Optimimization of the Acoustic Performance of a Thick Plate with Embedded Actuators.

Abstract

The acoustic response of a simple thick plate with embedded actuators is investigated in this work, and its performance optimized. The actuators themselves are not modeled. The plate is subjected to an incident wave and the actuators (pistons) do not necessarily cover the entire plate. A standard minimization method as well as a genetic algorithm are used to minimize the acoustic intensity of the far field at both selected field points and the entire far field. A closed form solution is derived for the case of the actuators being driven at the same velocity amplitude, while a genetic algorithm is employed to fwd optimum solutions for the actuators being driven at different velocity amplitudes. The results indicate that the closed form solution, which can include actuators functioning below 100% capacity, produces excellent solutions when the plate is mostly covered with actuators. However, for plates that are only partially covered with actuators or if some of the actuators are not firing or well below 100%, the closed form approach can produce very poor results. The genetic algorithm generate viable solutions in all cases, but shows less spectacular improvements in the reduction of the far field intensities.

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Document Details

Document Type
Technical Report
Publication Date
Dec 31, 1998
Accession Number
ADA358867

Entities

People

  • C. T. Dyka
  • G. C. Kirby

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Acoustic Waves
  • Acoustics
  • Actuators
  • Algorithms
  • Amplitude
  • Bits
  • Demographic Cohorts
  • Equations
  • Far Field
  • Frequency
  • Genetic Algorithms
  • Intensity
  • Military Research
  • Optimization
  • Standards
  • Waves

Fields of Study

  • Physics

Readers

  • Control Systems Engineering.
  • Operations Research
  • Structural Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Biotechnology