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Eric C. Stewart

Publications and source records attributed to Eric C. Stewart.

4 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

A Piloted Simulation Study of Advanced Controls and Displays for General Aviation Airplanes

A decoupled, fly-by-wire control system combined with a pictorial head-up display has been developed and evaluated on a piloted simulator. The test subjects used in the evaluation were primarily non-pilots who were given no training or practice before the data runs. Despite their lack of experience and training, all of the test subjects were able to complete a complex piloting task on their very first run. The piloting task was performed in reduced visibility conditions and consisted of a continuous series of common maneuvers beginning with a takeoff and ending with a landing. Severe upsets from turbulence and emergency situations such as engine failure were not simulated. Quantitative qualitative data are presented to illustrate the large improvements in subject performance when using the advanced controls and displays compared to the conventional ones.

Eric C. Stewart↗
NASA NTRSDate not supplied

To the Moon! Space Launch System Modal Testing with Video and Motion Magnification

MIT Lincoln Laboratory and NASA Marshall Space Flight Center have been collaborating on using video camera measurements and motion magnification for modal testing of large aerospace components for several years. This presentation will discuss results from the Space Launch System Integrated Modal Test (IMT) and Dynamic Rollout/Rollback Test (DRRT) in support of the Artemis I mission. During the IMT, the data collection focused on operational mode shapes. In addition, the cameras were repositioned mid-test to better understand the physics of a low-frequency torsion mode. The non-contact nature of video data capture allowed for the quick redeployment of the cameras while not causing any delay in test schedule, whereas traditional instrumentation would have required a pause in testing to attach the sensors to the test article. The motion magnification analysis was able to find the low-frequency operational mode shapes and help the test team better understand the physics of the torsion mode. Building upon the success of the IMT motion magnification work, a camera system was used during the DRRT to find operational mode shapes, if the physics of the low-frequency torsion mode remained with different boundary conditions, and relative deflection of the vehicle and ML tower during the roll. In this paper we will present operational mode shape results, discuss the physics of the torsion mode, and review experimental setup idiosyncrasies to help the community in designing video camera measurement systems.

optical↗
NASA NTRSDate not supplied

Machine Learning for Dynamic Test Sensor Placement

There are multiple different algorithms to perform modal test sensor placement optimization: effective independence, residual kinetic energy, iterative Guyan reduction, genetic algorithms, or a brute-force methodology. However, any of these methods may be computationally expensive, especially for structural models with a large number of degrees of freedom. Given the high-cost and the need to optimize the solution, modal sensor placement is a great application for machine learning (ML) algorithms. In this paper, we will apply ML algorithms to determine the optimal sensor locations for simple and complex structures. We will also discuss the benefits and drawbacks of using machine learning over other sensor placement algorithms.

Kelsey Buckles↗