Design and Optimization of the Internal Cooling Channels of a High Pressure Turbine Blade—Part II: Optimization

Abstract

This second paper presents the aerothermal optimization of the first stage rotor blade of an axial high pressure (HP) turbine by means of the conjugate heat transfer (CHT) method and lifetime model described in Paper I. The optimization system defines the position and diameter of the cooling channels leading to the maximum lifetime of the blade while limiting the amount of cooling flow. It is driven by the results of a CHT and subsequent stress analysis of each newly designed geometry. Both temperature and stress distributions are the input for the Larson–Miller material model to predict the lifetime of the blade. The optimization procedure makes use of a genetic algorithm (GA) and requires the aerothermal analysis of a large number of geometries. Because of the large computational cost of each CHT analysis, this results in a prohibitive computational effort. The latter has been remediated by using a more elaborate optimization system, in which a large part of the CHT analyzes is replaced by approximated predictions by means of a metamodel. Two metamodels, an artificial neural network and a radial basis function network, have been tested and their merits have been discussed. It is shown how this optimization procedure based on CHT calculations, a GA, and a metamodel can lead to a considerable extension of the blade lifetime without an increase in the amount of cooling flow or the complexity of the cooling geometry.

An Error Indicator-Based Adaptive Reduced Order Model for Nonlinear Structural Mechanics—Application to High-Pressure Turbine Blades

“…These blades are responsible for a large part of the maintenance budget of the engine, with temperature creep rupture and high-cycle fatigue [35,18] as possible failure causes. Various technological efforts have been spent to increase the durability of these blades as much as possible, such as thermal barrier coatings [43], advanced superalloys [10] and complex internal cooling channels [5,46], see Figure 1 for a representation of a high-pressure turbine blade. Figure 1: Illustration of a high-pressure turbine blade [1].…”

Numerical Optimization of Advanced Monolithic Microcoolers for High Heat Flux Microelectronics

“…[16] was used, implemented in the in-house developed optimization code CADO of the Von Karman Institute for Fluid Dynamics (VKI). For details on the algorithm and the implementation, the reader is referred to [17] and [18]. The used genetic algorithm is based on the idea of natural selection, using the principle of the survival of the fittest.…”

CAD Integrated Multipoint Adjoint-Based Optimization of a Turbocharger Radial Turbine

“…Gradient-free (or stochastic) optimization algorithms, which only require the evaluation of the objective function, are widely used due to their robustness, simple integration into a standard design process, and ability to handle multimodal functions of complex design problems. They have been successfully applied to many turbomachinery applications including multipoint, multiobjective, and multidisciplinary optimization problems [1][2][3][4]. Despite their success, gradient-free optimization techniques are computationally expensive, especially for applications with many design parameters, resulting in a long runtime due to the large number of required function evaluations.…”