Abstract

This paper describes the use of a structural/acoustic model of a section of a large aircraft to help define the sensor/actuator architecture that was used in a hardware demonstration of adaptive noise cancellation. Disturbances considered were representative of propeller-induced disturbances from an open fan aircraft. Controller on and controller off results from a hardware demonstration on a portion of a large aircraft are also included. The use of the model has facilitated the development of a new testing technique, closely related to modal testing, that can be used to find good structural actuator locations for adaptive noise cancellation.

Highlights

  • A wealth of technical research has accumulated starting in the late 1980’s exploring the concept of active acoustic control in aircraft interiors

  • Because much of the annoying cabin noise is caused by propellers and is tonal and at a relatively low frequency, reducing the sound with adaptive noise cancellation becomes much more tractable than the more general problem of mostly broadband, flow-induced noise

  • Cancellation is limited by the conditioning of the mathematical model. Since other limitations such as noise were not considered in this calculation, the cancellation achieved at the error sensors for a given location represents a best possible case

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Summary

Introduction

A wealth of technical research has accumulated starting in the late 1980’s exploring the concept of active acoustic control in aircraft interiors. The first demonstration of active structure-borne noise reduction on a full-size aircraft [23] used the aft section of a Douglas DC-9 to show that a small number of structural actuators provide good global control when the error sensors are microphones and can reduce vibration when the error sensors are accelerometers. The use of such a model in this work has made possible the development of a new testing technique, closely related to modal testing, that can be used to find good actuator locations for adaptive noise cancellation

Analytical model
Sensor locations
Actuator location metric
Adaptive feedforward control approaches
Testing
Adaptive noise cancellation results
Findings
Conclusion

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