FE calculations on a three stage metal forming process of Sandvik Nanoflex

Sandvik NanoflexTM combines good corrosion resistance with high strength. This steel has good deformability in\ud austenitic conditions. It belongs to the group of metastable austenites, which means that during deformation a strain-induced\ud transformation into martensite takes place. After deformation, transformation continues as a result of internal stresses. Both\ud transformations are stress-state and temperature dependent. A constitutive model for this steel has been formulated, based\ud on the macroscopic material behaviour measured by inductive measurements. Both the stress-assisted and the strain-induced\ud transformation into martensite have been incorporated in this model. Path-dependent work hardening has also been taken\ud into account. This article describes how the model is implemented in an internal Philips FE code called CRYSTAL, which is\ud a dedicated robust and accurate finite element solver. The implementation is based on lookup tables in combination with\ud feed-forward neural networks. The radial return method is used to determine the material state during and after plastic\ud flow, however, it has been extended to cope with the stiff character of the partial differential equation that describes the\ud transformation behaviour

Investigating the performance of different fluidized bed membrane reactor geometries

The hydrodynamics and mass transfer phenomena occurring in different fluidized bed membrane reactors have been studied with a Two Fluid Model (TFM). The present work focuses on the in-situ selective extraction of hydrogen from a fluidized bed membrane reactor, aiming to study and quantify the membrane performance, including possible concentration polarization. Using a hydrogen-nitrogen gas mixture as fluidizing gas, various fluidized bed geometries containing vertically or horizontally immersed membranes were simulated. The hydrodynamics and mass transfer phenomena of a fluidized bed can be strongly affected by the membrane configuration. Previous work by the group of Van Sint Annaland (1) showed the appearance of densified particle zones near the membranes, which could affect their performance. Furthermore, so called gas pockets (solids free non-rising bubbles, attached to the membrane) are formed underneath horizontal membrane tubes, see Medrano et al. (2). These phenomena are identified, their adverse effect on the membrane flux is quantified and possible remedies are discussed. Hydrogen fluxes of a membrane placed vertically in a fluidized bed were obtained from the TFM, experiments and a 1D model that does not take concentration polarization into account. These fluxes are compared in Figure 1. Concentration polarization is clearly very important in fluidized bed reactors with state-of-the-art high-flux membranes. The TFM predicts the fluxes quite accurately, whereas the model that does not account for concentration polarization severely overpredicts them. Please click Additional Files below to see the full abstract

Model selection in continuous test norming with GAMLSS

To compute norms from reference group test scores, continuous norming is preferred over traditional norming. A suitable continuous norming approach for continuous data is the use of the Box–Cox Power Exponential model, which is found in the generalized additive models for location, scale, and shape. Applying the Box–Cox Power Exponential model for test norming requires model selection, but it is unknown how well this can be done with an automatic selection procedure. In a simulation study, we compared the performance of two stepwise model selection procedures combined with four model-fit criteria (Akaike information criterion, Bayesian information criterion, generalized Akaike information criterion (3), cross-validation), varying data complexity, sampling design, and sample size in a fully crossed design. The new procedure combined with one of the generalized Akaike information criterion was the most efficient model selection procedure (i.e., required the smallest sample size). The advocated model selection procedure is illustrated with norming data of an intelligence test

Characterisation of a mitochondrial iron transporter of the pathogen Trypanosoma brucei

Similar to higher eukaryotes, the protist parasite T. brucei harbours several iron-containing proteins that regulate DNA and protein processing, oxidative stress defence and mitochondrial respiration. The synthesis of these proteins occurs either in the cytoplasm or within the mitochondrion. For mitochondrial iron cluster protein synthesis, iron needs to be transported across the solute impermeable mitochondrial membrane. In T. brucei we previously identified 24 mitochondrial carrier proteins (TbMCPs) sharing conserved structural and functional features with those from higher eukaryotes. One of these carriers (TbMCP17) displayed high similarity with the iron carriers MRS3, MRS4 from yeast and mitoferrin from mammals, insects and plants. In the present study we demonstrated that TbMCP17 functions as an iron carrier by complementation studies using MRS3/4-deficient yeast. Depletion of TbMCP17 in procyclic form T. brucei resulted in growth deficiency, increased sensitivity to iron deprivation, and lowered mitochondrial iron content. Taken together our results suggest that TbMCP17 functions as a mitochondrial iron transporter in the parasite T. brucei

Bias-variance trade-off in continuous test norming

In continuous test norming, the test score distribution is estimated as a continuous function of predictor(s). A flexible approach for norm estimation is the use of generalized additive models for location, scale, and shape. It is unknown how sensitive their estimates are to model flexibility and sample size. Generally, a flexible model that fits at the population level has smaller bias than its restricted nonfitting version, yet it has larger sampling variability. We investigated how model flexibility relates to bias, variance, and total variability in estimates of normalizedzscores under empirically relevant conditions, involving the skew Studenttand normal distributions as population distributions. We considered both transversal and longitudinal assumption violations. We found that models with too strict distributional assumptions yield biased estimates, whereas too flexible models yield increased variance. The skew Studenttdistribution, unlike the Box-Cox Power Exponential distribution, appeared problematic to estimate for normally distributed data. Recommendations for empirical norming practice are provided