Optimization of Tollmien-Schlichting waves control: comparison between a deep reinforcement learning and particle swarm optimization approach

B. Mohammadikalakoo*, M. Kotsonis, Nguyen Anh Khoa Doan

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

This work focuses on the suppression of Tollmien-Schlichting (TS) waves in a two-dimensional laminar boundary layer using optimized unsteady suction and blowing jets as an Active Flow Control (AFC) method. The suppression of TS waves via this AFC system is enabled through two artificial intelligence-based optimization methodologies: Single-Step Deep Reinforcement Learning (SDRL) and Particle Swarm Optimization (PSO). The primary aim of this research is to assess the performance of these methods in optimizing the AFC parameters with respect to convergence rate, computational efficiency, and ability to find an optimum control state. The findings demonstrate the success of both methods in finding appropriate control parameters resulting in TS wave attenuation by up to 40 dB in the maximum convective instability amplitude for the linear and nonlinear stages of development. The comparative study in this paper presents the effectiveness of the SDRL algorithm in optimizing the AFC system for TS waves’ suppression and demonstrates that it can outperform PSO in terms of convergence rate and computational efficiency alongside a better performance in finding an improved optimum for linear control cases. However, the advantage of the SDRL-based controller over the PSO-based one diminishes in multi-frequency nonlinear control cases where the controller is located downstream and attempting to control highly amplified multi-modal TS waves.
Original languageEnglish
Title of host publicationProceedings of the AIAA SCITECH 2024 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc. (AIAA)
Number of pages14
ISBN (Electronic)978-1-62410-711-5
DOIs
Publication statusPublished - 2024
EventAIAA SCITECH 2024 Forum - Orlando, United States
Duration: 8 Jan 202412 Jan 2024

Conference

ConferenceAIAA SCITECH 2024 Forum
Country/TerritoryUnited States
CityOrlando
Period8/01/2412/01/24

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