Experimental characterization of a planar jet by hot-wire anemometry

The study of turbulence is also nowadays a problem that does not have solution from the mathematical point of view due to the lack of solution to link the mean part of the flow with the fluctuating one. To solve this problem, in the CICLoPE laboratory of Predappio, experiments on different type of jets are performed in order to derive a closure model able to close our mathematical model. One of the most interesting type of jet that could be studied is the planar turbulent free jet which is a two dimensional canonical jet characterized by the self-similarity condition of the velocity profiles. To study this particular jet, a new facility was built. The aim of this project is to characterize the jet at different distances from the nozzle exit, for different values of Reynolds number, to demonstrate that the self-similarity condition is respected. To do that, the evaluation of quantities such as spreading rate, centerline velocity decay and relation between fluctuations and mean part of the flow has to be obtain. All these parameters could be detected thanks to the use of single and X hot-wire anemometry with which it is possible to analyzed the fluctuating behaviour of the flow by associating to an electric signal a physical variable expressed in terms of velocity. To justify the data obtain by the measures, a comparison with results coming from the literature has to be shown.

A deep learning model for controlling the galvanizing process in a production line

In the industry of steelmaking, the process of galvanizing is a treatment which is applied to protect the steel from corrosion. The air knife effect (AKE) occurs when nozzles emit a steam of air on the surfaces of a steel strip to remove excess zinc from it. In our work we formalized the problem to control the AKE and we implemented, with the R&D dept.of MarcegagliaSPA, a DL model able to drive the AKE. We call it controller. It takes as input the tuple : a tuple of the physical conditions of the process line (t,h,s) with the target value of the zinc coating (c); and generates the expected tuple of (pres and dist) to drive the mechanical nozzles towards the (c). According to the requirements we designed the structure of the network. We collected and explored the data set of the historical data of the smart factory. Finally, we designed the loss function as sum of three components: the minimization between the coating addressed by the network and the target value we want to reach; and two weighted minimization components for both pressure and distance. In our solution we construct a second module, named coating net, to predict the coating of zinc resulting from the AKE when the conditions are applied to the prod. line. Its structure is made by a linear and a deep nonlinear “residual” component learned by empirical observations. The predictions made by the coating nets are used as ground truth in the loss function of the controller. By tuning the weights of the different components of the loss function, it is possible to train models with slightly different optimization purposes. In the tests we compared the regularization of different strategies with the standard one in condition of optimal estimation for both; the overall accuracy is ± 3 g/m^2 dal target for all of them. Lastly, we analyze how the controller modeled the current solutions with the new logic: the sub-optimal values of pres and dist can be optimize of 50% and 20%.