The control of the entrainment and diffusion of the heat and mass ejected along the jet flow requires the control of fluid motion. In the present study, optimal initial velocity distributions for suppressing and promoting jet entrainment and diffusion were identified using deep reinforcement learning under constant initial jet flow rate conditions. Two-dimensional jet flow simulations using OpenFOAM were combined with a deep Q-network as the learning algorithm. The spatial distribution of the initial jet (velocity and ejection angle) changed. The results show that to suppress the entrainment of the jet, an optimal initial velocity distribution shape in which the velocity in the centre is almost 0, with an inward velocity just outside of it, and a large outward velocity at the outer edge, is desirable. However, to promote entrainment, a shape with a large velocity in the centre portion and small velocity at the outer edge, with an overall inward angle, is desirable. To suppress the diffusion of the jet, an optimal initial velocity distribution shape with a large velocity in the centre portion, a small velocity at the outer edge, and an overall inward angle is desirable. Conversely, to promote diffusion, a shape with a large outward velocity at both the centre and outer edges is desirable. It is also indicated that, when the initial velocity distribution of the jet has a special shape, and the relationship between flow rate, centre velocity, and centre temperature that is normally observed in a jet flow may not hold. In other words, by changing the initial velocity distribution, the entrainment and diffusion of the jet can be separated and controlled, which is usually difficult to achieve using conventional engineering methods.
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