Deep reinforcement learning-based jet control for tandem cylinders

The flow dynamics around tandem cylinders are complex, with significant engineering implications, especially in applications like high-rise buildings. This study presents a jet flow control framework for two tandem cylinders with a Reynolds number (Re) of 100, based on deep reinforcement learning. We compare two control strategies: (1) a single-agent strategy, where one controller manages the jet flow for two cylinders and (2) a dual-agent strategy, where separate controllers regulate each cylinder independently. The effectiveness of both strategies is evaluated under varying cylinder radii and inter-cylinder spacing. The results show that the single-agent strategy achieves drag reductions of approximately 28% and 40% for the front and rear cylinders, respectively, while the dual-agent strategy results in reductions of about 32% and 31%. While the single-agent strategy is more effective at reducing drag on the rear cylinder, the dual-agent strategy provides superior drag reduction for the larger cylinder and exhibits smaller fluctuations in drag across all conditions.