Additive manufacturing (AM), widely known as 3D printing, is a direct digital manufacturing process, where a component can be produced layer by layer from 3D digital data with no or minimal use of machining, molding, or casting. AM has developed rapidly in the last 10 years and has demonstrated significant potential in cost reduction of performance-critical components. This can be realized through improved design freedom, reduced material waste, and reduced post processing steps. Modeling AM processes not only provides important insight in competing physical phenomena that lead to final material properties and product quality but also provides the means to exploit the design space towards functional products and materials. The length-and timescales required to model AM processes and to predict the final workpiece characteristics are very challenging. Models must span length scales resolving powder particle diameters, the build chamber dimensions, and several hundreds or thousands of meters of heat source trajectories. Depending on the scan speed, the heat source interaction time with feedstock can be as short as a few microseconds, whereas the build time can span several hours or days depending on the size of the workpiece and the AM process used. Models also have to deal with multiple physical aspects such as heat transfer and phase changes as well as the evolution of the material properties and residual stresses throughout the build time. The modeling task is therefore a multi-scale, multi-physics endeavor calling for a complex interaction of multiple algorithms. This paper discusses models required to span the scope of AM processes with a particular focus towards predicting as-built material characteristics and residual stresses of the final build. Verification and validation examples are presented, the over-spanning goal is to provide an overview of currently available modeling tools and how they can contribute to maturing additive manufacturing.

Adhesion of leukocytes to substrate involves the coupling of disparate length and timescales between molecular mechanics and macroscopic transport, and existing models of cell adhesion do not use full cellular information. To address these challenges, a multiscale computational approach for studying the adhesion of a cell on a substrate is developed and assessed. The cellular level model consists of a continuum representation of the field equations and a moving boundary tracking capability to allow the cell to change its shape continuously. At the receptor-ligand level, a bond molecule is mechanically represented by a spring. Communication between the macro/micro- and nanoscale models is facilitated interactively during the computation. The computational model is assessed using an adherent cell, rolling and deforming along the vessel wall under imposed shear flows. Using this approach, we first confirm existing numerical and experimental results. In this study, the intracellular viscosity and interfacial tension are found to directly affect the rolling of a cell. Our results also show that the presence of a nucleus increases the bond lifetime, and decreases the cell rolling velocity. Furthermore, it is found that a cell with a larger diameter rolls faster, and decreases the bond lifetime. This study shows that cell rheological properties have significant effects on the adhesion process contrary to what has been hypothesized in most literature.

Many critical issues in biofluid dynamics occur at the boundaries between fluids, solids, or both. These issues can be very complex since in many cases the boundaries are deformable and moving. Furthermore, different characteristic times, lengths, and material properties are often present which make any computational task taxing. The present review focuses on computational modeling techniques for moving boundaries and multi-component systems with emphasis on micro-scale biofluid physics, including i) the dynamics of leukocyte (white blood cell) deformation, recovery, and adhesion; and ii) the thin-film dynamics involving tear–structure interaction in soft contact lens applications. In these problems, multiple length scales exist, and at least one of them is on the order of 10 μm or smaller. After presenting appropriate computational techniques for moving boundaries, recent research on leukocyte deformation, recovery, and adhesion is reviewed in the context of multi-component, multi-time-scale, and micro-macro interactions. The soft contact lens problem is discussed from the viewpoint of large disparities in length scales due to high aspect ratios. Depending on the nature of the problem and the goal of the computation, alternative computational techniques can successfully address the physical and numerical challenges. A major interest of this article is to stress how moving boundary techniques can be applied to provide new insights into the physico-chemical behavior of complex biological systems. To treat different time and length scales with due care in a moving boundary framework is a grand challenge in developing first-principle-based computational capabilities. There are 175 references in this review article.

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