Dans notre travail, nous avons utilisé la technique Monte Carlo afin de simuler l'effet des interactions dipolaires sur le retournement de l'aimantation d'une assemblée de nanoparticules ferromagnétiques. Dans notre modèle, nous avons considéré des nanoparticules à anisotropie uniaxiale, plus précisément le Cobalt. Avec la condition KV/kBT >> 1, le moment magnétique de chaque nanoparticule est figé dans ses positions d'équilibre et il ne peut pas fluctuer autour. Nous avons choisi de faire notre étude par le biais de la susceptibilité alternative car elle apporte l'avantage de pouvoir faire l'étude sur une large gamme de fréquences sans avoir à changer de technique de mesure puisque le temps de mesure est égale à l'inverse de la fréquence du champ appliqué. Nous avons étudié douze types d'assemblées chacune diffère de l'autre soit par sa forme, la distribution spatiale des nanoparticules ou la distribution des axes d'anisotropie. Et nous avons discerné l'influence de chaque paramètre sur l'effet des interactions dipolaires sur le retournement de l'aimantation. / In our work, we used Monte Carlo technique to simulate the effect of dipolar interactions on the magnetization reversal of a ferromagnetic nanoparticles assembly. In our model, we considered nanoparticles with uniaxial anisotropy and specifically the Cobalt. With the condition KV/kBT >> 1, the magnetic moment of each nanoparticle is fixed in its equilibrium positions and it can not fluctuate around. We chose to our investigation the ac susceptibility because it provides the advantage of making the study with a wide frequency range without changing measurement technique, because the measurement time is equal to the inverse of the frequency of the alternating magnetic field applied. We studied twelve types of assemblies each one differs from the other by some properties : The shape of the assembly, the spatial distribution of the nanoparticles or the distribution of the anisotropy axes. And we discerned the influence of each of theses parameters on the effect of dipolar interactions on the magnetization reversal.

This doctoral thesis deals with modelling and simulation of investment projects and linking risk management with new trends in construction industry. Process of balancing of acceptable risk level and investment costs is really complex and is influenced by several uncertainties. Simulation methods are able to model future scenarios of project development and quantify impact of risk factors. The aim of the doctoral thesis is to find an optimal methodology for risk analysis during decision-making process using simulation methods. The methodology links modeling by simulation method Monte Carlo with CBA, with risk analysis respectively. The aim is to reach more effective process of planning of investment projects. An accurate project plan in preinvestment phase will influence effectiveness of life cycle costs significantly. This is proved also by BIM methodology which works on base of transfer and storage of actual and complete information about investment plan within its whole lifecycle. Expected output of the thesis is effective application of simulation methods in risk prediction for modeling of outputs of investment project. Data from model are useful for decision making process, risk management, controlling and postaudit of investments. Projects can be evaluated by their complex benefits and quality with respect to sustainability.