Early detection of thermoacoustic instability in solid rocket motors
Project Description

Thermoacoustic instability is a recurring problem that occurs in nearly all continuous combustion systems. Physically, it is caused by positive feedback between heat-release-rate oscillations and sound waves. In saturated form, it manifests as high-amplitude self-excited tonal pressure oscillations in the combustor. Such oscillations can cause various operational problems, such as accelerated fatigue cycling, excessive heat transfer, and elevated noise and vibration. These problems can reduce the reliability and safety of the overall system. It is therefore important to be able to forecast the onset of thermoacoustic instability in advance, so that preventative action can be taken.

In this project, you will use advanced tools from dynamical systems theory, complex systems theory and machine learning to develop early warning indicators of thermoacoustic instability. The demonstration platform is a solid rocket motor, which is widely used in the aerospace industry to launch orbital vehicles. Working with real full-scale experimental data, you will become proficient in data analysis and visualization, computer programming, data-driven tools, and machine learning algorithms. The key objective of this project is to consolidate the foundational knowledge gained in the classroom to develop a reliable early warning indicator of thermoacoustic instability for real-world combustion systems.

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