Risk Assessment of Hydrogen-Powered Aircraft: An Integrated HAZOP and Fuzzy Dynamic Bayesian Network Framework

Abstract

To advance the hydrogen energy-driven low-altitude aviation sector, it is imperative to establish sophisticated risk assessment frameworks tailored for hydrogen-powered aircraft. Such methodologies will deliver fundamental guidelines for the preliminary design phase of onboard hydrogen systems by leveraging rigorous risk quantification and scenario-based analytical models to ensure operational safety and regulatory compliance. In this context, this study proposes a comprehensive hazard and operability analysis-fuzzy dynamic Bayesian network (HAZOP-FDBN) framework, which quantifies risk without relying on historical data. This framework systematically maps the risk factor relationships identified in HAZOP results into a dynamic Bayesian network (DBN) graphical structure, showcasing the risk propagation paths between subsystems. Expert knowledge is processed using a similarity aggregation method to generate fuzzy probabilities, which are then integrated into the FDBN model to construct a risk factor relationship network. A case study on low-altitude aircraft hydrogen storage systems demonstrates the framework's ability to (1) visualize time-dependent failure propagation mechanisms through bidirectional probabilistic reasoning, and (2) quantify likelihood distributions of system-level risks triggered by component failures. Results validate the predictive capability of the model in capturing emergent risk patterns arising from subsystem interactions under low-altitude operational constraints, thereby providing critical support for safety design optimization in the absence of historical failure data.

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