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Small Business Innovation Research/Small Business Tech Transfer

High-Fidelity Prediction of Launch Vehicle Lift-off Acoustic Environment

Completed Technology Project

Project Description

High-Fidelity Prediction of Launch Vehicle Lift-off Acoustic Environment
Launch vehicles experience extreme acoustic loads during liftoff driven by the interaction of rocket plumes and plume-generated acoustic waves with ground structures. Currently employed predictive capabilities to model the complex turbulent plume physics are too dissipative to accurately resolve the propagation of acoustic waves throughout the launch environment. Higher fidelity liftoff acoustic analysis tools to design mitigation measures such as deluge water and launch pad geometry are critically needed to optimize launch pads for SLS and commercial launch vehicles. This STTR project will deliver breakthrough technologies to drastically improve predictive capabilities for launch vehicle lift-off acoustic environments. Hybrid RANS/LES modeling presently established in NASA production flow solvers will be used for simulation of the acoustic generation physics, and a high-order accurate unstructured discontinuous Galerkin (DG) solver developed in the same production framework will be employed to accurately propagate acoustic waves across large distances. An innovative hybrid CFD-CAA method will be developed in which the launch-induced acoustic field predicted from hybrid RANS/LES will be transmitted to a DG solver and propagated using high-order accurate schemes ideal for acoustic propagation modeling. This new paradigm enables: (1) Improved fidelity over linear methods for modeling nonlinear launch-induced acoustics; (2) Greatly reduced numerical dissipation and dispersion; and (3) Improved acoustics modeling for attenuation, reflection, and diffraction from complex geometry. The merits of the proposed approach will be investigated and demonstrated in Phase I for benchmark CAA applications and plume impingement problems. In Phase II, the methodology will be refined and validated against realistic targeted applications. More »

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