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Aeroacoustics of innovative aircraft cannot disregard the development of low-order models for the jet noise source, which are essential to assess the propulsion–airframe interactions from the conceptual design stage. The main scope of this work is to provide a noise source model that can be coupled with relatively low computational cost methods for aeroacoustic scattering. To this end, this paper presents for the first time a multiobjective optimization of the zeroth-mode wave-packet in the jet near field. The importance of calibrating the model with near-field pressure data stems from the fact that in new aircraft, the engine nacelles are typically positioned at a few diameters from the wing or fuselage. In this work, the near field of a high subsonic jet at a Mach number of 0.9 is represented as a cylindrical surface radiating the pressure disturbances of a wave-packet source, which is optimized using large-eddy simulation data from three lines at different radial distances. The optimized model has been tested at Strouhal numbers between 0.25 and one, and the optimized solutions have been chosen using a Pareto-ranking criterion considering the wave-packet prediction over an extra line. A good agreement is achieved between the reference data and model predictions for multiple near-field radial distances.

The present work reports an investigation on the use of adaptive metamodels based on radial basis functions (RBFs) for aeroacoustic applications of highly innovative configurations. The relevance of the topic lies on the paramount importance of metamodelling techniques within the design optimisation process of disruptive aircraft layouts. Indeed, the air traffic growth, consequently the hard environmental constraints imposed by regulations, will make a technological breakthrough, an imperative need within few years. As a consequence, the engineering community is paying particular attention to the development of innovative techniques for the design of unconventional configurations. For this class of applications, the designer cannot successfully rely on historical data or low-fidelity models, and the expensive direct simulations remain the only valuable design strategy. In this regard, it can be demonstrated that the use of surrogate models, i.e., metamodels, significantly reduces the computing costs, especially in view of a robust approach to the optimised design. In order to further improve the efficiency of metamodel-based techniques, dynamic approaches based on hyperparameter optimisation and adaptive sampling procedures have been recently developed. The case study presented here pertains the exploiting of dynamic RBF-based metamodels for noise shielding applications. The analysis of the metamodel performances and its convergence properties shows how the final number of direct simulations is significantly reduced by the hyperparameter optimisation algorithm, still strongly depending on the choice of the RBF kernel function.

This work deals with the investigation and modelling of wall pressure fluctuations induced by a supersonic jet over a tangential flat plate. The analysis is performed at several nozzle pressure ratios around the nozzle design Mach number, including slightly over-expanded and under-expanded conditions, and for different radial positions of the rigid plate. Pitot measurements and flow visualizations through the background oriented schlieren technique provided a general overview of the aerodynamic interactions between the jet flow and the plate at the different regimes and configurations. Wall pressure fluctuations were measured using a couple of piezoelectric pressure transducers flush mounted over the plate surface. The spectral analysis has been carried out to clarify the effect of the plate position on the single and multivariate wall pressure statistics, including the screech tone amplitude. The experimental dataset is used to assess and validate a surrogate model based on artificial neural networks. Sound pressure levels and coherence functions are modelled by means of a single fully connected network, built on the basis of a recently implemented fully deterministic topology optimization algorithm. The metamodel uncertainty is also quantified using the spatial correlation function. It is shown that the flow behaviour as well as the screech and broadband noise signatures are significantly influenced by the presence of the plate, and the effects on spectral quantities are correctly reproduced by the proposed data-driven model that provides predictions in agreement with the available data.

This article deals with the validation of two analytical spacetime corrections for the extension of the acoustic metafluid model to aeroacoustics. The need for convective metafluid corrections is due to the performance decay of a statically-designed acoustic metafluid device in presence of a mean flow in the hosting fluid. Aeronautical applications cannot disregard the presence of a Mach number which causes the distortion of acoustic propagation pattern due to the coupling between acoustic propagation and fluid dynamics phenomena. Hence, analytical corrections represent one of the possible strategies to overcome such performance decay. For a more comfortable manipulation and the introduction of the analytical corrections, a spacetime reinterpretation of the characteristic equations is needed. Two analytical corrections, derived from Taylor’s and Prantdl-Glauert’s transformations, and several Mach numbers up to 0.3 are considered during the validation process. The test case considers a rectangular domain occupied by a metafluid embedded in a conventional medium domain, characterized by a uniform background flow and where a monopole source emits acoustic perturbations. The numerical setup is chosen to eliminate any spurious effects introduced by the analytical approximations and to limit, as much as possible, those related to the numerical implementation. Moreover, it legitimates the comparison between the corrections used. To ensure the continuity of the acoustic propagation between the two media, a crucial role is covered by the correct imposition of the boundary conditions. The importance of using primitive variables in doing so is underlined. Numerical simulations are implemented in the frequency domain using the commercial FEM solver COMSOL Multiphysics.

The expected increase of the air traffic and the emergence of new paradigms of air transportation induced by the evolution of the market demand are going to significantly worsen the community noise scenario of our cities. An accurate and timely prediction of the noise emission is, therefore, a mandatory aspect of the design process of any innovative air transport solution. Unfortunately, aeroacoustic simulations are extremely expensive and a popular approach to circumvent the problem is the development of surrogate models equally accurate yet computationally cheaper for the evaluation of the objective functions. This work deals with a multi-fidelity approach to surrogate modeling for the identification of optimal engine installation to maximise noise shielding. The paper explores the possibility to couple reinforcement learning techniques with a trust-region-based multi fidelity approach to meta modelling. Specifically, an accelerated variant of the well known Q-learning algorithm, namely the Evolutionary Q-learning (EVQL) is used to train a group of agents, each one deployed in one of a set of trust subregions, defined around the high-accuracy samples. The subregions of confidence are updated according to the exit directions of each agent. These directions are also used to enrich the set of high-accuracy samples by evaluating the meta model uncertainties along them in the untrusted regions of the domain.

The use of surrogate models in recent times plays a key role in the design process and, overthe past few years, machine learning algorithms are adapting to the use of active metamodellingtechniques. In the present study, Artificial Neural Networks formulation is used as a data–driven nonlinear model aimed at describing the dynamics of an experimental test campaignrelated to the noise emitted by a single–stream jet in under–expanded conditions. The architectureof the neural network is selected employing a deterministic optimization algorithm,coupled with data informed tuning of the input parameters. The data set explored here wasacquired in the state–of–the–art aeroacoustic facility at the University of Bristol. Both the nearandfar–field acoustic measurements were carried out for a cold under–expanded jet for Machnumbers ranging from 1.1 to 1.4. The predictions by the metamodel are in good agreementwith the experimental data, and the results demonstrate the capability of metamodels as areliable tool to estimate jet noise in under-expanded flow conditions for a wide range of Machnumbers and near–field locations.

Two major issues affecting the solution of Boundary Integral Equations via collocation methods (such as the Boundary Element Method) are the high memory requirement/usage for large problems resulting, \textit{e.g.}, in high frequency acoustics, and the need for regularisation due to the non-uniqueness of the solution for the so-called spurious frequencies for exterior problems. The first problem can be effectively mitigated introducing methods such as the Adaptive-Cross-Approximation technique, which builds a hierarchical block-wise approximation of the matrix arising from the discretisation of the BIE, with a combination of low-rank approximated and fully populated blocks, reducing the storage requirement. Typical strategies to address the regularisation of the solution are the CHIEF points and the Burton \& Miller methods. More recently, the Dual Surface method has been introduced and adapted for acoustics. The Dual Surface method is a regularization technique that does not increase the problem size and avoids the evaluation of hyper--singular integrals, combining the advantages of the Burton \& Miller and CHIEF methods, requiring, however, the evaluation of an increased number of integrals for each matrix entry. This drawback is partially compensated when using the ACA partial pivoting algorithm, which limits the number of entries to be evaluated when building the system matrix. In this paper, the implementation of the Dual Surface method will be discussed for the classic Kirchhoff-Helmholtz integral equation and for a BIE for the sound scattered by moving bodies based on the Ffowcs Williams \& Hawkings equation.

Aeroacoustic analyses of new generation and highly innovative aircraft configurations such as Hybrid and Blended Wing Body cannot disregard the development of low-order models for the jet noise source, essential to assess the propulsion-airframe interactions since the conceptual/preliminary design stage. The use of wave-packets to model jet noise is based on the widely accepted hypothesis that the large-scale turbulent structures, responsible for noise peaks emitted by subsonic and supersonic jets, can be modelled as instability waves that grow and then decay with axial distance. In this study, a M=0.9 high-subsonic jet is represented as a cylindrical surface radiating the pressure disturbances of a wave-packet source, whose parameters are optimized using near-field information from LES simulations. The importance of calibrating the model with near-field pressure data stems from the fact that innovative aircraft configurations have engines nacelles typically positioned at a few diameters from the wing or fuselage. The main scope of this analysis is to provide a noise source model that can be coupled with Boundary Elements Method (BEM) codes for aeroacoustic scattering evaluations. A quite good agreement is achieved at multiple near-field radial distances between the simulation data and model prediction for the dominating 0th azimuthal mode at the selected Strouhal numbers up to 1.