This paper presents a multi-objective and multi-disciplinary design optimization and data mining of gas turbine blade profile and cooling system by using conjugate heat transfer analysis. A 3D multi-disciplinary aerothermal optimization and data mining is proposed and developed by integrating the global optimization method of self-adaptive multi-objective differential evolution (SMODE) algorithm based on constraint-handling method, the CHT method for aerothermal performance evaluation of gas turbine blade, the 3D blade parameterization method and the self-organization map (SOM) based data mining technique. Using CHT, a numerical investigation was carried out to evaluate the aerothermal performance of C3X model, which consists of the blade passage, the blade solid domain and the internal coolant flow passages. The results calculated by the CHT method were validated by the experimental results. A new parameterization method for modeling the blade profile and cooling system has been developed. The optimization is intended to minimize the maximum blade temperature and the temperature gradient with constraints on the coolant mass flow rate, total mass flow rate and total pressure recovery coefficient of the blade. 27 Pareto solutions are obtained after the multidisciplinary design optimization for the gas turbine blade. Detailed aerothermal analysis shows that the thermal performance of the blade is significantly improved without deteriorating the related aerodynamic performance, thereby the correctness and effectiveness of our proposed optimization method are demonstrated. The SOM-based data mining on optimization design space is also applied to explore the trade-off relations between objective functions and correlations among design variables and objective function.

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