Multi-goal forced convection heat transfer control for cylinder under online and offline scenarios via deep reinforcement learning

Key Points

• Heat transfer control strategies significantly enhance performance metrics in various scenarios.
• Key metrics improved include a notable increase in efficiency and response time during forced convection.
• Analysis using deep reinforcement learning across both online and offline scenarios demonstrates adaptability and effectiveness.
• Highlights the need for further data integration to refine model prediction capabilities and enhance practical applications.