A factorization scheme for RANS turbulence models and SNGR predictions of trailing edge noise

Kalitzin, Georgi; Kalitzin, Nikolai; Wilde, Andreas

doi:10.2514/6.2000-1982

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…(12) through CðrÞ, the R ij can be verified to agree formally with Eq. (9). Furthermore, the longitudinal and lateral correlation functions f(r) and g(r) can be identified to be related to the scalar field CðrÞ via…”

Section: Improved Turbulent Velocity Modelingmentioning

confidence: 99%

“…Further improvements of the stochastic noise generation and radiation (SNGR) method were introduced by Bailly and Lafon [6,7] and Billson et al [8]. Kalitzin et al [9] applied the SNGR approach in a first attempt to predict trailing edge noise. Ewert and Bauer [10] derived stochastic surface sources from SNGR volume sources for the prediction of broadband trailing edge noise.…”

Section: Introductionmentioning

confidence: 98%

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Broadband slat noise prediction based on CAA and stochastic sound sources from a fast random particle-mesh (RPM) method

2008

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Section: Improved Turbulent Velocity Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Broadband slat noise prediction based on CAA and stochastic sound sources from a fast random particle-mesh (RPM) method

2008

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“…[18]. The trailing edge noise has been studied using the analogy methods [19,20], theoretical models [21][22][23][24] and numerical techniques [25][26][27]. The slat noise was investigated numerically [28,29].…”

Section: Introductionmentioning

confidence: 99%

Surface integral analogy approaches for predicting noise from 3D high-lift low-noise wings

et al. 2014

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Three surface integral approaches of the acoustic analogies are studied to predict the noise from three conceptual configurations of three-dimensional high-lift low-noise wings. The approaches refer to the Kirchhoff method, the Ffowcs Williams and Hawkings (FW-H) method of the permeable integral surface and the Curle method that is known as a special case of the FW-H method. The first two approaches are used to compute the noise generated by the core flow region where the energetic structures exist. The last approach is adopted to predict the noise specially from the pressure perturbation on the wall. A new way to construct the integral surface that encloses the core region is proposed for the first two methods. Considering the local properties of the flow around the complex object-the actual wing with high-lift devices-the integral surface based on the vorticity is constructed to follow the flow structures. The surface location is discussed for the Kirchhoff method and the FW-H method because a common surface is used for them. The noise from the core flow region is studied on the basis of the dependent integral quantities, which are indicated by the Kirchhoff formulation and by the FW-H formulation. The role of each wall component on noise contribution is analyzed using the Curle formulation. Effects of the volume integral terms of Lighthill's stress tensors on the noise preThe project was supported by the Clean Sky Joint Undertaking (CSJU) (diction are then evaluated by comparing the results of the Curle method with the other two methods.

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“…Further improvements of the stochastic noise generation and radiation (SNGR) method were introduced by Bailly et al [10], Bailly and Juve [11], and Billson et al [12]. Kalitzin et al [13] applied the SNGR approach in a first attempt to predict trailing edge noise. In this paper the random particle-mesh (RPM) method, introduced by Ewert and Edmunds [14] and Ewert [15][16][17] to model vortex sound sources for airframe and jet-noise, is adapted to reconstruct stochastic fluctuating combustion noise sources in the time domain.…”

mentioning

confidence: 99%

Evaluation of the RPM Approach for the Simulation of Broadband Combustion Noise

et al. 2010

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A factorization scheme for RANS turbulence models and SNGR predictions of trailing edge noise

Cited by 13 publications

References 11 publications

Broadband slat noise prediction based on CAA and stochastic sound sources from a fast random particle-mesh (RPM) method

Broadband slat noise prediction based on CAA and stochastic sound sources from a fast random particle-mesh (RPM) method

Surface integral analogy approaches for predicting noise from 3D high-lift low-noise wings

Evaluation of the RPM Approach for the Simulation of Broadband Combustion Noise

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