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Advanced Aerial Mobility (AAM) platforms are poised to begin high-density operations in urban areas nationwide. This new category of aviation platforms spans a broad range of sizes, from small package delivery drones to passenger-carrying vehicles. Unlike traditional aircraft, AAM vehicles operate within the urban boundary layer, where large structures, such as buildings, interrupt the flow. This study examines the response of a package delivery drone, a general aviation aircraft, and a passenger-carrying urban air mobility aircraft through an urban wind field generated using Large Eddy Simulations (LES). Since it is burdensome to simulate flight dynamics in real-time using the full-order solution, reduced-order wind models are created. Comparing trajectories for each aircraft platform using full-order or reduced-order solutions reveals little difference; reduced-order wind representations appear sufficient to replicate trajectories as long as the spatiotemporal wind field is represented. However, examining control usage statistics and time histories creates a stark difference between the wind fields, especially for the lower wing-loading package delivery drone where control saturation was encountered. The control saturation occurrences were inconsistent across the full-order and reduced-order winds, advising caution when using reduced-order models for lightly wing-loaded aircraft. The results presented demonstrate the effectiveness of using a simulation environment to evaluate reduced-order models by directly comparing their trajectories and control activity metrics with the full-order model. This evaluation provides designers valuable insights for making informed decisions for disturbance rejection systems. Additionally, the results indicate that using Reynolds-averaged Navier–Stokes (RANS) solutions to represent urban wind fields is inappropriate. It was observed that the mean wind field trajectories fall outside the 95% confidence intervals, a finding consistent with the authors’ previous research.
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A non-intrusive reduced order model using deep learning for realistic wind data generation for small unmanned aerial systems in urban spaces
Realistic wind data are essential in developing, testing, and ensuring the safety of unmanned aerial systems in operation. Alternatives to Dryden and von Kármán turbulence models are required, aimed explicitly at urban air spaces to generate turbulent wind data. We present a novel method to generate realistic wind data for the safe operation of small unmanned aerial vehicles in urban spaces. We propose a non-intrusive reduced order modeling approach to replicate realistic wind data and predict wind fields. The method uses a well-established large-eddy simulation model, the parallelized large eddy simulation model, to generate high-fidelity data. To create a reduced-order model, we utilize proper orthogonal decomposition to extract modes from the three-dimensional space and use specialized recurrent neural networks and long-term short memory for stepping in time. This paper combines the traditional approach of using computational fluid dynamic simulations to generate wind data with deep learning and reduced-order modeling techniques to devise a methodology for a non-intrusive data-based model for wind field prediction. A simplistic model of an isolated urban subspace with a single building setup in neutral atmospheric conditions is considered a test case for the demonstration of the method.
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- Award ID(s):
- 1925147
- PAR ID:
- 10597310
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- AIP Advances
- Volume:
- 12
- Issue:
- 8
- ISSN:
- 2158-3226
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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