openCFS-Data: Implementation of the Stochastic Noise Generation and Radiation Model (SNGR)

TL;DR

This paper introduces the SNGR algorithm within openCFS-Data, enabling rapid stochastic noise generation for aeroacoustic source modeling, facilitating early-stage virtual prototyping in aeroacoustic applications.

Contribution

It presents the implementation of the SNGR algorithm in openCFS-Data, enhancing stochastic turbulence modeling for aeroacoustic simulations based on RANS data.

Findings

Enables quick aeroacoustic source estimation from RANS simulations.

Supports early-stage aeroacoustic design and analysis.

Integrates stochastic noise generation into open-source workflow.

Abstract

Preliminary aeroacoustic investigations in competitive industries require rapid numerical simulation techniques to gain initial insight into the flow and acoustic field. Although there are capabilities to resolve virtually all turbulence length scales, these techniques are often impractical in early stages of component development. Therefore, the flow field is typically assessed by a Reynolds-averaged Navier Stokes Simulation. Building upon the results of that flow simulation, a stochastic approach to reconstruct the turbulent velocity fluctuations. In conjunction with a hybrid aeroacoustic workflow, this approach is useful in early stage virtual prototyping of aeroacoustic applications. In this working paper, we present the SNGR algorithm of CFS-Data, the open-source pre-post-processing part of openCFS, with a focus on the computation of aeroacoustic sources.