Training the Powered-Lift Evaluation Pilot

Abstract

Verbatim from NASA NTRS. Not paraphrased, not summarized.

This poster describes a project to prepare pilots for a study assessing novel aircraft automation concepts for electric Vertical Takeoff and Landing (eVTOL) aircraft using NASA’s Vertical Motion Simulator (VMS). By exploring the operational and learning challenges related to transitioning between forward flight and vertical landing, we seek to establish baselines of pilot workload and aircraft handling qualities across varying atmospheric conditions and automation states. The simulated eVTOL design differentiates flight control allocations as a function of airspeed across four speed ranges as the vehicle transitions between fully thrust-borne lift and wing-borne lift. As speed increases, side stick controls command: translational ground speeds, vertical and lateral acceleration, vertical rate, vertical flight path angle, and bank angle. This novel approach to flight control allocation creates a significant learning challenge for pilots. Since initial eVTOL aircraft may have limitations on hover capabilities, automation and flight guidance cues also vary with airspeed to provide efficient landing profiles while still providing cues suitable for cruise flight. The NASA team prepared the study pilots to follow these flight guidance cues along curved Required Navigation Performance (RNP) approaches and along 6o and 12o glide paths to energy-efficient assistive-hover landing and goarounds. The pre-VMS preparation sought to prepare pilots from diverse levels of experience and background. To do this, NASA researchers designed and developed a fixed-based, large field-ofview simulator with terrain, structures, and air traffic. With one day of combined classroom learning and skill development in the fixedbase simulator, pilots were largely able to fly the simulated eVTOL in the VMS with sufficient mastery to provide handling quality assessments using the Cooper-Harper Handling Qualities Rating and workload assessments through the Bedford Workload Scale.

Authors

• Loran Haworth San Jose State University
• Michael Feary Ames Research Center
• John Kaneshige Ames Research Center
• Kimberlee Shish Ames Research Center
• Thomas Lombaerts Wyle (United States)
• Kevin Monk Ames Research Center
• Amber Villa Ames Research Center
• Mieczyslaw Steglinski KBR (United States)
• Nelson Iwai Artic Slope Regional Corporation
• John Archdeacon Artic Slope Regional Corporation
• Barth Baron Jr San Jose State University

Keywords

• AAM
• eVTOL
• human-automation interaction
• training