PhD Oral Exam - Mohsen Hamedi, Mechanical Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Aviation noise poses significant challenges to both the quality of life for those living near airports and the sustainable growth of the aviation industry. Accurate aeroacoustic shape optimization methods are required to reduce this noise. To address this, we propose an innovative approach for aeroacoustic shape optimization using Large Eddy Simulation (LES). The gradient-free Mesh Adaptive Direct Search (MADS) algorithm minimizes sound pressure levels for observers at varying distances. Near-field predictions rely on a high-order unstructured flow solver, while far-field predictions employ an acoustic solver based on the Ffowcs Williams and Hawkings (FW-H) time-domain formulation. The acoustic solver is verified and validated using analytical test cases and comparison with results obtained directly from the flow solver. The optimization framework is implemented in parallel, enabling concurrent objective function evaluations at each iteration. This removes the dependency of the computational runtime of the MADS algorithm on the total number of design variables, given the availability of sufficient resources. Different problems are considered to evaluate the performance of the proposed optimization framework, including flow over a deep open cavity, tandem cylinders configuration, and a NACA 4-digit airfoil. Initially, noise reduction for a near-field observer is addressed for these geometries under low Reynolds numbers and in two-dimensional settings. Subsequently, the approach is extended to three dimensions, and finally, shape optimization is conducted to minimize noise for a far-field observer. The results affirm the efficacy of the proposed optimization framework by significant noise reduction across all cases.