1_c034196

Unlike the previous record, the magnitude of the modified energy error rate is quite high, with between 2.5 and 0.5 n miles distance remaining. This is the region where the energy profile of the current record shifts away from the reference profile (hence the positive value of the metric). The energy profile swings back toward the reference for a while and overshoots it. This is when the metric again becomes positive. The glideslope is very steep in the initial part of the approach where the energy rate is also very high (magnitude). In this region, the thrust margin is higher than average, indicating that the aircraft has a higher ability to add more energy into the system. For some portion of the approach, the aircraft is gaining energy or is energy neutral instead of losing it; this is where the inverse energy rate efficiency is negative and the glideslope almost positive or zero. The thrust margin also drops during this region. For most of the approach, the aircraft (even though flying a low-energy profile) has a sufficient thrust margin, indicating the ability to possibly recover the aircraft from this low-energy state (assuming that the engine has not failed).

Similar to the earlier flight record, a visualization of the raw flight parameters is seen in Fig. 10. As seen for the earlier case, a lot of unusual behavior is seen the raw flight parameters. The vertical speed, pitch, and roll all exhibit values going outside the nominal bounds of operations for this dataset.

C. Observations

Through the visualization of energy metrics for two cases, we can see how different energy metrics can be used to interpret the energy state of the aircraft from various viewpoints. Although a particular record might seem abnormal from the point of view of a metric or subset of metrics, it may be well behaved for other metrics. This is because different metrics deal with different aspects of the aircraft performance. Therefore, each energy metric can be viewed as a different dimension (not necessarily completely independent) of

aircraft safety. These dimensions of aircraft safety would also come in handy when trying to identify subsections within an abnormal flight record that are worthy of further inspection.

These two simple examples have shown how energy metrics can be useful in a FDM setting. Although the examples shown here pertain to the approach and landing phase, the same methodology can be applied to other phases of flight [37]. Energy metrics can be a powerful concept in retrospective flight data analysis because they not only capture the energetic state of the aircraft but can also be used as an indicator of broader aircraft limits (e.g., thrust margin, energy rate margin or maximum/minimum PFPA). The importance of specific energy metrics lies in the fact that these can be generalized more easily because they do not depend on the weight of the aircraft. This means that certain limits of the aircraft operating envelope that vary from one aircraft to another can be captured more broadly by limits on values of specific energy metrics.

VI. Conclusions

In this paper, a thorough survey of existing literature on energy management and energy metrics was conducted. A few different ways of classifying the existing literature were demonstrated for the readers to understand the existing body of work. Through this classification and further research, important challenges and opportunities for using energy metrics in general aviation aircraft operations were identified. Modifications to existing metrics and the definition of some new energy metrics were proposed. A methodology was demonstrated for unifying and implementing all the existing metrics and newly defined metrics in a systematic way. Visualization of useful energy metrics along with raw flight data parameters were shown. A comparison of abnormal approaches identified using energy metrics to safety-related events in traditional exceedance detection highlighted the potential of using energy

metrics for safety assessments. Two examples were presented that demonstrated the use of energy metrics in a retrospective flight analysis setting.

With the energy metrics defined and implemented, different ways of using energy metrics for automatically identifying potential anomalous flight data records will be examined [37]. Recommendations on which energy metrics prove to be more useful for identifying unsafe events will be provided. Another important direction of future work is to quantify/validate what constitutes an actual unsafe event from the perspective of energy metrics.

Acknowledgments

This work was sponsored by the Federal Aviation Administration (FAA) under project no. 5 of the FAA Center of Excellence for General Aviation and the Partnership to Enhance General Aviation Safety Accessibility and Sustainability via grant no. 12-C-GA-GIT- 004 to the Georgia Institute of Technology. The authors are grateful to FAA Project Technical Monitor Michael Vu and to project partners at Purdue University and the Ohio State University. The information in this research does not constitute FAA flight standards or FAA aircraft certification policy. The authors would also like to thank Evan Harrison and Sanggyu Min for the aircraft performance models, as well as Imon Chakraborty of the Aerospace Systems Design Laboratory for his valuable feedback and comments.

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