A benchmark dedicated to RANS-informed analytical methods for the prediction of turbofan rotor–stator interaction broadband noise was organised within the framework of the European project TurboNoiseBB. The second part of this benchmark focuses on the impact of the acoustic models. Twelve different approaches implemented in seven different acoustic solvers are compared. Some of the methods resort to the acoustic analogy, while some use a direct approach bypassing the calculation of a source term. Due to differing application objectives, the studied methods vary in terms of complexity to represent the turbulence, to calculate the acoustic response of the stator and to model the boundary and flow conditions for the generation and propagation of the acoustic waves. This diversity of approaches constitutes the unique quality of this work. The overall agreement of the predicted sound power spectra is satisfactory. While the comparison between the models show significant deviations at low frequency, the power levels vary within an interval of ±3 dB at mid and high frequencies. The trends predicted by increasing the rotor speed are similar for almost all models. However, most predicted levels are some decibels lower than the experimental results. This comparison is not completely fair—particularly at low frequency—because of the presence of noise sources in the experimental results, which were not considered in the simulations.

This article presents an integral validation of a synthetic turbulence broadband noise prediction methodology for fan/outlet-guide-vane (OGV) interaction. The test vehicle is the ACAT1 fan, a modern scaled-down fan, experimentally analyzed in 2018 within the TurboNoiseBB project. Three operating points, namely, Approach, Cutback, and Sideline, and two different rig configurations in terms of the axial gap between the fan and OGV are examined within this work. The methodology consists of using a Reynolds-averaged Navier–Stokes (RANS) solver to model the fan wake and the use of two-dimensional frequency domain linearized Navier–Stokes simulations to resolve the acoustics, including quasi-3D corrections to obtain representative results. The RANS results with no ad hoc tuning are compared in detail against hotwire data to determine the degree of uncertainty incurred by this kind of approach. The predicted broadband noise spectra and noise azimuthal decompositions are compared against the experimental data. The spectral levels are well predicted despite an average underprediction of around 3dB. The noise azimuthal decompositions feature a remarkable agreement with the experiment, denoting accurate modeling of the main physics governing the problem. The impact of increasing the fan/OGV axial gap is quantified numerically for the first time. It is concluded that increasing the gap is detrimental for the broadband noise footprint, unlike intuitively could be expected. Overall, the presented broadband noise methodology yields robust broadband noise predictions at an industrially feasible cost and enables a deeper understanding of the problem.

A linear frequency domain Navier-Stokes solver is used to retain the influence of turning, thickness, and main geometric parameters on turbine broadband noise. The methodology has been applied to predict the broadband interaction noise produced by a representative low-speed low-pressure turbine section. The differences in the spectra with respect to those yielded by state-of-the-art flat plate based methodologies are up to 6 dB. The differences are caused by multiple effects that semi-analytical methodologies do not account for. The most important are blade thickness and turning, which have been studied separately to quantify their impact on the broadband noise footprint. The influence of changing the turbine operating conditions has been discussed as well. The outlet sound pressure level scales with the third and second power of the inlet and outlet Mach number, respectively, for constant turbulence intensity, within most of the frequency range considered.