Engineering topics

Keiko Nagami

Publications and source records attributed to Keiko Nagami.

3 records

NASA NTRSDate not supplied

Multirotor Configuration Trades Informed by Handling Qualities for Urban Air Mobility Application

Many contemporary Advanced Air Mobility (AAM), and more specifically, urban air mobility (UAM) vehicle designers are attracted to variable rotor speed-controlled designs with multiple rotors because of the great potential for mass savings compared to more traditional, variable blade pitch-controlled vehicles. These designs are based on the assumption that the stability and control of recreation or basic utility-sized drones can be scaled to larger passenger-sized vehicles. Previous work had shown the challenges in stabilizing passenger-sized quadcopters. In this study, power constraints were made less restrictive and varied, allowing more control power. Motor parameters such as efficiency, nominal voltage and current operating point, and rise time of the rotor speed controller step response were studied. By fixing the efficiency of the motor to 95% and assuming a motor voltage to current ratio of 2.0 (previously, assumed to be 1.0), the authors were able to stabilize the quadcopter in the roll axis because this allowed the vehicle to achieve adequate rise times between 0.4 and 0.8 s. This motor optimization was extended to a hexacopter and octocopter designed to the same payload size and mission as the quadcopter. The three vehicle configurations and their motor speed controllers were compared. It was found that while hexacopter and octocopter required more mass and overall power; all three configurations had similar margins required for control. However, the hexacopter and octocopter were able to use this power margin to achieve lower rise times (i.e. the vehicle responded more quickly to pilot inputs) than the quadcopter, with the octocopter having the lowest rotor response rise time of the three vehicle configurations studied.

Multirotor↗

NASA NTRSDate not supplied

Impact of Handling Qualities on Motor Sizing for Multirotor Aircraft with Urban Air Mobility Missions

Control models of three NASA Urban Air Mobility (UAM) reference vehicles (the quadrotor, octocopter, and Lift+Cruise (LPC)) were created and compared to determine the effect of rotor number and disk loading on control margin and design. The heave and yaw axes demand more actuator usage than the roll and pitch axes. Between heave and yaw, heave was the more demanding of the two because of the dependence of heave on the engine speed controller (ESC). When the feedback gains for all three vehicles were optimized to Level 1 handling qualities (HQs) specifications using CONDUIT, the ESC for the octocopter was the most stable and had the highest rise time (time for the rotor to respond to an input), while the LPC ESC was the least stable and had the smallest rise time. Rise time corresponds to the time required for rotor response. When actuator usage was translated to current margin, torque margin, and power margin, heave was the most demanding axis, followed by yaw, roll, and then pitch for all three vehicles. The results emphasize the importance of an accurate motor model within the control system architecture.

Impact↗

NASA NTRSDate not supplied

Pilot Workload Rating Predictions Using Image Data and Recurrent Neural Networks

In this work, we augmented existing methods for estimating pilot workload ratings with deep neural networks trained using data from simulated flight tests in the Vertical Motion Simulator (VMS). We used an existing method, Spare Capacity Operations Estimator (SCOPE), along with a recurrent neural network and conducted comparison studies between the two methods individually, and when used together. We found that using both methods together can improve the result over using either approach alone. In our first test case, we achieved an improved linear correlation coefficient of 0.409 over that of SCOPE alone at 0.352 on the training dataset. Through cross validation, we also found that the results may be dependent on the split of training vs. validation data, and that further investigation should be conducted to understand what additional inputs to the neural network model should be made.

Image Data↗