Multiple Jet

Multiple jet systems are being investigated for their ability to control fluid flow and other complex phenomena, with primary objectives focused on optimizing jet placement, flow rate, and overall system efficiency. Current research utilizes machine learning, particularly deep reinforcement learning and various supervised learning algorithms (e.g., neural networks, random forests), to model and predict jet behavior in diverse applications, from drag reduction on cylindrical bodies to high-explosive interactions and the printing of flexible biosensors. These advancements offer significant potential for improving the design and control of multiple jet systems across various fields, leading to enhanced efficiency and precision in applications ranging from aerospace engineering to additive manufacturing.