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UAM Demand Capacity Modeling through Ensemble Learning (UDC-ModEL)

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: 80NSSC21C0361
Agency Tracking Number: 213268
Amount: $125,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: A3
Solicitation Number: SBIR_21_P1
Solicitation Year: 2021
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-05-06
Award End Date (Contract End Date): 2021-11-19
Small Business Information
15400 Calhoun Drive, Suite 190
Rockville, MD 20855-2814
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Sricharan Ayyalasomayajula
 (301) 294-5246
Business Contact
 Robin Beahm
Phone: (301) 294-5220
Research Institution

UAM operations are estimated in the hundreds to thousands of flights every day in each of their metropolitan market regions. Further, most of these flights are expected to employ electric propulsion vertical takeoff/landing aircraft (eVTOL). While eVTOLs offer many advantages over conventional gasoline fueled aircraft, huge strides are needed in battery or energy storage technologies to enable long duration flights. The immediate implication for this is that airborne eVTOLs may not have large reserves of energy to implement congestion mitigation procedures such as hold patterns. Further, current technology requires many hours to recharge the batteries on these aircraft, which implies that the UAM operators require accurate predictions of available airspace capacity to schedule their operations and manage their fleetrsquo;s energy resources. Given this situation, there is a need for accurate estimation of available capacity and how the prevalent demand can be balanced to take full advantage of this capacity, also known as demand capacity balancing (DCB). From another perspective, accurate DCB estimation offers the opportunity to evaluate which technological and operational enhancements best serve the prevalent and anticipated demand. The concept of DCB has been implemented within the commercial aviation world at some of the busiest airports across the world. However, those DCB approaches do not readily translate for the UAM paradigm. To address these needs and gaps, IAI proposes UAM Demand Capacity Modeling through Ensemble Learning (UDC-ModEL) to accurately and rapidly model DCB at UAM vertiports. As the name suggests, our technology leverages the latest advances in machine learning and artificial intelligence to erect a rapid estimation capability that is agnostic to the UAM marketrsquo;s location or the eVTOL fleet mix used by a UAM operator. UDC-ModEL will be a valuable decision support tool for UAM operators and the proposed Providers of Services for UAM (PSUs).

* Information listed above is at the time of submission. *

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