Graduation Semester and Year

2008

Language

English

Document Type

Thesis

Degree Name

Master of Science in Aerospace Engineering

Department

Mechanical and Aerospace Engineering

First Advisor

Brian Dennis

Abstract

This paper outlines the use of Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) Optimization to enhance the design of a 3-dimensional internally cooled turbine blade. The optimization code utilizes principles of Genetic Algorithms to optimize (i.e. minimize) the integrated heat flux through the turbine blade. The optimization is accomplished by adjusting the size and position (within pre-determined constraints) of cooling passages internal to the turbine blade. Starting with an aerodynamically optimized airfoil shape, cooling passage designs are generated by the optimization process and coupled with boundary and airfoil domains. The analysis becomes a Conjugate Heat Transfer (CHT) problem analyzing convective heat transfer between the hot gas boundary and the solid airfoil, thermal conduction within the airfoil and convective heat transfer between the solid airfoil and cold gas flow. The turbine blade is analyzed aerodynamically and thermodynamically using FLUENT CFD software. The analysis process is fully automated to produce thermodynamic results of the turbine blade design. Solution of the CHT problem is output as integrated heat flux over the blade surface and maximum temperature within the turbine blade. These values are transferred back to the optimization code as quantities of interest for the optimizer. The optimization code and CFD analysis work iteratively until convergence is achieved through minimized heat flux while maintaining a maximum allowable temperature within the airfoil. Aerodynamic and thermal solutions for the initial airfoil and final airfoil geometry are presented.

Disciplines

Aerospace Engineering | Engineering | Mechanical Engineering

Comments

Degree granted by The University of Texas at Arlington

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