CFD simulations are valuable tools in evaluating and deploying oxy-fuel and other carbon capture technologies either as retrofit technologies or for new construction. However, accurate predictive simulations require physically realistic submodels with low computational requirements. In particular, comprehensive char oxidation and gasification models have been developed that describe multiple reaction and diffusion processes. This work extends a comprehensive char conversion code (the Carbon Conversion Kinetics or CCK model), which treats surface oxidation and gasification reactions as well as processes such as film diffusion, pore diffusion, ash encapsulation, and annealing. In this work, the CCK model was thoroughly investigated with a global sensitivity analysis. The sensitivity analysis highlighted several submodels in the CCK code, which were updated with more realistic physics or otherwise extended to function in oxy-coal conditions. Improved submodels include a greatly extended annealing model, the swelling model, the mode of burning parameter, and the kinetic model, as well as the addition of the Chemical Percolation Devolatilization (CPD) model. The resultant Carbon Conversion Kinetics for oxy-coal combustion (CCK/oxy) model predictions were compared to oxy-coal data, and further compared to parallel data sets obtained at near conventional conditions.

This work focuses on the construction of functional representations in high-dimensional spaces.Attention is focused on the modeling of ignition phenomena using detailed kinetics, and on the ignition delay time as the primary quantity of interest (QoI). An iso-octane air mixture is first considered, using a detailed chemical mechanism with 3,811 elementary reactions. Uncertainty in all reaction rates is directly accounted for using associated uncertainty factors, assuming independent log-uniform priors. A Latin hypercube sample (LHS) of the ignition delay times was first generated, and the resulting database was then exploited to assess the possibility of constructing polynomial chaos (PC) representations in terms of the canonical random variables parametrizing the uncertain rates. We explored two avenues, namely sparse regression (SR) using LASSO, and a coordinate transform (CT) approach. Preconditioned variants of both approaches were also considered, namely using the logarithm of the ignition delay time as QoI. A global sensitivity analysis is performed using the representations constructed by SR and CT. Next, the tangent linear approximation is developed to estimate the sensitivity of the ignition delay time with respect to individual rate parameters in a detailed chemical mechanism. Attention is focused on a gas mixture reacting under adiabatic, constant-volume conditions. The approach is based on integrating the linearized system of equations governing the evolution of the partial derivatives of the state vector with respect to individual random variables, and a linearized approximation is developed to relate the ignition delay sensitivity to the scaled partial derivatives of temperature. In particular, the computations indicate that for detailed reaction mechanisms the TLA leads to robust local sensitivity predictions at a computational cost that is order-of-magnitude smaller than that incurred by finite-difference approaches based on one-at-a-time rate parameters perturbations. In the last part, we explore the potential of utilizing TLA-based sensitivities to identify active subspace and to construct suitable representations. Performance is assessed based contrasting experiences with CT-based machinery developed earlier.

This master thesis is focused on the sensitivity analysis of selected kinds of state reconstructors. They are realized in a general form, via direct and parallel programing. Quantity that determines the quality of sensitivity is output signal difference of the reconstructor with the general form of the system. Testing will be based on different initial state conditions and on the parameters change of the feedback A matrix due to the rested reconstructors.

This thesis focuses on automated trading systems for financial markets trading. It describes theoretical background of financial markets, different technical analysis approaches and theoretical knowledge about automated trading systems. The output of the present paper is a diversified portfolio comprising four different investment models aimed to trading futures contracts of cocoa and gold. The portfolio tested on market data from the first quarter 2013 achieved 46.74% increase on the initial equity. The systems have been designed in Adaptrade Builder software using genetic algorithms and subsequently tested in the MetaTrader trading platform. They have been finally optimized using sensitivity analysis.

The master´s thesis was focused on parametric computer simulations of the energy performance of buildings. The aim is to determine the sensitivity of the individual operating and climatic influences on the final energy demand of the building, which will be reflected in final form Et-curve. Parametric simulation for building energy efficiency is planned to use the BSim and ready climatic database for a specific location in place of the assessed building. The outcome of the evaluation of the real use of property in Et-curve using a custom application.

<div>This dissertation is devoted to developing a first-of-a-kind uncertainty characterization framework (UCF) providing comprehensive, efficient and scientifically defendable methodologies for uncertainty characterization (UC) in best-estimate (BE) reactor physics simulations. The UCF is designed with primary application to CANDU neutronics calculations, but could also be applied to other thermal-spectrum reactor systems. The overarching goal of the UCF is to propagate and prioritize all sources of uncertainties, including those originating from nuclear data uncertainties, modeling assumptions, and other approximations, in order to reliably use the results of BE simulations in the various aspects of reactor design, operation, and safety. The scope of this UCF is to propagate nuclear data uncertainties from the multi-group format, representing the input to lattice physics calculations, to the few-group format, representing the input to nodal diffusion-based core simulators and quantify the uncertainties in reactor core attributes.</div><div>The main contribution of this dissertation addresses two major challenges in current uncertainty analysis approaches. The first is the feasibility of the UCF due to the complex nature of nuclear reactor simulation and computational burden of conventional uncertainty quantification (UQ) methods. The second goal is to assess the impact of other sources of uncertainties that are typically ignored in the course of propagating nuclear data uncertainties, such as various modeling assumptions and approximations.</div>To deal with the first challenge, this thesis work proposes an integrated UC process employing a number of approaches and algorithms, including the physics-guided coverage mapping (PCM) method in support of model validation, and the reduced order modeling (ROM) techniques as well as the sensitivity analysis (SA) on uncertainty sources, to reduce the dimensionality of uncertainty space at each interface of neutronics calculations. In addition to the efficient techniques to reduce the computational cost, the UCF aims to accomplish four primary functions in uncertainty analysis of neutronics simulations. The first function is to identify all sources of uncertainties, including nuclear data uncertainties, modeling assumptions, numerical approximations and technological parameter uncertainties. Second, the proposed UC process will be able to propagate the identified uncertainties to the responses of interest in core simulation and provide uncertainty quantifications (UQ) analysis for these core attributes. Third, the propagated uncertainties will be mapped to a wide range of reactor core operation conditions. Finally, the fourth function is to prioritize the identified uncertainty sources, i.e., to generate a priority identification and ranking table (PIRT) which sorts the major sources of uncertainties according to the impact on the core attributes’ uncertainties. In the proposed implementation, the nuclear data uncertainties are first propagated from multi-group level through lattice physics calculation to generate few-group parameters uncertainties, described using a vector of mean values and a covariance matrix. Employing an ROM-based compression of the covariance matrix, the few-group uncertainties are then propagated through downstream core simulation in a computationally efficient manner.<div>To explore on the impact of uncertainty sources except for nuclear data uncertainties on the UC process, a number of approximations and assumptions are investigated in this thesis, e.g., modeling assumptions such as resonance treatment, energy group structure, etc., and assumptions associated with the uncertainty analysis itself, e.g., linearity assumption, level of ROM reduction and associated number of degrees of freedom employed. These approximations and assumptions have been employed in the literature of neutronic uncertainty analysis yet without formal verifications. The major argument here is that these assumptions may introduce another source of uncertainty whose magnitude needs to be quantified in tandem with nuclear data uncertainties. In order to assess whether modeling uncertainties have an impact on parameter uncertainties, this dissertation proposes a process to evaluate the influence of various modeling assumptions and approximations and to investigate the interactions between the two major uncertainty sources. To explore this endeavor, the impact of a number of modeling assumptions on core attributes uncertainties is quantified.</div><div>The proposed UC process has first applied to a BWR application, in order to test the uncertainty propagation and prioritization process with the ROM implementation in a wide range of core conditions. Finally, a comprehensive uncertainty library for CANDU uncertainty analysis with NESTLE-C as core simulator is generated compressed uncertainty sources from the proposed UCF. The modeling uncertainties as well as their impact on the parameter uncertainty propagation process are investigated on the CANDU application with the uncertainty library.</div>

Les matériaux à structures complexes (anisotropes, multicouches et hétérogènes comme poreux) sont de plus en plus utilisés dans de nombreuses applications (ex. automobile,aéronautique, industrie chimique, génie civil et biomédical), notamment en raison de leur amélioration des propriétés mécaniques et physiques. L’identification des propriétés thermophysiques de ces matériaux devient un enjeu incontournable dans plusieurs applications afin de prédire correctement l’évolution de la température au sein de ces structures et d’assurer le contrôle et la modélisation des transferts de chaleur au cours des processus. Dans ce contexte,l’identification des propriétés thermophysiques de tels matériaux, suscitent depuis de nombreuses années une préoccupation importante et croissante. La principale caractéristique de cette thèse concerne la mise en œuvre d’une méthode d’identification directe et simultanée des diffusivités thermiques de matériaux monocouches ou multicouches à l’aide d’un modèle3D transitoire analytique et d’une expérience unique et non intrusive. La méthode proposée est d’abord validée sur un matériau monocouche opaque et isotrope, puis appliquée et vérifiée sur un matériau orthotrope. La méthode d’identification est basée sur l’expérience bien connue de la méthode flash, qui utilise l’évolution de la température sur la face avant ou arrière de l’échantillon, enregistrée via une caméra infrarouge, pour identifier les paramètres inconnus. Compte tenu de la complexité et de la non-linéarité du problème inverse, un algorithme d’optimisation hybride couplant un algorithme stochastique (Optimisation par essaims particulaires) et un déterministe (de type gradient), a été choisi. L’estimation repose sur la minimisation de l’écart entre les mesures et la réponse d’un modèle semi-analytique inspiré de l’approche des quadripôles thermiques qui prédit l’évolution de la température sur la face avant ou la face arrière. L’excitation thermique, générée par un laser CO2, est représentée par un flux de chaleur localisé imposé qui peut être de type Dirac ou créneau. Les estimations sont comparées aux valeurs trouvées dans la littérature et aux résultats obtenus en utilisant d’autres méthodes bien établies. Enfin, quelques améliorations de la méthode sont étudiées, en termes de temps de calcul et de précision, avec une optimisation des conditions expérimentales241RÉSUMÉ(durée et intensité des créneaux, face de mesure. . . ). La méthode est ensuite généralisée aux matériaux multicouches, puis appliquée expérimentalement à un matériau bicouche. Cette stratégie, qui peut être considérée comme une tâche difficile, est motivée par l’impossibilité,dans certains cas, de séparer les 2 couches, en particulier pour les revêtements déposés sur des substrats, qui sera la dernière application investiguée dans ce travail. Une analyse de sensibilité est souvent effectuée afin de tester la faisabilité de l’estimation et de la comparaison,pour les matériaux à deux couches et multicouches, de plusieurs configurations possibles en termes de faces d’excitation/de mesures. La pré-évaluation des méthodes d’identification et les études paramétriques sont effectuées à l’aide de données synthétiques bruitées et obtenues à l’aide du modèle ou d’un code numérique d’éléments finis (pseudo-expérience) afin de vérifier la faisabilité et la robustesse des approches. L’une des caractéristiques les plus distinctes de cette approche est que l’estimation peut être réalisée, et avec succès, sans aucune connaissance préalable de la forme ou de l’intensité de l’excitation. En effet, outre l’estimation simultanée des diffusivités thermiques, la méthode peut prédire la quantité de chaleur absorbée parle matériau ainsi que la distribution spatiale de l’excitation thermique. / Advanced materials with complex structures (anisotropic, multilayers and heterogeneous like porous) are increasingly used in many applications, (e.g. automotive, aeronautics, chemical industry, civil and biomedical engineering) due to their advantages, in terms of mechanical and physical properties enhancements. Estimating thermophysical properties of such materials becomes a crucial issue in several applications in order to correctly predict temperature evolution inside these structures and to ensure the control and the modelling of heat transfers through the processes. In this context, the identification of such materials thermophysical properties, has taken from many years, a significant and increasing concern. The main feature of this thesis relies on the devolvement of a direct and simultaneous identification method of the thermal diffusivities of monolayer or multilayer materials using an analytical 3D transient model and a unique and non-intrusive experiment. The proposed method is firstly validated on an isotropic opaque monolayermaterial, then applied and verified on an orthotropic one. The identificationmethod is based on the well-known flash-method experiment whose temperature evolution on the front or rear face on the sample, recorded via an IR camera, is used to identify the unknown parameters. Considering the complexity, and the non-linearity of the inverse problem, a hybrid optimization algorithm combining a stochastic algorithm (Particles Swarm Optimization) and a deterministic one (gradient based), has been chosen. This minimization procedure is applied to fit the observation to the output of a pseudo- analytical model inspired from the thermal quadrupoles approach that predicts the temperature evolution on the front or rear face. The thermal excitation, generated by a CO2 laser, is mimicked by an imposed localized heat flux that may be of Dirac or pulse type. The estimations are compared with values from literature and results obtain from well-established methods. Finally, some improvement of the method are investigated, in terms of time consumption and accuracy, with an optimization of the experiment design (pulse time and intensity, measurement face). The method is then generalised to multi-layer materials, then applied experimentally to a two-layer material. This strategy, which can be considered as a challenging task, is motivated by the impossibility, in some cases, to separate the 2 layers, especially for coatings deposited on substrates which is the last application investigated in this work. A sensitivity analysis is often conducted in order to test the feasibility of the estimation and compare, for two-layer and multilayers materials, several possible configurations in terms of excitation/measurements faces. Pre-evaluation of the overall identification methods and parametric studies are performed using synthetic noisy data generated using the model or a numerical finite element code(pseudo-experiment) to verify the approaches feasibility and robustness. One of the most distinctive features of our approach is that the estimation may be successfully achieved without any a priori knowledge about the shape or the intensity of the excitation. Indeed, besides the simultaneous estimation of the thermal diffusivities, the method predicts the total amount of heat absorbed by the material as well as the space shape of the thermal excitation.

Cette thèse présente une approche probabiliste de la modélisation de la mécanique des matériaux et des structures. Le dimensionnement est influencé par l'incertitude des paramètres d'entrée. Le travail est interdisciplinaire et les méthodes décrites sont appliquées à des exemples de biomécanique et de génie civil. La motivation de ce travail était le besoin d'approches basées sur la mécanique dans la modélisation et la simulation des implants utilisés dans la réparation des hernies ventrales. De nombreuses incertitudes apparaissent dans la modélisation du système implant-paroi abdominale. L'approche probabiliste proposée dans cette thèse permet de propager ces incertitudes et d’étudier leurs influences respectives. La méthode du chaos polynomial basée sur la régression est utilisée dans ce travail. L'exactitude de ce type de méthodes non intrusives dépend du nombre et de l'emplacement des points de calcul choisis. Trouver une méthode universelle pour atteindre un bon équilibre entre l'exactitude et le coût de calcul est encore une question ouverte. Différentes approches sont étudiées dans cette thèse afin de choisir une méthode efficace et adaptée au cas d’étude. L'analyse de sensibilité globale est utilisée pour étudier les influences des incertitudes d'entrée sur les variations des sorties de différents modèles. Les incertitudes sont propagées aux modèles implant-paroi abdominale. Elle permet de tirer des conclusions importantes pour les pratiques chirurgicales. À l'aide de l'expertise acquise à partir de ces modèles biomécaniques, la méthodologie développée est utilisée pour la modélisation de joints de bois historiques et la simulation de leur comportement mécanique. Ce type d’étude facilite en effet la planification efficace des réparations et de la rénovation des bâtiments ayant une valeur historique. / This thesis presents a probabilistic approach to modelling the mechanics of materials and structures where the modelled performance is influenced by uncertainty in the input parameters. The work is interdisciplinary and the methods described are applied to medical and civil engineering problems. The motivation for this work was the necessity of mechanics-based approaches in the modelling and simulation of implants used in the repair of ventral hernias. Many uncertainties appear in the modelling of the implant-abdominal wall system. The probabilistic approach proposed in this thesis enables these uncertainties to be propagated to the output of the model and the investigation of their respective influences. The regression-based polynomial chaos expansion method is used here. However, the accuracy of such non-intrusive methods depends on the number and location of sampling points. Finding a universal method to achieve a good balance between accuracy and computational cost is still an open question so different approaches are investigated in this thesis in order to choose an efficient method. Global sensitivity analysis is used to investigate the respective influences of input uncertainties on the variation of the outputs of different models. The uncertainties are propagated to the implant-abdominal wall models in order to draw some conclusions important for further research. Using the expertise acquired from biomechanical models, modelling of historic timber joints and simulations of their mechanical behaviour is undertaken. Such an investigation is important owing to the need for efficient planning of repairs and renovation of buildings of historical value.

Thermo-economic modeling refers to the process of estimating the cost and performance of a power plant using cost oriented equations and reference data. In this thesis the fundamentals of cost and performance modeling as well as sensitivity analysis is researched and applied to an existing model in the field of concentrated solar power. The thesis aims to isolate the sources of possible errors and presents comprehensible methods of minimizing the sensitivity these give rise to. The extensive literature study provides the knowledge and methodologies necessary to perform an evaluation of a computer model and these methodologies are applied to the tool DYESOPT developed at the Royal Institute of Technology.   The evaluation highlights the importance of reliable references of operational solar power plants and also the current lack of such data. A particular area suffering from this is the cost estimation, which includes assumptions and requires future revisions. The sensitivity analysis methodologies one-at-a-time and the sensitivity index are used to locate the areas where extra care must be taken in order to minimize error as well as provide an understanding of the internal correlation of critical inputs.   The results show that the accuracy of the model is dominated by three inputs: solar multiple, tower height and storage time, and that certain intervals and combinations of these decide the overall error of the model. By isolating the intervals in which the sensitivity is at its minimum the model error can be roughly quantified with a class system using standard error intervals. For a model such as DYESOPT a minimum error of 20 to 30 percent is a reasonable assumption.

In 2013, Stockholms Hamnar began a development project for Värtahamnen, one of Stockholms most important harbors, and also decided to build a new ferry terminal that is better suited to meet the increasing capacity demand. The new terminal will feature a borehole storage that will be used to cover the building’s heating and cooling demands. The boreholes have already been drilled and currently the construction of the building is being planned. The overall objective of this project is to study the new terminal and its borehole storage regarding certain input parameters (such as internal heat gains and the U-value of windows) that affect the building’s annual heating and cooling demands, as well as long-term temperature of the borehole storage. To do this, two modeling programs are used: IDA ICE and EED (Earth Energy Designer). The project focuses on three main parts. Part one is a sensitivity analysis of internal loads and construction specific parameters that shows how a variation in these affects the heating and cooling demands. To accomplish this, several models are created and simulated in IDA ICE. In part two, the long-term ground temperature is studied for two of the models analyzed in part one. This is done in both IDA (through a new borehole module) and EED, followed by a comparison of these results. The last part presents the possible amount of free cooling that can be taken from the ground. This estimation is made through simulations in EED, using altered load profiles of the two previously mentioned models. Additionally, this part covers the effects of a changed borehole configuration (number of boreholes, depth, layout, etc.). The results of the first part (the sensitivity analysis) show that there is a rather large variation in annual heating and cooling demands depending on what approach is used for estimating a reasonable amount of internal loads. One way to do this is to first determine the maximum possible load in each zone and then, when simulating the annual energy demand, reduce the total load in the whole building by a certain factor. Another approach is to, from the start of the building modeling, more accurately try to estimate the average amount of internal loads in each zone. In the second part, due to unbalanced load profiles for both analyzed models, the temperature of the borehole storage will increase over time if there is no limitation of the amount of cooling taken from the ground. The results of IDA generally agree with those of EED. In the last part of the project it is shown that a thermally more favorable borehole installation could increase the relative amount of free cooling from the ground, compared to the current installation.