Kaiserslautern - Fachbereich Maschinenbau und Verfahrenstechnik
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Microcrystalline cellulose pellets for oral drug delivery are often produced by a combined wet extrusion-spheronization process. During the entire process, the cylindrical as well as the spherical pellets are exposed to various stresses resulting in a change of their shape and size due to plastic deformation and breakage. In this work, the effect of moisture content of pellets on their mechanical behavior is studied. In static compression tests, the strong influence of water content on deformation behavior of pellets is confirmed. Moreover, impact tests are performed using a setup consisting of three high-speed cameras to record pellet-wall collisions. Material properties, such as stiffness, restitution coefficient, breakage force, and displacement, were analyzed depending on the water content.
Machining is very common in industry, e.g. automotive industry and aerospace industry, which is a nonlinear dynamic problem including large deformations, large strain, large strain rates and high temperatures, that implies some difficulties for numerical methods such as Finite element method. One way to simulate such kind of problems is the Particle Finite Element Method (PFEM) which combines the advantages of continuum mechanics and discrete modeling techniques. In this work we introduce an improved PFEM called the Adaptive Particle Finite Element Method (A-PFEM). The A-PFEM introduces particles and removes wrong elements along the numerical simulation to improve accuracy, precision, decrease computing time and resolve the phenomena that take place in machining in multiple scales. At the end of this paper, some examples are present to show the performance of the A-PFEM.
One technique to describe the failure of mechanical structures is a phase field model for fracture. Phase field models for fracture consider an independent scalar field variable in addition to the mechanical displacement [1]. The phase field ansatz approximates crack surfaces as a continuous transition zone in which the phase field variable varies from a value that indicates intact material to another value that represents cracks. For a good approximation of cracks, these transition zones are required to be narrow, which leads to steep gradients in the fracture field. As a consequence, the required mesh density in a finite element simulation and thus the computational effort increases. In order to circumvent this efficiency problem, exponential shape functions were introduced in the discretization of the phase field variable, see [2]. Compared to the bilinear shape functions these special shape functions allow for a better approximation of the steep transition with less elements. Unfortunately, the exponential shape functions are not symmetric, which requires a certain orientation of elements relative to the crack surfaces. This adaptation is not uniquely determined and needs to be set up in the correct way in order to improve the approximation of smooth cracks. The issue is solved in this work by reorientating the exponential shape functions according to the nodal value of phase field gradient in a particular element. To be precise, this work discusses an adaptive algorithm that implements such a reorientation for 2d and 3d situations.
In grinding, the crystal grain size of the workpiece material is relatively same range compared to the removal depth. This raises a question if an anisotropic material model, which considers the effect of the crystal grain size and orientations, would better predict the process forces when compared to an isotropic material model. Initially, a simple micro-indentation process is chosen to compare the two models. In this work, a crystal plasticity model and an isotropic Johnson-Cooke plasticity model are employed to simulate micro-identation of a twinning induced plasticity (TWIP) steel. The results of the two models are compared using the force-displacement curves from the micro-indentation experiments. In the future, the study will be extended to describe the material removal process during a single grit scratch test.
This contribution presents a novel approach to investigate entrainment in distillation and absorption columns. An image-based probe allows precise droplet detection at various radial and axial positions above trays. Validations achieve an aver-age error of 6.4 % (monospheres 9.2–114.4mm) and 3 % (monodisperse droplet stream up to 19 m s–1and 74.5mm).Experiments in a DN 450 cold flow test rig show an increasing (decreasing) share of larger droplets with higher gas (liq-uid) loads. Locally measured droplet sizes depend on probe position as well as tray design and enable an extrapolation tointegral entrainment rates.
Organic solutions of lithium bis(fluorosulfonyl)imide (LiFSI) are promising electrolytes for Li-ion batteries. Information on the diffusion coefficients of the species in these solutions is needed for battery design. Therefore, the self-diffusion coefficients in such solutions were studied experimentally with the pulsed-field gradient nuclear magnetic resonance technique. The self-diffusion coefficients of the ions Li+ and FSI− as well as those of the solvents were measured for LiFSI solutions in pure dimethyl carbonate and ethylene carbonate as well as in mixtures of these solvents at 298 K and ambient pressure. Despite the Li+ ion being the smallest species in the solution, its self-diffusion coefficient is the lowest as a result of its strong coordination with the solvent molecules.
Measuring Particle Size Distributions in Multiphase Flows Using a Convolutional Neural Network
(2019)
The efficiency of many chemical engineering applications depends on the surface/volume ratio of the dispersed phase. Knowledge of this particle size distribution is a key factor for better process control. The challenge of measurements acquired by optical imaging techniques is the segmentation of overlapping particles, especially in high phase fraction flows. In this work, a convolutional neural network is trained to segment droplets in images acquired by a shadowgraphic approach. The network is trained on artificial images and implemented into a droplet size algorithm. The results are compared to an OpenSource segmentation approach.
The interest in micro applications increases in recent years due to new methods of fabrication. One fabrication process is direct laser writing, which can fabricate high-precision structures in the micrometer range. The material properties of the micro structures are related to the writing parameters, such as laser power, scan speed, distance between written lines and writing direction. This work presents investigations of the thermal length expansion coefficients of a laser-written polymer in regard to laser power. To this end cantilever structures are fabricated. The small cantilevers are heated and their length expansions observed using a microscope. Images of the cantilevers at different temperatures are taken and by image post processing, the change in length and their coefficients of thermal expansion is determined.
With significant technological growth and computing power it is possible to simulate metal cutting processes with different discretization techniques. Classically the Lagrangian or Eulerian finite element formulations are used to model metal cutting process. Lagrangian approach is accurate with it's representation of the domain boundary, but requires a re-meshing procedure to avoid element distortions. Eulerian approach provides a steady state solution of the chip-workpiece separation, however its limitation lies in the treatment of convective terms during motion. The Arbitrary Lagrangian-Eulerian method can be used to combine the advantages of both methods and avoid the disadvantages. In the Lagrangian framework, use of a meshless technique– Smooth Particle Hydrodynamics (SPH) has its advantage in large strain deformation problems without the need for re-meshing algorithms. This work compares the LAG, ALE and SPH approaches by modelling a turning process.
This work reviews the state-of-the-art models for the simulation of bubble columns and focuses on methods coupled with computational fluid dynamics (CFD) where the potential and deficits of the models are evaluated. Particular attention is paid to different approaches in multiphase fluid dynamics including the population balance to determine bubble size distributions and the modeling of turbulence where the authors refer to numerous published examples. Additional models for reactive systems are presented as well as a special chapter regarding the extension of the models for the simulation of bubble columns with a present solid particle phase, i.e., slurry bubble columns.