Probabilistic Prediction of Location-Specific Microstructure in Turbine Disks

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,968.00
Award Year:
2010
Program:
STTR
Phase:
Phase I
Contract:
N00014-10-M-0264
Award Id:
95137
Agency Tracking Number:
N10A-028-0201
Solicitation Year:
n/a
Solicitation Topic Code:
NAVY 10T028
Solicitation Number:
n/a
Small Business Information
2545 Farmers Drive Suite 200, Columbus, OH, 43235
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
789156841
Principal Investigator:
Wei-Tsu Wu
Executive Vice President
(614) 451-8322
wwu@deform.com
Business Contact:
Juipeng Tang
President
(614) 451-8320
jtang@deform.com
Research Institution:
Carnegie Mellon University
Anthony Rollett
5000 Forbes Avenue
Pittsburgh, PA, 15213-
(412) 268-3177
Nonprofit college or university
Abstract
While there are established methods available in determining the fatigue life of critical rotating components, there is still room for improvement for better understanding and prediction of life limiting factors. Improved risk assessment of jet engine disk components would require probabilistic modeling capability of the evolution of microstructural features, residual stresses and material anomalies as the disk components undergo thermo-mechanical processing. Currently, the integrated process modeling system DEFORM can only predict the evolution of microstructure deterministically during thermo-mechanical processing. Scientific Forming Technologies Corporation is teaming with Carnegie Mellon University in this project. The objective of this project is to develop a probabilistic modeling framework that enables probabilistic prediction of microstructure evolution and bulk residual stresses due to thermo-mechanical processing. The probabilistic modeling framework in DEFORM will enable the user to systematically analyze the variabilities and uncertainties associated with the processing conditions, boundary conditions, material properties and incoming starting grain size distribution of the billet material, thus providing a probabilistic location specific microstructure response which can be used as an input to the probabilistic lifing model. At the end of phase I, we intend to demonstrate a proof of concept models for probabilistic grain size evolution and residual stresses as a result of thermo-mechanical processing. Our team will work closely with a major jet engine OEM, GE Aviation to develop an implementation and a validation plan. It is envisioned that the implementation and validation of probabilistic modeling of microstructure evolution will be undertaken in the phase II of this project.

* information listed above is at the time of submission.

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