Reduced-order modelling of jet noise using a map-based stochastic turbulence approach

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Jet engines produce noise in different ways, but mainly this noise comes from the high-speed exhaust stream that leaves the nozzle at the rear of the engine. And planes are loudest when they move slowly, such as at takeoff or landing. As the exhaust stream meets relatively still air, it creates tremendous shear that quickly becomes unstable. The evaluation of the performance of new noise-reducing concepts crucially depends on robust but economical numerical methods for modelling of source mechanisms of the acoustic processes in the relevant Mach number flow regimes. The purpose of this research proposal is to gain a better understanding of the missing noise in the range of high-frequencies, which exhibits a high annoyance penalty, and limited frequency bandwidth predictions and to develop a simulation tool to predict the same. This proposal contributes to the later via a map-based stochastic reduced-order modelling approach. Simulating a complex phenomenon like jet turbulence requires the use of an extremely high-resolution mesh to represent the dynamics involved. A typical simulation could have 500 million grid points. Multiply that by five to account for pressure, density and three components of velocity to describe the flow at every grid point. That equates to billions of degrees of freedom or the number of variables a computer uses to simulate the noise from a single idealised jet. The important question that arises here is whether the current status of these affordable high-resolution numerical simulations can be evolved further to answer the remaining questions about jet noise. In the proposed research, I propose the development of a reduced-order modelling approach that has a unique possibility to incorporate detailed and resolved physics with the aim of increasing the fidelity of the simulation results on that level.The outcome of the intended research will be a jet noise prediction model that is scientifically well-grounded, involving a synthetic pressure field generated by the reduced-order model. More broadly, the research is intended to demonstrate reliable prediction of the pressure field of a turbulent jet, with implications well beyond the scope of the present study.