Master Thesis, 30 hp Surrogate-Based Optimization of High-Performance Airfoils and Air Intake Ducts
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Job Summary
Are you a student eager to apply your theoretical knowledge and fresh perspectives to real-world challenges At Saab we believe that innovation thrives on new ideas and your master thesis project could be the spark that ignites our next technological breakthrough.
We recognize the immense value that students bring to our company. Your academic rigor combined with your enthusiasm for cutting-edge technology allows you to approach problems with a unique and insightful lens. At Saab youll have the opportunity to collaborate with experienced engineers and specialists gaining invaluable practical experience while making a tangible contribution to our growth and development.
Background
Aerodynamic shape optimization using high-fidelity Computational Fluid Dynamics (CFD) such as Reynolds-Averaged NavierStokes (RANS) solvers - is computationally intensive. Direct optimization loops using standard gradient-free or evolutionary algorithms often require hundreds of evaluations making global optimization prohibitive for complex aerodynamic components like transonic airfoils and engine air intake ducts (e.g. S-ducts or nacelles). Surrogate-Based Optimization (SBO) mitigates this computational cost by replacing expensive CFD solvers with statistical or data-driven metamodels (such as Kriging/Gaussian Process Regression or Radial Basis Functions) coupled with adaptive infill sampling strategies (e.g. Efficient Global Optimization via Expected Improvement).
Description of the Thesis Project
This thesis project focuses on developing and evaluating an efficient SBO framework tailored for aerodynamic shape design applied to both 2D airfoils and 3D air intake geometries. The primary objectives are:
Geometric Parameterization & Sampling: Implement compact parameterization schemes (e.g. Class-Shape Transformation (CST) or Free-Form Deformation (FFD)) and construct initial Design of Experiments (DoE) datasets using Latin Hypercube Sampling (LHS).
Surrogate Model Construction & Adaptive Infilling: Train Kriging and/or multi-fidelity Co-Kriging models using CFD evaluations incorporating adaptive sampling criteria (e.g. Expected Improvement) to systematically explore and exploit the design space.
Application & Validation:
Case Study 1 (Airfoil): Multi-point drag minimization of a transonic airfoil subject to lift and geometric thickness constraints.
Case Study 2 (Air Intake): Multi-objective optimization of an engine air intake duct (or S-duct) to maximize total pressure recovery while minimizing compressor-face flow distortion.
Performance Benchmark: Compare the efficiency convergence rate and accuracy of the SBO framework against standard direct (gradient based) optimization baselines.
The project is suitable for one masters student in Aerospace Engineering Mechanical Engineering Computational Mechanics or Applied Mathematics. You are in the final stage of your education or equivalent and are about to begin a 30 HP thesis project.
We are looking for someone with a solid background in optimization algorithms and scientific computing combined with proficiency in Python and/or Matlab for scripting optimization loops and mesh processing. A background in Fluid Mechanics and Computational Fluid Dynamics (CFD) as well as prior experience with CAD and mesh generation tools is considered a strong merit.
We provide the support and guidance you need to translate your theoretical knowledge into practical solutions. Join us and become a driving force behind Saabs technological advancements!
This position requires that you pass a security vetting based on the current regulations around/of security protection. For positions requiring security clearance additional obligations on citizenship may apply.
Kindly observe that this is an ongoing recruitment process and that the position might be filled before the closing date of the advertisement.
Contact information
Nor Al-Mosawi Manager Propulsion System
Magnus Carlsson. Master Thesis Supervisor Propulsion System Department
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