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Energy management and energy state awareness are important concepts in aircraft safety analysis. Many loss-of-control accidents can be attributed to poor energy management. Energy-based metrics provide a measurable quantity of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions. In this work, we have surveyed key energy-based metrics from various domains and identified the challenges of implementing these metrics for General Aviation operations. Modifications to existing metrics and definition of some new energy metrics are proposed. A methodology is developed that can be used to evaluate and visualize the energy metrics. These energy metrics can then be used to understand and enhance General Aviation aircraft safety using retrospective flight data analysis.

General Aviation (GA) is likely to be at the forefront of a paradigm change in aviation, where the introduction of novel concepts such as Urban Air Mobility (UAM), architectures like −VTOL, and technologies like Distributed Electric Propulsion (DEP) are expected to make aircraft more efficient and reduce their environmental footprint. However, these architectures carry with them an uncertainty related to the off-nominal operational risk they pose. The limitations and off-nominal operational considerations generally postulated during traditional safety analysis may not be complete or correct for new technologies. While a lot of the literature surveyed focuses on improving traditional methods of safety analysis, it still does not completely address the limitations caused due to insufficient knowledge and experience with transformative technologies. The research objective of the present work is to integrate the Bayesian safety assessment framework developed previously by the authors with conceptual and 6-DoF performance models for DEP aircraft to evaluate off-nominal performance and reliability using information that is typically available in conceptual or preliminary design phases. A case study on the electric power architecture of the the NASA Maxwell X-57 Mod. IV is provided. A maximum potential flight path angle metric, as well as trimmability considerations using a 6-DoF model constructed using available literature help determine hazard severity of power degradation scenarios. Bayesian failure rate posteriors are constructed for the different components in the traction power system, which are used in a Bayesian decision framework. The results indicate that while most of the components in the traction power architecture of the X-57 Mod. IV are compliant with failure rate requirements generated, the batteries, cruise motors, and cruise motor-inverters do not meet those requirements.