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  1. اثر عدم قطعیت های هندسی بر عملکرد یک طبقه از کمپرسور محوری به کمک هوش مصنوعی
اثر عدم قطعیت های هندسی بر عملکرد یک طبقه از کمپرسور محوری به کمک هوش مصنوعی
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اثر عدم قطعیت های هندسی بر عملکرد یک طبقه از کمپرسور محوری به کمک هوش مصنوعی

کلابی، مهراب Kalabi, Mehrab

Artificial Intelligence-Based Analysis of Geometric Uncertainties on a Single-Stage Axial Compressor Performance

Kalabi, Mehrab | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58881 (45)
  4. University: Sharif University of Technology
  5. Department: Aerospace Engineering
  6. Advisor(s): Farahani, Mohammad
  7. Abstract:
  8. In this study, the effect of geometric uncertainties on the performance of a single-stage transonic axial compressor is investigated. As one of the main components of an aircraft engine, the compressor increases the internal energy of the flow by raising the total pressure. Due to the adverse pressure gradient in compressors, examining this issue is of greater importance compared to other engine components. Deviations of geometric parameters from their nominal values can lead to detrimental phenomena such as flow separation, surge, and choking. Therefore, compressor performance under geometric uncertainties is analyzed at the design point, near choking, and near instability conditions. By identifying the parameters with the greatest influence on compressor performance in each operating region, optimal tolerances for geometric parameters can be determined. This approach not only reduces manufacturing costs but also ensures reliable compressor operation. To achieve this, a surrogate model based on machine learning is developed, enabling efficient evaluation of geometric uncertainties with minimal computational expense. The considered parameters include blade thickness, leading and trailing edge radii, blade inlet angle, sweep angle of rotor and stator blades, and rotor tip clearance. The parameters are ranked according to their impact on compressor performance. The results are in full agreement with the aerodynamic principles of axial compressors. Findings reveal that rotor tip clearance is the most influential parameter affecting compressor performance. Moreover, the rotor is identified as more sensitive than the stator, highlighting the importance of precise manufacturing of rotor blades. For instance, rotor blade thickness is recognized as a key parameter, and results show that reducing positive thickness tolerance can guaranty compressor performance
  9. Keywords:
  10. Axial Compressor ; Machine Learning ; Artificial Neural Network ; Geometric Uncertainties ; Parametrization

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