Hybrid Prediction Method for Aircraft Interior Noise

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$68,685.00
Award Year:
2005
Program:
SBIR
Phase:
Phase I
Contract:
NNL05AA88P
Award Id:
72689
Agency Tracking Number:
041354
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
202 North Curry Street, Suite 100, Carson City, NV, 89703
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
009737904
Principal Investigator:
Bryce Gardner
Principal Investigator
(858) 350-0057
bgardner@vasci.com
Business Contact:
Jane Trenaman
COO
(858) 350-0057
jane.trenaman@esi-group-na.com
Research Institute:
n/a
Abstract
This proposal discusses the development and application of new methods of structural-acoustic analysis in order to address existing problems in aircraft interior noise prediction. The proposed methods are based on a hybrid modeling strategy that combines Finite Element Analysis (FEA) and Statistical Energy Analysis (SEA). Over the past five years, Vibro-Acoustic Sciences has devoted a considerable research effort towards the development of a framework for combining these two analysis methods. Recent research carried out by over the past two years has resulted in the development of a rigorous solution to this problem. The resulting Hybrid approach has been derived in general terms and validated for a number of simple structural-acoustic problems. However, the method has not yet been applied to aircraft interior noise prediction. A number of candidate aircraft interior noise problems have been identified which would benefit greatly from the use of the Hybrid method. The aims of the research described in this proposal are therefore: (i) to demonstrate the application of the Hybrid method to a number of existing aircraft interior noise problems, (ii) to develop the method to ensure it contains sufficient functionality to address practical aircraft interior noise problems and (iii) to demonstrate the value of the method in the prediction and reduction of noise in airframe systems.

* information listed above is at the time of submission.

Agency Micro-sites


SBA logo

Department of Agriculture logo

Department of Commerce logo

Department of Defense logo

Department of Education logo

Department of Energy logo

Department of Health and Human Services logo

Department of Homeland Security logo

Department of Transportation logo

Enviromental Protection Agency logo

National Aeronautics and Space Administration logo

National Science Foundation logo
US Flag An Official Website of the United States Government