Using MFOQA to Quantify Fuel Degrade
The results presented in Chapter IV demonstrate the usefulness of MFOQA in terms of quantifying a fuel degrade figure for an individual C-5M. Harnessing MFOQA required quite a bit of data cleansing, but not to the point that implementation on a wide scale is prohibitive. There are a few reasons for this. First off, there is only a very small subset of the MFOQA database needed for fuel consumption analysis. The methodology employed in this study really only pulls four quantities from the larger dataset: 1) Fuel Flow (1 reading per engine), 2) Gross Weight, 3) Altitude, and 4) True Airspeed
(interchangeable with Mach speed). This relatively small subset of MFOQA was pulled for this study using contracted algorithms and VBA code. The process can easily be replicated.
The methodology summarized in Figure 8 is intended to provide a good estimate of the actual performance of the engine in terms of fuel consumption. It utilizes various techniques to reduce noisy autothrottle excursions while attempting to preserve the underlying trend in fuel flow responses. As outlined in Chapter II and Chapter III, there are many other mathematical and statistical techniques which could apply to quantifying MFOQA fuel flow. The technique used to determine the fuel degrade value should be validated. The precision and quantity of MFOQA data makes it a very reliable source for any number of techniques.
The case study outlined in Chapter III and demonstrated in Chapter IV provides a framework for determining a tail-specific fuel degrade quantity for an individual C-5M.
For the reasons discussed in Chapter I, a precise fuel degrade figure can lead to improved mission planning and better overall fuel efficiency for an individual C-5M. The
individual fuel degrade figure coupled with aircraft-specific fuel planning can lead to better overall fleet fuel efficiency.
Fleet Fuel Efficiency and Maintenance/Operations Implications
AMC is operating the C-5M under the assumption that the engines are operating at a 4% degrade value. The 4% value accounts for deterioration and other differences between the operating conditions and the flight test data provided in the original technical order. This value translates into a “pad” factor added to the ramp fuel calculated to perform a mission. Chapter IV results show the case study aircraft degrade at an average of 4.2% meaning the case study aircraft is likely being under-fueled. As discussed in Chapter I, razor thin fuel loading procedures could lead to costly diverts if a degrade factor is undercut. Conversely, Aircraft #2 samples are trending toward a number less than 4% meaning that this aircraft could be loading excess fuel and incurring unnecessary cost. Chapter I discusses how over-fueling the aircraft wastes fuel and incurs added cost.
Whatever the case, there is evidence that a blanket value of 4% for the entire C-5M fleet is most likely incorrect and wasting money and resources. Examining each C-5M individually would lead to refined values for each aircraft and a possible overall increase in fleet-wide fuel efficiency.
Although quantifying the fuel degrade number is useful for fuel planning, the drastic differences in the performance of the individual engines provide evidence that the engines themselves are consuming fuel at different rates. The overall goal of this study is
not anomaly detection per se, and analysis of MFOQA alone cannot account for the variation in fuel burn. This analysis simply presents the results as they are, namely, that a difference exists. In other words, this MFOQA fuel study cannot state “why” a certain engine exhibits anomalous fuel consumption, but simply that it is occurring. Many of the studies outlined in Chapter II harness FOQA for anomaly detection in a similar manner.
Within the context of AMC operations, identifying anomalous fuel consumption in an engine is still very useful even if there is no upfront solution to the problem. Simply identifying the problem is useful. Utilizing MFOQA provides maintenance personnel a method of identifying “bad actors” within the fleet independent of the normal
maintenance and inspection procedures. Of course this also highlights the associated challenges of implementing such analysis fleet-wide. For example, if an aircraft is sent off to Depot maintenance and the engines are fine-tuned (or even swapped) then the entire analysis must be re-run. Logically, the degrade number before and after a major maintenance action would tend to differ.
Finally, aircraft operations should include consideration of differing fuel degrade values. For instance, aircraft exhibiting consumption above 4% could be relegated to missions of short duration and/or training missions. This would mitigate the impact of the overconsumption. Aircraft performing at or below the 4% threshold should be judiciously employed on long-haul missions to affect a lesser overall fuel burn. Despite the inherent challenges with both the data and implementation of the analysis, there are substantial fleet-wide benefits to quantifying the tail-specific fuel degrade.
Future Work
One of the drawbacks of this study was the limited scope of the case study.
Harnessing data from a single aircraft alongside only five additional samples from a second aircraft severely limited the extent to which inferences could be drawn about the C-5M in general. Clearly, the next step is to gather more MFOQA data on several different aircraft. The methodology employed in this study could then be applied to determine how these aircraft are consuming fuel. The confidence interval approach shown in Chapter IV is still valid and the question of interest is whether or not the CIs on the test aircraft’s engines contain the 4% value. Departures from the 4% value (either above or below) would signal the need for a refined fuel “pad”. Aircraft operating below 4% should have their degrade factors lowered and ramp fuel loads reduced. Aircraft operating above 4% should have their degrade factors raised and ramp fuel loads
increased. Degrade factors above 4% could also alert maintenance personnel to possible malfunctions. After enough time that a large sample size is gathered, AMC may even reconsider the “baseline” 4% value itself. If most aircraft are trending one direction or the other (either above or below 4%), then there would be evidence that the population fuel degrade value is something other than 4%.
In terms of the methodology itself, the use of the LOESS smoother upon MFOQA fuel flow readings culled from stable cruise subsets is not the only procedure available.
The use of various regression algorithms such as those utilized by Srivastava (2012) could apply. Control functions such as Kalman or Weiner filters may also be useful in capturing the fuel flow readings while simultaneously reducing the autocorrelated noise.
the use of such techniques upon MFOQA. Simulation techniques such as the Batched Means method described by Law (2007) are effective at reducing autocorrelation and could be effectively employed on the MFOQA fuel flow data. Any or all of these
avenues could be explored in future studies aimed at quantifying tail specific fuel degrade values.
Recommendations for Action
Based on the outcomes of this study, it is recommended that AMC explore methods of utilizing MFOQA to obtain tail-specific fuel degrade values for the C-5M.
Much of the groundwork is outlined in this study and the Lockheed (2014) study already briefed to AMC. The data is available and the cost savings potential is real.
Summary
This thesis outlined the use of MFOQA data to improve C-5M fuel efficiency.
The first chapter explored the background of various issues surrounding the fueling and operation of aircraft fleets and outlined the cost savings potential of the project. The dataset itself is described and the applicable research outlined in Chapter II. Chapter III provides an in-depth discussion of the thesis methodology and the techniques used to translate MFOQA data into meaningful fuel degrade figures. The results of this study are outlined in Chapter IV and demonstrate the usefulness of MFOQA in quantifying the fuel consumption behavior of AMC’s C-5M aircraft. Although conducted as a case study, the results have fleet-wide implications for both fuel efficiency and maintenance operations.
The analysis could be easily expanded to a greater number of aircraft which would yield further insights on the fuel consumption behavior of the fleet.
Appendix A: Algorithms
Algorithm 1 Estimate Fuel Degrade using MFOQA
1: Obtain MFOQA readings at 1 sample/sec
2: Parse MFOQA dataset into stable cruise segments
3: while 27,000’ < Alt < 42,000’ and Alt±25’ do
4: Record stable cruise segment if > 3600 records (1 hr)
5: Trim first/last 5 min of cruise (300 records) to ensure level, stable flight
6: end while
7: Calculate baseline fuel burn
8: for all stable cruise segments do
9: Interpolate Specific Range values using MFOQA inputs
10: Convert each Specific Range value to an instantaneous Fuel Flow reading
11: Integrate Fuel Flow readings over stable cruise flight time to obtain total baseline burn
12: end for
13: Calculate actual fuel burn
14: for all stable cruise segments do
15: Smooth MFOQA-observed Fuel Flow to reduce noise {this study uses LOESS}
16: Integrate Fuel Flow readings over stable cruise flight time to obtain total actual burn
17: end for
18: Calculate difference and percent degrade
19: for all stable cruise segments do
20: Subtract baseline estimate from actual estimate to obtain
"overconsumption" figure
21: Divide overconsumption figure by baseline estimate to obtain
"percent degrade" figure
22: end for
23: Aggregate output from each engine and each cruise segment to estimate aircraft overall degrade
Algorithm 2 Compare CFP and MFOQA-observed Fuel Burn
1: Match stable cruise segments to CFP waypoints
2: for all stable cruise segments do
3: Match MFOQA Gross Weight to CFP Waypoint using ’FLRM’
4: Record first/last CFP Waypoint corresponding to MFOQA Gross Weight
5: end for
6: for all matching cruise segments do
7: Match Lat/Long coordinates for each Waypoint in the span
8: - CFP Waypoint coords in DD MM S format
9: - MFOQA output in DD.nnnn format
10: - Conversion: MM S/60 ≈ nnnn
11: Calculate baseline fuel burn {Algorithm 1, steps 9-11}
12: Calculate acutal fuel burn {Algorithm 1, steps 14-15}
13: end for
14: Compare outputs
15: - Model output for baseline and MFOQA-observed
16: - Difference in ’FLRM’ for CFP
Appendix B: Quad Chart
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C-5M Fuel Efficiency Through MFOQA Data Analysis
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Mariotti, Michael, P., Major, USAF
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14. ABSTRACT
This study investigates the usage of Military Flight Operations Quality Assurance (MFOQA) data as a means to obtain precise, aircraft-specific fuel loads. Currently, USAF C-5M aircraft include a 4%
“degrade” value in their fueling practices. MFOQA data are analyzed in an attempt to refine this value.
Case study data are analyzed from a single C-5M. A model is constructed using smoothing techniques which compare MFOQA actual observations to a baseline flight test model. The resulting figures are applicable to fuel planning and fuel efficiency concepts. Validation is presented through comparison with computerized flight planning software output. Results from the case study analysis are presented within the framework of fleet-wide implementation and maintenance practices.
15. SUBJECT TERMS
Fuel Efficiency, Fuel Consumption, FOQA Data Analysis, Time Series Data, Military Transport Aircraft
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