SPRiNT Smart Panels for the Reduction of Noise Transmission
Significant components of the interior noise in high speed vehicles are caused by the Turbulent Boundary Layer (TBL) disturbances which are inherently random and broadband. In fact, aircraft cabin noise during cruise is dominated...
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Descripción del proyecto
Significant components of the interior noise in high speed vehicles are caused by the Turbulent Boundary Layer (TBL) disturbances which are inherently random and broadband. In fact, aircraft cabin noise during cruise is dominated by the TBL noise components. The noise is generated by the airflow over the fuselage surface and transmitted to the vehicle interior especially through vibration and sound radiation of panels of the aircraft fuselage. This problem is traditionally solved passively, by applying sound absorbing materials onto the panels. However, the low-frequency effectiveness of passive treatments is not sufficient. For this reason an interest has grown in using active noise transmission control. It can be implemented through smart panels with embedded sensor-actuator pairs which apply decentralised velocity feedback control. Such control generates active damping and thus significantly decreases the sound transmission at low frequencies, where it is dominated by resonances of fuselage panels. The major challenge in development of smart panels with decentralised velocity feedback control loops is the stability of practical systems, which limits the maximum feedback gain, and thus the maximum performance. Furthermore, due to the cross-talk between the control units, the increase in the number of feedback loops does not necessarily lead to the increase in global performance, as the maximum stable gain tends to decrease. In that case, there is a danger that unnecessary mass is added to the structure. Therefore the three objectives of the project SPRiNT are: to improve on the stability of the feedback loops, to investigate ways to reduce mass added to the structure by the active control system components, and finally, to optimize the number of control units per smart panel area. This will be done theoretically by numerical simulations and experimentally on prototype smart panels.