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- W2335740236 abstract "Launch vehicles experience extreme acoustic loads during lift-off, driven by the interaction of exhaust plumes and plume-generated acoustic waves with ground structures. Prediction of the noise associated with launch vehicle acoustic environments poses several unique challenges when compared to traditional jet noise modeling techniques. In particular, acoustic and Mach waves propagating from the exhaust plume are in the vicinity of obstructions and are generally in the nonlinear regime, which renders the classical acoustic analogy and linearized approaches invalid. In this work, a novel approach for the accurate prediction of launch vehicle lift-off acoustic environments is presented. A well-established hybrid RANS/LES unstructured mesh solver with low-dissipation schemes is utilized to efficiently and accurately predict the complex exhaust plume physics, and a high-order accurate discontinuous Galerkin solver is used to accurately propagate acoustic waves across large distances throughout the launch environment. The two solvers operate on separate overlapping meshes and an innovative overset coupling approach is used to transmit the plume-generated acoustics to the farfield in a one-way manner in which the exhaust plume prediction is unaffected by the outer acoustic propagation physics. The framework upon which the solvers are developed is described along with details outlining the overset grid assembly and interpolation methods. Results are presented which demonstrate the accuracy of the capability for aeroacoustic predictions, and the merits of the approach are evaluated for transonic jet noise prediction for a two-dimensional free jet. The method is shown to be effective and accurate in terms of predicted sound pressure level spectra when compared to direct noise computation." @default.
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- W2335740236 date "2016-01-02" @default.
- W2335740236 modified "2023-09-27" @default.
- W2335740236 title "Coupled Overset Unstructured Discontinuous Galerkin Method for Jet Noise Prediction" @default.
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- W2335740236 doi "https://doi.org/10.2514/6.2016-0762" @default.
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