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Comparison of Surrogate Modeling Techniques for Life Cycle Models of Advanced Air Mobility

Pohya, Ahmad Ali and Wende, Gerko and Wicke, Kai and Corbetta, Matteo and Kulkarni, Chetan S. (2023) Comparison of Surrogate Modeling Techniques for Life Cycle Models of Advanced Air Mobility. In: AIAA Aviation Forum 2023. AIAA Aviation Forum 2023, 2023-06-12 - 2023-06-16, San Diego, USA. doi: 10.2514/6.2023-3857.

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Official URL: https://dx.doi.org/10.2514/6.2023-3857


Insights from life cycle simulations of Unmanned Air Vehicles (UAVs) can help in the introduction of the anticipated Advanced Air Mobility in a safe and economical manner. This includes (but is not limited to) effects between demand, utilization, fleet availability, and maintenance downtime. In a collaborative effort between NASA and DLR, we aim to evaluate different maintenance strategies for UAVs using uncertainty-driven and discrete event-based life cycle simulation. Computational efficiency is a prevalent issue with this type of research, particularly when expensive-to-evaluate submodels are added into already complex life cycle simulation frameworks. Surrogate modeling solutions reduce execution time but sacrifice output accuracy to do so. In this paper, we present the initial outcomes of the collaboration, which comprise the derivation and development of operational scenarios and a performance model. With the latter being the computational bottleneck, we have performed a comparative analysis of four commonly used surrogate modeling techniques, namely (a) Multilinear Interpolation (MLI), (b) Multilinear Regression, (c) Random Forest (RFo) supervised machine learning, and (d) Polynomial Chaos Expansions (PCE). Inputs for the models include the UAV's flown distance and direction, carried payload, wind magnitude and direction, turbulence level, and battery health. The model's output is the change in the battery's state of charge. The comparison focuses on accuracy, and decrease in computational expense. Calculated sensitivity measures revealed the flown distance, carried payload, and battery health to be the most influential parameters. All models show good overall accuracy values of 99\% and above but differ significantly in execution time. In addition, only the MLI model was able to capture the influences of head winds and tail winds correctly.

Item URL in elib:https://elib.dlr.de/195904/
Document Type:Conference or Workshop Item (Speech)
Additional Information:Kollaboration mit NASA Ames Research Center, Intelligent Systems Division, Diagnostics and Prognostics Group
Title:Comparison of Surrogate Modeling Techniques for Life Cycle Models of Advanced Air Mobility
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Pohya, Ahmad AliUNSPECIFIEDhttps://orcid.org/0000-0002-2734-3199UNSPECIFIED
Wende, GerkoUNSPECIFIEDhttps://orcid.org/0000-0003-2368-2369UNSPECIFIED
Wicke, KaiUNSPECIFIEDhttps://orcid.org/0000-0002-8220-6437UNSPECIFIED
Date:June 2023
Journal or Publication Title:AIAA Aviation Forum 2023
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Life-Cycle Cost Benefit Assessment, Advanced Air Mobility, System-Wide Safety, Uncertainty Quantification
Event Title:AIAA Aviation Forum 2023
Event Location:San Diego, USA
Event Type:international Conference
Event Start Date:12 June 2023
Event End Date:16 June 2023
Organizer:American Institute of Aeronautics and Astronautics
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Air Transportation and Impact
DLR - Research area:Aeronautics
DLR - Program:L AI - Air Transportation and Impact
DLR - Research theme (Project):L - Air Transport Operations and Impact Assessment
Location: Hamburg
Institutes and Institutions:Institute of Maintenance, Repair and Overhaul > Product Life Cycle Management
Deposited By: Pohya, M.Sc. Ahmad Ali
Deposited On:17 Jul 2023 08:13
Last Modified:24 Apr 2024 20:56

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