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A Data-Driven Approach for Performance Evaluation of Autonomous eVTOLs
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  • Mrinmoy Sarkar ,
  • Xuyang Yan ,
  • Biniam Gebru ,
  • Abdul-Rauf Nuhu ,
  • Kishor Datta Gupta ,
  • Kyriakos G. Vamvoudakis ,
  • Abdollah Homaifar
Mrinmoy Sarkar
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Xuyang Yan
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Biniam Gebru
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Abdul-Rauf Nuhu
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Kishor Datta Gupta
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Kyriakos G. Vamvoudakis
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Abdollah Homaifar
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In this paper, we develop a data-driven performance-based evaluation framework for a novel electric vertical takeoff and landing aircraft (eVTOL) in the context of Urban Air Mobility (UAM) applications. First, a two-stage comprehensive simulation framework is developed to generate a benchmark database for the performance evaluation of both UAS Traffic Management (UTM) algorithms and high-fidelity eVTOL dynamical models. In the developed simulation framework, we implement UTM algorithms and incorporate real-world constraints, e.g., vertiport infrastructures and different wind conditions. From the developed simulation framework, we generate 1,213,010 flight profiles. These flight profiles are used in a model based eVTOL performance evaluation tool as inputs to compute the physical performance of 3 types of eVTOLs. Due to the high computational cost of model-based eVTOL performance evaluation approaches, a clustering-based sampling procedure is employed to reduce the redundancy in the generated flight profiles and utilize the re-sampled flight profiles to form an eVTOL performance analysis dataset. We then train and compare several machine learning models on the eVTOL performance analysis dataset to predict: performance variables-flight conditions, aerodynamic coefficients, aircraft electronics, electric motor and propeller efficiencies. Finally, we deploy the proposed data-driven models in the framework and reduce the eVTOL performance inference time to real-time. The implementation of the proposed framework can be found on GitHub: https://github.com/mrinmoysarkar/eVTOL_performance_evaluation.git