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Untersuchungen zur Tragfähigkeit und Steifigkeit eines neuartigen Wandelements in Holzbauweisen
(2018)
Manufacturing process simulation (MPS) has become more and more important for aviation and the automobile industry. A highly competitive market requires the use of high performance metals and composite materials in combination with reduced manufacturing cost and time as well as a minimization of the time to market for a new product. However, the use of such materials is expensive and requires sophisticated manufacturing processes. An experience based process and tooling design followed by a lengthy trial-and-error optimization is just not contemporary anymore. Instead, a tooling design process aided by simulation is used more often. This paper provides an overview of the capabilities of MPS in the fields of sheet metal forming and prepreg autoclave manufacturing of composite parts summarizing the resulting benefits for tooling design and manufacturing engineering. The simulation technology is explained briefly in order to show several simplification and optimization techniques for developing industrialized simulation approaches. Small case studies provide examples of an efficient application on an industrial scale.
In this paper, a coupled multiphase model considering both non-linearities of water retention curves and solid state modeling is proposed. The solid displacements and the pressures of both water and air phases are unknowns of the proposed model. The finite element method is used to solve the governing differential equations. The proposed method is demonstrated through simulation of seepage test and partially consolidation problem. Then, implementation of the model is done by using hypoplasticity for the solid phase and analyzing the fully saturated triaxial experiments. In integration of the constitutive law error controlling is improved and comparisons done accordingly. In this work, the advantages and limitations of the numerical model are discussed.
Magnetic nanoparticles (MNPs) are used as therapeutic and diagnostic agents for local delivery of heat and image contrast enhancement in diseased tissue. Besides magnetization, the most important parameter that determines their performance for these applications is their magnetic relaxation, which can be affected when MNPs immobilize and agglomerate inside tissues. In this letter, we investigate different MNP agglomeration states for their magnetic relaxation properties under excitation in alternating fields and relate this to their heating efficiency and imaging properties. With focus on magnetic fluid hyperthermia, two different trends in MNP heating efficiency are measured: an increase by up to 23% for agglomerated MNP in suspension and a decrease by up to 28% for mixed states of agglomerated and immobilized MNP, which indicates that immobilization is the dominant effect. The same comparatively moderate effects are obtained for the signal amplitude in magnetic particle spectroscopy.
Many efforts are made worldwide to establish magnetic fluid hyperthermia (MFH) as a treatment for organ-confined tumors. However, translation to clinical application hardly succeeds as it still lacks of understanding the mechanisms determining MFH cytotoxic effects. Here, we investigate the intracellular MFH efficacy with respect to different parameters and assess the intracellular cytotoxic effects in detail. For this, MiaPaCa-2 human pancreatic tumor cells and L929 murine fibroblasts were loaded with iron-oxide magnetic nanoparticles (MNP) and exposed to MFH for either 30 min or 90 min. The resulting cytotoxic effects were assessed via clonogenic assay. Our results demonstrate that cell damage depends not only on the obvious parameters bulk temperature and duration of treatment, but most importantly on cell type and thermal energy deposited per cell during MFH treatment. Tumor cell death of 95% was achieved by depositing an intracellular total thermal energy with about 50% margin to damage of healthy cells. This is attributed to combined intracellular nanoheating and extracellular bulk heating. Tumor cell damage of up to 86% was observed for MFH treatment without perceptible bulk temperature rise. Effective heating decreased by up to 65% after MNP were internalized inside cells.
Suspension depletion approach for exemption of infected Solanum jasminoides cells from pospiviroids
(2018)
Despite numerous studies, viroid elimination from infected plants remains a very challenging task. This study introduces for the first time a novel ‘suspension depletion’ approach for exemption of Solanum jasminoides plants from viroids. The proposed method implies initial establishment of suspension cultures of the infected plant cells. The suspended cells were then physically treated (mild thermotherapy, 33 °C), which presumably delayed the replication of the viroid. The viroid concentration in the treated biomass was monitored weekly using pospiviroid-specific PCR. After 10–12 weeks of continuous treatment, a sufficient decrease in viroid concentration was observed such that the infection became undetectable by PCR. The treated single cells then gave rise to microcolonies on a solid culture medium and the obtained viroid-negative clones were further promoted to regenerate into viroid-free plants. Three years of accumulated experimental data suggests feasibility, broad applicability, and good efficacy of the proposed approach.
In energy economy forecasts of different time series are rudimentary. In this study, a prediction for the German day-ahead spot market is created with Apache Spark and R. It is just an example for many different applications in virtual power plant environments. Other examples of use as intraday price processes, load processes of machines or electric vehicles, real time energy loads of photovoltaic systems and many more time series need to be analysed and predicted.
This work gives a short introduction into the project where this study is settled. It describes the time series methods that are used in energy industry for forecasts shortly. As programming technique Apache Spark, which is a strong cluster computing technology, is utilised. Today, single time series can be predicted. The focus of this work is on developing a method to parallel forecasting, to process multiple time series simultaneously with R and Apache Spark.