This paper presents a comprehensive computational model capable of simulating fouling layer thickness evolution using dynamic mesh model. This computational methodology has been developed to reproduce the deposit generation during fouling process with an innovated work method. Dynamic mesh model, from Ansys Fluent software, and external routines have been used to implement this advanced numerical model which allows to move the boundaries of a region relative to other boundaries of the zone. The displacement of the nodes of the mesh is the mechanism that this model uses to adjust the geometry according to the fouling layer evolution. During the simulation process, the geometry under investigation is modified to reproduce the emergence and gradual change of the fouling layer. Different rules of deposition and removal of the fouling process can be implemented in the proposed algorithm. The direct interaction between fouling expressions and governing equations of the main flow is used to predict deposits formation and growth. In this paper, numerical simulations of soot fouling layer formation have been presented. Deposits evolution has been calculated inside different heat exchanger technologies used in exhaust gas recirculation systems to analyse fouling process and to verify the advantages of this new computational strategy.

This work introduces the use of machine vision in the massive bubble recognition process, which supports the validation of boiling models involving bubble dynamics, as well as nucleation frequency, active site density and size of the bubbles. The two algorithms presented are meant to be run employing quite standard images of the bubbling process, recorded in general-purpose boiling facilities. The recognition routines are easily adaptable to other facilities if a minimum number of precautions are taken in the setup and in the treatment of the information. Both the side and front projections of subcooled flow-boiling phenomenon over a plain plate are covered. Once all of the intended bubbles have been located in space and time, the proper post-process of the recorded data become capable of tracking each of the recognized bubbles, sketching their trajectories and size evolution, locating the nucleation sites, computing their diameters, and so on. After validating the algorithm’s output against the human eye and data from other researchers, machine vision systems have been demonstrated to be a very valuable option to successfully perform the recognition process, even though the optical analysis of bubbles has not been set as the main goal of the experimental facility.