Design optimization of an s-shaped subsonic intake using bayesian approach and bezier curves

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2022-5-26
Atasoy, Mete
Engine is a prominent part for the systems of a cruise missile. Reliable and efficient engine performance significantly depends on the quality of the incoming airflow. In this thesis, the aerodynamic performance of a subsonic intake is improved by using open-source multi-task Bayesian optimization (MTBO) tool. The main difference of Bayesian approaches from other optimization methods is that it has information about all design space, along with the uncertainty value. The learning process in the Bayesian approach is developed using the correlation of two different numerical methods. Also, machine learning-based optimization combined with multi-tasking used in this thesis is a pioneering work for air-intake design. The commercial computational fluid dynamics (CFD) solver ANSYS Fluent is used to calculate the flow variables through the intake. The flow quality at a plane interfacing with the engine (denoted as the aerodynamic interfacing plane, AIP) are usually quantified with the flow uniformity and level of pressure loss. MTBO employs a Gaussian process with both high and low cost solutions for machine learning. Then, an acquisition function scores the system output of the surrogate model. In addition, turbulence model selection studies for both cost levels are conducted. An objective function is utilized based on the weighted sum of non-dimensional values of Distortion Coefficient (DC), Pressure Recovery (PR) and weight of the S-duct. Initial sampling approach for multi-tasking approaches is also investigated. Multi-tasking is compared with single-tasking for total execution time when similar optima is achieved. Furthermore, parameters in the objective functions are investigated with Pareto plots.

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Citation Formats
M. Atasoy, “Design optimization of an s-shaped subsonic intake using bayesian approach and bezier curves,” M.S. - Master of Science, Middle East Technical University, 2022.