An experimental investigation of pressure fluctuations generated by a single-stream compressible jet is carried out in an anechoic wind tunnel. Measurements are performed using a linear array of microphones installed in the near region of the jet and a polar arc of microphones in the far field. The main focus of the paper is on the analysis of the pressure fluctuations in the near field. Three novel signal processing techniques are presented to provide the decomposition of the near-field pressure into hydrodynamic and acoustic components. The procedures are all based on the application of the wavelet transform to the measured pressure data and possess the distinctive property of requiring a very simple arrangement to obtain the desired results (one or two microphones at most). The hydrodynamic and acoustic pressures are characterized separately in terms of their spectral and statistical quantities and a direct link between the acoustic pressure extracted from the near field and the actual noise in the far field is established. The analysis of the separated pressure components sheds light on the nearly Gaussian nature/intermittent behaviour of the acoustic/hydrodynamic pressure. The higher sensitivity of the acoustic component to the Mach number variation has been highlighted as well as the different propagation velocities of the two pressure components. The achieved outcomes are validated through the application to the same data of existing separation procedures evidencing the advantages and limitations of the new methods.

In the last decade, the drone market has grown rapidly for both civil and military purposes. Due to their versatility, the demand for drones is constantly increasing, with several industrial players joining the venture to transfer urban mobility to the air. This has exacerbated the problem of noise pollution, mainly due to the relatively lower altitude of these vehicles and the proximity of their routes to extremely densely populated areas. In particular, both the aerodynamic and aeroacoustic optimization of the propulsive system and of its interaction with the airframe are key aspects of unmanned aerial vehicle design that can signify the success or the failure of their mission. The industrial challenge involves finding the best performance in terms of loading, efficiency and weight, and, at the same time, the most silent configuration. For these reasons, research has focused on an initial localization of the noise sources and, on further analysis, of the noise generation mechanism, focusing particularly on directivity and scattering. The aim of the present study is to review the noise source mechanisms and the state-of-the-art control strategies, available in the literature, for its suppression, focusing especially on the fluid-dynamic aspects of low Reynolds numbers of the propulsive system and on the interaction of the propulsive system flow with the airframe.

In this work an experimental investigation of the near-field pressure of a compressible jet is presented. The proper orthogonal decomposition (POD) of the pressure fluctuations measured by a linear array of microphones is performed in order to provide the streamwise evolution of the jet structure. The wavenumber–frequency spectrum of the space–time pressure fields re-constructed using each POD mode is computed in order to provide the physical interpretation of the mode in terms of hydrodynamic/acoustic nature. Specifically, non-radiating hydrodynamic, radiating acoustic and ‘hybrid’ hydro-acoustic modes are found based on the phase velocity associated with the spectral energy bumps in the wavenumber–frequency domain. Furthermore, the propagation direction in the far field of the radiating POD modes is detected through the cross-correlation with the measured far-field noise. Modes associated with noise emissions from large/fine scale turbulent structures radiating in the downstream/sideline direction in the far field are thus identified.

Exposure to pollutants is usually higher in cities than in the countryside. Generally, in the urban areas pollution sources as traffic, power generator and domestic heating system are more intense and spatially distributed. The pollutants can be classified as a function of long-term toxicological effects due to an exposure and inhalation. In the present work, several kinds of pollutants concentration generated in Rome during 2015 have been analyzed applying different advanced post-processing technique. In particular, statistic and cross-statistic have been computed in time and phase space domain. As main result, it is observed, as expected, that all the pollutant concentrations increase during the winter season into a couple of time ranges despite of [O₃] that has high values in summer. It can be clearly concluded that Rome has a strongly unsteady behaviour in terms of a family of pollutant concentration, which fluctuate significantly. It is worth noticing that there is a strong linear dependence between [C₆H₆] and [NO] and a more complex interdependence of [O₃] and [C₆H₆]. Qualitatively is provided that, to a reduction of [C₆H₆] under a certain threshold level corresponds an increase of [O₃].

This paper presents a nonlinear time-series analysis of the thermoacoustic instabilities of an experimental slot burner. The main objective was the calculation of indexes capable of detecting in advance the combustion instabilities by gradually increasing the flow Reynolds number of the pilot burner. A chaotic analysis based on diagonalwise measurements of the recurrence plots was performed on the basis of which the following indexes were calculated: the τ-recurrence rate index RR_τ, the τ-determinism index DET_τ, the τ-average diagonal line length index L_τ, and the τ-entropy index s_τ. A quantification carried out by means of the standard deviation σ and mean values μ of the diagonalwise measurements showed that the aforementioned indexes were successfully able to sort all cases under analysis mainly into two groups: the first three cases that correspond to the stable regime named “Combustion Noise” and the remaining cases that were associated with the unstable regime called “Combustion Instability.” Additionally, the particle image velocimetry optical method was applied in order to compute a new index based on the velocity fields. The results showed that the index V_h, based on the local heights of the velocity profiles of the central flame, was also capable of detecting the same two groups previously identified by the nonlinear analysis. Nevertheless, the most sensitive indexes were the indexes $R{R}_{{\tau }_{\mu }}$ , $DE{T}_{{\tau }_{\sigma }}$ , and $s_{{\tau }_{\sigma }}$ since these indexes were able to detect the transition between the combustion noise and combustion instability regimes. Therefore, the present results proved that the proposed five indexes were effective precursors in order to detect in advance the combustion instabilities.

Recent experiments reveal that even simple thermoacoustic systems may exhibit nonlinear behavior, far more elaborate than period-1 limit cycle oscillations. Consequently, a new approach based on nonlinear dynamics is emerging besides conventional linear analysis to characterize unstable regimes in gas turbine combustors. This approach reduces or avoids the risk of misunderstanding the deterministic chaos, hidden in the measured signals also during stable combustion regimes, as stochastic noise. The gained information will be available to analytically formulate an index acting as the earliest warning signal of an impending oscillatory combustion instability. In light of this, we first apply the chaotic analisys to an unsteady thermoacoustic time series coming from a typical industrial combustor operated in a stability-to-humming/back-to-stability transition. Then, observing that the Rayleigh and extended Chu indices describe the instability as a linear-interaction-induced synchronization between heat release and flow disturbances, to account for nonlinear interactions, the chaotic synchronization theory is applied. In particular, the interdependence index $E$, a nonlinear causality detector, with no previous applications in combustion instability sensing, is proven to be more effective than linear analysis detectors, both in the early perception of self-sustained (chaotic or not) thermoacoustic oscillations and in gaining meaningful insight on unsteady combustor physics.