We analyze and compare various empirical models of wall pressure spectra beneath turbulent boundary layers and propose an alternative machine learning approach using Artificial Neural Networks (ANNs). The analysis and the training of the ANN are performed on data from experiments and high-fidelity simulations by various authors, covering a wide range of flow conditions. We present a methodology to extract all the turbulent boundary layer parameters required by these models, also considering flows experiencing strong adverse pressure gradients. Moreover, the database is explored to unveil important dependencies within the boundary layer parameters and to propose a possible set of features from which the ANN should predict the wall pressure spectra. The results show that the ANN outperforms traditional models in adverse pressure gradients, and its predictive capabilities generalize better over the range of investigated conditions. The analysis is completed with a deep ensemble approach for quantifying the uncertainties in the model prediction and integrated gradient analysis of the model sensitivity to its inputs. Uncertainties and sensitivities allow for identifying the regions where new training data would be most beneficial to the model's accuracy, thus opening the path toward a self-calibrating modeling approach.

The distortion of homogeneous isotropic turbulence interacting with a porous cylinder is calculated by means of the rapid distortion theory (RDT). The porous treatment, characterised by a constant static permeability, is modelled as an impedance boundary condition accounting for the Darcy's flow within the body. The RDT algorithm is first validated through comparisons with published velocity measurements in the stagnation region of an impermeable cylinder placed downstream of a turbulence grid. Subsequently, the impact of porosity on the velocity field is investigated through the analysis of the one-dimensional spectra at different locations near the body and the velocity variance along the stagnation streamline. The porous surface affects the incoming turbulence distortion near the cylinder by reducing the blocking effect of the body and by altering the vorticity deformation caused by the mean flow. The former leads to an attenuation of the one-dimensional velocity spectrum in the low-frequency range, whereas the latter results in an amplification of the high-frequency components. This trend is found to be strongly dependent on the turbulence scale and influences the evolution of the velocity fluctuations in the stagnation region. The porous RDT is finally adapted to model the turbulence distortion in the vicinity of the leading edge of a NACA-0024 profile fitted with melamine foam. The good agreement between the calculations and the experimental results demonstrates that the present methodology can improve the understanding of the physical mechanisms involved in the aerofoil-turbulence interaction noise reduction through porosity and be instrumental in designing such passive noise-mitigation treatments.

In solid rocket motors, vortex nozzle interactions can be a source of large-amplitude pressure pulsations. Using a two-dimensional frictionless flow model, a scaling law is deduced, which describes the magnitude of a pressure pulsation as being proportional to the product of the dynamic pressure of the upstream main flow and of vortex circulation. The scaling law was found to be valid for both an integrated nozzle with surrounding cavity and a nozzle geometry without surrounding cavity that forms a right angle with the combustion chamber side wall. Deviations from the scaling law only occur when unrealistically strong circulations are considered.

The low-speed fans used for automotive engine cooling contribute to a significant part of the global noise emitted by the vehicle. A low-order sound-prediction methodology is developed considering the blade sweep-angle effect on the acoustic predictions of the turbulence-impingement and the trailing-edge noise-generating mechanisms. We modeled these through the application of a semianalytical method based on Amiet’s airfoil theory, appropriately adapted via a strip-theory approach accounting for rotation and modified to include the blades forward curvature. Sweep was already shown in the literature to reduce the noise emitted by isolated airfoils, but its effect on rotating machines was not yet well understood. In this study, we show that the effect of the sweep-angle is to globally reduce the emitted noise by the fan and to change the sound distribution of the sources along the blade span. Thus, the sweep-angle must be considered not only because it yields a better comparison with experimental results but also because wrong conclusions on the dominating noise-generating mechanisms can be drawn when this effect is not taken into account. The investigation is finally complemented by a sensitivity analysis focusing on some of the key parameters characterizing the acoustic prediction.