On a lower-order framework for jet noise prediction based on one-dimensional turbulence

TL;DR

This paper introduces a new lower-order modeling framework that combines a one-dimensional turbulence model with the Ffowcs-Williams and Hawkings method to predict far-field noise of subsonic turbulent jets efficiently.

Contribution

It presents a novel coupling approach that improves noise prediction accuracy by addressing the estimation of missing noise from modeled turbulent scales.

Findings

Effective prediction of jet noise using the new framework.

Reduced computational cost compared to high-resolution simulations.

Potential for application in real-time noise monitoring systems.

Abstract

Noise prediction requires the resolution of relevant acoustic sources on all scales of a turbulent flow. High-resolution direct numerical and large-eddy simulation would be ideal but both are usually too costly despite developments in high performance computing. Lower-order modeling approaches are therefore of general interest. A crucial but standing problem for accurate predictive modeling is the estimation of missing noise from the modeled scales. In this paper we address this problem by presenting a novel lower-order framework that couples the one-dimensional turbulence model to the Ffowcs-Williams and Hawkings approach for prediction of the far-field noise of a subsonic turbulent round jet.