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Probabilistic Prediction of Location-Specific Microstructure in Turbine Disks

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-10-M-0264
Agency Tracking Number: N10A-028-0201
Amount: $99,945.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N10A-T028
Solicitation Number: 2010.A
Solicitation Year: 2010
Award Year: 2010
Award Start Date (Proposal Award Date): 2010-06-28
Award End Date (Contract End Date): 2011-08-17
Small Business Information
2545 Farmers Drive Suite 200
Columbus, OH 43235
United States
DUNS: 789156841
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Wei-Tsu Wu
 Executive Vice President
 (614) 451-8322
Business Contact
 Juipeng Tang
Title: President
Phone: (614) 451-8320
Research Institution
 Carnegie Mellon University
 Anthony Rollett
5000 Forbes Avenue
Pittsburgh, PA 15213-
United States

 (412) 268-3177
 Nonprofit College or University

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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