Adaptive smoothed FEM for forming simulations

FEMsimulation of large deformations as occur in metal forming processes is usually accompanied with highly distorted meshes. This leads first to a reduction of accuracy and later to loss of convergence when implicit solvers are used. Remeshing can be used to reduce element distortion, but repeated remeshing will result in smoothing of data like equivalent plastic strain, due to averaging and interpolation. A meshless method circumvents the problem of mesh distortion, but depending on the integration of the weak formulation of equilibrium mapping of data and hence smoothing of data still remains unless a\ud nodal integration scheme is used. Starting with a LocalMaximum Entropy approach [1] with nodal integration, we end-up with a smoothed Finite Element formulation in the limit of local approximations [2]. It is straightforward to adapt the triangulation in every increment, yielding an Adaptive Smoothed Finite\ud Element Method, in which large deformations can be modelled with a Lagrangian description without the necessity to map data from one step to the other.\ud A cell based stabilized conforming nodal integration method (SCNI) [3] is used. Depending on the configuration of nodes, nodal integration can yield singular stiffness matrices, resulting in spurious displacement modes [4]. A stabilization is used, based on minimizing the difference between a ‘linear\ud assumed’ and the consistent strain field. The cells are based on the Delaunay triangulation, connecting mid-sides and centres of gravity of the triangles (Figure 1). Especially at the outer boundary, this yields a simpler formulation than using the dual Voronoi tesselatio

Nodal integration of meshless methods

Meshless methods offer interesting properties for the simulation of bulk forming\ud processes. This research concerns the investigation of the stabilized conforming nodal integration scheme (SCNI) for use in metal-forming processes. Two tests are carried out. Firstly, the performance of SCNI is compared to a standard integration scheme. The performance seems problem specific. Secondly the footing of a piece of nearly incompressible material is used for testing the locking behavior of the method. No volumetric locking was found

On the use of local max-ent shape functions for the simulation of forming processes

In this work we review the opportunities given by the use of local maximum-\ud entropy approximants (LME) for the simulation of forming processes. This approximation can\ud be considered as a meshless approximation scheme, and thus presents some appealing features\ud for the numerical simulation of forming processes in a Galerkin framework.\ud Especially the behavior of these shape functions at the boundary is interesting. At nodes\ud on the boundary, the functions possess a weak Kronecker-delta property, hence simplifying the\ud prescription of boundary conditions. Shape functions at the boundary do not overlap internal\ud nodes, nor do internal shape functions overlap nodes at the boundary. Boundary integrals can be\ud computed easily and efficiently compared to for instance moving least-squares approximations.\ud Furthermore, LME shapes also present a controllable degree of smoothness.\ud To test the performance of the LME shapes, an elastic and a elasto-plastic problem was\ud analyzed. The results were compared with a meshless method based on a moving least-squares\ud approximation

A comparative study on the performance of meshless approximations and their integration

The goal of this research is to study the performance of meshless approximations and their integration. Two diffuse shape functions, namely the moving least-squares and local maximum-entropy function, and a linear triangular interpolation are compared using Gaussian integration and the stabilized conforming nodal integration scheme. The shape functions and integration schemes are tested on two elastic problems, an elasto-plastic problem and the inf-sup test. The elastic computation shows a somewhat lower accuracy for the linear triangular interpolation than for the two diffuse functions with the same number of nodes. However, the computational effort for this interpolation is considerably lower. The accuracy of the calculations in elasto-plasticity depends to great extend on the used integration scheme. All shape functions, and even the linear triangular interpolation, perform very well with the nodal integration scheme and locking-free behavior is shown in the inf-sup test

Recalibration of the delirium prediction model for ICU patients (PRE-DELIRIC): a multinational observational study

Purpose Recalibration and determining discriminative power, internationally, of the existing delirium prediction model (PRE-DELIRIC) for intensive care patients. Methods A prospective multicenter cohort study was performed in eight intensive care units (ICUs) in six countries. The ten predictors (age, APACHE-II, urgent and admission category, infection, coma, sedation, morphine use, urea level, metabolic acidosis) were collected within 24 h after ICU admission. The confusion assessment method for the intensive care unit (CAM-ICU) was used to identify ICU delirium. CAM-ICU screening compliance and inter-rater reliability measurements were used to secure the quality of the data. Results A total of 2,852 adult ICU patients were screened of which 1,824 (64 %) were eligible for the study. Main reasons for exclusion were length of stay <1 day (19.1 %) and sustained coma (4.1 %). CAM-ICU compliance was mean (SD) 82 ± 16 % and inter-rater reliability 0.87 ± 0.17. The median delirium incidence was 22.5 % (IQR 12.8–36.6 %). Although the incidence of all ten predictors differed significantly between centers, the area under the receiver operating characteristic (AUROC) curve of the eight participating centers remained good: 0.77 (95 % CI 0.74–0.79). The linear predictor and intercept of the prediction rule were adjusted and resulted in improved re-calibration of the PRE-DELIRIC model. Conclusions In this multinational study, we recalibrated the PRE-DELIRIC model. Despite differences in the incidence of predictors between the centers in the different countries, the performance of the PRE-DELIRIC-model remained good. Following validation of the PRE-DELIRIC model, it may facilitate implementation of strategies to prevent delirium and aid improvements in delirium management of ICU patients