Simulation of forced ignition in an accelerator scramjet operating at Mach 5

Abstract: Scramjets are air-breathing engines that operate at hypersonic speeds, where the Mach number is greater than five. At these speeds, they significantly outperform rockets in terms of specific impulse by utilizing oxygen from the atmosphere. One potential application of scramjets is to power the second stage of a partially air-breathing, reusable space launch system that has been modelled to deliver payloads to orbit more efficiently than a pure rocket system. In such a system, the scramjet accelerates from Mach 5 to 10 and, to reliably survive the harsh aerothermal environment, is envisioned to have fixed geometry. This introduces design challenges: to produce useful thrust at high Mach numbers (9-10), the engine must have a low compression inlet and be unobstructed by drag generating features such as ramp or cavity flame-holders, all of which run contrary to typical Mach 5-6 scramjet design approaches. Thus at Mach 5, the flow in the accelerator engine does not autoignite and there is no physical flameholder as a natural site for forced ignition. In this work, we investigate forced ignition in the accelerator scramjet combustor during Mach 5 operation using a hybrid RANS-LES approach: the outflow from a RANS simulation of the inlet is fed into a simulation of the combustor that transitions to Wall-Modelled LES mode as unsteadiness develops in the jet/cross-flow interactions. We study the dynamics of forced ignition by impulsive energy deposition in designed shock-induced boundary layer separations in the wake of fuel injectors. Keywords: Hypersonics, Scramjets, Large-Eddy Simulation, Combustion

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-01
DOI
https://doi.org/10.5281/zenodo.14213618
Primary Topic
Machine Learning and Data Classification
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Simulation of forced ignition in an accelerator scramjet operating at Mach 5

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning and Data Classification
article

Simulation of forced ignition in an accelerator scramjet operating at Mach 5

article en

Abstract

Abstract: Scramjets are air-breathing engines that operate at hypersonic speeds, where the Mach number is greater than five. At these speeds, they significantly outperform rockets in terms of specific impulse by utilizing oxygen from the atmosphere. One potential application of scramjets is to power the second stage of a partially air-breathing, reusable space launch system that has been modelled to deliver payloads to orbit more efficiently than a pure rocket system. In such a system, the scramjet accelerates from Mach 5 to 10 and, to reliably survive the harsh aerothermal environment, is envisioned to have fixed geometry. This introduces design challenges: to produce useful thrust at high Mach numbers (9-10), the engine must have a low compression inlet and be unobstructed by drag generating features such as ramp or cavity flame-holders, all of which run contrary to typical Mach 5-6 scramjet design approaches. Thus at Mach 5, the flow in the accelerator engine does not autoignite and there is no physical flameholder as a natural site for forced ignition. In this work, we investigate forced ignition in the accelerator scramjet combustor during Mach 5 operation using a hybrid RANS-LES approach: the outflow from a RANS simulation of the inlet is fed into a simulation of the combustor that transitions to Wall-Modelled LES mode as unsteadiness develops in the jet/cross-flow interactions. We study the dynamics of forced ignition by impulsive energy deposition in designed shock-induced boundary layer separations in the wake of fuel injectors. Keywords: Hypersonics, Scramjets, Large-Eddy Simulation, Combustion

Zenodo (CERN European Organization for Nuclear Research)
The University of Queensland (AU)
Affordable and clean energy
Openalex Percentile: Top 61%
Machine Learning and Data Classification
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.