A benchmark of Reynolds-Averaged Navier-Stokes (RANS)-informed analytical methods, which are attractive for predicting fan broadband noise, was conducted within the framework of the European project TurboNoiseBB. This paper discusses the first part of the benchmark, which investigates the influence of the RANS inputs. Its companion paper focuses on the influence of the applied acoustic models on predicted fan broadband noise levels. While similar benchmarking activities were conducted in the past, this benchmark is unique due to its large and diverse data set involving members from more than ten institutions. In this work, the authors analyze RANS solutions performed at approach conditions for the ACAT1 fan. The RANS solutions were obtained using different CFD codes, mesh resolutions, and computational settings. The flow, turbulence, and resulting fan broadband noise predictions are analyzed to pinpoint critical influencing parameters related to the RANS inputs. Experimental data are used for comparison. It is shown that when turbomachinery experts perform RANS simulations using the same geometry and the same operating conditions, the most crucial choices in terms of predicted fan broadband noise are the type of turbulence model and applied turbulence model extensions. Chosen mesh resolutions, CFD solvers, and other computational settings are less critical.

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.

Turbulence parameters, in particular integral length scale (ILS) and turbulence intensity (Tu), are key input parameters for various applications in aerodynamics and aeroacoustics. The estimation of these parameters is typically performed using data obtained via hot-wire measurements. On the one hand, hot-wire measurements are affected by external disturbances resulting in increased measurement noise. On the other hand, commonly applied turbulence parameter estimators lack in robustness. If not addressed correctly, both issues may impede the accuracy of the turbulence parameter estimation. In this article, a procedure consisting of several signal processing steps is presented to filter non-turbulence related disturbances from the unsteady velocity data. The signal processing techniques comprise time- and frequency-domain approaches. For the turbulence parameter estimation, two different models of the turbulence spectra—the von Kármán model and the Bullen model—are fitted to match the spectrum of the measured data. The results of several parameter estimation techniques are compared. Computational Fluid Dynamics (CFD) data are used to validate the estimation techniques and also to assess the influence of the variation in window size on the estimated parameters. Additionally, hot-wire data from a high-speed fan rig are analyzed. ILS and Tu are assessed at several radial positions for two fan speeds. It is found that most techniques yield similar values for ILS and Tu. The comparison of the fitted spectra with the spectra of the measured data shows a good agreement in most cases provided that a sufficiently fine frequency resolution is applied. The ratio of ILS and Tu of the velocity components in longitudinal and transverse direction allows the assessment of flow-isotropy. Results indicate that the turbulence is anisotropic for the investigated flow fields.

Fan broadband noise is an important noise source of modern aircraft. Yet its accurate prediction using analytical or numerical techniques remains a challenge. RANS-informed synthetic turbulence methods bridge the gap between expensive but physical scale-resolving and fast but simplified analytical methods. In past works, the authors have used a two-dimensional (2D) approach based on the fast Random Particle Mesh (fRPM) method. While the 2D approach can be performed on a regular personal computer and has yielded a good agreement with experimental results, some difficulties remain: The 2D approach neglects three-dimensional (3D) flow effects and its results depend on the chosen streamline position. Therefore, the authors expanded the method to three-dimensional space. An effort was made to optimize the used settings in order to achieve accurate results while keeping the computation cost within reasonable limits. These findings were then verified by comparing the synthesized turbulence to analytical data for typical flow and turbulence characteristics found in the interstage region of a fan. The 3D synthetic turbulence method was subsequently applied to a realistic fan configuration. The results of the fan broadband noise prediction were compared with the results of an equivalent 2D simulation, the results of RANS-informed analytical methods, and with experimental data.