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ItemA Framework for Integrating Advanced Air Mobility Vehicle Development, Safety and Certification(Georgia Institute of Technology, 2022-04-28) Markov, AlexanderAs urbanization continues to grow world wide, 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 work load for novel aircraft makes piloted aircraft infeasible at the scale intended or Advanced Air Mobility. 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. The research objective of this work is to develop a framework for the development and safety assessment of autonomous Advanced Air Mobility aircraft by first examining the existing methods, techniques, and regulations. In doing so, several gaps are identified pertaining to the hazard analysis, reliability analysis of Integrated Modular Avionics systems, and the inclusion of a Run-Time Assurance architecture for vehicle control. 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. This information is traced back to identified hazards and used to establish constraints to mitigate unsafe behavior. 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. The improved approach develops requirements and determines component and system constraints so that requirements can be refined. It also develops a control structure of the system and assigns traceable items at each step to track how unsafe actions, losses, hazards, and constraints are linked. To improve the reliability modeling of complex modular avionics systems utilizing Multi-Core Processing, a Dynamic Bayesian Network modeling method is developed. 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. This is useful for identifying weak areas of the system that may be the most likely to cause an overall system failure. A secondary capability is the modeling of uncertainty and the reliability impacts of Multi-Core Processing factors. Subject Matter Expert input and test data can be used to develop conditional dependencies between factors like Worst-Case Execution time, complexity, and partitioning of multi-core systems and their impact on the reliability of the Real-Time Operating System. The added safety challenges of interference and system complexity can be modeled earlier in the design process and can quickly be updated as more information becomes available. 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 non0deterministic in nature and would otherwise not be certifiable as a result. 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 and the monitoring and triggering capability is demonstrated. The architecture successfully triggers the back up when a threshold is exceeded and hands control back over to the complex controller when the system is brought back to nominal conditions. The main contribution of this dissertation is the development of a modified development assurance and safety management framework that is applicable to Advanced Air Mobility aircraft. The modifications made are specifically targeted at the challenges of applying the existing framework to novel, integrated, complex, and autonomous aircraft. This supports the objective of this research and provides guidance for how existing well understood and trusted methods can be modified for novel applications.