Crystal Plasticity Modeling of Grey Cast Irons under Tension, Compression and Fatigue Loadings

The study of the micromechanical performance of materials is important in explaining their macrostructural behavior, such as fracture and fatigue. This paper is aimed, among other things, at reducing the deficiency of microstructural models of grey cast irons in the literature. For this purpose, a numerical modeling approach based on the crystal plasticity (CP) theory is used. Both synthetic models and models based on scanning electron microscope (SEM) electron backscatter diffraction (EBSD) imaging finite element are utilized. For the metal phase, a CP model for body-centered cubic (BCC) crystals is adopted. A cleavage damage model is introduced as a strain-like variable; it accounts for crack closure in a smeared manner as the load reverses, which is especially important for fatigue modeling. A temperature dependence is included in some material parameters. The graphite phase is modeled using the CP model for hexagonal close-packed (HCP) crystal and has a significant difference in tensile and compressive behavior, which determines a similar macro-level behavior for cast iron. The numerical simulation results are compared with experimental tensile and compression tests at different temperatures, as well as with fatigue experiments. The comparison revealed a good performance of the modeling approach

Decoding complex biological networks - tracing essential and modulatory parameters in complex and simplified models of the cell cycle

<p>Abstract</p> <p>Background</p> <p>One of the most well described cellular processes is the cell cycle, governing cell division. Mathematical models of this gene-protein network are therefore a good test case for assessing to what extent we can dissect the relationship between model parameters and system dynamics. Here we combine two strategies to enable an exploration of parameter space in relation to model output. A simplified, piecewise linear approximation of the original model is combined with a sensitivity analysis of the same system, to obtain and validate analytical expressions describing the dynamical role of different model parameters.</p> <p>Results</p> <p>We considered two different output responses to parameter perturbations. One was qualitative and described whether the system was still working, i.e. whether there were oscillations. We call parameters that correspond to such qualitative change in system response <it>essential</it>. The other response pattern was quantitative and measured changes in cell size, corresponding to perturbations of <it>modulatory </it>parameters. Analytical predictions from the simplified model concerning the impact of different parameters were compared to a sensitivity analysis of the original model, thus evaluating the predictions from the simplified model. The comparison showed that the predictions on essential and modulatory parameters were satisfactory for small perturbations, but more discrepancies were seen for larger perturbations. Furthermore, for this particular cell cycle model, we found that most parameters were either essential or modulatory. Essential parameters required large perturbations for identification, whereas modulatory parameters were more easily identified with small perturbations. Finally, we used the simplified model to make predictions on critical combinations of parameter perturbations.</p> <p>Conclusions</p> <p>The parameter characterizations of the simplified model are in large consistent with the original model and the simplified model can give predictions on critical combinations of parameter perturbations. We believe that the distinction between essential and modulatory perturbation responses will be of use for sensitivity analysis, and in discussions of robustness and during the model simplification process.</p

Crystal plasticity with micromorphic regularization in assessing scale dependent deformation of polycrystalline doped copper alloys

It is planned that doped copper overpacks will be utilized in the spent nuclear fuel repositories in Finland and in Sweden. The assessment of long-term integrity of the material is a matter of importance. Grain structure variations, segregation and any possible manufacturing defects in microstructure are relevant in terms of susceptibility to creep and damage from the loading evolution imposed by its operating environment. This work focuses on studying the microstructure level length-scale dependent deformation behavior of the material, of particular significance with respect to accumulation of plasticity over the extensive operational period of the overpacks. The reduced micromorphic crystal plasticity model, which is similar to strain gradient models, is used in this investigation. Firstly, the model’s size dependent plasticity effects are evaluated. Secondly, different microstructural aggregates presenting overpack sections are analyzed. Grain size dependent hardening responses, i.e., Hall-Petch like behavior, can be achieved with the enhanced hardening associated with the micromorphic model at polycrystalline level. It was found that the nominally large grain size in the base material of the overpack shows lower strain hardening potential than the fine grained region of the welded microstructure with stronger strain gradient related hardening effects. Size dependent regularization of strain localization networks is indicated as a desired characteristic of the model. The findings can be utilized to provide an improved basis for modeling the viscoplastic deformation behavior of the studied copper alloy and to assess the microstructural origins of any integrity concerns explicitly by way of full field modeling

Sim2Real for Environmental Neural Processes

Machine learning (ML)-based weather models have recently undergone rapid improvements. These models are typically trained on gridded reanalysis data from numerical data assimilation systems. However, reanalysis data comes with limitations, such as assumptions about physical laws and low spatiotemporal resolution. The gap between reanalysis and reality has sparked growing interest in training ML models directly on observations such as weather stations. Modelling scattered and sparse environmental observations requires scalable and flexible ML architectures, one of which is the convolutional conditional neural process (ConvCNP). ConvCNPs can learn to condition on both gridded and off-the-grid context data to make uncertainty-aware predictions at target locations. However, the sparsity of real observations presents a challenge for data-hungry deep learning models like the ConvCNP. One potential solution is 'Sim2Real': pre-training on reanalysis and fine-tuning on observational data. We analyse Sim2Real with a ConvCNP trained to interpolate surface air temperature over Germany, using varying numbers of weather stations for fine-tuning. On held-out weather stations, Sim2Real training substantially outperforms the same model architecture trained only with reanalysis data or only with station data, showing that reanalysis data can serve as a stepping stone for learning from real observations. Sim2Real could thus enable more accurate models for weather prediction and climate monitoring.Comment: 4 pages, 3 figures, To be published in Tackling Climate Change with Machine Learning workshop at NeurIP

The development and preliminary evaluation of Cognitive Behavioural Therapy (CBT) for Chronic Loneliness in Young People

Background: Approximately 10% of young people ‘often’ feel lonely, with loneliness being predictive of multiple physical and mental health problems. Research has found CBT to be effective for reducing loneliness in adults, but interventions for young people who report loneliness as their primary difficulty are lacking. // Method: CBT for Chronic Loneliness in Young People was developed as a modular intervention. This was evaluated in a single-case experimental design (SCED) with seven participants aged 11–18 years. The primary outcome was self-reported loneliness on the Three-Item Loneliness Scale. Secondary outcomes were self-reported loneliness on the UCLA-LS-3, and self- and parent-reported RCADS and SDQ impact scores. Feasibility and participant satisfaction were also assessed. // Results: At post-intervention, there was a 66.41% reduction in loneliness, with all seven participants reporting a significant reduction on the primary outcome measure (p < .001). There was also a reduction on the UCLA-LS-3 of a large effect (d = 1.53). Reductions of a large effect size were also found for parent-reported total RCADS (d = 2.19) and SDQ impact scores (d = 2.15) and self-reported total RCADS scores (d = 1.81), with a small reduction in self-reported SDQ impact scores (d = 0.41). Participants reported high levels of satisfaction, with the protocol being feasible and acceptable. // Conclusions: We conclude that CBT for Chronic Loneliness in Young People may be an effective intervention for reducing loneliness and co-occurring mental health difficulties in young people. The intervention should now be evaluated further through a randomised controlled trial (RCT)

A weighted configuration model and inhomogeneous epidemics

A random graph model with prescribed degree distribution and degree dependent edge weights is introduced. Each vertex is independently equipped with a random number of half-edges and each half-edge is assigned an integer valued weight according to a distribution that is allowed to depend on the degree of its vertex. Half-edges with the same weight are then paired randomly to create edges. An expression for the threshold for the appearance of a giant component in the resulting graph is derived using results on multi-type branching processes. The same technique also gives an expression for the basic reproduction number for an epidemic on the graph where the probability that a certain edge is used for transmission is a function of the edge weight. It is demonstrated that, if vertices with large degree tend to have large (small) weights on their edges and if the transmission probability increases with the edge weight, then it is easier (harder) for the epidemic to take off compared to a randomized epidemic with the same degree and weight distribution. A recipe for calculating the probability of a large outbreak in the epidemic and the size of such an outbreak is also given. Finally, the model is fitted to three empirical weighted networks of importance for the spread of contagious diseases and it is shown that $R_0$ can be substantially over- or underestimated if the correlation between degree and weight is not taken into account