Prediction of LAGOON landing-gear noise using an unstructured LES Solver

Giret, Jean-Christophe; Sengissen, Alois; Moreau, Stéphane; Jouhaud, Jean-Christophe

doi:10.2514/6.2013-2113

Cited by 18 publications

(14 citation statements)

References 25 publications

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“…Based on these noise contributions from all the five C-Ss, the overall far-field noise is calculated using Equation (3). Figure 11a presents the total PSD noise level of all the five C-Ss and compared them with the experimental [12] and 3D numerical results [44]. For the 2DMic, the result of the present approach has a good qualitative agreement with the experimental data at the medium-to-high frequency ranges (1.55-2.2 kHz), as illustrated in Figure 11a.…”

Section: The Lagoon Nlg Overall Far-field Acoustic Resultssupporting

confidence: 55%

“…The proposed methodology is applied to the LAnding Gear nOise database and CAA validatiON (LAGOON), a simplified nose landing gear (NLG), which was considered a benchmark problem during the second BANC workshop [6]. The LAGOON NLG experimental data [12] and the 3D numerical results [44] are used to validate the proposed method. One of the advantages of the proposed approach is that the CFD analysis of each 2D C-S is carried out individually.…”

Section: Proposed Approach For the Lg Far-field Noise Predictionmentioning

confidence: 99%

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Predicting Far-Field Noise Generated by a Landing Gear Using Multiple Two-Dimensional Simulations

2019

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Featured Application: Far-field noise prediction of flow past complex multi-component structures. Abstract: In this paper, a new approach is proposed to predict the far-field noise of a landing gear (LG) based on near-field flow data obtained from multiple two-dimensional (2D) simulations. The LG consists of many bluff bodies with various shapes and sizes. The analysis begins with dividing the LG structure into multiple 2D cross-sections (C-Ss) representing different configurations. The C-Ss locations are selected based on the number of components, sizes, and geometric complexities. The 2D Computational Fluid Dynamics (CFD) analysis for each C-S is carried out first to obtain the acoustic source data. The Ffowcs Williams and Hawkings acoustic analogy (FW-H) is then used to predict the far-field noise. To compensate for the third dimension, a source correlation length (SCL) is assumed based on a perfectly correlated flow. The overall noise of the LG is calculated as the incoherent sum of the predicted noise from all C-Ss. Flow over a circular cylinder is then studied to examine the effect of the 2D CFD results on the predicted noise. The results are in good agreement with reported experimental and numerical data. However, the Strouhal number (St) is over-predicted. The proposed approach provides a reasonable estimation of the LG far-field noise at a low computational cost. Thus, it has the potential to be used as a quick tool to predict the far-field noise from an LG during the design stage.The direct numerical simulation (DNS) of complex three-dimensional (3D) aircraft systems, such as the LGs, is computationally expensive. This is because the 3D model needs high spatial and temporal resolutions to resolve the wide range of energy and length scales between the flow and acoustic fields. Therefore, an efficient two-step hybrid computational aeroacoustics (CAA) approach was proposed, where the flow and the acoustic fields are computed using two independent solvers [8]. In the last decade, noise generated due to flow past a simplified LG geometry has been extensively investigated using hybrid CAA approaches. The numerical results were validated with experiments through benchmark problems for airframe noise computations (BANC) workshops [10][11][12][13][14]. The BANC workshops focus on improving the far-field noise prediction accuracy of the 3D simulations and reducing the computation time. There are a few semi-empirical tools developed to facilitate the noise prediction of the LG during the design stage [3,7]. Among these, the Landing Gear Model and Acoustic Prediction tool (LGMAP) has been developed for a quick noise estimate of the LG [15]. However, lower fidelity approaches are essential to predict the flow and acoustic quantities with better computational efficiency and reasonable accuracy.The two-dimensional (2D) Computational Fluid Dynamics (CFD) analysis provides a faster way to predict the near-field data. A few studies investigated the validity of the 2D simulations to predict the far-field noise of the flow...

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Section: The Lagoon Nlg Overall Far-field Acoustic Resultssupporting

confidence: 55%

Section: Proposed Approach For the Lg Far-field Noise Predictionmentioning

confidence: 99%

Predicting Far-Field Noise Generated by a Landing Gear Using Multiple Two-Dimensional Simulations

2019

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“…The same signal processing procedure has been applied for all numerical computations, including for reference LES. 7 The pressure signal has been sampled at a frequency of 60 kHz for a duration of 0.24 s out of the 0.33 s simulated, and the spectral estimate 26 built out of 5 averaged chunks with 50% of overlapping. Hann windowing has been considered as a standard.…”

Section: B Near-field Acoustic Resultsmentioning

confidence: 99%

Simulations of LAGOON landing-gear noise using Lattice Boltzmann Solver

Sengissen

Giret

Coreixas

et al. 2015

21st AIAA/CEAS Aeroacoustics Conference

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This paper aims at investigating and analyzing numerical simulations of landing-gear configurations of increasing complexity using the Lattice-Boltzmann solver "LaBS". The LAGOON (LAnding-Gear nOise database for CAA validatiON) project, supported by Airbus, 1, 2 provides an accurate experimental database on simplified landing-gear configurations perfectly suitable for this purpose. First, an assessment of the numerical approach accuracy is carried out on LAGOON1 configuration by comparing both aerodynamic and near-field acoustic results with the LAGOON database disclosed in the frame of the NASA BANC workshop. Then, further investigations are focused on the influence of mesh refinement, subgrid scale model and wall law parameters. Finally, the best practices obtained are applied on LAGOON2 & 3 configurations and allow to capture the impact of some geometrical components added onto LAGOON1 baseline.

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“…Consequently the boundary layer is assumed to be fully turbulent on all presented sensors. However, the intensity of the hydrodynamic fluctuations on Kulites #1 to #4 are rather low and these sensors are able to detect tonal acoustic waves at 1 kHz and 1.5 kHz, which are generated in the cavity between the wheels by modal mechanisms of acoustic resonance 19,23 . Globally, broadband levels predicted at low frequency (in the range 0-2 kHz) by all computations are in good agreement with experiments, and all computations correctly predict these two tones.…”

Section: F Wall Pressure Fluctuationsmentioning

confidence: 99%

Summary of the LAGOON Solutions from the Benchmark problems for Airframe Noise Computations-III Workshop

Manoha¹,

Caruelle

2015

21st AIAA/CEAS Aeroacoustics Conference

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and the BANC-III Category 5 LAGOON Team The Benchmark for Airframe Noise Computations (BANC) has been initiated by the BECAN Technical Discussion Group under AIAA. This continuous framework, mainly impulsed by NASA LaRC, aims at evaluating numerical methods for the simulation of unsteady flows and aerodynamic noise radiated by airframe components such as airfoil trailing edges, landing gears and high lift devices. In this context, eight test-cases are proposed, with problem statements relying on extended experimental databases. The 2-wheel LAGOON landing gear is one of them. Originally designed and tested in the homonym project funded by Airbus-France, it has a simplified shape, compatible with a wide range of numerical methods, although involving complex physics. This paper summarizes seven submissions that were presented in this category at the Third BANC Workshop in Atlanta in June 2014. Researchers employed various block-structured, unstructured and embedded Cartesian ("octree") grids and large computational resources to simulate the flow and radiated noise. The solutions are compared against each other and with experimental data gathered in two Onera's windtunnels, F2 for aerodynamic data and CEPRA19 for acoustic data. Overall, all simulations captured the main features of the unsteady flow and radiated noise, including cavity resonances occurring between the wheels. The acoustic spectra and directivity diagrams of overall sound pressure levels are in fair agreement with experiment, although a significant dispersion can be observed between all contributions. Nomenclature D wheel diameter (D = 300 mm) W wheel (tyre) width (W = 88 mm) S wheelbase or spanwise distance between the wheel's median planes (S = 198 mm) D a axle diameter (D a = 44 mm) D ml main leg lower diameter (D ml = 55 mm) D mh main leg upper diameter (D mh = 71 mm) M Mach number p pressure U

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Prediction of LAGOON landing-gear noise using an unstructured LES Solver

Cited by 18 publications

References 25 publications

Predicting Far-Field Noise Generated by a Landing Gear Using Multiple Two-Dimensional Simulations

Predicting Far-Field Noise Generated by a Landing Gear Using Multiple Two-Dimensional Simulations

Simulations of LAGOON landing-gear noise using Lattice Boltzmann Solver

Summary of the LAGOON Solutions from the Benchmark problems for Airframe Noise Computations-III Workshop

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