Aeromechanical optimization of first row compressor test stand blades using a hybrid machine learning model of genetic algorithm, artificial neural networks and design of experiments

Abstract

In this paper, optimization of the first blade of a new test rig is pursued using a hybrid model comprising the genetic algorithm, artificial neural networks and design of experiments. Blade tuning is conducted using three-dimensional geometric parameters. Taper and sweep angle play important roles in this optimization process. Compressor characteristics involving mass flow and efficiency, and stress and eigenfrequencies of the blades are the main objectives of the evaluation. Owing to the design of blade attachments and their dynamic isolation from the disk, the vibrational behavior of the one blade is tuned based on the self-excited and forced vibration phenomenon. Using a semi-analytical MATLAB code instability, the conditions are satisfied. The code uses Whitehead’s theory and force response theory to predict classical and stall flutter speeds. Forced vibrational instability is controlled using Campbell’s theory. The aerodynamics of the new blade geometry is determined using multistage computational fluid dynamics simulation. The numerical results show increasing performance near the surge line and improvement in the working interval along with a 4% increase in mass flow. From the vibrational point of view, the reduced frequency increases by at least 5% in both stall and classical regions, and force response constraints are satisfied.

Keywords

• axial compressor blade
• aeroelasticity
• multidisciplinary design optimization
• computational fluid dynamics (cfd)
• machine learning
• artificial neural network (ann)
• design of experiments (doe)