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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Design Optimization Study of Fault Tolerant and Redundant Motor Drivetrains for Urban Air Mobility Vehicles

Fully electric and hybrid electric aircraft will require extremely lightweight and reliable electric motor drivetrains to meet performance and safety goals. It is likely that to meet reliability targets some form of fault tolerance and/or redundancy will need to be used in the electric motor drivetrain. The use of either redundancy or fault tolerance will result in a reduction in drivetrain performance. In this paper, an example design study is carried out comparing redundant and fault tolerant drivetrains based on an example fault tolerant motor topology for a tilt rotor UAM application. Results show a minimal weight penalty for the incorporation of fault tolerance into drivetrains with the example motor explored here.

Thomas Francis Tallerico↗
NASA NTRSDate not supplied

Design Optimization Study of Fault Tolerant and Redundant Motor Drivetrains for Urban Air Mobility Vehicles

Fully electric and hybrid electric aircraft will require extremely lightweight and reliable electric motor drivetrains to meet performance and safety goals. It is likely that to meet reliability targets some form of fault tolerance and/or redundancy will need to be used in the electric motor drivetrain. The use of either redundancy or fault tolerance will result in a reduction in drivetrain performance. In this paper, an example design study is carried out comparing redundant and fault tolerant drivetrains based on an example fault tolerant motor topology for a tilt rotor UAM application. Results show a minimal weight penalty for the incorporation of fault tolerance into drivetrains with the example motor explored here.

Thomas Tallerico↗
NASA NTRSDate not supplied

Data Fusion for Urban Air Quality Assessment & Forecasting

This presentation provides an overview for our funded project with NASA's Health and Air Quality Applied Sciences Program. The project will expand an existing air quality data fusion tool implemented in Google Earth Engine (GEE) by our project team members at Sonoma Technology, Inc. (STI), a private air quality data company. We will expand the capabilities of this tool using new methods developed by the NASA GMAO which will give it the capability of providing sub-city scale resolution and hourly frequency estimates and forecasts of three key air quality indicators: surface-level particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3). We will combine a variety of Earth Observations including satellite data, global air quality forecasts, and local data from regulatory-grade monitors and/or low cost sensors. We will implement the new data fusion capabilities into the existing GEE tool in consultation with our end-users to best address their needs for sub-city scale air quality estimates and forecasts.

K Emma Knowland↗
NASA NTRS2023

ATM-X Urban Air Mobility: Assistive Detect and Avoid for UAM Operations Safety Evaluation Metrics

This document satisfies ATM-X UAM Task UID 989 “Evaluation Report on Tactical Separation Provision Services with Advisory Detect and Avoid Safety Metrics”. The first version of this document was delivered to the UAM team on October 3, 2022, for initial revision and comments.This current version incorporates all the comments received and is being delivered to the UAM subproject for a final revision before is submitted to satisfy the L1 milestone deliverable.

UAM↗