巴西航空工业公司

This study experimentally investigates the aeroacoustic characteristics of tandem, co-rotating rotors operating under non-axial inflow conditions. Measurements were conducted in an anechoic wind tunnel over a range of advance ratios and tilt angles to provide a sub-scale representation of the noise generated under moderate non-axial inflows. Far-field noise was characterised using multiple in-plane and out-of-plane microphone arrays to resolve directivity, spectral content, blade passing harmonics, and broadband components. The results show that the noise from the tandem, co-rotating rotors is strongly influenced by both advance ratio and tilt angle. Operation at low tilt angles leads to significant amplification of higher blade passing harmonics and broadband noise, particularly at out-of-plane observer locations, indicating increased unsteady loading. Increasing the tilt angle substantially reduces higher harmonic and broadband noise whilst the blade passing frequency component remains comparatively insensitive. These findings highlight the sensitivity of tandem-rotor noise to operating conditions and provide insight into noise mitigation opportunities during transition flight in urban air mobility applications.

The application of uncertainty quantification (UQ) methods to realistic industrial-scale structures remains a challenging task, due to the typical geometric complexity, the existence of various sources of uncertainties and the high dimension of structural models. These challenges are even bigger when dealing with composite material structures, which are knowingly prone to uncertainties arising from manufacturing processes and environmental influences. In this context, the present paper intends to contribute to increase the maturity level of UQ techniques to composite aeronautic structures, under the combined effects of space-dependent material and environmental fluctuations. Variations in temperature, laminate thickness and fiber volume fraction are jointly considered, being represented as random fields discretized using the Karhunen-Loève Expansion (KLE), while fiber angles are treated as random variables. Aiming at expanding the range of situations possibly found in practice, both Gaussian and non-Gaussian random fields are considered within a methodology combining the Iterative Translation Approximation Method (ITAM) and KLE. A micromechanical model is used to represent temperature- and moisture-dependent material properties, capturing the coupled effects of environmental degradation of material properties and hygrothermally-induced stresses. Monte Carlo Simulation (MCS) is employed to perform uncertainty quantification for buckling loads and vibration natural frequencies of a regional aircraft composite wing structure modeled with a relatively high-dimension finite element model. Additionally, global sensitivity analysis based on Sobol’ indices is conducted to identify the most influential random parameters, where structural responses are approximated using artificial neural network (ANN)-based surrogate models. From the simulation scenarios analyzed, accounting for different values of standard deviations attributed to random variables and correlation lengths assigned to random fields, the statistics of structural responses are assessed. The significant spread of structural responses highlights the importance of incorporating the considered types of uncertainty in analysis and design procedures for achieving robust and reliable aerospace composite structures.

With the increasing number of drones flying simultaneously under the supervision of a limited number of humans, it is essential to delegate more decision-making authority to the autopilot system to minimise intervention. The decision making capabilities of autonomous UAVs have a direct impact on mission safety and reliability. In this paper we propose a decision-making model for UAVs based on decision networks. The non-dominated sorting genetic algorithm (NSGA III) is used to train the model from a provided set of cases. Separate selected cases with the respective expected outcomes were used to test the model, showcasing its capacity to accurately represent cases while maintaining interpretability. We demonstrate that this model can facilitate mission accomplishment with risk minimisation under uncertainty in stochastic environments. In particular, the model provides a meaningful interpretation of the parameters and how they are taken into account when making decisions.

This paper presents a methodology for calculating the hinge moment and its derivatives through computational tools applied to compressible transonic unsteady flows for control surfaces. The hinge moment is expressed as a function of both the angle of attack and the deflection angle of the control surface. The open-source multiphysics software SU2 is used to impose movement in both space and time through grid movement algorithms. Several two- and three-dimensional simulations are performed under various operating conditions. For most of the cases presented, the computational results exhibited good behavior, with the hinge moment demonstrating a periodic sinusoidal wave pattern. Furthermore, the mean of these unsteady computational results showed strong agreement with available experimental data, particularly in terms of the predicted hinge moment behavior and its sensitivity to flow conditions. However, in certain cases, primarily at high angles of attack, where the presence of multiple dominant frequencies in the unsteady flow significantly affects the results and can lead to boundary-layer separation, the flow exhibited complex, nonlinear behavior. These results demonstrate the ability of the methodology to effectively model unsteady transonic flows and predict hinge moments for realistic aerodynamic configurations.