Interface-tracking approach

Broadly speaking, there are two computational approaches to solving fluid flow problems that include an evolving interface between two fluid phases. These can be categorized as interface tracking and interface capturing. The former refers to methods that explicitly parameterize the interface and follow the points on the surface... 

Other approaches that combine concepts from the volume-of-fluid and level set methods [14] are also likely to be developed further. In parallel, interface tracking methods which follow the sharp interface in a Lagrangian sense... 

In recent years, several methods have been developed for simulating the transport of fully resolved freely moving particles in laminar and turbulent two-phase flows. Methods like fictions domain [17], finite difference [38], finite element [26], immersed boundary [36] as well as interface tracking [6] are only a few examples of established simulation approaches. Nevertheless, aU methods mentioned above face the same problem the limitation is that transient computations of a time-varying particle-fluid interface of moving resolved particles require a significant amount of computational time. 

Up to date, several experimental techniques have been developed which are capable of detecting some of these particles under ordinary thermodynamic conditions. One can use these methods to keep track of transformations of the particles. For instance, it is relevant to mention here the method of electron paramagnetic resonance (EPR) with sensitivity of about 10 particles per cm [IJ. However, the above sensitivity is not sufficient to study physical and chemical processes developing in gaseous and liquid media (especially at the interface with solids). Moreover, this approach is not suitable if one is faced with detection of particles possessing the highest chemical activity, namely, free radicals and atoms. As for the detection of excited molecular or atom particles... 

The interface may be self-adaptive, noting the identity of the user each time they log in to use the system and tracking the interactions that follow, learning about the level of expertise of the user, and building up a personal profile of them so that the approach of the ES can be tailored to them individually. This is sometimes apparent in the Help system on a PC, which is usually based around a type of ES. Since the level of knowledge, experience, and confidence among PC users varies widely, if a Help system is to be of the greatest value, it is wise to construct it so that it can adjust its approach and presentation to match the level of expertise of the user. 

Fig. 1 Real-time tracking of cell adhesion [42]. (a) Components of a total internal reflection fluorescent microscope (TIRFM). (b) The cell adhesion process (7) a cell approaches the surface, (2) the cell lands, (3) the cell attaches, and (4) the cell spreads out on the surface. The evanescent field was generated by total internal reflection of a laser beam at the glass-water interface. Cells with fluorescently labeled membranes (dashed lines) were plated on SAMs. Cell membranes within the evanescent field (solid line) were observed by TIRFM. Corresponding TIRFM images are shown below...

In the microfluid dynamics approaches the continuity and Navier-Stokes equation coupled with methodologies for tracking the disperse/continuous interface are used to describe the droplet formation in quiescent and crossflow continuous conditions. Ohta et al. [54] used a computational fluid dynamics (CFD) approach to analyze the single-droplet-formation process at an orifice under pressure pulse conditions (pulsed sieve-plate column). Abrahamse et al. [55] simulated the process of the droplet break-up in crossflow membrane emulsification using an equal computational fluid dynamics procedure. They calculated the minimum distance between two membrane pores as a function of crossflow velocity and pore size. This minimum distance is important to optimize the space between two pores on the membrane... 

Recently there has been renewed interest in automated method development in which the optimization software directly interfaces with the instrument in order to run or suggest new experiments based on the prior results that generated the initial resolution maps. In the late 1980s, a number of approaches to this problem were attempted, but none of these tools prevailed, due in part to the challenges of tracking peaks between experiments. 

---