Enhancing Advanced Air Mobility through Digital Engineering and Predictive Analytics for Effective Maintenance Strategies

Abstract:
The operation of Urban Air Mobility Vehicles (UAMVs) presents significant technical and operational challenges, particularly in the areas of safety, training, and cost management. This paper explores how advanced simulation models and predictive algorithms can address these challenges. A digital transformation framework is developed and applied in an Urban Air Mobility (UAM) case study to illustrate the effectiveness of these tools. Through the development of simulation models, critical insights are provided on damage detection, impact analysis, and maintenance optimization. The application of predictive algorithms enables quick damage assessment, improving safety by facilitating timely maintenance and repair decisions. To help showcase the benefits of this research, a demonstration was designed and built that allows users to interact with the developed tools and get a better understanding through hands-on training.

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