Int. J. Simul. Multidisci. Des. Optim.
Volume 10, 2019
Special Issue - Uncertainty-Based Design Optimization
|Number of page(s)||13|
|Published online||13 March 2019|
Application of evolutionary algorithms to optimize cooling channels
1 University of Central Florida,
2 Universal Orlando Resort, 32819 Orlando, Florida, USA
* e-mail: firstname.lastname@example.org
Accepted: 24 February 2019
The design and development is a complex, repetitive, and more often difficult task, as design tasks comprising of restraining and conflicting relationships among design variables with more than one design objectives. Conventional methods for solving more than one objective optimization problems is to build one composite function by scalarizing the multiple objective functions into a single objective function with one solution. But, the disadvantages of conventional methods inspired scientists and engineers to look for different methods that result in more than one design solutions, also known as Pareto optimal solutions instead of one single solution. Furthermore, these methods not only involved in the optimization of more than one objectives concurrently but also optimize the objectives which are conflicting in nature, where optimizing one or more objective affects the outcome of other objectives negatively. This study demonstrates a nature-based and bio-inspired evolutionary simulation method that addresses the disadvantages of current methods in the application of design optimization. As an example, in this research, we chose to optimize the periodic segment of the cooling passage of an industrial gas turbine blade comprising of ribs (also known as turbulators) to enhance the cooling effectiveness. The outlined design optimization method provides a set of tradeoff designs to pick from depending on designer requirements.
Key words: Multi-objective optimization / Numerical simulation / Genetic algorithm / Evolutionary algorithm / Heat transfer / Fluid dynamics / Gas turbine / Blade / Internal cooling channel
© N.R. Nagaiah and C.D. Geiger, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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