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

Mohammad Ghalandari, Alireza Ziamolki, Amir Mosavi, Shahaboddin Shamshirband, Kwok Wing Chau, Saeed Bornassi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

45 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)892-904
Number of pages13
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - 1 Jan 2019


  • aeroelasticity
  • artificial neural network (ANN)
  • axial compressor blade
  • computational fluid dynamics (CFD)
  • design of experiments (DOE)
  • machine learning
  • multidisciplinary design optimization

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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