Stochastic Optimization of Aeroelastic Response (SOAR)

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NND04AB83C
Agency Tracking Number: 020070
Amount: $499,999.00
Phase: Phase II
Program: STTR
Awards Year: 2004
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: N/A
Small Business Information
Mechanical Solutions, Inc.
1719 Route 10 East, Suite 305, Parsippany, NJ, 07054
DUNS: 015334899
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 William Marscher
 Principal Investigator
 (973) 326-9920
Business Contact
 William Marscher
Title: President
Phone: (973) 326-9920
Research Institution
 Penn State University
 Madara Ogot
 101 Hammond Bldg
University Park, PA, 16802
 (814) 865-3272
 Nonprofit college or university
Using MSI?s Stochastic Optimization of Aeroelastic Response (SOAR) framework, time-savings of several-fold can be achieved during the airframe conceptual design stage. The availability of fast and inexpensive computers along with higher-fidelity design models has provided the opportunity to optimize aeroelastic behavior and consider uncertainty during the conceptual design phase. However, computational time becomes prohibitive in conceptual airframe development due to the many iterations that must be performed to optimize weight and/or lift/drag ratios, while ensuring that aeroelastic flutter is avoided, particularly when uncertainty effects are included. Automated Multi-Disciplinary Optimization (MDO) programs have attempted to address this shortcoming, but recent attempts have had only limited success, due to problems with slow convergence and local minima and due to the computational expense of performing uncertainty analysis. To provide a step improvement, MSI is developing an automated, robust and reliable multidisciplinary optimization method which uses a stochastic approach to avoid local minima, and a compromise response surface method to determine system performance and constraint probability density functions due to both controllable (design variables, operating conditions) and uncontrollable (material defects, wind gusts) random effects. The new SOAR framework combines uncertainty analysis with optimization techniques to obtain an integrated and powerful approach to optimal design.

* information listed above is at the time of submission.

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