CAA-RPM Prediction and Validation of Slat Setting Influence on Broadband High-Lift Noise Generation

Ewert, Roland; Dierke, Jürgen; Pott-Pollenske, Michael; Appel, Christina; Emunds, Rolf; Sutcliffe, Mark

doi:10.2514/6.2010-3833

Cited by 20 publications

(21 citation statements)

References 15 publications

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“…The second step consists of two parts, (a) the stochastic unsteady 3D modeling of the turbulence by DLR's stochastic turbulence generator fRPM (fast Random Particle Mesh Method) [11,12] and (b) the actual CAA computation of the (acoustic) perturbation field about the given RANS flow field by means of DLR's CAA code PIANO [13], see Figure 2. The approach requires an appropriate modeling of the unsteadiness of turbulence, which means that it has to cover those features of the turbulence which are relevant for the sound generation, see [14,15]. The respective turbulence representation is realized with the stochastic turbulence generator "fRPM" which takes the flow field and one point turbulence statistics from a pre-cursor RANS mean flow simulation as an input.…”

Section: Noise Assessment Of An Aircraft Component (Level 1)mentioning

confidence: 99%

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Aircraft Noise Assessment—From Single Components to Large Scenarios

Delfs¹,

Bertsch²,

Zellmann

et al. 2018

Energies

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Abstract:The strategic European paper "Flightpath 2050" claims dramatic reductions of noise for aviation transport scenarios in 2050: ". . . The perceived noise emission of flying aircraft is reduced by 65%. These are relative to the capabilities of typical new aircraft in 2000. . . ". There is a consensus among experts that these far reaching objectives cannot be accomplished by application of noise reduction technologies at the level of aircraft components only. Comparably drastic claims simultaneously expressed in Flightpath 2050 for carbon dioxide and NOX reduction underline the need for step changes in aircraft technologies and aircraft configurations. New aircraft concepts with entirely different propulsion concepts will emerge, including unconventional power supplies from renewable energy sources, ranging from electric over hybrid to synthetic fuels. Given this foreseen revolution in aircraft technology the question arises, how the noise impact of these new aircraft may be assessed. Within the present contribution, a multi-level, multi-fidelity approach is proposed which enables aircraft noise assessment. It is composed by coupling noise prediction methods at three different levels of detail. On the first level, high fidelity methods for predicting the aeroacoustic behavior of aircraft components (and installations) are required since in the early stages of the development of innovative noise reduction technology test data is not available. The results are transferred to the second level, where radiation patterns of entire conventional and future aircraft concepts are assembled and noise emissions for single aircraft are computed. In the third level, large scale scenarios with many aircraft are considered to accurately predict the noise exposure for receivers on the ground. It is shown that reasonable predictions of the ground noise exposure level may be obtained. Furthermore, even though simplifications and omissions are introduced, it is shown that the method is capable of transferring all relevant physical aspects through the levels.

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Section: Noise Assessment Of An Aircraft Component (Level 1)mentioning

confidence: 99%

“…Turbulence is only realized in the relevant source domain (called "fRPM source patch", see [14,15]) as shown in Figure 3a. A snapshot of the resulting sound pressure field is plotted in Figure 3b for illustration.…”

Section: Noise Assessment Of An Aircraft Component (Level 1)mentioning

confidence: 99%

Aircraft Noise Assessment—From Single Components to Large Scenarios

Delfs¹,

Bertsch²,

Zellmann

et al. 2018

Energies

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“…This mechanism (referred to as an edge-dipole source [79]) is regarded as a major contributor to slat noise generation. High-fidelity simulation of the multi-element aerofoil flow, and in particular of the slat flow, using improved versions of DES (see [6,88]), implicit DES [89,90], with wallmodel LES (see [10]) and stochastic source models coupled with acoustic perturbation equation solution [91,92] has provided important insights into slat noise generation and radiation. Strong collaboration between the computations and laboratory measurements (see [93]) even when simulations were limited to two-dimensional unsteady RANS, has resulted in faster progress.…”

Section: (I) Slat Noisementioning

confidence: 99%

A second golden age of aeroacoustics?

Lele

Nichols

2014

Phil. Trans. R. Soc. A.

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In 1992, Sir James Lighthill foresaw the dawn of a second golden age in aeroacoustics enabled by computer simulations (Hardin JC, Hussaini MY (eds) 1993 Computational aeroacoustics, New York, NY: Springer (doi:10.1007). This review traces the progress in large-scale computations to resolve the noise-source processes and the methods devised to predict the far-field radiated sound using this information. Keeping focus on aviation-related noise sources a brief account of the progress in simulations of jet noise, fan noise and airframe noise is given highlighting the key technical issues and challenges. The complex geometry of nozzle elements and airframe components as well as the high Reynolds number of target applications require careful assessment of the discretization algorithms on unstructured grids and modelling compromises. High-fidelity simulations with 200-500 million points are not uncommon today and are used to improve scientific understanding of the noise generation process in specific situations. We attempt to discern where the future might take us, especially if exascale computing becomes a reality in 10 years. A pressing question in this context concerns the role of modelling in the coming era. While the sheer scale of the data generated by large-scale simulations will require new methods for data analysis and data visualization, it is our view that suitable theoretical formulations and reduced models will be even more important in future.

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“…Initially frozen, the behavior of the turbulence carried by the mean flow in the RPM method is now driven by a Langevin equation [31,34,35], which is massively used to describe turbulence temporal evolution [34,36,37]. Working with a Langevin equation, random particles carrying the stream function are placed along the streamlines according to the time step and the mean flow.…”

Section: Doi: 102514/1j052368mentioning

confidence: 99%

“…Many formulations have been proposed to improve the former works of Careta et al [29], who generated a stochastic field from a random stream function. The random particle mesh (RPM) method developed by Ewert and Edmunds [30], followed by Ewert [13], has been applied to investigate slat [31] and jet [32] noise. In RPM, the streamlines are discretized regarding the time step and the local mean velocity of the flow.…”

Section: Doi: 102514/1j052368mentioning

confidence: 99%

Turbulence Generation from a Sweeping-Based Stochastic Model

et al. 2014

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Stochastic methods are widely used because they constitute a low-cost computational-fluid-dynamics approach to synthesize a turbulent velocity field from time-averaged variables of a flowfield. A new combined stochastic method based on the sweeping hypothesis is introduced in this paper. This phenomenon, stating that inertial range structures are advected by the energy containing eddies, is known to be an important mechanism of the turbulent velocity field decorrelation process. The proposed method presents the advantage of being easily implementable and applicable to any three-dimensional configuration as long as a steady Reynolds-averaged Navier-Stokes computation of the flow is available and assuming that the considered turbulence physics is compatible with the hypotheses made to build the current numerical model. The developed method is applied on a subsonic round cold free jet. The validation study shows that the synthesized turbulent velocity fields reproduce statistical features of the flow, such as two-point twotime velocity correlation functions, comparable to those found experimentally and integrates shear effects of the mean flow. The mean convection velocity of the turbulent structures is also correctly modeled. In addition, the turbulent kinetic energy spatial distribution is preserved by the stochastic method.

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CAA-RPM Prediction and Validation of Slat Setting Influence on Broadband High-Lift Noise Generation

Cited by 20 publications

References 15 publications

Aircraft Noise Assessment—From Single Components to Large Scenarios

Aircraft Noise Assessment—From Single Components to Large Scenarios

A second golden age of aeroacoustics?

Turbulence Generation from a Sweeping-Based Stochastic Model

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