Impact of wake modeling uncertainty on helicopter rotor aeroacoustic analysis

Free-wake models are routinely used in aeroacoustic analysis of helicopter rotors; however, their semi-empiricism is essentially accompanied with uncertainty related to physical wake parameters. In some cases, analysts have to resort to empirical adaption of these parameters based on previous experimental evidence. This paper investigates the impact of inherent uncertainty in wake aerodynamic modeling on the robustness of helicopter rotor aeroacoustic analysis. A freewake aeroelastic rotor model is employed to predict high-resolution unsteady airloads, including blade-vortex interactions. A rotor aeroacoustics model, fundamentally based on Acoustic Analogy, is utilized to calculate aerodynamic noise in the time-domain. The individual analytical models are incorporated into a stochastic analysis numerical procedure, implemented through non-intrusive Polynomial Chaos expansion. The possible sources of uncertainty in wake tip-vortex core modeling are identified and their impact on noise predictions quantified. When experimental data to adjust the tip-vortex core model are not available the uncertainty in acoustic pressure and ground noise impact at observers dominated by blade-vortex interaction noise can reach up to 25% and 3.50 dB respectively. This work aims to devise generalized uncertainty maps to be used as modeling guidelines for aeroacoustic analysis in the absence of the robust evidence necessary for calibration of semi-empirical vortex core models

A conceptual framework for combining artificial neural networks with computational aeroacoustics for design development.

This paper presents a preliminary method for improving the design and development process in a way that combines engineering design approaches based on learning algorithms and computational aeroacoustics. It is proposed that machine learning can effectively predict the noise generated by a coaxial jet exhaust by utilizing a database of computational experiments that cover a variety of flow and geometric configurations. A conceptual framework has been outlined for the development of a practical design tool to predict the changes in jet acoustics imparted by varying the fan nozzle geometry and engine cycle of a coaxial jet. It is proposed that computational aeroacoustic analysis is used to generate a training and validation database for an artificial neural network. The trained network can then predict noise data for any operational configuration. This method allows for the exploration of noise emissions from a variety of fan nozzle areas, engine cycles and flight conditions. It is intended that this be used as a design tool in order to reduce the design cycle time of new engine configurations and provide engineers with insight into the relationship between jet noise and the input variables.N/

Aeroacoustic simulation of rotorcraft propulsion systems.

Rotorcraft constitute air vehicles with unique capabilities, including vertical take- off and landing, hover and forward/backward/lateral flight. The efficiency of rotorcraft operations is expected to improve rapidly, due to the incorporation of novel technologies into current designs. Moreover, enhanced or even new capabilities are anticipated after the introduction of advanced fast rotorcraft configurations into the future fleet. The forecast growth in rotorcraft operations is essentially associated with an expected increase in adverse environmental impact. With respect to the forthcoming rotorcraft aviation advancements, regulatory and advisory bodies, as well as communities, have focused their attention on reducing pollutant emissions and acoustic impact of rotorcraft activity. Consequently, robust and computationally efficient noise modelling approaches are deemed as prerequisites towards quantifying the acoustic impact of present and future rotorcraft activity. Ultimately, these approaches need to cater for unique operational conditions encompassed by modern rotorcraft across designated flight procedures. Additionally, individual variations of key design variables need to be resolved, in the context of design or operational optimisation, targeted at noise mitigation. This work elaborates on the development and application of a robust and computationally efficient methodology for the aeroacoustic simulation of rotorcraft propulsion systems. A series of fundamental modelling methods is developed for the prediction of helicopter rotor noise at fully-integrated operational level. An extensive validation is carried out against existing experimental data with respect to prediction of challenging aeroacoustic phenomena arising from complex aerodynamic interactions. The robustness of the deployed method is confirmed through a cost-effective uncertainty analysis method focused on aerodynamic sources of uncertainty. A set of generalised modelling guidelines is devised for the case of not available input parameters to calibrate the aerodynamic models. The aspect of multi-disciplinary optimisation of rotorcraft at aircraft level in terms of maximising the potential benefits of novel technologies is also tackled within this work. A holistic schedule of optimal active rotor morphing control is derived, offering simultaneous mitigation of pollutant emissions and acoustic impact across a wide range of the helicopter flight envelope. Finally, the developed noise prediction method is incorporated into an operational-level optimisation algorithm, demonstrating the potential of active rotor morphing with respect to reduction of ground-noise impact. The contribution to knowledge arising from the successful completion of this work comprises both the development of methodologies for helicopter aeroacoustic analysis and the derivation of guidelines and best practices for morphing rotor control. Specifically, a generic operational-level simulation approach is developed which effectively advances the state-of-the-art in mission noise prediction. New insight is provided with respect to the impact of wake aerodynamic modelling uncertainty on the robustness of noise predictions. Moreover, the aeroacoustic aspects of a novel morphing rotor concept are explored and quantifications with respect to the trade-off between environmental and noise disciplines are offered. Finally, a generalised set of optimal rotor control guidelines is derived towards achieving the challenging environmental goals set for a sustainable future rotorcraft aviation.PhD in Aerospac

INDUSTRIAL CFD SIMULATION OF AERODYNAMIC NOISE

Real challenges to suppress undesirable fluid-excited acoustics are posed by a wide variety of engineering disciplines. Noise regulations, passenger comfort and component stability are motivators which are continuing to stimulate substantial efforts towards the understanding of aeroacoustic phenomena, and not least to quantify the usability (practicability and value) of traditional and advanced prediction methods. The latter is the primary focus of this thesis, particularly as applied to the transportation industries, aerospace, automotive and rail. Nowadays Computational Fluid Dynamics (CFD) is a tool well integrated into the industrial development and production life-cycles. This is possible now because of two main factors: the increase in the performance of relatively cheap personal computers and network facilities, and the progress made in general purpose CFD software between modeling complexity and practicability within the industrial environment. While CFD methodologies are well established for lots of applications such as aerodynamics, heat exchange, etc., aeroacoustic CFD simulations still represent a challenge, in particular their industrial practicability. In these years this has given rise to heavy investments by the automotive industry in international aeroacoustics consortia, whereby all the major car companies work together to study the limitations and advantages of aeroacoustics CFD. The general aim of these consortia is to develop methodologies which fit into, and improve upon, current design processes. The goal of the present work is to explore the multitude of different CFD modeling approaches for some typical industrial problems such as: cavity noise, vortex shedding noise, propeller and jet noise. Each of these problems has a particular mechanism for noise generation and different methods have been studied and tested, in order to develop and optimize a practical methodology for the analysis of each problem type. Furthermore each of the aeroacoustics problems considered are representative of a variety of industrial applications. Cavity noise is at the origin of phenomena such as sun-roof buffeting in convertibles or door-gap tonal noise. Vortex shedding noise is typical of any flows involving bluff bodies such as automobile antennas or aircraft landing gear. Propeller noise is typical to applications involving rotating machinery, such as fans, pumps and turbines. Different approaches ranging from steady and transient RANS simulations with the acoustic analogy (including porous and solid surface formulations), to Computational Aero Acoustics (CAA) and Large Eddy Simulation (LES) type computations have been studied and applied. Classic theories already exist to predict aerodynamically generated noise, which are both computationally and economically less expensive than CFD methods. However aeroacoustics CFD is the future, beginning as a promising present, for the following reasons: Industries are interested in modeling complex geometries. Many classic theories can be applied successfully but very often restrictions exist with respect to the configuration and flow conditions. For example, classic propeller theories cannot be used to model real-world configurations such as a propeller installed on a wing with some prescribed yaw or angle of attack. The progress of all other Computer Aided Design and Engineering tools, such as linear or non-linear structural codes, are driving design towards a virtual multi-physics approach for the simulation of complex geometries. Due to previous experience and the wide availability of modeling options, it was decided to use the general purpose CFD software package ANSYS FLUENT for CFD investigations in this study

Signal Processing and Propagation for Aeroacoustic Sensor Networking,” Ch

Passive sensing of acoustic sources is attractive in many respects, including the relatively low signal bandwidth of sound waves, the loudness of most sources of interest, and the inherent difficulty of disguising or concealing emitted acoustic signals. The availability of inexpensive, low-power sensing and signal-processing hardware enables application of sophisticated real-time signal processing. Among th

Innovative Helicopter In-Flight Noise Monitoring Systems Enabled by Rotor-State Measurements

The present contribution aims at providing a comprehensive illustration of a new approach to rotorcraft noise abatement, especially during terminal procedures, when the vehicle approaches the ground and the acoustic impact is higher. This approach pursues the development of technologies and tools for real-time, in-flight monitoring of the emitted noise. The effect of the acoustic radiation is presented to the pilot in a condensed, practical form on a new cockpit instrumentation, the Pilot Acoustic Indicator (PAI), to be used for performing quieter maneuvers. The PAI is based on the synergetic composition of pre-calculated acoustic data, which are used in a noise estimation algorithm together with the data gathered by an innovative contactless measurement system, capable of acquiring the main rotor blade motion. The paper reports on the current studies in unsteady and quasi-steady aeroacoustic prediction and tip-path-plane angle of attack and thrust coefficient observation. Results on novel methodologies are discussed, together with the main features of the PAI design and development process

SIMULATION OF WHISTLE NOISE USING COMPUTATIONAL FLUID DYNAMICS AND ACOUSTIC FINITE ELEMENT SIMULATION

The prediction of sound generated from fluid flow has always been a difficult subject due to the nonlinearities in the governing equations. However, flow noise can now be simulated with the help of modern computation techniques and super computers. The research presented in this thesis uses the computational fluid dynamics (CFD) and the acoustic finite element method (FEM) in order to simulate the whistle noise caused by vortex shedding. The acoustic results were compared to both analytical solutions and experimental results to better understand the effects of turbulence models, fluid compressibility, and wall boundary meshes on the acoustic frequency response. In the case of the whistle, sound power and pressure levels are scaled since 2-D models are used to model 3-D phenomenon. The methodology for scaling the results is detailed

Computational aeroacoustic modelling using hybrid RANS/LES methods with modified acoustic analogies

This study considers a numerical approach to identifying noise mechanisms in tandem cylinders to understand aircraft landing gear as a primary contributor to airframe noise during approach and landing. Fluctuations in the flow properties induced by turbulence are computed as well as the corresponding propagations. A hybrid IDDES turbulence model is employed, to compute the boundary layer and fluctuations in the flow properties. Larsson et al. modified Curle's analogy leading to the derivation of a version of Curle's analogy that makes use of strictly time derivatives which has been proven to be less sensitive to numerical errors. Brentner and Farassat derived a formulation of the Ffowcs-Williams and Hawkings analogy for a permeable surface enclosing the acoustic sources which accounts for the quadrupole acoustic sources in the flow without the costly calculation of a volume integral. This study will consider the impact of neglecting the volume sources through a comparison of the two modified versions of Curle's and FWH analogies with the results of other CFD practitioners as well as experimental data

Simulation of Airbus-A320 fuselage surface pressure fluctuations at cruise conditions in "Aeroacoustics research in Europe: The CEAS-ASC report on 2019 highlights"

The fuselage surface pressure fluctuations on an Airbus-A320 aircraft at cruise conditions are simulated by solving a Poisson equation. The right-hand-side source terms of the Poisson equation, including both the mean-shear term and the turbulence-turbulence term, are realized with synthetic anisotropic turbulence generated by the Fast Random Particle-Mesh Method. The stochastic realization is based on time-averaged turbulence statistics derived from a RANS simulation under the same condition as in the flight tests, conducted with DLR's Airbus-A320 research aircraft. The fuselage surface pressure fluctuations are calculated at three streamwise positions from front to rear corresponding to the measurement positions in the flight tests. One- and two-point spectral features of the pressure fluctuations relevant to the fuselage surface excitation are obtained and analysed

Improving the analysis of aeroacoustic measurements through machine learning

This thesis focuses on improving the analysis of aeroacoustic imaging methods using automated data processing and machine learning. Imaging methods result in beamforming maps that are challenging to explore manually since they comprise complex, high-dimensional data, including spatial coordinates, frequency, and flow properties. The manual, iterative analysis is time-consuming, biased, and typically based on 2D beamforming maps that only insufficiently capture the complex source distribution of airframe sources. Further, acoustic array imaging methods often assume monopole sources and, thus, suffer from mismatches between model assumptions and actual sources. This thesis addresses these issues by proposing novel broadband beamforming methods based on the observation that most airframe noise sources are spatially compact and parameters such as their location do not change over frequency. A broadband approach improves the ratio of known to unknowns in the mathematical formulation of the problem, which allows for the inclusion of advanced model assumptions, such as dipoles and distributed sources. The resulting novel methods are broadband Global Optimization, a gridless covariance matrix fitting method, Broadband-CLEAN-SC, an adaptation of CLEAN based on Source Coherence (CLEAN-SC), and gridless beamforming using artificial neuronal networks with a permutation invariant loss. All proposed methods share that a beamforming solution is obtained for multiple frequencies simultaneously. The broadband approach improves the resolution at low frequencies. It suppresses side- and grating lobes (aliasing), improves the identification and spectra extraction from the results, and outperforms the corresponding small-band methods. This thesis proposes two clustering methods that identify sources in the high-dimensional beamforming maps and extract their spectra to post-process the industrial gold standard conventional beamforming and CLEAN-SC methods. The proposed "Source Identification based on Spatial Normal Distribution" (SIND) method is a clustering algorithm similar to a Gaussian Mixture Model. It is tailored to the source identification problem, with spatial discretization, a large number of estimated sources, and statistical noise as its main challenges. To determine the number of clusters, SIND does not rely on a priori hyper-parameters but determines the unknown number of sources iteratively from the spatial distribution of the data. The proposed "Source Identification based on Hierarchical Clustering" (SIHC) method clusters the data directly in space and frequency using the established Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. The introduced automatic source identification capabilities allow the precise source identification and spectra extraction of 3D beamforming maps with no added effort, compared to the standard manual process, typically based on planar 2D beamforming maps with insufficient spatial resolution. This thesis provides an overview and insight into the aeroacoustic theory and introduces numerical features that explicitly formulate physical properties, enabling the deduction of aeroacoustic source mechanisms. The proposed formulas are robust towards noisy and degenerate spectra typically resulting from deconvolution methods such as CLEAN-SC. They are independent of the measured object, the amount of measured Mach numbers, and the Mach numbers themselves. Thus, they offer a comparability of the properties across different measurements, which was previously impossible. Sources can be visualized utilizing their high-dimensional feature space through dimensional-reduction methods and effectively clustered with HDBSCAN, offering a manual classification and interpretation guideline. This process facilitates the creation of an Expert Decision Support System (EDSS). The clusters proposed by the EDSS strongly correlate with the manually determined categories so that the expert can interpret them. Clustering results from industrial wind tunnel experiments on a Dornier 728 and Airbus 320 models are presented. The clustering accuracy, determined from a confusion matrix and a manual selection of its correct entries, is 77.04% for the Dornier 728 and 61.52% for the Airbus 320 experiment. The thesis presents a detailed aeroacoustic analysis and manual classification of all occurring airframe sources. Novel aeroacoustic observations are described, such as that the flap side edge and strake are composed of two sources each with different mechanisms and that many sources depend on the Mach number weaker than the one of a true Strouhal number. Also, some sources, such as the strake and cavity noise, show a Mach number dependency, even at a constant Reynolds number, while most sources are self-similar within a large Reynolds number range. In summary, this thesis presents an improved workflow for beamforming, post-processing, interpretation, and knowledge generation from aeroacoustic experiments. The proposed EDSS enables a complete aeroacoustic analysis of wind tunnel experiments, offering detailed insights into the nature of the sources. Further, the EDSS has proven its capabilities to be employed in situ to detect and fix spurious noise sources during experiments, offering new perspectives and a practical tool for researchers and practitioners in the field