Towards reduced order models of small-scale acoustically significant components in gas turbine combustion chambers
Suhas A. Kowshik, Sumukha Sridhar, N. C. W. Treleaven

TL;DR
This paper explores reduced order models like low-order dynamical systems and neural networks to efficiently simulate small-scale acoustic features in gas turbine combustion chambers, improving accuracy and reducing computational costs.
Contribution
It introduces and tests LODS and ANN models for representing acoustic dynamics in CFD simulations of combustion chamber components.
Findings
LODS and ANN models closely match CFD results.
Models significantly reduce computational costs.
Potential applications in flame quenching and turbine blade design.
Abstract
Gas turbine combustion chambers contain numerous smallscale features that help to dampen acoustic waves and alter the acoustic mode shapes. This damping helps to alleviate problems such as thermoacoustic instabilities. During computational fluid dynamics simulations (CFD) of combustion chambers, these small-scale features are often neglected as the corresponding increase in the mesh cell count augments significantly the cost of simulation while the small physical size of these cells can present problems for the stability of the solver. In problems where acoustics are prevalent and critical to the validity of the simulation, the neglected small-scale features and the associated reduction in overall acoustic damping can cause problems with spurious, nonphysical noise and prevents accurate simulation of transients and limit cycle oscillations. Low-order dynamical systems (LODS) and…
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