In regard to the development of more efficient and silent wind turbines, the present study consists of experimentally assessing the potential aero-acoustic benefits offered by bio-inspired, serrated blades. To this end, a conventional wind turbine blade (NREL Phase VI) is modified such that its leading edge exhibits serrations of various designs (amplitude and wavelength). The resulting serrated blades are then characterized from both an aerodynamic and an acoustic perspective, which is achieved through dual aero-acoustic tests. Involving a small-scale, fully instrumented wind turbine rig, these aero-acoustic tests are performed within both a closed-vein aerodynamic wind tunnel and an anechoic chamber, thereby allowing for measurement of the aerodynamic performance and the noise signatures entailed by the blades’ serrations, depending on their designs. From an aerodynamic standpoint, the serrated blades exhibit superior performance to their baseline counterpart, which translate into significantly higher spinning rates and power coefficients. These benefits are robust, being relatively insensitive to the inflow conditions (yaw, pitch, upstream turbulence) and, to a lesser extent, the serration design considered. From an acoustic perspective, the serrated blades exhibit more diverse noise signatures, depending on their serration design, the rotational regime and, in some cases, the radiative direction considered. Whereas no clear trend can be drawn at lower spinning rates, the higher rotational regime reveals more consistent patterns, e.g., the blades with smaller (resp. larger) serrations systematically radiate less (resp. more) noise than their baseline counterpart. All in all, these findings indicate that leading-edge serrations may offer an efficient passive flow control solution to improve the aero-acoustic characteristics of wind turbine blades. They nevertheless call for extra caution when designing serrated blades, owing to their sensitivity towards their serration design.

Sinusoidal leading-edge tubercles have shown potential for improving the aerodynamic performance and/or reducing the aerodynamic noise of lifting bodies (airfoils) - wind turbine blades included. Yet, the physical mechanisms underlying these benefits are still not fully understood. In that regard, the present study consists of a numerical characterization of the aeroacoustics of a representative wind turbine blade section (S809 airfoil) to be possibly equipped with leading-edge tubercles, when exposed to a low Reynolds number flow (Re = 60,000) at a post-stall incidence (alpha = 20 degrees). To this end, compressible, wall-resolved Large Eddy Simulations (LES) are performed, which allow capturing altogether the aerodynamic unsteady field around the airfoils and their subsequent noise emission. Compared to its baseline counterpart of straight leading-edge, the tubercled airfoil exhibits a noticeable reduction in drag (13%) with no real penalty in lift. Flow visualizations reveal the occasional formation of a laminar separation bubble (LSB), which alters the near-wall vortical dynamics both in the streamwise and spanwise directions, ultimately delaying the flow detachment and the wake vortex development. As a result, the surface pressure fluctuations are significantly reduced around the trailing edge, decreasing the unsteady aerodynamic loading and potentially mitigating noise emissions.

As part of a more global research effort towards greener aviation, the present study focuses on the prediction of noise impacts by the air traffic around major airports. A previous effort saw the present authors developing a simple yet efficient data-driven model based on machine learning (multivariable linear regression), which allows assessing the ground noise impacts incurred by air traffic upon the sole knowledge of aircraft characteristics and operational features. Notably, this data-driven model had been trained upon and successfully validated against an experimental database comprising 70+ flights operating around the Hong Kong International Airport. Such a limited database, however, does not encompass most of the aircraft types/variants and operations that exist, which may restrict the model's applicability. Thus, the database is here significantly expanded, being complemented with additional flight operational features (weather conditions) and experimental data pertaining to another major airport (New York's John F. Kennedy International Airport (JFK)). The data-driven model is trained upon and/or validated against this wider database, before specific analyses are performed to assess the model's sensitivity towards the data it is fed with. As a byproduct, this allows further discriminating the main drivers underlying the noise impacts by aircraft, as had been tentatively done using the limited database. All in all, this study demonstrates how a data-driven model based on a relatively simple machine learning approach can effectively predict the noise impacts of air traffic upon the sole knowledge of aircraft characteristics and operational features.

Wind turbine noise is a major concern worldwide, which calls for more research on the aerodynamic noise generated by blades, especially when exposed to low Reynolds number flows. Indeed, blade sections operating in laminar flow regimes are known to radiate intense tonal noises, which stem from the inception and shedding of vortical structures. In particular, the so-called dual vortex shedding (DVS) which is generated under specific conditions, can lead to distinctive tonal noises. The present study consists of numerically characterizing the DVS generated by a typical wind turbine blade profile (S809 airfoil) when exposed to a low Reynolds number flow (Re = 68,000) at zero incidence (alpha=0 degrees). To this end, compressible, wall-resolved Large Eddy Simulations (LES) are performed, which allow capturing altogether the unsteady aerodynamics occurring around the airfoil and its subsequent noise radiation. The LES successfully captures the dual vortex shedding phenomenon, with vortices shedding alternately from the upper and lower surfaces of the airfoil. As they interact, these alternating vortices generate quasi-periodic lift oscillations, whose characteristics depend on whether the vortices are shed in-phase or out-of-phase. This results in a noise emission that exhibits two dominant tones, surrounded by equally distributed side tones. Fourier-based modal decomposition reveals that these dominant tones correspond to the separate shedding events on the upper and lower surfaces.

As part of a more global research effort towards a greener aviation, the present study focuses on the noise impact by air traffic operations around major airports. A previous effort saw the present authors characterizing the ground noise impacts incurred by 70+ aircraft flying in and out of the Hong Kong International Airport (HKIA), using experimental and computational means. The experimental actions consisted in conducting field tests in two locations of Hong Kong city, to measure the ground noise signals incurred by actual aircraft departing from or approaching HKIA. Most of these ground noise signals were then further exploited to assess their psychoacoustic characteristics through commonly used noise metrics (e.g., sound quality criteria and psychoacoustic annoyance rate). To further evaluate these noise signals from a human-centric perspective, a laboratory psychoacoustic survey is performed, in which 30+ participants are asked to listen to a series of aircraft noise recordings and then compare their relative annoyance levels. This comparative assessment reveals rather consistent trends, e.g., the noise signals' loudness and sharpness stand out as prime contributors to the perceived annoyance, which aligns with common observations from the literature. This being said, the scores obtained in terms of psychoacoustic annoyance rate (PA) exhibit significant variabilities depending on both the PA model employed and the effective duration of the noise samples it is applied to, which may indicate some limitations in the state-of-the-art modelling of perceived annoyances. Overall, this study demonstrates the benefits of combining field-test measurements and laboratory psychoacoustic surveys when it comes to holistically assessing the perceived noise annoyance of air traffic operations.

This study discusses a machine learning-driven methodology for optimizing the aerodynamic performance of both conventional,like common research model(CRM),and non-conventional,like Bionica box-wing,aircraft configurations.The approach leverages advanced parameterization techniques,such as class and shape transformation(CST)and Bezier curves,to reduce design complexity while preserving flexibility.Computational fluid dynamics(CFD)simulations are performed to generate a comprehensive dataset,which is used to train an extreme gradient boosting(XGBoost)model for predicting aerodynamic performance.The optimization process,using the non-dominated sorting genetic algorithm(NSGA-Ⅱ),results in a 12.3%reduction in drag for the CRM wing and an 18%improvement in the lift-to-drag ratio for the Bionica box-wing.These findings validate the efficacy of machine learning based method in aerodynamic optimization,demonstrating significant efficiency gains across both configurations.

As part of a more global research effort towards greener aviation, the present study focuses on the noise impact of aircraft operations around major airports. To this end, two distinct aircraft noise prediction approaches are cross-fertilized, which are of semi-empirical or fully computational natures. Firstly, a well-established computational approach relying on Ray Tracing (RT) is improved from the viewpoint of its noise generation phase, whose source model is refined upon that of a popular semi-empirical method (Integrated Noise Model, INM) to incorporate more realistic spectral content. Secondly, the latter semi-empirical INM method is refined from the viewpoint of its noise propagation phase, whose sound attenuation model is refined using that of the RT approach to incorporate the convection and/or refraction effects stemming from realistic (i.e., heterogeneous) atmospheres. Each one of these two refined prediction approaches is validated through test cases involving representative aircraft operations and/or meteorological conditions. All in all, this study illustrates how some well-established prediction methodologies can be improved by proper cross-fertilization, thereby allowing to assess more accurately the environmental impacts of air traffic operations.

In regard to the development of more efficient yet silent wind turbines, this research consists in designing a wind turbine small-scale model. The latter allows characterizing experimentally both the aerodynamics and the acoustics of any kind of blade (whether traditional or innovative) to be considered under representative conditions (flow Reynolds number and subsequent rotational speed). The rig replicates a representative horizontal axis wind turbine from the US National Renewable Energy Laboratory (NREL), namely the NREL Phase VI, which is well documented throughout the scientific community. Unlike what is commonly done, the wind turbine rig is equipped with a dual motor/resistor so that it can either be used within an aerodynamic wind tunnel (to measure the blades aerodynamic efficiency e.g. the power delivered by the free rotation of the rotor, when exposed to an incoming flow) or within an anechoic chamber (to measure the blades acoustic discretion e.g. the noise signature coming from the powered-spinning rotor). The rig is fully instrumented (e. g. load balance, torque meter, optical encoder), as well as made easy enough to operate (with a flexible albeit simple and robust way to change the blades and adjust them in pitch). Aside from that, the rig is made modular, so that it can be easily dismantled, and then stored or displaced from one facility to another. The rig is tested within two facilities (aerodynamic wind tunnel, anechoic chamber), the aerodynamic results being validated against experimental data from the literature. This dual experimental campaign demonstrates the added value of this kind of equipment, when it comes to develop more efficient and silent wind turbines.

As part of a more global research effort towards greener aviation, the present study focuses on the prediction of noise impacts by the air traffic around major airports. To this end, a simple yet efficient data-driven model is developed which, relying on machine learning (multivariable linear regression), allows assessing the noise levels and annoyance incurred by air traffic upon the sole knowledge of aircraft characteristics and operational features. This model is then applied to the air traffic occurring in Hong Kong city, which presents some unique specificities, e.g., complex airspace, non-standard aircraft types and/or operations, significant weather variations; Upon a prior training using a pre-existing experimental database of actual aircraft flying in and out of the Hong Kong International Airport (HKIA), the model proves efficient in predicting most flights’ noise impacts, which are here quantified through three specific sound metrics. In a second stage, this model is further simplified via a systematic exploration and subsequent reduction of its constitutive variables, thereby leading to a reduced order model of similar accuracy and lower complexity. As a byproduct, this model reduction allows discriminating the main drivers underlying the noise impacts by aircraft. Overall, this study demonstrates how a data-driven model based on a relatively simple machine learning approach can effectively predict the noise impacts of air traffic upon the sole knowledge of aircraft characteristics and operational features, even when they are non-standard as in Hong Kong.

This study explores the prediction of airfoil self-noise through Deep Learning whilst focusing more specifically on the so-called turbulent boundary layer trailing edge (TBL-TE) noise. To this end, a predictive model relying on a Deep Neural Network (DNN) is developed, being then trained using an experimental database of TBL-TE noise signatures previously acquired by NASA. The DNN is favorably benchmarked against the test results, demonstrating its superiority over a popular semi-empirical prediction tool, i.e., the BPM model from NASA. Special attention is paid to the sensitivity of the DNN towards its architecture and/or its training extent. All in all, the DNN proves robust and accurate, reproducing faithfully the TBL-TE noise signatures with an average error of about 1.5 similar to 2.5 dB in terms of Sound Pressure Level. Special attention is also paid to sensitivity of the DNN towards the composition of its training dataset, whose consistency is enforced by clustering all datapoints belonging to an identical test configuration. This allows evaluating how far the model constitutes a true prediction tool, i.e., can extrapolate the experimental database instead of merely interpolating it through overfitting. Finally, the sensitivity of the DNN towards its training data is further explored to tentatively discriminate which quantities constituting the experimental database may contribute more significantly to the correct prediction of the noise signatures and - by extension - to their underlying physical mechanisms.

We explore the transition to chaos in a prototypical hydrodynamic oscillator, namely a globally unstable low-density jet subjected to external time-periodic forcing. As the forcing strengthens at an off-resonant frequency, we find that the jet exhibits a sequence of nonlinear states: period-1 limit cycle $\rightarrow $ quasiperiodicity $\rightarrow$ intermittency $\rightarrow$ low-dimensional chaos. We show that the intermittency obeys type-II Pomeau–Manneville dynamics by analysing the first return map and the scaling properties of the quasiperiodic lifetimes between successive chaotic epochs. By providing experimental evidence of the type-II intermittency route to chaos in a globally unstable jet, this study reinforces the idea that strange attractors emerge via universal mechanisms in open self-excited flows, facilitating the development of instability control strategies based on chaos theory.

In this experimental study, we use a data-driven machine learning framework based on genetic programing (GP) to discover model-free control laws (individuals) for suppressing self-excited thermoacoustic oscillations in a prototypical laminar combustor. This GP framework relies on an evolutionary algorithm to make decisions based on natural selection. Starting from an initial generation of individuals, we rank their performance based on a cost function that accounts for the trade-off between the state cost (thermoacoustic amplitude) and the input cost (actuator power). We then breed subsequent generations of individuals via a tournament in which the direct forwarding of elite individuals occurs alongside genetic operations such as mutation, replication, and crossover. We implement this GP control framework in both closed-loop and open-loop forms, followed by benchmarking against conventional open-loop control based on time-periodic forcing. We find that while all three control strategies can achieve similarly large reductions in thermoacoustic amplitude, GP closed-loop control consumes the least actuator power, making it the most efficient. It achieves this efficiency by learning an actuation mechanism that exploits the strong heat-release-rate amplification of the open flame at its preferred mode, even though the GP algorithm has never seen the open flame itself. This study demonstrates the feasibility of using GP to discover new and more efficient model-free individuals for suppressing self-excited thermoacoustic oscillations, providing a promising approach to data-driven feedback control of combustion devices.

We present experimental evidence of coherence resonance (CR) in an aerodynamic system, namely the flow around a prototypical airfoil. When stall occurs, this flow experiences a Hopf bifurcation, transitioning from a fixed-point state to a limit-cycle state of low-frequency oscillations. We inject white noise into the system en route to the Hopf point, while monitoring the power spectra of the aerodynamic force fluctuations. By evaluating the Lorentzian features of the spectra, we show that the coherence factor reaches a maximum at an intermediate noise amplitude, providing definitive evidence of CR. We then model the CR dynamics with a Van der Pol oscillator subjected to additive white Gaussian noise. We calibrate the model parameters by applying output-only system identification via the Fokker-Planck equation. We find good agreement between the model and experiments, enhancing the universality of CR in systems with Hopf bifurcations and paving the way for CR to be used for the early detection of airfoil stall.

Regarding the further development of wind energy solutions, this study explores the idea of morphing wind turbine blades as a means to improve their aero-structural merits. To this end, the aerodynamics of a representative, morphable wind turbine blade section is assessed using numerical simulation, this being done for a significant range of flow conditions (speed, incidence), with various levels of morphing applied. Upon this, diverse morphing scenarios are inferred, either to alleviate the aero-structural loads exerted on the blade, or to rather maximize its generated aerodynamic power. It is shown that these morphing strategies translate into significant benefits, among which substantial gains of power delivered across all flow regimes. The phenomenology behind the benefits brought by the morphing is then explored, which confirms that morphing a blade is superior to simply pitching or twisting it - as classically done or envisioned. Finally, the outcomes obtained from the computational investigation are replicated through a dedicated, small-scale experiment, thereby further confirming the aerodynamic merits brought by the morphing. All this advocates for a further exploration of morphable wind turbine blades.

Focusing on a bio-inspired passive flow control strategy with a potential application to wind turbines, this study consists of an experimental characterization of the aerodynamics of a representative wind turbine blade section to be possibly equipped with leading-edge tubercles. A total of five airfoils are tested in a low-speed wind tunnel, their aerodynamic characteristics being explored over a wide range of flow conditions by the means of load measurements and wall-flow visualizations. Results reveal that the leading-edge tubercles alter differently the airfoil's aerodynamics, depending on their specific design (amplitude and wavelength) as well as on the flow conditions (speed and incidence). The magnitude of these effects is seen to vary with the tubercles' amplitude and/or wavelength, ultimately leading to an optimal trade-off between pre- and post-stall regimes being offered by an intermediate amplitude-to-wavelength ratio. Second, the aerodynamic benefits/penalties induced by the leading-edge tubercles are explored through wall-flow visualizations; The tubercled airfoils exhibit pairs of counter-rotating vortices, whose characteristics are driven by the tubercles' design, the airfoil shape, and/or the flow conditions. Overall, these vortex pairs are more prominent for large-amplitude and/or small-wavelength tubercles, each design altering the near-wall flow and, thus, the aerodynamic performances in a specific fashion.

As part of a more global research effort towards a greener aviation, the present study focuses on the noise impact by aircraft operations around a major airport, namely the Hong Kong International Airport (HKIA). To assess a pre-existing aircraft noise prediction platform vis-à-vis the specificities of the local aviation scene, the ground noise impacts of 70+ aircraft flying in and out of HKIA are live-measured in two locations of Hong Kong city. This is achieved through in-situ audio recordings of aircraft during take-off/landing flight phases, along with a real-time tracking of their characteristics (aircraft and engine types, flightpaths, atmospheric conditions, etc.). Once suitably post-processed, the measurements are analyzed to characterize better the noise impacts by these various flights in regard to their specificities (e.g., aircraft types, flight operations). In a second stage, these noise impacts are compared against their digital twins, which are simulated via the noise prediction platform, using the actual flight characteristics (aircraft types, flightpaths, thrust, airspeed, etc.). This comparative assessment is achieved for a total of 60 flights consisting of 37 departures (resp. 23 arrivals) and involving 6 (resp. 5) aircraft types. Overall, the comparison between the field tests and their digital twins proves to be reasonably good, with both measured and predicted noise levels falling within a range of a few decibels across all flights, on average. Finally, specific analyses are conducted to explore the uncertainties weighing on the predictions, possibly explaining some of the mismatches observed between the experimental and computational results. Overall, the study further demonstrates that aircraft noise prediction methods such as the present one constitute a valuable means to assess the environmental impacts of air traffic operations, including when the latter are highly specific — as in Hong Kong city, owing to its complex airspace and meteorological conditions.

As part of a more global research effort towards a greener aviation, the present study focuses on the noise impact by the air traffic in Hong Kong city, which presents some unique specificities (e.g., complex airspace, non-standard aircraft types and/or operations, significant weather variations). A previous effort saw the present authors characterizing the ground noise incurred by 70+ aircraft flying in and out of the Hong Kong International Airport (HKIA), using experimental and computational means. The experimental actions consisted in conducting field tests in two locations of Hong Kong city, to measure the ground noise signals incurred by actual aircraft departing from or approaching HKIA. Here, most of these ground noise signals are further exploited to assess their psychoacoustic characteristics, which are assessed through specific noise metrics, either traditional or more sophisticated. Upon this, an empirical prediction model is developed, which relies on machine learning (linear regression) and allows assessing the noise annoyances incurred by air traffic operations upon the sole knowledge of aircraft characteristics and their operational features. This empirical model is then further simplified via the systematic exploration and subsequent reduction of its constitutive parameters, thereby leading to a reduced order model of similar accuracy and lower complexity. Overall, this study demonstrates further the pertinence of assessing more holistically the noise impacts of air traffic operations, especially when they are non-standard as in Hong Kong.

The aerodynamic noise generated by the unsteady flow passing a cylinder is simulated via Computational Fluid Dynamics (CFD), using compressible Large Eddy Simulations (LES). Two flow regimes are considered, namely laminar periodic and sub-critical. The laminar periodic regime is simulated using two-dimensional calculations, which allows for the validation of the numerical approach. The sub-critical regime is simulated using 3D calculations of variable spanwise extent, thereby allowing exploration of the impact of three-dimensionality on the turbulent flow dynamics and its resulting noise emission. This parametric study reveals a strong dependency of both aerodynamic and acoustic characteristics towards the spanwise extent. This dependency is further investigated by examining how the vortices that are incepted within the shear layer and then shed into the wake correlate with the near- and far-field pressure; As the spanwise extent increases, the turbulent structures get finer, and the noise emissions lower. This spanwise increase, however, is not enough to mitigate the residual discrepancy observed between the simulation results and the reference one, which comes from an experiment. This residual discrepancy is believed to stem from the confinement of the computational domain, which prevents the expansion (and, thus, natural decay) of the noise waves that are emitted by the vortex shedding.

As part of a research effort towards greener aviation, this study focuses on the noise impact of aircraft operations around major airports. First, a pre-existing aircraft noise prediction platform based on a simplified (semi-empirical) approach is refined, to account for all refraction effects inherited from realistic (e.g., heterogeneous) atmospheres. To do so, the platform is enhanced with a more advanced noise propagation kernel relying on Ray Tracing, which combines state-ofthe-art functionalities and novel refinements (e.g., to account for the three-dimensional nature of winds). This improved prediction platform is then validated against various benchmark cases, delivering outcomes of both phenomenological and methodological natures (e.g., the importance of accounting for 3D wind patterns whilst numerically handling them with care). Finally, the platform is applied to real-life situations involving representative aircraft operations and actual meteorological conditions, revealing how weather variations can significantly alter the propagation -and thus impact -of aircraft noise.

As part of a more global research effort towards a greener aviation, the present study focuses on assessing the ground noise impact from air traffic operations around Hong Kong International Airport (HKIA). Owing to the unique specificities (and subsequent methodological complexities) pertaining to the local aviation scene in Hong Kong (e.g., complex airspace, non-standard aircraft types and/or operations, significant weather variations), both computational and experimental means are deployed, to better characterize the ground noise incurred by aircraft flying in and out of HKIA. From a computational perspective, real-life situations involving representative aircraft operations and meteorological conditions are simulated using a pre-existing aircraft noise prediction platform, which relies on two different computational approaches (semi-empirical and fully computational) and had been previously validated through canonical benchmark cases. From an experimental viewpoint, dedicated field tests are conducted in two locations of Hong Kong city, to measure the ground noise signals incurred by actual aircraft departing from or approaching HKIA. In both cases, comparative analyses are conducted to highlight how far the ground noise impact by aircraft may depend on their characteristics (type) and/or operational conditions (flightpaths, weather, etc.). In a second time, most of the field test experiments are simulated using the aircraft noise prediction platform, to further benchmark and validate its relevance in regard to the specificities pertaining to Hong Kong. A straight comparison between the experimental and computational results reveals a fairly good agreement between the measurements and their digital twins, especially for what concerns departure flights. It is shown how accounting more accurately for the meteorological conditions may help improving the predictions.