Sensitivity-Based Simulation Software for Optimization of Turbine Blade Cooling Strategies

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$99,792.00
Award Year:
2009
Program:
SBIR
Phase:
Phase I
Contract:
NNX09CC90P
Award Id:
90590
Agency Tracking Number:
084216
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
3819 Sunnycroft Place, West Lafayette, IN, 47906
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
806890807
Principal Investigator:
Sanjay Mathur
Principal Investigator
(765) 497-3653
sm@jabirusoft.com
Business Contact:
Sanjay Mathur
Business Official
(765) 497-3653
sm@jabirusoft.com
Research Institute:
n/a
Abstract
In recent years, there has been a tendency to use ever-higher gas turbine inlet temperatures, resulting in ever-higher heat loads necessitating efficient cooling. Internal cooling designs have evolved from the use of simple curved ducts in early designs to very complex geometries. Similar complexities govern film cooling as well, leading to complex fluid-structure interactions and turbulence physics. These complexities make it impossible to obtain optimal cooling designs by intuition alone. In this project we propose to develop optimization software for the design and optimization of turbine blade cooling strategies. The objectives of Phase I are to (i) demonstrate the feasibility of accurate single-point physical modeling of internal and film cooling geometries using our CFD solver TETHYS, (ii) demonstrate the feasibility of sensitivity computation and uncertainty quantification using TETHYS, (iii) apply these sensitivity and uncertainty quantification approaches to turbine blade cooling and to demonstrate their advantage over single-point CFD simulations, and (iv) develop and demonstrate multivariate optimization of a chosen turbine blade cooling problem. Phase II will extend our methodology to geometry optimization, the improvement of physical models and numerical schemes, parallel processing on shared and distributed memory platforms and multicore architectures, as well as application to more complex optimization problems.

* information listed above is at the time of submission.

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