History matching is a critical process used for calibrating simulation models and assessing subsurface uncertainties. This common technique aims to align the reservoir models with the observed data. However, achieving this goal is often challenging due to the non uniqueness of the solution, underlying subsurface uncertainties, and usually the high computational cost of simulations. The traditional approach is often based on trial and error, which is exhaustive and labor-intensive. Some analytical and numerical proxies combined with Monte Carlo simulations are utilized to reduce the computational time. However, these approaches suffer from low accuracy and may not fully capture subsurface uncertainties. This study proposes a new robust method utilizing Bayesian Markov Chain Monte Carlo (MCMC) to perform assisted history matching under uncertainties. We propose a novel three-step workflow that includes 1) multi-resolution low-fidelity models to guarantee high-quality matching; 2) Long-Short Term Memory (LSTM) network as a low-fidelity model to reproduce continuous time-response based on the simulation model, combined with Bayesian optimization to obtain the optimum low fidelity model; 3) Bayesian MCMC runs to obtain the Bayesian inversion of the uncertainty parameters. We perform sensitivity analysis on the LSTM’s architecture, hyperparameters, training set, number of chains, and chain length to obtain the optimum setup for Bayesian-LSTM history matching. We also compare the performance of predicting the recovery factor using different surrogate methods, including polynomial chaos expansions (PCE), kriging, and support vector machines for regression (SVR). We demonstrate the proposed method using a water flooding problem for the upper Tarbert formation of the tenth SPE comparative model. This study case represents a highly heterogeneous nearshore environment. Results showed that the Bayesian-optimized LSTM has successfully captured the physics in the high-fidelity model. The Bayesian-LSTM MCMC produces an accurate prediction with narrow ranges of uncertainties. The posterior prediction through the high-fidelity model ensures the robustness and accuracy of the workflow. This approach provides an efficient and practical history-matching method for reservoir simulation and subsurface flow modeling with significant uncertainties.

The purpose of the study was to research leadership during uncertainty to identify managerial implications on organizational performance and enhance the limited body of academic literature available on the topic currently. The implications regarding leadership are not limited to the COVID-19 pandemic but instead widely applicable to leadership during uncertainty overall. The pandemic simply provided an example of uncertainty, which was the context. Additionally, as mining is a global industry with widespread follow-on effects on global economies - authors believe that the results from this research are not limited to the mining industry itself and instead widely applicable to senior-level managers in multinational organizations worldwide. The methodology implemented ensures that leaders at any level can extract practical guidance from this research to determine how they may act during times of uncertainty and potential flow-on effects of their chosen leadership style.  Organizations can remain relevant, achieve long-term success and maintain the expected level of performance through uncertainty by adopting a transformational model of leadership that addresses the needs of a fluctuating environment. Uncertainty is the shortage of knowledge and information about probabilities of the future state of events. Organizational leaders need to provide the necessary guidance, inspiration, and motivation to the members because their approach influences the organizational performance. Essentially, people look up to their leaders during uncertain times. The research results showed that transformational managers could offer idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration to team members during uncertainty. These outcomes were achieved through encouraging two-way communication, providing necessary guidance and inspiration to bring out and encourage new idea generation and critical thinking.  Results of the research further showed that transformational leadership has positive effects on organizational performance. Teams functioned properly without much supervision, members remained motivated, the workforce interacted more efficiently, and creative ideas came out when leaders and managers practiced a transformational leadership style. Organizational performance increased through meeting deadlines, maintaining the flow of work, KPIs, production, and non-financial metrics such as culture and safety considerations.

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Yes / This paper proposes a probabilistic method for active distribution networks planning with integration of demand response. Uncertainties related to solar irradiance, load demand and future load growth are modelled by probability density functions. The method simultaneously minimizes the total operational cost and total energy losses of the lines from the point of view of distribution network operators with integration of demand response over the planning horizon considering active management schemes including coordinated voltage control and adaptive power factor control. Monte Carlo simulation method is employed to use the generated probability density functions and the weighting factor method is used to solve the multi-objective optimization problem. The effectiveness of the proposed method is demonstrated with 16-bus UK generic distribution system.

Because of the inherent complexity of fiber-reinforced laminated composites, it can be challenging to manufacture composite structures according to their exact design specifications, resulting in unwanted material and geometric uncertainties. Thus the understanding of the effect of uncertainties in laminated structures on their static and dynamic responses is highly important for a reliable design of such structures. In this research, we focus on the deterministic and probabilistic stability analysis of laminated structures subject to subtangential loading, a combination of conservative and nonconservative tangential loads, using the dynamic criterion. Thus a shear-deformable laminated beam element, including warping effects, is derived to study the deterministic and probabilistic response of laminated beams. This twenty-one degrees of freedom element can be used for solving both static and dynamic problems. In the first-order shear deformable model used here we have employed a more accurate method to obtain the transverse shear correction factor. The dynamic version of the principle of virtual work for laminated composites is expressed in its nondimensional form and the element tangent stiffness and mass matrices are obtained using analytical integration. The stability is studied by giving the structure a small disturbance about an equilibrium configuration, and observing if the resulting response remains small. In order to study the dynamic behavior by including uncertainties into the problem, three models were developed: Exact Monte Carlo Simulation, Sensitivity-Based Monte Carlo Simulation, and Probabilistic FEA. These methods were integrated into the developed finite element analysis. Also, perturbation and sensitivity analysis have been used to study nonconservative problems, as well as to study the stability analysis using the dynamic criterion. / Ph. D.

Autonomous navigation with high speed in rough terrain is one of the most challenging tasks for wheeled robots. To achieve mobility over this terrain, a high agility wheeled robot should adapt and react fast to optimally traverse this challenging environment. Therefore, this dissertation is geared towards the design and development of a novel reconfigurable wheeled robot paradigm for rough terrain applications. This research focuses on the design, modeling, analysis and control of the reconfigurable wheeled robot, TIGER, with an elastic actuated mechanism for improving the robot's dynamic stability on rough terrain by controlling the robot's ground clearance, body roll and pitch angles. The elastic actuated mechanism mainly consists of a linear actuator connected in series with a shock absorber. Four sets of the elastic actuated mechanism are used to create different robot configurations to adapt to the terrain. Three main aspects were considered in this research in order to extend the ability of the robot to effectively navigate in rough terrain. The first aspect focuses on designing an agile reconfigurable wheeled robot by including an elastic actuated mechanism for improving maneuverability, longitudinal/lateral stability, and rollover prevention. Robot agility, stability, and high speed have been considered during the design process. The new design provides different configuration modes. These configurations allow for controlling the robot's Center Of Mass (COM) height and optimally distribute the vertical force on each tire for enhancing the tractive efficiency, mobility and dynamic stability. The second aspect presents the robot kinematic and dynamic modeling and analysis. The robot dynamics model is represented with fourteen degrees of freedom (DOF), where the dynamic behaviors of the robot body, suspension system, forces and moments on the tires are included. The dynamic behavior is controlled using the linear actuators' position and speed as inputs to determine the resulting ground clearance, body roll, and pitch angles. Sensors are integrated onboard the robot to calculate the robot's states in real time for use in feedback control. The third aspect focuses on introducing a technique for estimating the robot state-space dynamic model and control the Elastic Actuated Mechanism (EAM) using only a noisy Inertial Measurement Unit (IMU) with COM position uncertainty. The simulation results show that the observer estimates the actual behavior of the robot with 95% accuracy and up to 20% COM uncertainty. The Root Mean Square (RMS) has been reduced by 21% for bounce, 51% for pitch and 50% for roll acceleration. / Ph. D. / Wheeled mobile robots are being used for rough terrain applications in the field of robotics as a practical solution to accomplish various tasks. Unfortunately, most of the wheeled robots are not able to perform high dynamically tasks with high speed in rough terrain due to complex suspension design, high power-to-weight ratio, high cost and complexity of controlling highly nonlinear model in real-time. Therefore, this dissertation is geared towards the design and development of a novel reconfigurable wheeled robot paradigm for rough terrain applications. This research focuses on the design, modeling, analysis and control of the reconfigurable wheeled robot, TIGER, with an elastic actuated mechanism for improving the robot’s dynamic stability on rough terrain by controlling the robot’s ground clearance, body roll and pitch angles. The elastic actuated mechanism mainly consists of a linear actuator connected in series with a shock absorber. Four sets of the elastic actuated mechanism are used to create different robot configurations to adapt to the terrain. Three main aspects were considered in this research in order to extend the ability of the robot to effectively navigate in rough terrain. The first aspect focuses on designing an agile reconfigurable wheeled robot by including an elastic actuated mechanism for improving maneuverability, longitudinal/lateral stability, and rollover prevention. Robot agility, stability, and high speed have been considered during the design process. The new design provides different configuration modes. These configurations allow for controlling the robot’s COM height and optimally distribute the vertical force on each tire for enhancing the tractive efficiency, mobility and dynamic stability. The second aspect presents the robot kinematic and dynamic modeling and analysis. The robot dynamics model is represented with fourteen degrees of freedom (DOF), where the dynamic behaviors of the robot body, suspension system, forces and moments on the tires are included. The dynamic behavior is controlled using the linear actuators’ position and speeds as inputs to determine the resulting ground clearance, body roll, and pitch angles. Sensors are integrated onboard the robot to calculate the robot’s states in real time for use in feedback control. The third aspect focuses on introducing a technique for estimating the robot state-space dynamic model and control the EAM using only a noisy IMU with COM position uncertainty. The simulation results show that the observer estimates the actual behavior of the robot with 95% accuracy and up to 20% COM uncertainty. The RMS has been reduced by 21% for bounce, 51% for pitch and 50% for roll acceleration.

Yes / Given that the probability of extreme weather has been dramatically increasing, this study contributes to the existing literature by bridging the relation between weather risks and global commodity prices with a secondary dataset (e.g., weather risks of Canada and the United States, agricultural raw materials price, gold price, and crude oil price). The results from the vector autoregression model and impulse response functions show that rising weather risks increase the price of agricultural raw materials and gold. However, the negative impact of weather risks on the crude oil price is found. Finally, the paper discusses the findings' potential implications (e.g., developing decarbonised supply chains) for decreasing weather risks' effects on commodity market uncertainties.

Yes / This paper proposes a probabilistic multi-objective optimization method for the operation of three-phase distribution networks incorporating active network management (ANM) schemes including coordinated voltage control and adaptive power factor control. The proposed probabilistic method incorporates detailed modelling of three-phase distribution network components and considers different operational objectives. The method simultaneously minimizes the total energy losses of the lines from the point of view of distribution network operators (DNOs) and maximizes the energy generated by photovoltaic (PV) cells considering ANM schemes and network constraints. Uncertainties related to intermittent generation of PVs and load demands are modelled by probability density functions (PDFs). Monte Carlo simulation method is employed to use the generated PDFs. The problem is solved using ɛ-constraint approach and fuzzy satisfying method is used to select the best solution from the Pareto optimal set. The effectiveness of the proposed probabilistic method is demonstrated with IEEE 13- and 34- bus test feeders.

The purpose of this study is to examine the most suitable valuation methods for Swedish property companies when appraising specialty properties, and to analyze the uncertainties associated with valuing such properties and the strategies used to mitigate these uncertainties. The study is based on a combination of literature reviews, theoretical frameworks, and 15 semi-structured interviews with property valuers and experts in the field. Previous research has explored the most appropriate valuation methods for specialty properties based on value principles. Other research has explored property valuation on a global scale to identify applicable methods. This study specifically focuses on the valuation methods most commonly used in the Swedish market, their associated uncertainties, and how companies manage these uncertainties. A qualitative research method is employed to gain a deeper understanding of Swedish property companies' perspectives on these issues. The 15 semi-structured interviews primarily involved companies of various sizes acting as both consultants and owners, along with two interviews with experts in property valuation from KTH and Lund University. This approach facilitated a comprehensive investigation and identification of differences and similarities among the respondents. The results indicate that the income approach and the sales comparison approach are widely used as valuation methods. There is a greater variation in perceptions of uncertainties, with particular emphasis placed on the stability of tenants and the uncertainty surrounding lease terminations, given that the properties are tailored to specific business needs. Some respondents also point to the lack of comparable properties and a limited transaction market for the segment as contributing factors to valuation uncertainties. Finally, the study presents companies' strategies for minimizing uncertainties, with a common theme being thorough analysis of tenants and lease agreements. Companies emphasize the importance of internal databases and continuous updating of knowledge in the field to minimize uncertainties. One difference among companies is how they present uncertainties in their valuation reports. Some companies provide reports with a risk range, while others believe that more consideration should be given to uncertainties to minimize uncertainty in valuation. They highlight that a descriptive analysis of how changes in required rates of return and net rental income can affect valuation may be significant, especially for investors. / Syftet med denna studie är att undersöka vilka värderingsmetoder som är mest lämpliga för svenska fastighetsbolag när de ska värdera specialfastigheter, samt att analysera de osäkerheter som är förknippade med värderingen av sådana fastigheter och vilka strategier som används för att hantera dessa osäkerheter. Studien bygger på en kombination av litteraturstudier, teoretiska ramverk och 15 semistrukturerade intervjuer med fastighetsvärderare och experter inom området. Tidigare forskning har undersökt de mest lämpliga värderingsmetoderna för specialfastigheter baserat på värdeprinciper. Annan forskning har utforskat fastighetsvärdering på en global nivå för att identifiera tillämpliga metoder. Denna studie fokuserar specifikt på vilka värderingsmetoder som används mest på den svenska marknaden, dess medföljande osäkerheter och hur företagen hanterar dessa osäkerheter. En kvalitativ forskningsmetod används för att få en djupare förståelse av svenska fastighetsbolagens perspektiv på dessa frågor. De 15 semistrukturerade intervjuerna omfattade främst företag av olika storlek som agerar både som konsulter och ägare, samt två intervjuer med experter inom fastighetsvärdering från KTH och Lunds Universitet. Detta tillvägagångssätt möjliggjorde en omfattande undersökning och identifiering av skillnader och likheter mellan respondenterna. Resultaten visar att avkastningsmetoden och ortsprismetoden används i stor utsträckning som värderingsmetoder. Det finns en större variation i synen på osäkerheter, där framför allt hyresgästens stabilitet och osäkerheten kring uppsägning av hyresavtal får stor vikt, då fastigheten är utformad för specifika verksamheters behov. Vissa respondenter pekar också på bristen på jämförbara objekt och en begränsad transaktionsmarknad för segmentet som bidragande faktorer till osäkerheter vid värdering. Slutligen presenteras företagens strategier för att minimera osäkerheter, där en gemensam nämnare är noggrann analys av hyresgäster och hyresavtal. Företagen betonar vikten av interna databaser och ständig uppdatering av kunskap inom området för att minimera osäkerheter. En skillnad bland företagen är hur de presenterar osäkerheter i sina värderingsutlåtanden. Vissa företag presenterar utlåtanden med ett riskintervall, medan andra anser att mer hänsyn behöver tas till osäkerheter för att minimera osäkerheten i värderingen. De pekar på att en deskriptiv analys av hur förändringar i krav på avkastning och netto hyresintäkter kan påverka värderingen kan vara av betydelse, särskilt för investerare.

La gestion des incertitudes constitue un sujet particulièrement important pour le processus de développement des systèmes (DS) complexes. Il faut donc améliorer la flexibilité et la prédictibilité des processus de DS en proposant une méthodologie pour appréhender et maîtriser les incertitudes qu’ils comportent, ce qui passe par la modélisation. Le travail présenté dans ce mémoire se concentre sur deux questions indispensables dans ce but : (1) Comment gérer les incertitudes pendant le processus de DS ? (2) Comment représenter les incertitudes dans la modélisation de processus de DS ? Pour répondre à ces questions, la thèse propose une méthodologie incluant l’identification des incertitudes, l’analyse de leurs effets, leur réduction et/ou leur exploitation, et leur surveillance et leur contrôle ; elle définit également une démarche de modélisation des incertitudes avec le langage UML en proposant un méta-modèle des incertitudes et un nouveau profil UML "Uncertainty-of" qui permet de rendre ce méta-modèle opérable avec les outils UML. La validité de la méthodologie et du profil proposés a été testée par l’étude d’un cas industriel. Cette étude a montré que la définition d’une telle méthodologie était utile pour l’organisation de la gestion des incertitudes dans la conception et le développement de systèmes, et que la création d’un nouveau profil UML apportait une solution pertinente dans la représentation des incertitudes. / Uncertainty management is a particularly important issue for complex systems development (SD) processes. The overall objective to be attained in this context is to improve the flexibility and predictability of SD process by proposing a methodology to understand and master the uncertainties. We believe that modeling uncertainties is an indispensable step in this direction. For this purpose, the presented work in this thesis focuses on two critical issues : (1) How should we manage uncertainties in SD processes ? (2) How can we represent uncertainties in modeling such processes ? In order to answer to these questions, this thesis proposes a methodology including identification, analysis of impact, diminution and / or exploitation, and monitoring and control of uncertainties ; and it also defines an approach to modeling uncertainties with UML by proposing a meta-model of uncertainties and a new UML profile named "Uncertaintyof", which allows to make this meta-model operable with UML tools. The validity of our proposals was tested by an industrial case study, which showed that the definition of such a methodology is helpful to manage the uncertainties in systems design and development, and that the creation of a new UML profile provides a valid solution in the representation of uncertainties.