Evaluation of an AI-aided Document Framework for Certification of Novel Aircraft Propulsion Systems

Abstract

In contrast to the development of conventional civil aircraft propulsion systems, novel propulsion technology or non-standard energy sources disclose a lack of flexibility in the current aviation certification framework. Additionally, aircraft certification in 2025relies heavily on manual effort, causing major difficulties in maintaining the traceability of data across vast sets of regulation documents. One promising solution to overcome such challenges offers the field of Artificial Intelligence (AI), particularly Large Language Models (LLMs). This paper introduces an AI-aided framework designed to streamline the certifiability of novel aircraft propulsion systems. To meet the AI trustworthiness demands set by the European Union Aviation Safety Agency (EASA), the framework proposes a concept for achieving data and model transparency in Machine Learning (ML) applications. To address the demand for data transparency, aviation regulatory context data will be unified and stored in a Unified Regulations Database (URD). This unified data is classified and enriched with related information for ML purposes. The URD enables the creation of modern, transparent AI features for civil aircraft certification. This AI-aided framework will enable certification measures for the development and allows for certifiability checks for novel aircraft technologies. Both, the aviation industry and regulatory authorities may equally benefit from the existence of the URD as starting point for certification AI features.