Validation of a Density Base Navier-Stokes solver simulating the startup deflagration to detonation process of Rotating Detonation Engine (RDE)
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-0171.vid Validation of a Density Based Navier-Stokes solver simulating the start-up deflagration to detonation process of Rotating Detonation Engine (RDE) Bruce Crawford Ansys Inc., 2600 Ansys Dr, Canonsburg, PA 15317 USA Ishan Verma Ansys Pune, Vari Tech Park, India Stefano Orsino Ansys Inc., 10 Cavendish Court, Lebanon NH, 03766, USA Jean-Sebastien Cagnone Ansys Canada Ltd., 1000 Sherbrooke Street West, Montreal QC, H3A 3G4 Canada Modeling pressure gain combustion systems, in-particular rotating detonation engines (RDE), has been a growing area of interest in academia and industry for the last decade. The main application for RDEs is in rocket motors and gas-turbine combustors. Following this industry trend, there has been a push to expand the capabilities of the Ansys Fluent CFD solver, especially in its high-speed Density Based Navier-Stokes solver (DBNS), to improve the modeling and design of new RDE systems. High-fidelity CFD simulations can provide invaluable insights into the high-speed flow mixing and combustion processes in a typical RDE combustor. These CFD simulations require physical models that can predict physical phenomena such as compressibility, turbulence, and chemical non-equilibrium. Implementing new enhancements towards chemical non-equilibrium (combustion) and turbulence combustion interaction models has been done to meet the requirement of predictive high-speed reactive flow simulations. The present work shows the validation of the newly implemented capabilities and physical model of Ansys Fluent for RDEs. The computational domain is based on the experiments from AFRL/NETL RDE, and the validation of CFD simulations is done with the experimental results. Three different mesh resolutions using Poly-Hexcore Mosaic mesh topology are evaluated, showing the performance of the RDE modeling. Additionally, the system demonstrates the computationally efficient modeling capabilities offered by the direct source chemistry solver with a global mechanism and various chemistry solvers using detailed chemistry kinetic mechanisms for higher fidelity combustion modeling. The validation cases presented here use the spectrum of turbulence models to validate RDE operating conditions by using realizable k-epsilon, k-omega Shear Stress Transport (SST), and Large Eddy Simulations (LES).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".