Using data mining and support vector machines to optimize cooling effectiveness in a gas turbine blade leading edge

Abstract

This paper discusses a research related with the innovative use of a decision support system based on data mining (to evaluate historical information) and the support vector machine method to determine the optimal values related with the cooling efficiency of a gas turbine blade and to determine the adequate selection of components to build scenarios under uncertainty. This research allows the selection of a specific number of optimal values for components, in a time horizon of a power energy installation (approximately four hours). These components are evaluated with data from an information repository of a successful energy system. The intent of this research is to apply the computational properties of an established model of intelligent optimization. The case study allowed the analysis of the individual features of each component with the emulation from set matching features (optimal values reached by our hybrid algorithm). This way it is possible to predict a better functionality in this kind of system.

Keywords

• data mining
• support vector machine
• pattern recognition
• decision support system