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Global feedforward active noise control in vibro-acoustic cavities without increasing structural vibrations.
Amrita Puri1, S V Modak1, K Gupta1
1Department of Mechanical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.
The Journal of the Acoustical Society of America
|January 3, 2019
Summary
This study introduces a new feedforward active noise control technique for vibro-acoustic cavities. It minimizes interior noise without increasing structural vibration, validated in a car-like cavity.
Area of Science:
- Vibro-acoustic systems
- Active noise control
- Structural dynamics
Background:
- Interior noise in cavities arises from acoustic and structural disturbances.
- Optimizing actuators reduces acoustic potential energy but may increase structural kinetic energy.
- Existing methods lack control over structural kinetic energy during noise reduction.
Purpose of the Study:
- To develop a feedforward active noise control technique for vibro-acoustic cavities.
- To ensure noise reduction does not increase the kinetic energy of the cavity structure.
- To formulate the problem as a constrained minimization to balance acoustic and kinetic energy.
Main Methods:
- Formulation of the problem as a constrained minimization problem.
- Minimizing acoustic potential energy subject to a constraint on kinetic energy.
- Utilizing a penalty function method for solving sequential unconstrained problems.
Main Results:
- Numerical studies demonstrate favorable outcomes in reducing both acoustic potential and kinetic energy.
- The proposed method effectively controls interior noise without exacerbating structural vibrations.
- Validation on a car-like cavity confirms efficacy for single- and multi-tonal noise.
Conclusions:
- The developed feedforward technique successfully reduces interior noise while constraining kinetic energy.
- This approach offers a balanced solution for active noise control in vibro-acoustic systems.
- The penalty function method provides an effective means to solve the constrained optimization problem.
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