Flow and performance analysis of a natural gas-air rotating detonation engine with high-speed velocimetry
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Presented by NETL at ASME Turbo Expo 2019, 06/19
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Presented by NETL at AIAA SciTech, 01/20
2023 AIAA SciTech presentation describing quantitative ion probe analysis in an RDE
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Presented by NETL at The AIAA SciTech Forum & Exposition, 01/20
Submission from 2021 AIAA SciTech describing RDE inlet and fill zone model
<p style="text-align: left;">AIAA SciTech 2023 paper describing quantitative interpretation of ion probe measurements in an RDE.
ASME Turbo Expo 2023
Paper presented at Turbo Expo 2023, Boston, Massachusetts, June 26-30, 2023.
In this study, the focus is on the mixing and detonation processes within a model RDE configuration experimentally studied at Air Force Research Lab (AFRL), which is an annular configuration operating on ethylene fuel. In the simulations below, it is shown that due to the lower detonability of ethylene (compared to hydrogen) and the specifics of the mixing process, a hybrid detonation-deflagration mode persists within the combustor. In particular, a distributed combustion process driven by a weak azimuthal wave (roughly Ma 1.5) is observed.
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.
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.
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.