Abstract

Few engineering tools are suitable for predicting supersonic jet noise, and the development of engine exhaust noise reduction technology for tactical aircraft continues to rely heavily on laboratory scale parametric testing. Aside from intuition and experience with the generally subtle issues involved in low-noise design, there is currently no way to rapidly and cheaply assess whether proposed designs will be effective, and no way to determine whether such designs are optimal. Arguably, this gap in jet noise modeling capability is an impediment toward achieving significant noise reduction for tactical aircraft. The objective of the on-going research program presented in this paper is to develop and demonstrate innovative, highly-efficient computational methodologies for simultaneous nozzle acoustic and aerodynamic design applicable to subsonic and supersonic jet exhaust noise reduction in tactical aircraft. The approach comprises of three major elements: (1) Reynolds-averaged NavierStokes (RANS)-CFD for computing the jet turbulent mean flow, (2) pressure wave packet-based methods for predicting near-field sound generation from the largest scales, based on the Linear Parabolized Stability Equations (LPSE) and the aforementioned RANS solutions, and (3) a method based on the solution of the linear wave equation for determining the acoustic radiation field from the LPSE solution. While these three procedures have received significant attention in the literature, their integration into a single tool for far-field noise prediction has not. To assess the accuracy and robustness of the simulation tool, experimental data has been acquired with near field array directed at detecting changes in the organized turbulence scale structure to link cause (nozzle geometry) and effect (near field and far field noise changes). Both ideallyand non-ideally expanded conditions are being investigated. Forward flight effects have also been measured using an open jet acoustic wind tunnel (at United Technologies Research Center) to evaluate the noise dependence on different operating conditions. The development and preliminary validation of the integrated tool is presented in this paper, with a focus on individual components and their translation into a common format for integration.

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