Jet mixing optimization using a flexible nozzle, distributed actuators, and machine learning

In this paper, we introduce the first jet nozzle allowing simultaneous shape variation and distributed active control, termed ¿Smart Nozzle¿ in the sequel. Our Smart Nozzle manipulates the jet with an adjustable flexible shape via 12 equidistant stepper motors and 12 equidistantly placed inward-pointing minijets. The mixing performance is evaluated with a 7¿×¿7 array of Pitot tubes at the end of the potential core. The experimental investigation is carried out in three steps. First, we perform an aerodynamic characterization of the unforced round jet flow. Second, we investigate the mixing performance under five representative nozzle geometries, including round, elliptical, triangular, squared, and hexagonal shapes. The greatest mixing area is achieved with the square shape. Third, the symmetric forcing parameters are optimized for each specified nozzle shape with a machine learning algorithm. The best mixing enhancement for a symmetric active control is obtained by the squared shape, which results in a 1.93-fold mixing area increase as compared to the unforced case. Symmetrically unconstrained forcing achieves a nearly 4.5-fold mixing area increase. The Smart Nozzle demonstrates the feasibility of novel flow control techniques that combine shape variation and active control, leveraging the capabilities of machine learning optimization algorithms.

Jet mixing optimization using a flexible nozzle, distributed actuators, and machine learning Articles