RANS-based prediction of noise from isothermal and hot subsonic jets

Current civil aircraft are significantly quieter than the civil aircraft of the 20th century. But the overall impact of aircraft noise has not been reduced by the same token because the number of aircraft operations has been steadily increasing. So to compensate for the increase in aircraft operations and reduce the overall impact of aircraft noise, we must design quieter aircraft.The noise generated by the jet leaving the engine exhaust is the dominant source when the aircraft is taking off, so its reduction will lead to significant reduction of the total aircraft noise. The current engine design employs decades of research on jet noise, so noise technology has reached a mature state. Thus to further reduce jet noise we must assess the impact of once secondary elements or employ disruptive designs. These assessments would have such a large design space that it is not possible to rely on experimental campaigns and scaling laws, hence the need to develop numerical methods to predict jet noise.This thesis studies methods to predict jet noise that use an acoustic analogy and information from a steady RANS solution of the flow to compute turbulence two-point statistics. RANS-based methods rely on empirical modelling but may provide the optimum balance between computational cost and generality needed by the industry to design the next generation of jet engines.The goal of the thesis is to reduce the empiricism of RANS-based prediction whilst keeping the low computational cost. The contributions of the thesis are summarised in three aspects: (1) introduce a model for the additional sound source in hot jets, (2) formulate the empirical model of turbulence statistics in frequency domain, and (3) compute the effect of turbulence anisotropy on jet noise directivity.The contributions of the thesis update an existing prediction method (C. R. S. Il´ario et al. Prediction of jet mixing noise with Lighthill’s acoustic analogy and geometrical acoustics. J. Acoust. Soc. Am., 141 (2), 2017.) which can be applied by industry, and provide background for further research in academia.