Jet Noise Prediction Research Articles

Use of semi-empirical models to predict jet noise radiated by unsuppressed and suppressed nozzles

ONERA is involved in two R&T projects related to supersonic aircraft : a self-funded project called PINSON in which a 10-passengers supersonic concept-plane is developed with an aim of reducing consumption, sonic boom and take-off noise, and EU-Project SENECA aiming at providing insights on i) achievable noise levels during takeoff and landing and ii) global climate impact of supersonic aircraft. In both project, the semi-empirical models developed by Stone et al. have been identified as valuable tools for predicting jet noise, which is assumed to be the main contributor to supersonic aircraft noise at take-off. With an aim of extending the work initiated by Jaron et al. in the SENECA framework, ONERA applied Stone et al.'s models to various jet configurations to better understand their domain of validity. Ultimately, the objective is to use those models during engine pre-sizing phase of PINSON project in which a low-noise nozzle equiped with chevrons will be optimized from a multidisciplinary point of view. The objectives of the paper are i) to present the results obtained by applying those models on the EXEJET configurations and ii) to discuss the ability of the latest version of the model to predict chevrons acoustic impact.

Subsonic Jet Noise Prediction in Near and Far Field with Optimized Wave-Packet Approach

In this study, the parameters of a wave-packet model for subsonic jet noise prediction are systematically optimized by leveraging near- and far-field data obtained from the large-eddy simulation (LES) of a free jet at a Mach number of 0.9 across various radial distances. The utilization of near-field information is justified by the observation that the scattering surfaces are typically situated within a few nozzle diameters from the jet axis in the radial direction, both in the current and in innovative aircraft configurations. The far-field information is used to guarantee the correct subdivision between the wave-packet radiating noise and the hydrodynamic components. The results show a notable agreement between the LES data and the wave-packet solutions, consistent with findings documented in the existing literature. This agreement underscores the validity and applicability of the implemented methodology, offering an effective method for obtaining an equivalent jet noise acoustic source, easily implementable in acoustic scattering codes, and accounting for the directional behavior of jet noise.

Nektar++: Development of the compressible flow solver for jet aeroacoustics

A recently developed computational framework for jet noise predictions is presented. The framework consists of two main components, focusing on source prediction and noise propagation. To compute the noise sources, the turbulent jet is simulated using the compressible flow solver implemented in the open-source spectral/hp element framework Nektar++, which solves the unfiltered Navier-Stokes equations on unstructured grids using the high-order discontinuous Galerkin method. This allows high-order accuracy to be achieved on unstructured grids, which in turn is important in order to accurately simulate industrially relevant geometries. For noise propagation, the Ffowcs Williams - Hawkings method is used to propagate the noise between the jet and the far-field. The paper provides a detailed description of the computational framework, including how the different components fit together and how to use them. To demonstrate the framework, two configurations of a single stream subsonic jet are considered. In the first configuration, the jet is treated in isolation, whereas in the second configuration, it is installed under a wing. The aerodynamic results for these two jets show strong agreement with experimental data, while some discrepancies are observed in the acoustic results, which are discussed. In addition to this, we demonstrate close to linear scaling beyond 100,000 processors on the ARCHER2 supercomputer.

A physics merged deep neural network-based prediction method for jet turbulent mixing noise

Turbulent mixing noise is a vital component of jet noise, and its rapid, accurate prediction has always been persistently pursued. Recent advancement in machine learning has been applied to jet noise prediction. However, these applications are pure curve fitting and lack physical constraints. In this study, a physics-merged deep neural network (PMNN)-based prediction method was developed for turbulent mixing jet noise by merging the physics of the jet noise. This deep neural network (DNN)-based method employed recent advancements in jet turbulent mixing noise containing large- and fine-scale turbulence structures. Two simple rational functions for large- and fine-scale turbulent noise similarity spectra were proposed to replace the original complex similarity spectra functions and incorporated into the DNN-based prediction method. For comparison, we present two data-driven DNN-based prediction methods (DDNN). The first DDNN method used the sound pressure level (SPL) as the output of neural networks, directly establishing the nonlinear relationship between the input features and SPL. In the second DDNN method, the dominant modes of the jet noise spectra extracted using the proper orthogonal decomposition method were merged with DNN. These DNN-based methods were then trained using a set of experimental data over a wide range of jet operating conditions. Their performance was evaluated and compared. It was evident that all these DNN-based methods were capable of predicting turbulent mixing noise reasonably well. In contrast to the DDNN methods, the PMNN method could provide insights into the jet turbulent mixing noise components. It demonstrates that the turbulent mixing jet noise spectra at the mid polar angle is generated by the large-scale noise component at low-frequency range and by the fine-scale noise component at high-frequency range.

Jet Noise Prediction Benchmark for Landing and Takeoff Noise of Supersonic Aircraft

This paper presents a brief overview of the EU-Project SENECA (LTO noiSe and EmissioNs of supErsoniC Aircraft) aiming to support certification authorities and the International Civil Aviation Organization's Committee on Aviation Environmental Protection (ICAO CAEP) by providing comprehensive and reliable data regarding the achievable noise levels during takeoff and landing and the global climate impact of civil supersonic aircraft. The focus of the paper is on the jet noise prediction benchmark test conducted by the project members to increase the reliability of the calculated noise certification levels. The objectives are to validate a correct implementation of the models and to identify the most suitable models for predicting jet noise of this specific type of supersonic aircraft engines. Overall three different semi-empirical jet noise models were used on four different test cases from literature. Results show remarkable differences of the predicted spectra. As expected the models fit their respective experimental data sets better than other models.

New Approaches for Analysis of Jet Noise Data

Despite the significant advances in Computational Aeroacoustics for predicting jet noise, there is still a considerable need for experimental data. However, traditional measures of the directivity of Overall Sound Pressure Levels or the spectral distribution of the acoustic energy yield limited understanding of the nature of the sources. There is hence a need to use different approaches to the analysis of jet acoustics, among them applications of statistical methods such as auto correlation, self-correlation etc to understand the noise sources. In this paper, the acoustic characteristics of supersonic and high subsonic jets from nozzles of different exit geometries are measured and analyzed using these methods. The results show that these statistical methods of analysis can reveal the presence of two distinct noise sources in the flow and are applicable to both subsonic and supersonic flows. The information can be used to determine the appropriate model for the data and is useful for forming new theories about the noise generation mechanism in turbulent jets.

Survey of Semi-Empirical Jet Noise Models for Preliminary Aircraft Engine Design

Scientific research studies on jet noise generation have been ongoing since the early 1950s, when turbojets were first used in commercial aircraft. Several numerical methods have been developed with the aim of reducing the environmental issues related to the impact of jet noise on community annoyance. Among them, the development of fast and comprehensive tools for jet noise prediction captured the attention of researchers and engineers, being very useful in the preliminary design phase of aircraft engines. This work deals with an extensive survey of James R. Stone’s models, initially formulated for the National Aeronautics and Space Administration (NASA) by Modern Technologies Corporation (MTC), and their implementation in a contemporary numerical framework. The models and their implementation are validated by simulating different engine settings and nozzle configurations taken from the literature with the main scope of highlighting the strengths and weaknesses of the different semi-empirical formulations and setting guidelines for their effective use during the design phases of the next generation of supersonic aircraft.

Robustness of Reduced-Order Jet Noise Models

Three statistical jet noise prediction models are compared for a representative set of single-stream jet cases, which include cold and hot jets of the Strategic Investment in Low-Carbon Engine Technology experiment at an acoustic Mach number of 0.875 as well as the cold jets of the NASA small hot jet acoustic rig experiment at acoustic Mach numbers of 0.5 and 0.9. The implemented models are those proposed by Tam and Auriault (Jet Mixing Noise from Fine-Scale Turbulence,” AIAA Journal, Vol. 37, No. 2, 1999, pp. 145–153), Khavaran and Bridges (“An Empirical Temperature Variance Source Model in Heated Jets,” NASA TM 2012-217743, 2012), and the Goldstein’s “A Generalized Acoustic Analogy,” Journal of Fluid Mechanics, Vol. 488, July 2003, pp. 315–333) generalized acoustic analogy (GAA) model. By virtue of reduced-order modeling, which is based on the single-point mean flow and turbulence statistics, all of these implementations use a number of empirical dimensionless source parameters for far-field noise spectra predictions. In comparison with the Tam and Auriault model, the Khavaran and GAA model implementations use several dimensionless parameters, which are available from previous literature and assumed to be more or less universal for a class of single-stream jets. These parameters include the fluctuating enthalpy function and the dimensionless amplitudes of autocovariances of turbulent fluctuating stresses and velocities available from the literature. The comparison of the three models is aimed at not only assessing their accuracy for a range of jet conditions, observer angles, and frequencies but also to examine their robustness outside of a reference jet experiment for which their source models were calibrated. For the input to each model, the mean flow, turbulence kinetic energy, and dissipation rate extracted from Large Eddy Simulations and Reynolds-averaged Navier–Stokes solutions are considered.

Features of far-downstream asymptotic velocity fluctuations in a round jet: A one-dimensional turbulence study

An accurate prediction of the turbulent jet noise is usually only possible with direct numerical simulation (DNS) or high-resolution large-eddy simulation (LES) of the turbulent sources in the acoustic near field. The required level of fidelity comes at the price of high numerical resolution requirements, a severe restriction of the accessible parameter space, and high computational costs in general. These limitations can be partially mitigated by reduced-order models. In the present work, the stochastic one-dimensional turbulence (ODT) model is utilized as a stand-alone tool in order to study turbulent fluctuations in the far downstream region of turbulent round jets with finite co-flow velocity. ODT is a dimensionally reduced turbulence model that aims to resolve flow-field over a broad range of scales and, thus, the turbulent noise sources at all relevant scales, but only for a single, radially oriented, physical coordinate that is advected downstream with the flow during a simulation run. Here, unheated round jets with nozzle diameter D, nominal Mach number Ma = 0.9 but Reynolds number ReD∈{9×104,2×105,4×105} are studied as a canonical problem. An ensemble of ODT realizations is used to obtain flow statistics from a detailed representation of fluctuations that may be used to estimate turbulent noise by small-scale resolved sources in the near future. As the first step in this direction, we analyze the model representation of the flow field and the participating flow scales in detail. This is done even far downstream of the nozzle, which is not possible with high-resolution LES or DNS. The present ODT results agree well with the available reference data. The model accurately reproduces the asymptotic mean and fluctuating velocity behavior, and radial turbulence spectra of the jet that approximately obey large-scale jet similarity but are modified by axially decreasing the turbulence intensity. Based on these results, an outlook on the model application for turbulent jet noise prediction is given.

Jet noise predictions by time marching of single-snapshot tomographic PIV fields

This work combines the latest advancements in time marching of 3D vector fields from tomographic particle image velocimetry, with an adapted version of Lighthill’s formulation, for the prediction of far-field jet noise. Three-dimensional velocity vector fields of the jet flow are first reconstructed from a tomographic volume of 4times3times9.5 D_{j}^3, with D_{j} = 5 cm being the jet-exit diameter. (The jet-exit Mach number M_{j} ranges from 0.10 to 0.20.) The obtained vector fields are then used as input to a recently developed procedure for the time marching of the vorticity field, which relies upon the vortex-in-cell methodology. This yields time series of each three-dimensional velocity field, from which the far-field pressure is computed via Lilley’s acoustic analogy (through evaluation of the Lighthill’s stress tensor). It is shown that the estimate of the far-field noise spectrum compares well with the spectrum measured directly from a far-field microphone in the anechoic A-tunnel facility of TU Delft, in the Strouhal number range from approximately 1 to 12.Graphical

A Coupled LES-Synthetic Turbulence Method for Jet Noise Prediction

Large-eddy simulation (LES)-based jet noise predictions do not resolve the entire broadband noise spectra, often under-predicting high frequencies that correspond to un-resolved small-scale turbulence. The coupled LES-synthetic turbulence (CLST) model is presented which aims to model the missing high frequencies. The CLST method resolves large-scale turbulent fluctuations from coarse-grid large-eddy simulations (CLES) and models small-scale fluctuations generated by a synthetic eddy method (SEM). Noise is predicted using a formulation of the linearized Euler equations (LEE), where the acoustic waves are generated by source terms from the combined fluctuations of the CLES and the stochastic fields. Sweeping and straining of the synthetic eddies are accounted for by convecting eddies with the large turbulent scales from the CLES flow field. The near-field noise of a Mach 0.9 jet at a Reynolds number of 100,000 is predicted with LES. A high-order numerical algorithm, involving a dispersion relation preserving scheme for spatial discretization and an Adams–Bashforth scheme for time marching, is used for both LES and LEE solvers. Near-field noise spectra from the LES solver are compared to published results. Filtering is applied to the LES flow field to produce an under-resolved CLES flow field, and a comparison to the un-filtered LES spectra reveals the missing noise for this case. The CLST method recovers the filtered high-frequency content, agreeing well with the spectra from LES and showing promise at modeling the high-frequency range in the acoustic noise spectrum at a reasonable expense.

Acoustic measurement of under-expanded jet and its numerical prediction

AbstractIntense acoustic loads from jet noise cause noise pollution and induce failures, such as the malfunctioning of electronic devices and fatigue failure of internal/external structures. Consequently, the prediction of jet noise characteristics is crucial in the development of high-speed vehicles. This study presents acoustic experiments and predictions for an under-expanded, unheated jet using a small-scale prototype. Outdoor measurements are carried out using a vertical ejection setup. Acoustic characteristics are measured using both linear and circular microphone arrays. Additionally, numerical prediction of the same jet noise is performed using a detached eddy simulation and the permeable Ffowcs-Williams and Hawkings acoustic analogy. The vertical experimental setup exhibits the typical acoustic characteristics of a supersonic jet in terms of directivity and broadband shock-associated noise. Moreover, the numerical prediction exhibits satisfactory accuracy for the jet downstream, where the large-scale turbulence structures of the directivity predominate. However, discrepancy increases in the domain of lower directivity. The presented experiment and prediction will be extended to future studies regarding the noise of various deflector duct configurations impinging on supersonic jets.

A time-domain linear method for jet noise prediction and control trend analysis

Large-scale turbulent structures in the form of coherent wavepackets play a significant role in the generation of prominent shallow-angle noise radiation of jets. Economical prediction tools often model these wavepackets in the frequency-domain using stability modes of the mean flow. Simplifying choices, such as parabolized equations and azimuthal decomposition, provide efficient prediction methods but can impose constraints on rate of streamwise variation of the mean state or geometric complexity, thus limiting the range of application to mostly perfectly-expanded circular jets. The current investigation develops a time-domain linearized Navier-Stokes-based approach predicated on the mean basic state with two goals: (i) to obtain the radiated shallow-angle noise field, including that from imperfectly-expanded jets containing shock trains, and (ii) to estimate noise control trends with frequency imposed by a notional actuator. Implicit linearization about the mean flow is achieved through a general method that repurposes native non-linear Navier-Stokes code capabilities; this avoids any additional constraints on nozzle geometry or flow features such as shocks. A crucial step is to sift the linearized perturbations to isolate the acoustic component according to Doak's momentum potential theory (MPT). Comparisons with well-validated Large Eddy Simulation (LES) databases in different spectral ranges show accurate model predictions for super-radiative shallow angle noise, including for hot jets from military-style nozzles. Transient pulse response enabled by the time-domain nature of the procedure also facilitates estimation of control frequency effectiveness. For this, the transient perturbation growth characteristics are segregated into the distinct effects of the excitation on vorticity relative to the MPT acoustic variable. For a jet with extensive published experimental data using plasma actuators, it is shown that the method correctly predicts noise amplification at lower frequencies and reduction at higher values, at the observed crossover location. Considerations on the costs associated with the approach, which exploits linearity to extract results at multiple frequencies with each simulation, are outlined.

Acoustic Inversion for Uncertainty Reduction in Reynolds-Averaged Navier–Stokes-Based Jet Noise Prediction

The Reynolds-averaged Navier–Stokes (RANS)-based method is a practical tool to provide rapid assessment of jet noise-reduction concepts. However, the RANS-based method requires modeling assumptions to represent noise generation and propagation, which often reduces the predictive accuracy due to the model-form uncertainties. In this work, the ensemble Kalman filter-based acoustic inversion method is introduced to reduce uncertainties in the turbulent kinetic energy and dissipation rate based on the far-field noise and the axial centerline velocity data. The results show that jet noise data are more effective from which to infer turbulent kinetic energy and dissipation rate compared to velocity data. Moreover, the inferred noise source is able to improve the estimation of the turbulent flowfield and the far-field noise at unobserved locations. Further, the noise model parameters are also considered uncertain quantities, demonstrating the ability of the proposed framework to reduce uncertainties in both the RANS and noise models. Finally, one realistic case with experimental data is investigated to show the practicality of the proposed framework. The method opens up the possibility for the inverse modeling of jet noise sources by incorporating far-field noise data that are relatively straightforward to be measured compared to the velocity field.

Real-time supersonic jet noise predictions from near-field sensors with a wavepacket model.

Parabolized stability equations (PSE) have been shown to model wavepackets and, consequently, the near-field of turbulent jets with reasonable accuracy. In this work, PSE were employed to obtain a reduced-order model that could estimate both the fluid-dynamic and the acoustic fields of a supersonic jet in a computationally efficient approximation for resolvent-based estimation based on a single input. From the unsteady pressure data at an input position, the time-domain pressure field was estimated using transfer functions obtained using PSE and a data-driven method based on a well-validated large-eddy simulation (LES). The prediction scheme employed is a single-input single-output, linear model. The unsteady pressure predicted by the PSE showed good agreement with the LES results, especially if the input position is outside the mixing layer, where the prediction capabilities of the PSE are comparable to those of the data-driven transfer functions. The good agreement indicates that PSE could not only be used to predict the sound generation but also to open up different potentialities to attenuate the noise by flow control. The exploration of the regions where the method displayed good agreement, which are presented in this work, can guide the positioning of the sensors for experimental implementation of closed-loop control in a jet.

Jet noise reduction of spherical tuyeres with serrated trailing edges

The geometry of a jet nozzle has a direct effect on the noise of spherical tuyeres. The addition of a serrated structure to the trailing edge of a spherical tuyere is proposed to reduce the jet noise. A full-scale test bed is built to verify the jet velocity distribution of the spherical tuyere, and the tuyere jet noise prediction is verified through analogy experiments. The large eddy simulation (LES) model and Ffowcs Williams–Hawkings (FW–H) equation are used to predict the jet flow field and jet noise, respectively. The results show that the serrated trailing edge reduces the velocity attenuation and turbulent kinetic energy (TKE) on the downstream axis of the jet, and the radiated noise is significantly reduced at 10–60 Hz. However, when the serration width is excessively large, the serration increases the radiated sound pressure level (SPL) of the tuyere. In addition, optimal serration parameters can be used to reduce the effect of tuyere radiation noise on the standing and sitting postures of residents. The maximum radiated overall SPL can be reduced by 3.78 dB at the sitting height. The simulation data and analysis results can provide reference for the low-noise optimization design of spherical tuyeres.

A history of jet noise research at the National Aeronautics and Space Administration.

This paper reviews jet noise research conducted at the National Aeronautics and Space Administration (NASA) from the early 1950s to the present day. Research conducted by NASA's predecessor, the National Advisory Committee for Aeronautics (NACA), and during the early years of NASA focused on turbojet noise, where a common approach for reducing jet noise was to limit the jet exit velocity to speeds that provided acceptable noise levels. Suppressors tested during this time resulted in thrust losses that were too severe to be implemented. With the introduction of turbofan engines in the 1960s, NASA shifted research to programs for both subsonic and supersonic aircraft applications with specific noise reduction goals. Subsonic research focused on increasing the bypass ratio of the engine to reduce the jet exit velocity of the core exhaust and adding mixers to the dual exhaust streams. Advances in computational methods improved aerodynamic designs and jet noise prediction tools. Supersonic applications proved to be more troublesome as programs aimed at large commercial transports required higher specific thrust engines. Changing the engine cycle to reduce jet noise was not compatible with mission range and speed requirements. Research for supersonic commercial aircraft remains an area of interest today at NASA.

Empirical prediction of flight effect on subsonic coaxial-jet noise by introducing an adjusted flight velocity term

The effect of forward flight on jet noise is difficult to quantify through flyover tests since only the total noise is measured in a full-scale flyover test, and the contribution of the jet noise is difficult and sometimes nearly impossible to identify. Thus, most studies on the flight effect have been carried out through model-scale experiments with a single-stream jet simulator in a free jet facility. In this paper, the effect of forward flight was captured by using an adjusted flight velocity term (αV) to describe jet velocity in a new prediction of coaxial-jet noise. The new jet noise prediction method assumes that there are three components: primary, secondary, and mixed components with no filter functions. The coefficient α is determined by a thorough investigation of the model-scale data gained from an experiment in the anechoic wind tunnel of ONERA. The value of α is 1 for the primary component, 0.5 for the secondary component, and a linear function of the angle for the mixed component. The simple adjustment of the flight velocity successfully embodied the effect of forward flight at all angles, with no separate velocity exponent or an additional term.

Compressibility Modified RANS Simulations for Noise Prediction of Jet Exhausts with Chevron

The impact of compressibility modified RANS turbulence closures is investigated for high subsonic round and chevron jet flows with Mach = 0.9 and Re = 1.03×106, including the predicted acoustic noise generation. The well-documented chevron jet flow and noise cases, namely NASA SMC000 and SMC006 are selected as the simulation configurations. Two compressibility RANS closures are considered, which are based on the k-ε turbulence model. The first type only considers the compressibility dissipation rate, and the second type accounts for three modifications of compressibility dissipation rate, pressure dilation and production limiter. The acoustic noise is calculated employing the SNGR (Stochastic Noise Generation and Radiation) method using the flow prediction of the three-dimensional RANS simulations. The results show that both of the two types of compressibility modified RANS models improve the accuracy of the mean flow and turbulence quantities. This results in more accurate jet noise predictions than with the standard RANS model. The first type modification is found to be moderate and the second type is remarkable. The noise results by the second type model, i.e. Sarkar2 model, agree with the experimental data quite well. For the mean flow field, the compressibility modified model (Sarkar2 model) estimates a shorter potential jet core, and improved predictions of the velocity in the downstream region are observed. The study demonstrates the importance of considering the compressibility modified RANS closure for the noise prediction of high-speed jets via the comparison to experimental data. Hence, the SNGR method is found to be cost effective for jet noise prediction, when compared to other approaches.

Aeroacoustics Analysis of a Dual-Stream Subsonic Jet

This work was part of a major investigation for coaxial jet noise prediction and refers to a study of geometrical and velocity parameters and their influence on the noise generated by coaxial nozzles. The Reynolds Averaged Navier-Stokes (RANS) approach coupled with a fluctuation synthetization model and the integral formulation of Curle's Acoustic Analogy were employed to calculate the noise spectrum and compare it to experimental data from JEAN EU project.