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...
Landing gears (LG) are primarily designed to support the entire loads of an aircraft during landing, taxiing, and taking off. From aerodynamic design prospective, many of the LG components are exposed to the air flow giving rise to what so-called aerodynamic noise. Numerical study of complex systems such as LG as a three-dimensional (3D) model is not only CPU and memory consuming, but also it is way beyond the demand of industries for quick estimate during the design stage [1–3]. To understand the underlying physics of the flow induced noise, a two-dimensional (2D) flow past a circular cylinder is simulated using ANSYS Fluent. Two different Reynolds numbers, Re = 150 and 90000 are examined. For low Re, two distinct numerical conditions relevant to steady and unsteady flow are simulated and compared to examine the effect of the time dependency on the acoustic field. At high Re, the acoustic field is computed using the built-in Ffowcs William and Hawkings (FW-H) acoustic analogy solver in Fluent. The results show the importance of including the unsteady state term to extract the flow data. The far-field noise prediction is found to be highly dependent on the location of the near-field data.
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