, filtration may only prolong the point at which corrective action is required to return the fuel system to normal operation. Failure to take prompt corrective action may result in greater total costs due to possible fuel downgrade or complete shutdown of fuel delivery system. In order to provide the most useful analysis of contaminants, laboratory personnel may need guidance on appropriate analysis protocols based on sample source, for example bulk storage, filter separator, aircraft and refueler sumps, etc.. Additional guidance should be included suggesting probable root-causes based on contaminant identification and quantities or ratios. Frequently, a sample analysis is produced in great detail but will be of limited usefulness to field personnel without some reference to correlate contaminant analysis with typical contaminant sources. The study will gather DoD, industry and International Air Transport Association IATA type best practice information as it applies to specific DoD fuel system components. Collected material composition data for all fuel wetted components seen in normal operation will be compared with field histories of normal component failure modes and typical component wear patterns to develop a profile of likely contaminate composition. Where possible this model would be compared with data from recent case histories to further improve the predictive ability of the process.