Computer Vision

Advancing Diagnostic Efficiencies of Rotating Detonation Engine (RDE) with ML

Real-time diagnostic methods for gas turbines

Objective: Application of computer vision and machine learning techniques to monitor detonation wave behavior in real time to experimental RDEs.
Methods: Convoluted neural networks (CNNs) applied to high-speed conventional detonation imaging for image classification and object detection with You Only Look Once (YOLO) architecture, and time-series classification.
Results: No single diagnostic structure outperforms the others in all metrics. Each method offers solutions uniquely beneficial to a given study based on primary objectives and constraints, justifying the need for a portfolio of ML capabilities with networks tailored to specific needs throughout the research community.

Falyn Eisiminger2024-09-18T17:41:21+00:00September 18th, 2024|

Machine-Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Application in Experimental Facilities

Johnson, K. B., Ferguson, D., and Nix, A., (2023). Machine-Learning-Based Rotating Detonation Engine Diagnostics: Evaluation for Application in Experimental Facilities. Journal of Propulsion and Power, 1-14. https://doi.org/10.2514/1.B39287

Falyn Eisiminger2024-07-16T16:14:48+00:00November 7th, 2023|

Wave Detection and Tracking Within a Rotating Detonation Engine Through Object Detection

Johnson, K.B., Ferguson, D.H., Nix, A.C., and Tallman, Z., (2023). Wave Detection and Tracking Within a Rotating Detonation Engine Through Object Detection. Journal of Propulsion and Power, 39(4). https://doi.org/10.2514/1.B38960

Falyn Eisiminger2024-04-08T14:32:21+00:00April 1st, 2023|