Numerical study of 3-D finlets using Reynolds-averaged Navier–Stokes computational fluid dynamics for trailing edge noise reduction

Abstract

This paper uses Reynolds-averaged Navier–Stokes computational fluid dynamics to study trailing edge noise reduction with 3-D finlets. Reynolds-averaged Navier–Stokes computational fluid dynamics provides boundary layer parameters near a trailing edge for an empirical wall pressure spectrum model, and then an acoustic model predicts far-field noise based on pressure fluctuations obtained from the wall pressure spectrum model. First, this numerical approach is validated against experiments. Second, a comprehensive trend analysis is conducted to give insight into the design of 3-D finlets under different flow conditions. A data-driven turbulence spanwise length scale model is developed to tackle finlets with small spacing. Combined with acoustic results, detailed computational flow field results are analyzed to understand the physical mechanism of noise reduction. While the major part of the proposed mechanism is the same as prior work, several new observations are shown which better understand the physical mechanism of noise reduction with 3-D finlets. The goals of the current paper are to provide an efficient Reynolds-averaged Navier–Stokes-based approach to predict trailing edge noise of 3-D finlets, to give complete trend analysis results with various finlets under different flow conditions, and to advance an understanding of the underlying physics.

Finlet Rails for the Reduction of the Trailing-Edge Noise

“…These mean and r.m.s. velocity profiles are consistent with the velocity profiles obtained from finlet fences in previous experimental and numerical studies (see Afshari et al [28], Shi and Lee [30], and Bodling and Sharma [29]). A phenomenon analogous to that described by [31] as a 'lift-up of energy carrying eddies' seems therefore to take place here, which increases the separation distance between source and scattering edge, and reduces the noise production efficiency [29].…”

“…Also, the spanwise coherence decreases for length scales larger than the fences spacing, which attenuates TE noise at the mid-frequencies. Shi and Lee [30] found that the surface treatment retards the flow in the finlet channels, with a reduction of the turbulent kinetic energy at the wall. The recent study of Ananthan and Akkermans [31] identifies several interconnected noise reduction mechanisms.…”

Broadband Noise Reduction of a Two-Stage Fan with Wavy Trailing-Edge Blades

“…According to the need for a noise prediction method for the flow field results, the Reynolds-averaged Navier-Stokes (RANS) method is used for the flow field numerical calculation. The k ω − shear stress transport (SST) turbulence model is used in the flow field calculation, which is capable of solving the rotating flow, secondary flow, and boundary layer separation [24,25]. Due to the simulation of the rotor rotation, the computational domain is divided into the rotor domain and stator domain, and the computational domain is divided into four parts (as shown in Figure 3).…”

Experimental and Numerical Investigation of Bio-Inspired Airfoil Trailing-Edge Designs for Noise Reduction

“…Lastly, the finlet-spacing to boundary layer thickness ratio was found to be a critical parameter for achieving optimum surface pressure power spectral density reduction. Shi et al [31] conducted a comprehensive trend analysis of finlets. The effects of height, spacing, thickness, position and incoming flow velocity were considered.…”

“…Computational aeroacoustics (CAA) simulations simultaneously calculate the hydrodynamic and acoustic fields. This can be done using direct numerical simulations (DNS) [52] or using high fidelity flow simulations coupled with acoustic noise propagation models [31,[53][54][55]. Since noise is nothing but pressure fluctuations that reach the observer, explicitly solving the governing equations of fluid flow is the most accurate numerical approach available.…”