In dense cities, the interactions between all structures, from tall skyscrapers to complex underground tunnel systems, need to be carefully analysed as soon as a new project is considered. This is necessary because of the stress changes in the soil induced by each new construction. Demolishing a building could cause heave at the base of the excavation, deflections in supporting structures and settlements of the surroundings. The behaviour can be modelled in order to predict how large the deformations will be. This thesis investigates the effectiveness of such models. This is done through the application of a parameter sensitivity analysis on models created in Plaxis. The purpose of the analysis is to identify which factors cause discrepancies between the models and the actual displacements monitored on site. The project being examined is located in central London. The analysis focuses on the displacements of existing tunnels below the site caused by the demolition of two buildings. An analysis was carried out to investigate the significance of different parameters, of different material models and methods of analysis, of 3D effects and of inaccurate groundwater data. Ground investigations, laboratory tests and published data were the main sources used to collect reliable initial input parameters for the material models. A model was created in Plaxis 2D using the Mohr-Coulomb and the Hardening Soil with small-strain stiffness material models, using two types of undrained analysis. A model using the Mohr-Coulomb material model was created in Plaxis 3D as well. A sensitivity analysis was then carried out on the 2D models to identify which input parameters were most significant to the tunnel displacements. The results were compared to monitoring data and a back-analysis was carried out to produce more accurate results. The initial and adjusted input parameters were also tested on the 3D model. Finally, the groundwater level was altered. The results indicate that soil stiffness and effective cohesion are the most significant. Small-strain stiffness is shown to be especially important when analysing small tunnel deformation. The 3D model generally yielded more accurate results than the 2D model, while the groundwater level did not appear to affect the deformations. / I tätbebyggda städer bör samspelet mellan olika konstruktioner, från skyskrapor till tunnelsystem, analyseras noga så fort ett nytt projekt ska påbörjas. Detta är kritiskt på grund av den förändrade spänningsfördelning som uppstår i marken vid varje ny byggnation. Marken häver sig, stödmurar deformeras och den omkringliggande marken sätter sig när en byggnad rivs. Denna process kan modelleras för att uppskatta hur stora deformationerna kommer att bli. Det här examensarbetet utvärderar hur effektiv en sådan modell är. En känslighetsanalys utfördes på modeller skapade i Plaxis. Syftet med denna analys är att undersöka vilka faktorer som orsakar skillnader mellan modellerna och mätdata. Projektet ligger i centrala London och analysen fokuserar på tunneldeformationer orsakade av att två byggnader rivs. Tunnlarna befinner sig i ett lager av Londonlera under byggarbetsplatsen. En analys utfördes för att undersöka huruvida olika parameterar, olika materialmodeller och analysmetoder, 3D effekter och grundvattennivån påverkar tunnelförflyttningarna. Markundersökningar, labbtester och publicerad data användes som grund för att bestämma indatavärden. En 2D modell skapades i Plaxis genom att använda materialmodellerna Mohr-Coulomb och ”Hardening Soil with small-strain stiffness”. En Mohr-Coulomb modell skapades dessutom i Plaxis 3D. En känslighetsanalys utfördes sedan på 2D modellen för att identifiera vilka parametrar som påverkade tunnelförflyttningarna mest. Resultaten jämfördes med mätdata och viktiga parameterar ändrades för att ge bättre resultat. Inverkan av att ändra dessa värden undersöktes även i 3D modellen. Slutligen undersöktes påverkan av en förhöjd grundvattennivå. Resultaten antyder att jordens styvhet och den effektiva kohesionen har störst inverkan på resultaten. Styvheten vid små töjningar visar sig vara särskilt viktigt eftersom deformationerna år små. 3D modellen gav generellt sätt mer korrekta resultat än modellen i 2D. En högre grundvattennivå påverkade inte resultaten nämnvärt.

Steelmaking industry, one of the most electricity-intensive industrial processes, is seeking for new approaches to improve its competitiveness in terms of energy savings by taking advantage of the volatile electricity prices. This fluctuation in the price is mainly caused by the increasing share of renewable energy sources, the liberalization of energy markets and the growing demand of the energy. Therefore, making the production scheduling of steelmaking processes with knowledge about the cost of the energy may lead to significant cost savings in the electricity bills. With this aim in mind, different models are developed in this project in order to improve the existing monolithic models (continuous-time based scheduling) to find an efficient formulation of accounting for electricity consumption and also to expand them with more detailed scheduling of Electric Arc Furnace stage in the production process. The optimization of the energy cost with multiple electricity sources and contracts and the production planning are usually done as stand-alone optimizers due to their complexity, therefore as a new approach in addition to the monolithic model an iterative framework is developed in this work. The idea to integrate the two models in an iterative manner has potential to be useful in the industry due to low effort for reformulation of existing models. The implemented framework uses multiparametric programming together with bilevel programming in order to direct the schedule to find a compromise between the production constraints and goals, and the energy cost. To ensure applicability heuristic approaches are also examined whenever full sized models are not meeting computational performance requirements. The results show that the monolithic model implemented has a considerable advantage in terms of computational time compared to the models in the literature and in some cases, the solution can be obtained in a few minutes instead of hours. In the contrary, the iterative framework shows a bad performance in terms of computational time when dealing with real world instances. For that matter a heuristic approach, which is easy to implement, is investigated based on coordination theory and the results show that it has a potential since it provides solutions close to the optimal solutions in a reasonable amount of time. Multiparametric programming is the main core of the iterative framework developed in this internship and it is not able to give the solutions for real world instances due to computational time limitations. This computational problem is related to the nature of the algorithm behind mixed integer multiparametric programming and its ability to handle the binary variables. Therefore, further work to this project is to develop new approaches to approximate multiparametric technique or develop some heuristics to approximate the mp-MILP solutions.

The Gulf of Finland is the most eutrophied water body in the Baltic Sea, which is mainly caused by nutrient loads produced by human activities in its surrounding cities. In order to solve this environmental problem, a computational model based on the understanding the relations between eutrophication, water quality and sediments is needed to forecast the water quality variance in response to the natural and anthropogenic influences. A precise water quality model can be useful to assist the policy making in the Gulf of Finland, and even for the whole Baltic Sea. Kiirikki model, as one of these models describing the water quality of Baltic Sea in response of water quality variance, is a sediment and ecosystem based model, treating different sub-basins and layers as boxes. This study aims to assess the parameters’ sensitivity level on the scale of the Gulf of Finland. Firstly, the Morris sampling strategy is applied to generate economic OAT (One factor At a Time) samples before screening 50 out of 100 trajectories with distance as large as possible. In order to assess their sensitivity, index and indicator are needed. EE (elementary effect) is adopted to be the assessment index and four core eutrophication indicators from HELCOM 2009a are to be analyzed. By comparing the (σ,μ) and (σ,μ*) plots of each parameters’ EE values (σ is standard deviation, μ is mean value and μ* is the absolute mean value), some parameters are identified as potential sensitive parameter, such as the minimum biomass of cyanobacteria (Cmin), critical point of CO2 flux (CCr), the optimal temperature for detritus phosphorous mineralization (Toptgamma), maximum loss rate of algae (RAmax), optimal temperature for the growth of other algae (ToptmuA), Coefficient for temperature limiting factor for the growth of cyanobacteria (aTmuC), half-saturation coefficient of radiation for cyanobacteria (KIC) and so on. In contrast, the other parameters are ruled out as having very low values in terms of σ, μ and μ*. This is because the feature of Morris sampling strategy makes it easier to achieve high variance of the outputs, resulting into generally higher σ. Therefore, further investigation with different strategies is needed after the initial screening of the non-sensitive parameters in this study.

The reliable operation of Integrally Geared Centrifugal Compressors (IGCCs), used in the coal-fired power generation industry of South Africa, is essential for economic, environmental and safety considerations. However, due to the unavailability of individual stage performance curves, the ability of a compressor owner to identify underperforming stages to maintain these compressors proactively remains limited. This study addresses the stage performance prediction of an IGCC when only the compressor’s overall performance characteristic, in conjunction with the impeller diameters and tip speeds, are known. The study is limited to IGCCs used in the coal-fired power generation industry of South Africa. Based on the limited inputs, two performance modelling methods were considered for this application, namely stage stacking and 1-dimensional modelling. However, stage stacking requires known operating points on each stage performance curve from which the rest of the curve can be extrapolated while 1-dimensional models require detailed stage design information to model stage performance. This study developed a revised stage stacking procedure which in contrast to the traditional stage stacking procedure, does not require a known operating point on each stage’s performance curve, for it assesses the relative stage performance at the compressor’s surge flow rate. The relative maximum pressure ratio of each stage is acquired through the application of similarity principles while a simplified 1-dimensional impeller analysis model is used to assess relative impeller head coefficients. The modelling process was developed based on performance and design data for IGCCs obtained from a compressor manufacturer. Performance data of four IGCCs, consisting of 13 stages, were obtained, including the design data for ten impellers. Hence, the IGCCs satisfy the requirements of geometric and aerodynamic similarity, unveiling a linear relationship between the stage impeller tip speed and maximum pressure ratio. A simplified 1-dimensional performance model was used to assess relative impeller head coefficients. A verification procedure ensured the integrity of the findings of the 1-dimensional model was maintained by comparing the model results to findings obtained using commercial compressor performance modelling software. A sensitivity analysis was conducted on the 1-dimensional performance model to ascertain which input parameters could be scaled as a function of the impeller tip diameter. For the four IGCCs for which data were obtained, the stage-discharge pressure and isentropic efficiency curves were calculated using the developed model. The maximum variation between the measured and calculated pressure and isentropic efficiency curves equaled 8.20% and 10.84%, respectively. The prediction accuracy of the developed modelling procedure is similar to map-based models found in literature and is considered adequate for identifying an underperforming stage. Thus, the developed model could serve as a valuable conditioning monitoring tool for site-based compressor owners. / Dissertation (MEng)--University of Pretoria, 2019. / Mechanical and Aeronautical Engineering / MEng / Unrestricted

The increase in antibiotic resistance continues to pose a public health risk as very few new antibiotics are being produced, and bacteria resis- tant to currently prescribed antibiotics is growing. Within a typical hospital setting, one may find patients colonized with bacteria resistant to a single an- tibiotic, or, of a more emergent threat, patients may be colonized with bacteria resistant to multiple antibiotics. Precautions have been implemented to try to prevent the growth and spread of antimicrobial resistance such as a reduction in the distribution of antibiotics and increased hand washing and barrier pre- ventions; however, the rise of this resistance is still evident. As a result, there is a new movement to try to re-examine the need for the development of new antibiotics. In this paper, we use mathematical models to study the possible benefits of implementing a new antibiotic in this setting; through these models, we examine the use of a new antibiotic that is distributed in various ways and how this could reduce total resistance in the hospital. We compare several dif- ferent models in which patients colonized with both single and dual-resistant bacteria are present, including a model with no additional treatment proto- cols for the population colonized with dual-resistant bacteria as well as models including isolation and/or treatment with a new antibiotic. We examine the benefits and limitations of each scenario in the simulations presented.

La résilience concerne une capacité importante d'un système à résister et à se remettre des événements perturbateurs. L'objectif de cette thèse est de construire un cadre d'analyse et d'optimisation de la résilience des infrastructures critiques interconnectées (ICIs). Dans ce travail, les contributions scientifiques originales comprennent: 1) une approche de modélisation générique pour décrire le comportement dynamique et les processus d'échec en cascade dans les ICIs. 2) basé sur le modèle proposé, une approche quantitative d'évaluation de la résilience de ICIs est développée, où les aspects d'atténuation et de récupération sont évalués; 3) afin de réduire le coût de calcul dans le cas de systèmes à grande échelle, trois méthodes Différentes échelle, trois méthodes différentes d'analyse de sensibilité globale (ANN estimation, ensemble-based, given-data estimation), sont mis en place pour identifier les paramètres de modèle les plus pertinents affectant la résilience du système, puis les performances de ces méthodes sont comparées;4) un modèle hiérarchique est développé pour caractériser les facteurs de stratégies d'amélioration de la résilience. Un problème d’optimisations multi-objectif est formulé et résolu par l'algorithme NSGA-II, afin de fournir un plan optimal pour l'amélioration de la résilience du système. Les méthodes proposées sont mises en œuvre dans les applications, par exemple, un réseau d'alimentation en gaz et un réseau électrique. / Resilience is the ability of a system to resist to and recover from disruptive events. The objective of this thesis is to build a framework of analysis and optimization of interconnected critical infrastructures (ICIs) resilience. In this work, the original scientific contributions include: 1) a generic modeling approach to describe the dynamic behavior and the physical cascading failure processes in ICIs.2) on the basis of the model, a quantitative resilience assessment approach for ICIs is proposed, where both the mitigation and recovery aspects of system resilience are evaluated; 3) in order to reduce the computational cost in the case of large-scale systems, three different global sensitivity analysis methods (ANN estimation, ensemblebased, give-data estimation) are implemented to identify the most relevant model parameters affecting the system resilience, and then the performance of these methods are compared; 4) a hierarchical model is developed to characterize the factors of resilience improvement strategies. A multi-objectives optimization problem is formulated and solved by NSGA-II algorithm, to provide the optimal plan for system resilience improvement. The methods proposed are implemented to applications, e.g., a gas supply network and an electrical power grid.

Wind storms pose dangerous threats to human lives and are an enormous drain on the economy. Their damage to buildings usually starts with the failure of structural components that are subjected to excessive wind loads. In this dissertation, we investigate the characteristics of extreme loads on low-rise structures through analysis of full-scale and numerical data. We also use numerical simulations to evaluate different approaches to control the separated flow over a surface-mounted prism with the objective of reducing extreme pressure coefficients or loads on its surface. In the first part, we use a probabilistic approach to characterize peak loads as measured on a subject house during Hurricane Ivan on 2004. Time series of pressure coefficients collected on the roof of that house are analyzed. Rather than using peak values, which could vary due to the stochastic nature of the data, a probabilistic analysis is used to determine the probability of non-exceedence of specific values of pressure coefficients and associated wind loads. The results show that the time series of the pressure coefficients follow a three-parameter Gamma distribution, while the peak pressure follows a two-parameter Gumbel distribution. The results of the analysis are contrasted with the design values. In the second part, we perform numerical simulations of the flow over a surface-mounted prism as a simplified example for the flow over a low-rise structure. A Direct Numerical Simulation (DNS) code is developed to solve the unsteady two-dimensional incompressible Navier-Stokes equations of the flow past the prism. The pressure coefficients are then computed on the prism surface in order to assess the wind loads. The code is written on a parallel platform using the Message Passing Interface (MPI) library. We use the simulations to study the effects of inflow disturbances on the extreme loads on structures. The sensitivities of peak loads on a surface mounted prism to variations in incident gust parameters are determined. Latin Hypercube Sampling (LHS) is applied to obtain different combinations of inflow parameters. A non-intrusive polynomial chaos expansion is then applied to determine the sensitivities. The results show that the gust enhances the destabilization of the separation shear layer, forces it to break down and moves it closer to the roof of the prism. As for the sensitivities, the results show that the extreme loads are most sensitive to the transverse amplitude of the disturbance. Because the separated flow over sharp edges is responsible for the extreme pressure peaks, we investigate the use of active and passive control strategies to reduce wind loads. The studied active flow control strategies include blowing, suction, and synthetic jets. We implement them by using different flux injections, different slot locations and different angles. Investigation of the possible peak pressure reduction for two Reynolds numbers is performed. For Re = 1000, a reduction by nearly 50% of the peak pressure is obtained. For Re = 10, 000, the highest achieved reduction is nearly 25%. For passive control, we mount a flexible membrane on the top of the prism. In a two-dimensional framework, the membrane equation is modeled by a forced string equation. This mechanical equation is coupled with the DNS solver and integrated in time using a fourth order Hamming predictor corrector scheme. The results show that this strategy is as efficient as the active control approach, in terms of reducing extreme loads, for Re = 10, 000. / Ph. D.

Indiana University-Purdue University Indianapolis (IUPUI) / Powder bed fusion (PBF) process is a metal additive manufacturing process, which can build parts with any complexity from a wide range of metallic materials. Research in the PBF process predominantly focuses on the impact of a few parameters on the ultimate properties of the printed part. The lack of a systematic approach to optimizing the process parameters for a better performance of given material results in a sub-optimal process limiting the potential of the application. This process needs a comprehensive study of all the influential parameters and their impact on the mechanical and microstructural properties of a fabricated part. Furthermore, there is a need to develop a quantitative system for mapping the material properties and process parameters with the ultimate quality of the fabricated part to achieve improvement in the manufacturing cycle as well as the quality of the final part produced by the PBF process. To address the aforementioned challenges, this research proposes a framework to optimize the process for 316L stainless steel material. This framework characterizes the influence of process parameters on the microstructure and mechanical properties of the fabricated part using a series of experiments. These experiments study the significance of process parameters and their variance as well as study the microstructure and mechanical properties of fabricated parts by conducting tensile, impact, hardness, surface roughness, and densification tests, and ultimately obtain the optimum range of parameters. This would result in a more complete understanding of the correlation between process parameters and part quality. Furthermore, the data acquired from the experiments are employed to develop an intelligent parameter suggestion multi-layer feedforward (FF) backpropagation (BP) artificial neural network (ANN). This network estimates the fabrication time and suggests the parameter setting accordingly to the user/manufacturers desired characteristics of the end-product. Further, research is in progress to evaluate the framework for assemblies and complex part designs and incorporate the results in the network for achieving process repeatability and consistency.

The goal of the master´s thesis is to approach the isue of risk of investment projects and to deal with situations regarding these risks. The thesis describe the types of risks, their classification, methods of their identification, analysis, and their subsequent processing. In the practical part there will be clearly shown how the influence of some risks can affect the project and its cash flows. The subject of illustrative application is the Aquapark project in Uherské Hradiště.

付記する学位プログラム名: デザイン学大学院連携プログラム / 京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21099号 / 工博第4463号 / 新制||工||1693(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 西脇 眞二, 教授 椹木 哲夫, 教授 松原 厚 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM