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Johnson, Kristyn

Publications and source records attributed to Johnson, Kristyn.

11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Experimental Study of Instabilities in Hydrogen-Air Fueled Rotating Detonation Combustion Presentation

Conventional gas turbine engines rely on an idealized constant pressure combustion process that in reality produces a pressure decrease as a result of viscous and other non-reversible losses. An alternative approach is rotating detonation combustion (RDC) which is a form of pressure gain combustion in which one or more detonation waves propagate an annular channel resulting in an increase in pressure across, subsequently providing greater work availability compared to deflagration ultimately leading to opportunities for greater thermodynamic efficiency when used in gas turbine engines that conventionally relies on constant. Modern gas turbine engines often rely on pre-mixed reactants to limit NOx emissions, although this may result in greater susceptibility to instabilities such as flashback and thermoacoustic oscillation, particularly for applications that utilize hydrogen as the fuel. Research in RDC has focused on non-premixed reactants thus limiting the occurrence of flashback, and high frequency detonation wave propagation (kHz) may interfere with the occurrence of thermoacoustic oscillations. Thermal NOx emissions are controlled through rapid combustion and sudden expansion of the working fluid. Although RDC may not be susceptible to instabilities encountered in conventional state of the art gas turbine engine combustion, there may be other mechanisms occurring that support instabilities that could be detrimental to performance.

Weber, Justin↗
DOE OSTI2023

Consideration of Non-Ideal Detonation Regimes Influenced by Wave Modes in a Water-Cooled Rotating Detonation Engine Using OH Chemiluminescence

Within this study, the presence and proportion of ideal and non-ideal combustion regimes is compared across a variety of process conditions and wave modes. Large-scale data analysis seeks to summarize proportional heat release associated with commensal, parasitic, and detonative combustion averaged across individual traces of OH* chemiluminescent data acquired at the detonation plane. Means of regime partitioning based on the anatomy of the time-resolved OH* signal are proposed to ensure consistent analysis throughout the current and future studies concerning combustion regimes.

Johnson, Kristyn↗
DOE OSTI2023

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

This work evaluates and compares various convolutional neural networks (CNNs) trained in previous NETL studies according to metrics effecting diagnostic feasibility, external applicability, and performance. Each CNN surveyed, including image classification, object detection, and time series classification, is used to develop total RDE diagnostics and evaluated alongside conventional techniques with respect to real-time capabilities. Real-time capable diagnostics are deployed and evaluated in the laboratory environment using an altered experimental setup, which is outlined herein for possible adaptations in external experimental facilities.

Johnson, Kristyn↗
DOE OSTI2023

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

This work evaluates and compares various convolutional neural networks (CNNs) trained in previous NETL studies according to metrics effecting diagnostic feasibility, external applicability, and performance. Each CNN surveyed, including image classification, object detection, and time series classification, is used to develop total RDE diagnostics and evaluated alongside conventional techniques with respect to real-time capabilities. Real-time capable diagnostics are deployed and evaluated in the laboratory environment using an altered experimental setup, which is outlined herein for possible adaptations in external experimental facilities.

Johnson, Kristyn↗
DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗
DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗