Inclusión en modelos de riesgo de presas de una metodología de estimación hidrológica basada en técnicas de Monte Carlo

En este estudio se aplica una metodología de obtención de las leyes de frecuencia derivadas (de caudales máximo vertidos y niveles máximos alcanzados) en un entorno de simulaciones de Monte Carlo, para su inclusión en un modelo de análisis de riesgo de presas. Se compara su comportamiento respecto del uso de leyes de frecuencia obtenidas con las técnicas tradicionalmente utilizadas.

Differential algebra techniques for space applications

This project investigates the utility of differential algebra (DA) techniques applied to the problem of orbital dynamics with initial uncertainties in the orbital determination of the involved bodies. The use of DA theory allows the splitting of a common Monte Carlo simulation in two parts: the generation of a Taylor map of the final states with regard to the perturbation in the initial coordinates, and the evaluation of the map for many points. A propagator is implemented exploiting DA techniques, and tested in the field of asteroid impact risk monitoring with the potentially hazardous 2011 AG5 and 2007 VK184 as test cases. Results show that the new method is able to simulate 2.5 million trajectories with a precision good enough for the impact probability to be accurately reproduced, while running much faster than a traditional Monte Carlo approach (in 1 and 2 days, respectively).

Aspectos metodológicos para la evaluación de sistemas de tomografía por emisión de positrones empleando técnicas Montecarlo, protocolos estandarizados y diferentes trazadores

Esta tesis analiza los elementos que afectan a la evaluación del rendimiento dentro de la técnica de radiodiagnóstico mediante tomografía por emisión de positrones (PET), centrándose en escáneres preclínicos. Se exploran las posibilidades de los protocolos estándar de evaluación sobre los siguientes aspectos: su uso como herramienta para validar programas de simulación Montecarlo, como método para la comparación de escáneres y su validez en el estudio del efecto sobre la calidad de imagen al utilizar radioisótopos alternativos. Inicialmente se estudian los métodos de evaluación orientados a la validación de simulaciones PET, para ello se presenta el programa GAMOS como entorno de simulación y se muestran los resultados de su validación basada en el estándar NEMA NU 4-2008 para escáneres preclínicos. Esta validación se ha realizado mediante la comparación de los resultados simulados frente a adquisiciones reales en el equipo ClearPET, describiendo la metodología de evaluación y selección de los parámetros NEMA. En este apartado también se mencionan las aportaciones desarrolladas en GAMOS para aplicaciones PET, como la inclusión de herramientas para la reconstrucción de imágenes. Por otro lado, la evaluación NEMA del ClearPET es utilizada para comparar su rendimiento frente a otro escáner preclínico: el sistema rPET-1. Esto supone la primera caracterización NEMA NU 4 completa de ambos equipos; al mismo tiempo que se analiza cómo afectan las importantes diferencias de diseño entre ellos, especialmente el tamaño axial del campo de visión y la configuración de los detectores. El 68Ga es uno de los radioisótopos no convencionales en imagen PET que está experimentando un mayor desarrollo, sin embargo, presenta la desventaja del amplio rango o distancia recorrida por el positrón emitido. Además del rango del positrón, otra propiedad física característica de los radioisótopos PET que puede afectar a la imagen es la emisión de fotones gamma adicionales, tal como le ocurre al isótopo 48V. En esta tesis se evalúan dichos efectos mediante estudios de resolución espacial y calidad de imagen NEMA. Finalmente, se analiza el alcance del protocolo NEMA NU 4-2008 cuando se utiliza para este propósito, adaptándolo a tal fin y proponiendo posibles modificaciones. Abstract This thesis analyzes the factors affecting the performance evaluation in positron emission tomography (PET) imaging, focusing on preclinical scanners. It explores the possibilities of standard protocols of assessment on the following aspects: their use as tools to validate Monte Carlo simulation programs, their usefulness as a method for comparing scanners and their validity in the study of the effect of alternative radioisotopes on image quality. Initially we study the methods of performance evaluation oriented to validate PET simulations. For this we present the GAMOS program as a simulation framework and show the results of its validation based on the standard NEMA NU 4-2008 for preclinical PET scanners. This has been accomplished by comparing simulated results against experimental acquisitions in the ClearPET scanner, describing the methodology for the evaluation and selection of NEMA parameters. This section also mentions the contributions developed in GAMOS for PET applications, such as the inclusion of tools for image reconstruction. Furthermore, the evaluation of the ClearPET scanner is used to compare its performance against another preclinical scanner, specifically the rPET-1 system. This is the first complete NEMA NU 4 based characterization study of both systems. At the same time we analyze how do the significant design differences of these two systems, especially the size of the axial field of view and the detectors configuration affect their performance characteristics. 68Ga is one of the unconventional radioisotopes in PET imaging the use of which is currently significantly increasing; however, it presents the disadvantage of the long positron range (distance traveled by the emitted positron before annihilating with an electron). Besides the positron range, additional gamma photon emission is another physical property characteristic of PET radioisotopes that can affect the reconstructed image quality, as it happens to the isotope 48V. In this thesis we assess these effects through studies of spatial resolution and image quality. Finally, we analyze the scope of the NEMA NU 4-2008 to carry out such studies, adapting it and proposing possible modifications.

A simulation method to estimate closing forces in car-sealing rubber elements

The door-closing process can reinforce the impression of a solid, rock-proof, car body or of a rather cheap, flimsy vehicle. As there are no real prototypes during rubber profile bidding-out stages, engineers need to carry out non-linear numerical simulations that involve complex phenomena as well as static and dynamic loads for several profile candidates. This paper presents a structured virtual design tool based on FEM, including constitutive laws and incompressibility constraints allowing to predict more realistically the final closing forces and even to estimate sealing overpressure as an additional guarantee of noise insulation. Comparisons with results of physical tests are performed.

Preference intensity in MCDM when an additive utility function represents DM preferences

We propose a new method for ranking alternatives in multicriteria decision-making problems when there is imprecision concerning the alternative performances, component utility functions and weights. We assume decision maker?s preferences are represented by an additive multiattribute utility function, in which weights can be modeled by independent normal variables, fuzzy numbers, value intervals or by an ordinal relation. The approaches are based on dominance measures or exploring the weight space in order to describe which ratings would make each alternative the preferred one. On the one hand, the approaches based on dominance measures compute the minimum utility difference among pairs of alternatives. Then, they compute a measure by which to rank the alternatives. On the other hand, the approaches based on exploring the weight space compute confidence factors describing the reliability of the analysis. These methods are compared using Monte Carlo simulation.

A comparative study of Monte Carlo-coupled depletion codes applied to a Sodium Fast Reactor design loaded with minor actinides

inor actinides (MAs) transmutation is a main design objective of advanced nuclear systems such as generation IV Sodium Fast Reactors (SFRs). In advanced fuel cycles, MA contents in final high level waste packages are main contributors to short term heat production as well as to long-term radiotoxicity. Therefore, MA transmutation would have an impact on repository designs and would reduce the environment burden of nuclear energy. In order to predict such consequences Monte Carlo (MC) transport codes are used in reactor design tasks and they are important complements and references for routinely used deterministic computational tools. In this paper two promising Monte Carlo transport-coupled depletion codes, EVOLCODE and SERPENT, are used to examine the impact of MA burning strategies in a SFR core, 3600 MWth. The core concept proposal for MA loading in two configurations is the result of an optimization effort upon a preliminary reference design to reduce the reactivity insertion as a consequence of sodium voiding, one of the main concerns of this technology. The objective of this paper is double. Firstly, efficiencies of the two core configurations for MA transmutation are addressed and evaluated in terms of actinides mass changes and reactivity coefficients. Results are compared with those without MA loading. Secondly, a comparison of the two codes is provided. The discrepancies in the results are quantified and discussed.

Monte Carlo uncertainty propagation approaches in ADS burn-up calculations

In activation calculations, there are several approaches to quantify uncertainties: deterministic by means of sensitivity analysis, and stochastic by means of Monte Carlo. Here, two different Monte Carlo approaches for nuclear data uncertainty are presented: the first one is the Total Monte Carlo (TMC). The second one is by means of a Monte Carlo sampling of the covariance information included in the nuclear data libraries to propagate these uncertainties throughout the activation calculations. This last approach is what we named Covariance Uncertainty Propagation, CUP. This work presents both approaches and their differences. Also, they are compared by means of an activation calculation, where the cross-section uncertainties of 239Pu and 241Pu are propagated in an ADS activation calculation.

MMonCa: An Object Kinetic Monte Carlo simulator for damage irradiation evolution and defect diffusion

In this work, we introduce the Object Kinetic Monte Carlo (OKMC) simulator MMonCa and simulate the defect evolution in three different materials. We start by explaining the theory of OKMC and showing some details of how such theory is implemented by creating generic structures and algorithms in the objects that we want to simulate. Then we successfully reproduce simulated results for defect evolution in iron, silicon and tungsten using our simulator and compare with available experimental data and similar simulations. The comparisons validate MMonCa showing that it is powerful and flexible enough to be customized and used to study the damage evolution of defects in a wide range of solid materials.

Dominance intensity measure within fuzzy weight oriented MAUT: an application

We introduce a dominance intensity measuring method to derive a ranking of alternatives to deal with incomplete information in multi-criteria decision-making problems on the basis of multi-attribute utility theory (MAUT) and fuzzy sets theory. We consider the situation where there is imprecision concerning decision-makers’ preferences, and imprecise weights are represented by trapezoidal fuzzy weights.The proposed method is based on the dominance values between pairs of alternatives. These values can be computed by linear programming, as an additive multi-attribute utility model is used to rate the alternatives. Dominance values are then transformed into dominance intensity measures, used to rank the alternatives under consideration. Distances between fuzzy numbers based on the generalization of the left and right fuzzy numbers are utilized to account for fuzzy weights. An example concerning the selection of intervention strategies to restore an aquatic ecosystem contaminated by radionuclides illustrates the approach. Monte Carlo simulation techniques have been used to show that the proposed method performs well for different imprecision levels in terms of a hit ratio and a rank-order correlation measure.

Review and comparison of effective delayed neutron fraction calculation methods with Monte Carlo codes

The calculation of the effective delayed neutron fraction, beff , with Monte Carlo codes is a complex task due to the requirement of properly considering the adjoint weighting of delayed neutrons. Nevertheless, several techniques have been proposed to circumvent this difficulty and obtain accurate Monte Carlo results for beff without the need of explicitly determining the adjoint flux. In this paper, we make a review of some of these techniques; namely we have analyzed two variants of what we call the k-eigenvalue technique and other techniques based on different interpretations of the physical meaning of the adjoint weighting. To test the validity of all these techniques we have implemented them with the MCNPX code and we have benchmarked them against a range of critical and subcritical systems for which either experimental or deterministic values of beff are available. Furthermore, several nuclear data libraries have been used in order to assess the impact of the uncertainty in nuclear data in the calculated value of beff .

Hybrid Monte Carlo dose algorithm for low energy X-rays intraoperative radiation therapy

Low energy X-rays Intra-Operative Radiation Therapy (XIORT) treatment delivered during surgery (ex: INTRABEAM, Carl Zeiss, and Axxent, Xoft) can benefit from accurate and fast dose prediction in a patient 3D volume.

Efficient Monte Carlo methods for multi-dimensional learning with classifier chains

Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance – at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.

Monte Carlo limit cycle characterization

The fixed point implementation of IIR digital filters usually leads to the appearance of zero-input limit cycles, which degrade the performance of the system. In this paper, we develop an efficient Monte Carlo algorithm to detect and characterize limit cycles in fixed-point IIR digital filters. The proposed approach considers filters formulated in the state space and is valid for any fixed point representation and quantization function. Numerical simulations on several high-order filters, where an exhaustive search is unfeasible, show the effectiveness of the proposed approach.

Monte Carlo simulations of densely-packed athermal polymers in the bulk and under confinement

We review the main results from extensive Monte Carlo (MC) simulations on athermal polymer packings in the bulk and under confinement. By employing the simplest possible model of excluded volume, macromolecules are represented as freely-jointed chains of hard spheres of uniform size. Simulations are carried out in a wide concentration range: from very dilute up to very high volume fractions, reaching the maximally random jammed (MRJ) state. We study how factors like chain length, volume fraction and flexibility of bond lengths affect the structure, shape and size of polymers, their packing efficiency and their phase behaviour (disorder–order transition). In addition, we observe how these properties are affected by confinement realized by flat, impenetrable walls in one dimension. Finally, by mapping the parent polymer chains to primitive paths through direct geometrical algorithms, we analyse the characteristics of the entanglement network as a function of packing density.