This paper investigates the aeroacoustic interactions of small hovering rotors, using both experiments and computations. The experiments were conducted in an anechoic chamber with arrays of microphones setup to evaluate the azimuthal and polar directivity. The computational methodology consists of high fidelity detached eddy simulations coupled to the Ffowcs-Williams and Hawkings equation, supplemented by a trailing edge broadband noise code. The aerodynamics and aeroacoustics of a single rotor are investigated first. The simulations capture a Reynolds number effect seen in the performance parameters that results in the coefficient of thrust changing with the RPM. The acoustic analysis enables the identification of self-induced noise sources. Next, dual side-by-side rotors are studied in both counter-rotating and co-rotating configurations to quantify the impact of their interactions. Higher harmonics appear due to the interactions and it is verified that the counter-rotating case leads to more noise and a less uniform azimuthal directivity. Difficulties that arise when trying to validate small rotor calculations against experiments are discussed. Comparisons of computational and experimental results yield further insight into the noise mechanisms that are captured by each methodology.

Artificial intelligence (AI) can be used to optimize the prediction of pressure fluctuations over the external surfaces of aerospace launchers and minimize the number of wind tunnel tests. In the present research, various machine learning (ML) techniques capable of predicting the acoustic load were tested and validated. The methods included decision trees, Gaussian Process Regression (GPR), Support Vector Machines (SVMs), artificial neural networks (ANNs), linear regression, and ensemble methods such as bagged and boosted trees. These algorithms were trained using experimental data from an extensive wind tunnel test campaign conducted to support the design of a VEGA (Advanced Generation European Vehicle) launcher vehicle and provide wall pressure fluctuations in many configurations. The main objective of this study was to identify, among several algorithms, the most suitable method able to process such complex databases efficiently and to provide reliable predictions. Different statistical indices, including the root mean square error (RMSE), the mean square error (MSE), and a correlation coefficient (R-squared), were employed to evaluate the performance of the ML methods. Among all the methods, the bagged tree algorithm outperformed the others, providing the most accurate predictions, with low RMSE and high R-squared values across all test cases. Other methods, such as the ANNs and GPR, exhibited higher errors, indicating their reduced suitability for this dataset. The results demonstrate that ensemble decision tree methods are highly effective in predicting acoustic loads, offering reliable predictions, even for configurations outside the training database. These findings support the application of ML-based models to optimize experimental campaigns and enhance the design of aerospace launch vehicles.

The flight demonstrator FD1 is being developed by AVIO and ESA in order to test key technologies enabling re-usability in the design of the future launch vehicles. The wind tunnel test campaign described herein has contributed to the aerodynamic database for FD1, providing accurate data for the study of the critical re-entry phase, with deployed grid fins. Data include forces and moments measurements, fluctuating pressure on the model surface and Schlieren visualisations. Tests were carried out in the Mach number range 0.5-2, representative of dense atmosphere conditions, at different attitude angles. Schlieren visualisations at supersonic speeds reveal the detached shock at the base of the grid fins and the shock train across the fin cells. The most severe aerodynamic loads are measured at $Ma\approx1.2$, with the grid fins in $\times$ position at increasing pitch angles. The highest unsteady loads are measured at $Ma\approx0.8$, with the most stressed areas near the flow reattachment after the massive separation at the base and upstream the fins. Spectral and coherence analysis relate wall pressure fluctuations to the underlying flow structure. The current experimental database is useful for the future design of reusable stages or launch vehicles and for the validation of related numerical tools.

In this paper, noise emissions from two pusher propellers installed on a wing are investigated both experimentally and numerically. Aerodynamic and aeroacoustic measurements were carried out in the acoustically treated open test section of the Pininfarina Wind Tunnel and encompassed extensive parametric analyses in terms of geometrical configurations and flight conditions. Significant noise reduction is achieved by installing the propeller at the wingtip (up to 3 dB), as well as increasing its distance from the wing (up to 2 dB). The experimental results provided the identification of optimal configurations in terms of noise impact and originated a database used to validate and assess a numerical model. The numerical approach is based on a lattice Boltzmann method combined with a Ffowcs Williams–Hawkings analogy providing predictions of noise installation effects. Numerical simulations comprised different wing–propeller configurations and flight conditions, reproducing test cases explored in the wind-tunnel test campaign. Comparisons with experimental results are given, with focus on tonal noise and directivity. It is shown that the numerical method is able to reasonably predict the noise installation effects with maximum deviations of 4 dB and reproduce its sensitivity to the variation of relevant flight parameters.