Three-dimensional numerical simulation of blood flow in mouse aortic arch around atherosclerotic plaques

Atherosclerosis is a progressive disease, involving the build-up of lipid streaks in artery walls, leading to plaques. Understanding the development of atherosclerosis and plaque vulnerability is critically important since plaque rupture can result in heart attack or stroke. Plaques can be divided into two distinct types: those likely to rupture (vulnerable) or less likely to rupture (stable). In the last decade, researchers have been interested in studying the influence of the mechanical effects (blood shear stress, pressure forces and structural stress) on the plaque formation, progression and rupture processes but no general agreement has been found. The purpose of the present work is to include more realistic conditions for the numerical calculations of the blood flow by implementing real geometries with plaques in the numerical model. Hemodynamical parameters are studied in both diseased and healthy configurations. The healthy configuration is obtained by removing numerically the plaques from three dimensional geometries obtained by micro-computed tomography. A new hemodynamical parameter is also introduced to relate the location of plaques to the characteristics of the flow in the healthy configuration. © 2014 .

Minimizing bounded uncertainty impact on scheduling with earliest start and due-date constraints via interval computation

Bounded uncertainty is a major challenge to real life scheduling as it increases the risk and cost depending on the objective function. Bounded uncertainty provides limited information about its nature. It provides only the upper and the lower bounds without information in between, in contrast to probability distributions and fuzzymembership functions. Bratley algorithm is usually used for scheduling with the constraints of earliest start and due-date. It is formulated as . The proposed research uses interval computation to minimize the impact of bounded uncertainty of processing times on Bratley’s algorithm. It minimizes the uncertainty of the estimate of the objective function. The proposed concept is to do the calculations on the interval values and approximate the end result instead of approximating each interval then doing numerical calculations. This methodology gives a more certain estimate of the objective function.

Minimizing impact of bounded uncertainty on McNaughton's scheduling algorithm via interval programming

Uncertainty of data affects decision making process as it increases the risk and the costs of the decision. One of the challenges in minimizing the impact of the bounded uncertainty on any scheduling algorithm is the lack of information, as only the upper bound and the lower bound are provided without any known probability or membership function. On the contrary, probabilistic uncertainty can use probability distributions and fuzzy uncertainty can use the membership function. McNaughton's algorithm is used to find the optimum schedule that minimizes the makespan taking into consideration the preemption of tasks. The challenge here is the bounded inaccuracy of the input parameters for the algorithm, namely known as bounded uncertain data. This research uses interval programming to minimise the impact of bounded uncertainty of input parameters on McNaughton’s algorithm, it minimises the uncertainty of the cost function estimate and increase its optimality. This research is based on the hypothesis that doing the calculations on interval values then approximate the end result will produce more accurate results than approximating each interval input then doing numerical calculations.

Numerical investigation of influence of the Martensite Volume Fraction on dp steels fracture behavior on the basis of digital material representation model

Development of the methodology for creating reliable digital material representation (DMR) models of dual-phase steels and investigation of influence of the martensite volume fraction on fracture behavior under tensile load are the main goals of the paper. First, an approach based on image processing algorithms for creating a DMR is described. Then, obtained digital microstructures are used as input for the numerical model of deformation, which takes into account mechanisms of ductile fracture. Ferrite and martensite material model parameters are evaluated on the basis of micropillar compression tests. Finally, the model is used to investigate the impact of the martensite volume fraction on the DP steel behavior under plastic deformation. Results of calculations are presented and discussed in the paper.