While the operating conditions are the main factors that influence engine design, it is important to understand ignition in any potential design to ensure reliable light-ability. Ignition probability maps can be generated, either experimentally or numerically, to inform design of ignition mechanisms. Recent models have been proposed to estimate ignition probability using non-reacting computational fluid dynamic (CFD) simulations. These models have not been applied to scramjet flame holding cavities. A qualitative model is described that uses tracer particles that probe CFD data and are removed when the conditions are adverse to flame survivability. The parameters that influence ignition are investigated by changing the criteria to define the flammable region. A quantitative model is developed based on a frozen flow assumption and the assumption that a region exists such that the geometry can be considered ignited if a flame is able to be propagated to this region. A virtual flame begin in this "ignition region" and propagates backwards in time to all the cells that would be successful if forward time was used. This model is implemented with an Eulerian and a Lagrangian scheme (IMIT and LIMIT, respectively). The results are compared to a previous coldlow model, I-CRIT-LES, on a low speed, lifted jet geometry and a supersonic cavity geometry. The models generate similar results on the jet case. A diffusion-like effect in IMIT allows the virtual flame to propagate over streamlines and into cells that the flame should not be able to reach. Thus the cavity ignition map generated by IMIT overpredicts ignition. The diffusion-like problem is solved by using particles following the streamlines. Therefore, LIMIT results match the qualitative experimental data in the cavity better than the other models.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-2396 |
| Date | 09 August 2019 |
| Creators | Ivancic, Philip |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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