Alexander Markov
(Advisor: Prof. Dimitri Mavris)

will defend a doctoral thesis entitled,

A Framework for Integrating Advanced Air Mobility Vehicle Development, Safety and Certification

On

Monday, April 11 at 2:00 p.m.
Collaborative Visualization Environment (CoVE)

Weber Space Science and Technology Building (SST II)

And

https://bluejeans.com/131124466/8631

Abstract
As urbanization continues to grow worldwide, cities are experiencing challenges dealing with the increases in pollution, congestion, and availability of public transportation. A new market in aviation, Advanced Air Mobility, has emerged to address these challenges by engineering novel aircraft that are all electric and meant to transport people within and between cities quickly and efficiently. The scale of this market and the associated operations means that vehicles will need to fly with increased autonomy. The lack of highly trained and skilled pilots, along with the increased workload for novel aircraft makes piloted aircraft infeasible at the scale intended. While a variety of concepts have been created to meet the performance needs of such operations, the safety and certification requirements of these aircraft remain unclear. The paradigm shift from conventional aircraft to novel, highly integrated, and autonomous aircraft presents many challenges which motivate this work. An emphasis is placed on the safety assessment and the gaps between current regulations and the needs for Advanced Air Mobility.

First, an improved hazard analysis approach is developed to capture functional failures as well as systematic areas that can lead to unsafe system behavior. The Systems-Theoretic Process Analysis is supplemented to the Continuous Functional Hazard Assessment so that system behavior and component interactions can be captured. Unsafe system and component actions are identified and used to develop loss scenarios which provide context to the specific conditions that lead to loss of critical vehicle functionality. The Functional Hazard Assessment is then applied to applicable scenarios to provide severity and risk information so that quantitative metrics can be used in additional to qualitative ones.

Next, a Dynamic Bayesian Network modeling method is developed to improve the reliability modeling of complex modular avionics systems utilizing Multi-Core Processing. This method first utilizes the existing methods defined in ARP 4761 for reliability analysis, namely the Fault Tree Analysis. A mapping is identified for converting fault trees to Bayesian networks, before a Dynamic Bayesian Network is developed by defining how component reliability changes with time. The capability to model reliability of these kinds of systems overtime alone is useful for developing and evaluating maintenance schedules. Additionally, it can handle degradable and repairable components and has the capability to infer failure probabilities using observed evidence. A secondary capability is the modeling of uncertainty and the reliability impacts of Multi-Core Processing factors.

Finally, the safe inclusion of autonomy is addressed. To do so, a Simplex architecture is chosen for the development and testing of complex controllers. These controllers are non-deterministic in nature and would otherwise not be certifiable. The Simplex architecture uses an assured back up controller that is triggered when a monitor senses that some predefined safety threshold is breached and gives control back once the system is back to nominal operations. This architecture enables the use of complex control and functionality while also enabling the overall system to be certified. A model predictive control algorithm is developed using a recursive neural network and a receding horizon control scheme that allows a simple system to be controlled quickly and accurately. A PID controller is used as the assured back up controller

These modifications enable a development assurance and safety management framework that is applicable to Advanced Air Mobility aircraft. The modifications made specifically target the challenges presented by novel, integrated, complex, and autonomous aircraft and provide the groundwork for the eventual certification of these aircraft.

Committee

• Prof. Dimitri Mavris – School of Aerospace Engineering (advisor)
• Prof. Daniel Schrage – School of Aerospace Engineering
• Prof. Graeme Kennedy – School of Aerospace Engineering
• Dr. Gokcin Cinar – University of Michigan
• Dr. Mayank Bendarkar – School of Aerospace Engineering