A fast, flexible, and robust simulation-based optimization scheme using an ANN-surrogate model was developed, implemented, and validated. The optimization method uses Genetic Algorithm (GA), which is coupled with an Artificial Neural Network (ANN) that uses a back propagation algorithm. The developed optimization scheme was successfully applied to single-point aerodynamic optimization of a transonic turbine stator and multi-point optimization of a NACA65 subsonic compressor rotor in two-dimensional flow, both were represented by 2D linear cascades. High fidelity CFD flow simulations, which solve the Reynolds-Averaged Navier-Stokes equations, were used in generating the data base used in building the ANN low fidelity model. The optimization objective is a weighted sum of the performance objectives and is penalized with the constraints; it was constructed so as to achieve a better aerodynamic performance at the design point or over the full operating range by reshaping the blade profile. The latter is represented using NURBS functions, whose coefficients are used as the design variables. Parallelizing the CFD flow simulations reduced the turn-around computation time at close to 100% efficiency. The ANN model was able to approximate the objective function rather accurately and to reduce the optimization computing time by ten folds. The chosen objective function and optimization methodology result in a significant and consistent improvement in blade performance.
The present contribution fits into the frame of the ongoing 7th Framework European Project DREAM (valiDation of Radical Engine Architecture systeMs). One of its main themes targets the development of contra-rotating open rotors with variable pitch blades which are known to provide 10 to 15% fuel burn reduction but are noisier than high by-pass turbofans. More specifically, the present research was conducted in the frame of work package 3.4 lead by Techspace Aero, dedicated for one part to the design of a high speed booster adapted to open rotor configurations, and for the second part, from which this paper is issued, to the investigation of 3D geometries to improve LPC efficiency. A reference rotor blade has first been designed, with high loading, especially at hub. To improve its efficiency, a backward sweep has then been applied as it tends to unload midspan sections. However, this performance gain came at the price of severe stall margin degradation, the criticality of the hub region being increased. Based on 1.5 stage 3D RANS simulations, automated surrogate-assisted optimization has then been exploited to respectively evaluate the potential benefit of tailored 2D contouring and 3D hub profiling a posteriori applied to the swept rotor blade and of joint 3D hub profiling and sweep optimization of the unswept baseline rotor blade. The potential benefit of 3D profiling will be demonstrated while the joint 3D profiling and blade stacking optimization shed light on the achievable interesting 3D effects combinations.
Currently, most shape optimization activities for 2D blade sections focus on modifying the blade shape locally to get an optimum one, which implicitly assumes that the global shape is near optimum. Moreover, the common design parameters in most cases are not the variables used in shape optimization, hence the designer does not have control over the parameters that he or she uses in the design. In this work, the turbine blade shape at any given radial location, is represented with the MRATD model (Modified Rapid Axial Turbine Design), which is a low-order representation that describes the blade profile using a maximum of 17 aerodynamic design parameters that are given (and used) by the turbine designer, e.g. the blade axial chord, stagger, maximum thickness, throat, uncovered turning, inlet and exit blade and wedge angles, LE and TE radii etc... This representation is used in an optimization scheme to sweep the design space and identify the design parameters that would accomplish a certain optimization objective (e.g. maximum adiabatic efficiency) subject to some constraints (e.g. fixed throat area or minimum TE radius or maximum TE wedge angle or metal angles etc...). The optimization scheme uses evolutionary optimization algorithm, Genetic Algorithm(GA) and, to save computing time, Artificial Neural Network (ANN) is introduced to approximate the optimization objectives and constraints; it is trained and tested using a relatively small number of high fidelity CFD flow simulations. This approach to geometry representation is used to carry out a sensitivity study of the effect of the different design parameters on the blade performance of a highly efficient subsonic turbine blade. Its impact on the design process is also demonstrated.
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