The European Green Deal declared that Europe must decarbonize to become carbon-neutral within 2050. To do so, the European Parliament emphasized hydrogen as a major tool for energy transition. In regard of current environmental challenges, liquid hydrogen has raised interest as energy carrier for energy storage and transport. Due to growing use of renewable energy sources such as solar and wind energy, intermittent sources will increase. Hydrogen production methods will become mostly intermittent with renewable energies. However, due to historical hydrogen production by steam methane reforming, liquefaction was developed at steady nominal charge. In order to feed current liquefaction processes with renewable hydrogen, a buffer system will become required. This thesis studies the effect of buffer and liquefaction combination on performances and cost. In order to carry out this liquefaction from intermittent source, the study is performed based on industrial data from a variable co-product hydrogen profile. This profile acts as a simplified case. The scope of the study is drawn by considering compressed hydrogen as temporary storage for the buffer while liquefaction unit is modelled around Linde Leuna cycle. The technical-economical study covers sensitivity analysis on both buffer and liquefaction unit. For the buffer unit, storage capacity, storage pressure, liquefaction flexibility and recuperation rate impacts are examined. Liquefaction sensitivity analysis includes pressure drop, electricity cost and capacity study.  It is highlighted that 100% gaseous hydrogen recovery is not profitable due to high costs increase for recuperation higher than 95%. Storage pressure and capacity as well as liquefaction flexibility drive buffer cost and recuperation rate of the co-product hydrogen. Considering liquefaction study, results highlight that pressure drops cause first order deviations in energy consumption as well as on cost. Results show that the specific buffer cost is evaluated between 71% and 59% of liquefaction cost. Hence the thesis raises attention on future work on heat exchangers design, pressure drop optimization and liquefaction unit flexibility to allow an optimized renewable liquid hydrogen production.

<p dir="ltr">Uncertainty quantification is a science and engineering subject that aims to quantify and analyze the uncertainty arising from mathematical models, simulations, and measurement data. An uncertainty quantification analysis usually consists of conducting experiments to collect data, creating and calibrating mathematical models, predicting through numerical simulation, making decisions using predictive results, and comparing the model prediction with new experimental data.</p><p dir="ltr">The overarching goal of uncertainty quantification is to determine how likely some quantities in this analysis are if some other information is not exactly known and ultimately facilitate decision-making. This dissertation delivers a complete package, including theory, algorithms, and applications of information field theory, a Bayesian uncertainty quantification tool that leverages the state-of-the-art machine learning framework to accelerate solving the classical uncertainty quantification problems specified by differential equations.</p>

<p dir="ltr">Timely hemorrhage control is critical for preventing fatalities during active shooter incidents, particularly when interventions occur before hemorrhagic shock. Agent-Based Modeling (ABM) has been widely used to inform safety policies in such scenarios and is a well-researched domain. However, a major limitation in existing studies is the assumption that all injuries are fatal, oversimplifying the complex dynamics of injury outcomes. Real-world data paints a more nuanced picture: for example, a study of 1,226 gunshot wound patients in St. Louis emergency departments reported a 93% survival rate when victims received timely care (de Anda et al., 2018). In specific incidents, such as the 2019 Gilroy Garlic Festival shooting, rapid on-site response resulted in only 3 fatalities, while in the 2016 Orlando Pulse Nightclub shooting, studies suggest that 16 lives could have been saved with timely medical intervention (C. P. Smith et al., 2020a; E. R. Smith et al., 2018).</p><p dir="ltr">This study employed ABM to explore strategies for minimizing casualties by analyzing factors such as the spatial distribution and quantity of Hemorrhage Control Kits (HCKs), the number of trained responders, and the impact of their placement—in corridors, classrooms, or go-bags—on accessibility during lockdowns. It seeks to address the following research questions: How can survival rates in active shooter situations be improved? What impact do hemorrhage control training and the use of HCKs have on outcomes? How does the availability and distribution of medical supplies influence outcomes?</p><p dir="ltr">By incorporating models of injury progression and the stabilizing effects of timely treatment, the research revealed that increasing the number of trained responders significantly improves survival rates, with optimal results achieved when all teachers are trained in hemorrhage control. The most effective strategy combined HCKs in go-bags or on responders’ persons with well-distributed corridor stations, ensuring compatibility with lockdowns and faster response times. External responders, including tactical medics and EMTs, also benefited from these strategies due to the accessibility of medical supplies. Conversely, placing kits in individual rooms was less effective, as accessibility challenges often delayed critical interventions. This study underscores the importance of strategic planning in hemorrhage control and provides actionable insights and simulation tools to guide tailored emergency preparedness and response planning.</p>

<p dir="ltr">We present a framework for Trustworthy Artificial Intelligence (TAI) that dynamically assesses trust and scrutinizes past decision-making, aiming to identify both individual and community behavior. The modeling of behavior incorporates proposed concepts, namely trust pressure and trust sensitivity, laying the foundation for predicting future decision-making regarding community behavior, consensus level, and decision-making duration. Our framework involves the development and mathematical modeling of trust pressure and trust sensitivity, drawing on social validation theory within the context of environmental decision-making. To substantiate our approach, we conduct experiments encompassing (i) dynamic trust sensitivity to reveal the impact of learning actors between decision-making, (ii) multi-level trust measurements to capture disruptive ratings, and (iii) different distributions of trust sensitivity to emphasize the significance of individual progress as well as overall progress.</p><p dir="ltr">Additionally, we introduce TAI metrics, trustworthy acceptance, and trustworthy fairness, designed to evaluate the acceptance of decisions proposed by AI or humans and the fairness of such proposed decisions. The dynamic trust management within the framework allows these TAI metrics to discern support for decisions among individuals with varying levels of trust. We propose both the metrics and their measurement methodology as contributions to the standardization of trustworthy AI.</p><p dir="ltr">Furthermore, our trustability metric incorporates reliability, resilience, and trust to evaluate systems with multiple components. We illustrate experiments showcasing the effects of different trust declines on the overall trustability of the system. Notably, we depict the trade-off between trustability and cost, resulting in net utility, which facilitates decision-making in systems and cloud security. This represents a pivotal step toward an artificial control model involving multiple agents engaged in negotiation.</p><p dir="ltr">Lastly, the dynamic management of trust and trustworthy acceptance, particularly in varying criteria, serves as a foundation for causal AI by providing inference methods. We outline a mechanism and present an experiment on human-driven causal inference, where participant discussions act as interventions, enabling counterfactual evaluations once actor and community behavior are modeled.</p>

<p>The ongoing and rapidly accelerating integration of inverter-based resources (IBRs), such as solar panels, into power distribution systems has heightened the importance of computational tools that can be used to study the dynamics of such systems. IBRs use power electronics to interface the energy sources to the grid, thereby introducing faster dynamics than their electromechanical counterparts, which could lead to instabilities in distribution systems and microgrids. Hence, there is a need for conducting simulations of distribution systems containing large numbers of single- and three-phase inverters, which could be operating under either grid-following or grid-forming modes.</p> <p>A key objective of this thesis is to derive detailed, high-fidelity models of inverters and their controls in the context of unbalanced distribution networks. In addition, a diesel synchronous generator model is derived, for the sake of completeness. The main contribution of this work is that these models are developed for use in a novel simulation toolbox called Dynamic Simulation Tool using a Sparse Tableau Approach in Python, DynaSTPy (pronounced dynasty). This thesis outlines how the components can be modeled in the sparse tableau framework as electrical networks with topology described using a node-branch incidence matrix. In addition, the thesis explains how controller dynamics and constraints can be handled within this framework. These models are tailored to seamlessly integrate into the DynaSTPy toolbox. The proposed approach can be readily extended to model other such components in the future.</p>

An experimental investigation of an M level (M = 16, 64 and 256) Quadrature Amplitude Modulation (QAM) transmission system suitable for video transmission is presented. The communication system is based on layered video coding and unequal error protection to make the video bitstream robust to channel errors. An implementation is described in which H.264 video is protected unequally by partitioning the compressed data into two layers of different visual importance. The partition scheme is based on a separation of the group of pictures (GoP) in the intra-coded frame (I-frame) and predictive coded frame (P frame). This partition scheme is then applied to split the H.264-coded video bitstream and is suitable for Constant Bit Rate (CBR) transmission. Unequal error protection is based on uniform and non-uniform M-QAM constellations in conjunction with different scenarios of splitting the transmitted symbol for protection of the more important information of the video data; different constellation arrangements are proposed and evaluated to increase the capacity of the high priority layer. The performance of the transmission system is evaluated under Additive White Gaussian Noise (AWGN) and Rayleigh fading conditions. Simulation results showed that in noisy channels the decoded video can be improved by assigning a larger portion of the video data to the enhancement layer in conjunction with non-uniform constellation arrangements; in better channel conditions the quality of the received video can be improved by assigning more bits in the high priority channel and using uniform constellations. The aforementioned varying conditions can make the video transmission more successful over error-prone channels. Further techniques were developed to combat various channel impairments by considering channel coding methods suitable for layered video coding applications. It is shown that a combination of non-uniform M-QAM and forward error correction (FEC) will yield a better performance. Additionally, antenna diversity techniques are examined and introduced to the transmission system that can offer a significant improvement in the quality of service of mobile video communication systems in environments that can be modelled by a Rayleigh fading channel.

Ce travail s'inscrit dans le cadre de l'évaluation de la sûreté de fonctionnement des systèmes commandés en réseau (SCR). La capacité des systèmes de commandes à compenser les effets de certaines défaillances de composants amène à redéfinir le concept de défaillances du système. La conséquence est que l'évaluation de la fiabilité prévisionnelle du système est dépendante de l'évaluation fonctionnelle et devient impossible avec les méthodes traditionnelles de la sûreté de fonctionnement. Pour surmonter ces difficultés, une approche basée sur la modélisation en vue de la simulation est proposée. Nous avons choisi les Réseaux d'activités stochastiques (SAN) largement connus dans la modélisation des protocoles de communication ainsi que dans les études de la sûreté de fonctionnement. Dans un premier temps, nous avons cherché à identifier l'incidence de deux types de défaillances fugitives : la perte d'un échantillon d'une part et le retard d'un échantillon dans la boucle de régulation d'autre part. Après, nous simulons le comportement en présence des deux types de perturbations simultanément, mettant en évidence des effets cumulatifs. Si on tient compte maintenant du fait que l'origine des pertes ou retards est due à la présence du réseau, il faut l'introduire dans le modèle. On introduit alors dans le modèle global du système la représentation SAN d'un réseau CAN et l'injection des défaillances dans celui-ci. La méthode de Monte Carlo est utilisée pour estimer les indicateurs de sûreté de fonctionnement et on montre l'influence de certains facteurs comme la charge du réseau par exemple. Nous avons proposé une méthode et les outils associés pour approcher cette évaluation par simulation et ainsi apporter une aide à la conception des systèmes satisfaisant à des exigences critiques sur certains paramètres de performance / Achieved work in this thesis deals with dependability evaluation of networked controlled system (NCS). The ability of control system to offset the effects of some components’ failure leads to redefine the concept of system failure. Consequently the reliability evaluation is dependent on functional parameters and becomes impossible with traditional dependability methods. This work aims at bringing a contribution relative to this aspect. To overcome these difficulties, an approach based on both modelling and simulation is proposed. We choose to work with stochastic activity network (SAN) widely used in modelling communication protocols as well as in dependability studies. First we sought to identify the incidence of two types of transient faults: loss of samples and delay within the control loop. Next we simulate the behaviour in the presence of two types of disturbances at the same time highlighting the cumulative effects. In fact the origin of the loss or delay information inside the control loop is due to the presence of the network, this aspect must be taken into account, that is why we introduce a new model representing the Controller Area Network (CAN) and injection of possible perturbations. Monte-Carlo method is used to estimate dependability parameters showing the influence of some factors such as network load for example. We have proposed a method and associated tools to approach this evaluation by simulation and thus provide assistance in designing systems to meet requirements on certain performance parameters

Characterizing the uncertainty in rainfall using stochastic models has been a challenging area of research in the field of operational hydrology for about half a century. Simulated sequences drawn from such models find use in a variety of hydrological applications. Traditionally, parametric models are used for simulating rainfall. But the parametric models are not parsimonious and have uncertainties associated with identification of model form, normalizing transformation, and parameter estimation. None of the models in vogue have gained universal acceptability among practising engineers. This may either be due to lack of confidence in the existing models, or the inability to adopt models proposed in literature because of their complexity or both. In the present study, a new nonparametric Matched Block Bootstrap (MABB) model is proposed for stochastic simulation of rainfall at daily time scale. It is based on conditional matching of blocks formed from the historical rainfall data using a set of predictors (conditioning variables) proposed for matching the blocks. The efficiency of the developed model is demonstrated through application to rainfall data from India, Australia, and USA. The performance of MABB is compared with two non-parametric rainfall simulation models, k-NN and ROG-RAG, for a site in Melbourne, Australia. The results showed that MABB model is a feasible alternative to ROG-RAG and k-NN models for simulating daily rainfall sequences for hydrologic applications. Further it is found that MABB and ROG-RAG models outperform k-NN model. The proposed MABB model preserved the summary statistics of rainfall and fraction of wet days at daily, monthly, seasonal and annual scales. It could also provide reasonable performance in simulating spell statistics. The MABB is parsimonious and requires less computational effort than ROG-RAG model. It reproduces probability density function (marginal distribution) fairly well due to its data driven nature. Results obtained for sites in India and U.S.A. show that the model is robust and promising.

Immer wieder ist zu beobachten, wie schwierig es für Lernende im gewerblich-technischen Bereich ist, einen verständnisvollen Zugang zu komplexen technischen Systemen zu finden und wie schwer es ihnen fällt, geeignete mentale Modelle mit ausreichend großer Reichweite zum erfolgreichen Umgang mit komplexem Systemverhalten für ihren späteren Beruf zu entwickeln. Vor dem Hintergrund dieser unterrichtspraktischen Relevanz thematisiert der vorliegende Beitrag als übergeordnetes Ziel, mit Hilfe computergestützter Modellbildung und Simulation (CMS) das Erfassen und Beherrschen komplexer technischer Systeme zum Aufbau beruflicher Handlungskompetenz als wesentlicher Bestandteil des Bildungsauftrags der Berufsschule in verstärktem Maße unterrichtlich etablieren zu können. Eine vorrangige Aufgabe dabei ist es, Lehrenden bei der praktischen Gestaltung geeigneter Lernsituationen Hilfestellung zu leisten bei der Entscheidung, ob und in welcher Form computergestützte Modellbildung und Simulation zum Zwecke ihrer unterrichtlichen Entwicklung sowie einer geeigneten Diagnostik geeignet ist. Den Lehrenden im Bereich der gewerblichen Bildung soll so eine Möglichkeit aufgezeigt werden, wie die Entwicklung und Förderung dieser bereichsübergreifenden Kompetenzen angelegt sein sollte und wie sie diese in ihrem Unterricht entwickeln und nutzen können. Dabei wird offensichtlich, dass komplexe Problemlösekompetenz durch lediglich einen einzigen, umfassenden Indikator nicht bestimm- beziehungsweise darstellbar ist. Die Befähigung zum Lösen komplexer Probleme ist jeweils nur in einem Bündel einzelner Kompetenzen darstellbar. Im konkreten Einzelfall ist jedoch recht gut beschreibbar, welche Komponenten zu diesem Bündel gehören. Eine allgemeine, Domänen unabhängige komplexe Problemlösekompetenz, die in spezifischen Kontexten und unterschiedlichen Berufsfeldern flexibel eingesetzt werden könnte, gibt es nicht. Der Grund liegt u. a. darin, dass Wissen als auch Fähigkeiten und Kompetenzen kontextgebunden sind. Erst über mannigfaltige und zeitintensive Einübungen können sie zunehmend bereichsübergreifend verallgemeinert werden. Mit der im empirischen Teil vorgestellten Untersuchung wird versucht, zur bislang im gewerblich-technischen Bereich nur sehr gering ausgeprägten experimentellen Fundierung der Entwicklung und Erfassung von Kompetenzen mit computergestützter Modellbildung und Simulation (CMS) beizutragen. Hauptanliegen der quasiexperimentellen und explorativen Studie war es, in der beruflichen Erstausbildung an einem ausgewählten regelungstechnischen Beispiel zu prüfen, ob Lernenden über CMS unter der Verwendungsperspektive als so genanntes „Kognitives Tool“ eine (alternative) Zugangs- beziehungsweise Erschließungsmöglichkeit für den erfolgreichen Umgang mit komplexen technischen Systemen ermöglicht werden kann. An dem ausgewählten regelungstechnischen Beispiel wurde untersucht, ob Auszubildenden der Berufsschule, die über keine Kenntnisse in höherer Mathematik verfügen, ein solcher Zugang zu komplexem Systemverhalten gelingen kann. Im Rahmen der empirischen Untersuchungen der Arbeit konnte gezeigt werden, dass computergestützte Modellbildung und Simulation, wenn sie unter bestimmten Konstruktionsbedingungen entwickelt und unter bestimmten Implementationsbedingungen eingesetzt wird, Lernergebnisse erbringt, die den theoretischen Anforderungen an Entwicklung und Erfassung einer komplexen Problemlösekompetenz genügen. In diesem Zusammenhang konnte auch aufgezeigt werden, dass sich eine komplexe Problemlösekompetenz in einer beruflichen Domäne operationalisieren und es sich beobachten lässt, ob bei den Auszubildenden eine Bewegung in Richtung auf den Aufbau entsprechender Fähigkeiten festzustellen ist. Genau so wichtig ist es aber auch zu zeigen, dass der Einsatz computergestützter Modellbildung undSimulation kein Selbstläufer ist, sondern vielmehr seitens der Lehrenden ein didaktisches Expertenwissen voraussetzt. Das Problem der notwendigen Balance zwischen (Lerner-)Konstruktion und (Lehrer-)Instruktion wurde in der Arbeit ausführlich dargestellt. Sind diese Bedingungen gegeben, so führt die Gestaltung computerbasierter Lernumgebungen durch Modellbildung und Simulation bei der unterrichtlichen Förderung einer Problemlösekompetenz zum erfolgreichen Umgang mit komplexen technischen Systemen zu einer tiefen Elaboration von Konzepten und Zusammenhängen.

Les environnements arides végétalisés seront parmi les environnements les plus impactés par la désertification dans le cadre du changement climatique. Ces environnements légèrement végétalisés sont caractérisés par une balance précaire entre un état de résilience et de vulnérabilité qui est intrinsèquement menacé par la désertification pouvant potentiellement mener à une augmentation du transport des sédiments éolien et une dégradation des environnements. Le nombre d’interactions présentes entre la végétation, la pluie, le transport des sédiments et la présence d’herbivore en milieu aride, ainsi que leur nature non-linéaire rend difficile de représenter ces interactions à l’aide de modèle physique et mathématique. La modélisation complexe est mieux adaptée à la représentation des interactions complexes entre la végétation, la pluie, le transport des sédiments et la présence d’herbivores dans les systèmes arides. Un nombre considérable d’études ont utilisées les modèles complexes pour étudier l’effet de la végétation sur le transport des sédiments ou l’effet de la présence d’herbivore sur la végétation, mais peu d’études ont utilisées une approche intégrant ces trois composantes en un même modèle. Un nouveau modèle d’herbivorie basé sur l’agent (GrAM) est présenté sous forme d’extension du modèle ViSTA_M17 et permet une meilleure représentation de l’impact des régimes de pâturage en environnement aride végétalisé. Cet ajout ayant un modèle complexe de transport des sédiments et de végétation déjà établit vise présenter un modèle hybride pouvant représenter l’impact de l’herbivorie sur la composition végétale et le transport des sédiments en environnement aride à l’échelle du paysage. Le développement du nouveau module à l’intérieur de la structure du modèle ViSTA original a souligné certaines limites de ce dernier, notamment une sensitivité importante de la végétation et de la force de cisaillement du vent. Le modèle ViSTA_GrAM répond à certaines limites du modèle original par l’intégration d’un nouveau module d’herbivorie et présente une avancée vers une modélisation environnementale englobante permettant une meilleure compréhension des dynamiques spatiales et temporelles des environnements arides. L’approche englobante utilisée par le modèle ViSTA_GrAM est bénéfique à la prise de décision, puisqu’elle offre un outil permettant d’explorer les réponses des environnements arides à un changement de leur végétation, leur régime de pluie, leur régime de transport des sédiments ou leur régime d’herbivorie. Les modèles complexes et l’exploration de scénarios futurs des environnements arides peuvent permettre d’améliorer la gestion de ces mêmes environnements. / Vegetated arid environments will be among one of the most affected by desertification as a result of climate change. These sparsely vegetated regions exhibit a delicate balance of resilience and vulnerability that are profoundly challenged by desertification, potentially producing an important positive feedback leading to increased aeolian activity and therefore land degradation. The high level of interaction between rainfall, vegetation, sediment transport and grazing in these arid environments and the non-linear nature of these interactions make them difficult to predict by traditional mathematical modeling mean. Complex modeling, on the other hand, offer better representation of the intricate relation between vegetation, rainfall, sediment transport and grazing in an arid environment system. A sizable amount of studies has been conducted with complex models to explore the effect of vegetation on sediment transport or grazing effect on vegetation, but few have used a truly integrative approach where all tree components were represented in a complex model. This research present a novel agent-based model (GrAM) integrated as an extension to already complete sediment transport-vegetation complex model (ViSTA) allowing a more refined representation of grazer’s impact in vegetated arid environments. This addition to the ViSTA model is aimed to combine a land management and systematic approach in a coupled model, to represent, at a landscape level, the impact of grazing on the composition of vegetation and sediment movement in arid environments. The development of this new module within the original ViSTA model, has highlighted some limitations of this model, most notably concerning its sensitivity to vegetation and wind shear. The ViSTA_GrAM model addresses these limitations through integrating a new module of grazing as the next step toward an integrated modelling effort that permits models to effectively increase our spatial and temporal understanding of arid environments vegetation, sediment transport and grazing dynamics. Integrative approach, like the one provided by the ViSTA_GrAM model, is beneficial to decision making by providing tools to investigate the response of an arid environment to different state of their vegetation, rainfall regime, wind stress and grazing regime. By developing complex modeling in arid environment and exploring various future scenarios for arid environment, we hope to lead to better management plan of those same environment.