Aerospace: Airframe Noise

Accurate Simulation of Airframe Noise


Noise pollution from airplanes is steadily increasing - with proven adverse health effects for people in nearby communities. Government regulations of community noise are therefore getting more and more stringent in most countries. This creates significant challenges for aircraft manufacturers forced to meet these ever increasing regulatory requirements.

To have an impact on aircraft design noise control engineers need to be able to identify the main noise sources before the design is finalized. Assessing compliance with community noise regulations too late in the design process often leads to costly design changes, expensive repeat testing and may even jeopardize product certification.

Airframe noise - the noise created by the turbulent flow over airframe components like high-lift wings and landing gears - is one of the main sources of community noise during approach. While a great deal of progress has been made in understanding sound generation and propagation mechanisms of airframe noise, the use of this knowledge to improve acoustic design and meet community noise targets remains a major challenge for the transportation and heavy equipment industries.


A major challenge in meeting noise targets is to assess and reduce noise sources while dealing with multiple other design constraints. The time and cost of developing and testing physical prototypes are often prohibitive. Experimental testing challenges also include wind tunnel space limitations for extending measurements to the far-field, and relating stationary source wind tunnel measurements to the real life moving source scenario. Therefore a computational solution is highly desirable.

A key challenge for Computational Aero-Acoustic (CAA) methods is that sound propagated to the far-field consists of pressure perturbations which are very small relative to the turbulent pressure fluctuations in the near-field source region. Highly accurate prediction of the transient flow behavior with sufficiently low dissipation and dispersion is therefore required to resolve small amplitude fluctuations over the frequency range of interest.

Comparison between wind tunnel measurements and SIMULIA PowerFLOW predictions of unsteady pressure fluctuations on the surface of a complex nose landing gear


SIMULIA PowerFLOW® combined with the SIMULIA PowerACOUSTICS Far Field Noise Module provides accurate numerical prediction of flow-induced far-field noise for a single aircraft component such as the landing gear, or even a complete aircraft.

Accurate transient flow fluctuation prediction: Leverages PowerFLOW’s proven accuracy for the prediction of aerodynamically induced noise:  time-unsteady, very low dissipation, and ability to handle complex detailed geometry.

Fully coupled far-field noise solver: The fully-integrated Fowcs- Williams and Hawkings (FW-H) based solver predicts time signals at receiver/microphone locations based on PowerFLOW transient simulations. Options for fly-over vs. wind tunnel scenarios and solid vs. permeable configurations.

Noise metrics and digital certification: The Far-Field Noise Module outputs time-domain pressure signals, to which any user specific post-processing can be applied.

Insight on noise source locations: The Far-Field Noise Module provides a contribution analysis of the noise sources, highlighting the near-wall regions contributing the most to the far-field. Output far-field signals can be input to inverse methods such as beam forming or acoustic holography (not supplied with PowerACOUSTICS) providing spatial noise source localizations.

The complete PowerFLOW far-field noise solution allows early noise assessment and optimization, including noise certification evaluation (e.g. using the Effective Perceived Noise Level EPNL metric) before a final prototype is built. Complex geometry handling and efficient post-processing enable rapid turnaround time for noise assessment during the early design phase giving confidence that noise targets will be achieved.


Simulation Preparation: