Control of reacting flows with hybrid differentiable/deep learning flow solver

The control of turbulent reacting flows is very challenging due to the chaotic nature of flows, the strong nonlinearity of the chemical reactions and the complex interplay between flow and chemistry. Nonetheless, achieving such flow control in reacting flows could be of great importance given the various combustion instabilities that can occur in such systems. Typical approaches to this problem rely on the construction of ad-hoc reduced-order models of the combustion system for which control laws are designed, mainly based on expert knowledge. Recently, deep reinforcement learning was applied to nonreacting flow control with some success. Nevertheless, this latter approach tends to be computationally costly as it requires many episodes to train the controller. In this work, we propose a novel approach which combines a differentiable reacting flow solver and a deep learning controller. It uses the differentiable capability of the flow solver to provide the gradients of the controller parameters with respect to our objective function over multiple timesteps, enabling an acceleration of its training and ensuring stable control over a longer period. We test our framework on the problem of controlling an arbitrary given starting flame shape into another target flame shape which can be of relevance when wanting to enforce the position of the flame away from regions of low-velocity (to prevent flashback, for example). We show that the proposed framework identifies an efficient control approach to this problem.