Weather Impact Assessment for Urban Aerial Trips in Metropolitan Areas

Chao, Hsun; Maheshwari, Apoorv; DeLaurentis, Dan; Crossley, William A.

doi:10.2514/6.2021-3176

“…6) Wind: As noted in [23], [24], the effective deployment of eVTOLs depends on the convergence of several factors towards dependable performance. One such factor is the wind.…”

Section: A Utm Simulatormentioning

confidence: 99%

“…The work focused on operational aspects and didn't regard either vehicle dynamics or different vehicle technologies. In [24], the authors implemented a greedy algorithm in ranking 32 weather conditions while studying weather impact for urban aerial trips in two metropolitan areas. They identified that the common severe weather condition in both areas is wind.…”

Section: A Utm Simulatormentioning

confidence: 99%

“…We assumed, given the huge amount of data, the wind distribution as normal for each month in each city and calculated the respective means and standard deviations. Our objective in this work is to show that one can go beyond representing weather conditions, in particular wind, using a single value representation as in [24], where a median value of wind observations was used for analysis.…”

Section: A Utm Simulatormentioning

confidence: 99%

See 1 more Smart Citation

A Data-Driven Approach for Performance Evaluation of Autonomous eVTOLs

Sarkar¹,

Yan²,

Gebru³

et al. 2022

Preprint

1

0

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<p>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</p>

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“…6) Wind: As noted in [23], [24], the effective deployment of eVTOLs depends on the convergence of several factors towards dependable performance. One such factor is the wind.…”

Section: A Utm Simulatormentioning

confidence: 99%

“…The work focused on operational aspects and didn't regard either vehicle dynamics or different vehicle technologies. In [24], the authors implemented a greedy algorithm in ranking 32 weather conditions while studying weather impact for urban aerial trips in two metropolitan areas. They identified that the common severe weather condition in both areas is wind.…”

Section: A Utm Simulatormentioning

confidence: 99%

A Data-Driven Approach for Performance Evaluation of Autonomous eVTOLs

Sarkar¹,

Yan²,

Gebru³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

<p>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</p>

show abstract