This thesis provides a discrete-event simulation framework that can be used to analyze  and  dimension  computing  systems.  The  simulation  framework  can define  and  parametrize  the  flexible  queueing  system.  We  use  the  simulation framework to explore the data collected from the real-world system. We analyze the metrics, including waiting time and server utilization of single-server and multi-server  queueing  systems.  In  particular,  we  study  the  impact  of  the number of servers on waiting time and server utilization. The experiments show it  is  possible  to  increase  server  utilization  and  decrease  the  server  number without  significantly  increasing  waiting  time,  and  flexible  architectures  canlead to significant gains. / Detta  examensarbete  tillhandahåller  ett  ramverk  som  kan  användas  för  att analysera och dimensionera dator-system. Simuleringsramverket kan definera och parameterisera ett flexibelt kösystem baserat på data från ett system i drift. Vi använder simuleringsramverket för att undersöka datat insamlat från skarpa system.  Vi  analyserar  prestandatal,  såsom  väntetid  och  utnyttjandegrad  för system  med  en  och  flera  betjänare.  Framför  allt  undersöker  vi  hur  antalet betjänare  påverkar  väntetid  och  utnyttjandegrad.   Försöken  visar  att  det  är möjligt  att  öka  uttnyttjandegraden  och  minska  antalet  betjänare  utan  att märkbart öka väntetiden, och att en flexibel arkitektur kan leda till märkbaraförbättringar. / <p>Industrial Advisors: Olga Grinchtein and Johan Karlsson </p>

A human thermoregulation model has been adapted for predicting the thermal response of Typical Older Persons. The model known as the Older Persons Model predicts the core body temperature and regulatory responses of the older people in environmental exposures of cold, warm and hot. The model was developed by modifying an existing dynamic human thermoregulation model using anthropometric and thermo-physical properties of older people. The Model defines the body as two interrelating systems of the body structure (passive system) and the control system of the central nervous system (active system). The Older person's passive system of the model was developed by meticulously extracting relevant experimental data from selected published research works relating to anthropometric and thermo-physical properties of older people. The resultant body structure (passive system) is a multi-segmented representation of a Typical Older Person. The active system (central nervous system) was developed by the application of a novel optimization method based on the working principles of Genetic Algorithms. The use of Genetic Algorithm enables the complex characteristics of the central nervous system of the older persons to be well represented and evaluated based on available data. Active system control signal coefficients for sweating, shivering, vasodilation and vasoconstriction were explicitly derived based on experimental data sourced from literature. The Older Persons Model has been validated using independent experimental data and its results show good agreement with measured data. Furthermore, the Older Persons Model has been applied to several test cases extracted from published literature and its results show good agreement with published findings on the thermal behaviour of older persons. An interview study conducted as part of this research revealed that, professionals (built environment specialists) found the Older Persons Model useful in assisting to further understand the thermal response of the older persons. In conclusion, the adaptation of an existing human thermoregulation model has resulted in a new model, which allows improved prediction of heat and cold strain of the older person although there exist limitations.

Transportation has a major impact on our society and environment, contributing 70% of U.S petroleum use, 28% of U.S. greenhouse gas (GHG) emissions, over 34,000 fatalities and 2.2 million injuries in 2013. Punitive approaches to used to tackle environmental issues in the transportation sector, such as congestion pricing have been well documented, although the use of incentives or rewards lags behind in comparison. In addition to the use of more fuel-efficient, alternate energy vehicles and various other energy reduction strategies; energy consumption can be lowered through incentivizing alternative modes of transportation. This paper focused on modifying travelers’ behavior by providing rewards to enable shifts to more energy-efficient modes, (e.g., from auto to either bus or bicycles). Optimization conditions are formulated for the problem to understand solution properties, and numerical tests are carried out to study the effects of system parameters (e.g., token budget and coefficient of tokens) on the optimal solutions (i.e., energy savings). The multinomial logit model is used to formulate the full problem, comprised of an objective function and constraint of a token budget ranging from $5,000-$10,000. Comparably, the full problem is computationally reduced by various parameterization strategies, in that the number of tokens assigned to all travelers’ is parameterized and proportional to the expected energy savings. An optimization solution algorithm is applied with a global and local solver to solve a lagrangian sub-problem and a duo of heuristic solution algorithms of the original problem. These were determined necessary to attain an optimal and feasible solution. Input data necessary for this analysis is obtained for the Town of Amherst, MA from the Pioneer Valley Planning Commission (PVPC). The results demonstrated strong evidence to conclude a positive correlation between the system’s energy savings and the aforementioned system parameters. The local and global solvers solution algorithm reduced the average energy consumption by 11.48% - 19.91% and12.79% – 21.09% consecutively for the identified token budget range from a base case scenario with no tokens assigned. The duo of lagrangian heuristic algorithms improved the full problems solution i.e., higher energy savings, when optimized over a local solver, while the parameterized problem formulations resulted in higher energy savings when compared to the full problems’ formulation solution over local solver, but higher energy savings compared over the global solver. The Computational run-time for the global and local solvers solution algorithm for the full problem formulation required 43 hours and 24 minutes consecutively, while the local solver for the lagrangian heuristics and parameterized problem solution algorithm took 13 minutes and 7 minutes consecutively. Future research on this paper will be comprised of a bi-level optimization problem formulation where a high level optimization aims at maximizing system-wide energy savings, while a low-level consumer surplus maximization problem is solved for each system user.

This diploma thesis focuses on the optimization of water supply system in the town of Počátky. It describes the process of creating a mathematical model needed for the hydraulic analysis, which was used to assess the water supply system Počátky. This thesis task is to apprise reader, with a basic distribution in the drinking water supply, how to proceed in collecting data on hydraulic analysis requirements and using tools and means used in hydraulic analysis. Conclusions are processed variants for possible optimization of water supply system Počátky.

Energy recovery ventilation (ERV) systems are designed to decrease the energy consumed by building HVAC systems. ERV’s scavenge sensible and latent energy from the exhaust air leaving a building or space and recycle this energy content to pre-condition the entering outdoor air. A few studies found in the open literature are dedicated to developing detailed numerical models to predict or simulate the performance of energy recovery wheels and desiccant wheels. However, the models are often computationally intensive, requiring a lot of time to perform parametric studies. For example, if the physical characteristics of a study target change (e.g., wheel diameter or depth) or if the system runs at different operating conditions (e.g., wheel rotation speed or airflow rate), the model parameters need to be recalculated. Hence, developing a mapping method with better computational efficiency, which will enable the opportunity to conduct extensive parametric or optimal design studies for different wheels is the goal of this research. In this work, finite difference method (FDM) numerical models of energy recovery wheels and desiccant wheels are established and validated with laboratory test results. The FDM models are then used to provide data for the development of performance mapping methods for an energy wheel or a desiccant wheel. After validating these new mapping approaches, they are employed using independent data sets from different laboratories and other sources available in the literature to identify their universality. One significant characteristic of the proposed mapping methods that makes the contribution unique is that once the models are trained, they can be used to predict performance for other wheels with different physical geometries or different operating conditions if the desiccant material is identical. The methods provide a computationally efficient performance prediction tool; therefore, they are ideal to integrate with transient building energy simulation software to conduct performance evaluations or optimizations of energy recovery/ desiccant wheel integrated HVAC systems.

Presented PhD thesis aims at development of new methodologies and approaches to reduce lead times in production systems. Therefore the thesis examines in detail the design of new, more comprehensive approaches to shortening lead times. The dissertation describes the methodology of using these approaches in production systems, it is suggested their inclusion in the control structure of production and adequate managerial support for the successful application of these new approaches in the industry is developed. The first part summarizes current knowledge in the field of reducing lead times. The various methods that can be used for reducing lead times are defined and described, from which is selected discrete simulation of manufacturing systems as a suitable method for the purposes of issue development. The next part of this work is therefore devoted to simulation approach to reducing lead times and the factors that affect them, such as processing time, size of the batch, queue lengths, etc. In this part is also discussed the use of information technologies (programming languages, libraries, and software tools) that enable efficient creation of simulation models. The second part is focused on industrial applications implemented to reduce production lead times. Specific simulation analysis deal with, for example, reduction of the lead time for manufacture of surgical instruments, screws, slats, but also transport systems, optimization of production orders input to the production system, the issue of supply, etc. The second part also describes a practical approach to data transfer to / from the simulation model, simulation software connection to the ERP database of enterprise and energy intensity of production. The conclusion summarizes the findings that were detected on the issue of reducing lead times, and within the chosen methodology, simulation of manufacturing processes indicates possible further progress in the research and practical applications.

As the electricity demand increases dramatically in Sweden, the need of using the existing electricity grid as efficiently as possible gains more importance. Simultaneously as needs expand, so does production in the form of wind parks and solar parks. This has led to an increase in connection requests at Svenska Kraftnät, the Swedish transmission system operator. The current process for accepting or rejecting these requests is based on the first-come-first-serve principle, where each request is investigated separately. This thesis investigates an alternative way of processing the requests in clusters and optimizing which combination is the best to accept from a technical point of view. To handle this multiobjective combinatorial optimization problem, a multiobjective Genetic algorithm with a Pareto filter is developed. The Genetic Algorithm finds a refined Pareto front containing optimal solutions that are plotted with objective function values. The user can then easily analyze the optimal solutions and decide upon which the final optimal request combination is. The developed Genetic Algorithm reaches a close-optimal Pareto front estimation after exploring between 15-40% of the solution space.

The steel manufacturing industry is one of the largest emitters of CO2, accounting for upwards of 8.8% of all anthropogenic CO2 emissions. The governments are charging taxes on CO2 emissions, which incentivize the industry to further reduce CO2 emissions. At present, much of the CO2, produced in the steel manufacturing process occurs as a result of coke oven and blast furnace gas by-products. As such, two major strategies have been proposed to reduce steel-manufacturing-related CO2 emissions: producing more electricity via optimized combined cycle power plants (CCPP), and converting off-gas by-products into methanol (CBMeOH). The present research consists of an economic and environmental analysis of the status quo, CCPP, and CBMeOH systems for five locations: Ontario, the USA, Finland, Mexico, and China. The economic analysis considered factors such as carbon tax, electricity price, methanol price, electricity carbon intensity, power purchasing parity, and income tax. In the CCPP process, desulphurization is conducted using ProMax with MDEA as the solvent, while the CBMeOH process uses a membrane to separate the bulk H2S, with organic sulfurs such as thiophene being removed via CO2+steam reforming and middle-temperature removal. The results of the economic analysis revealed the CBMeOH plant to be the most profitable in Ontario, the USA, China, and Mexico, while the CCPP system was shown to be the most profitable in Finland. The environmental analysis was conducted using the TRACI, CML-IA, ReCiPe2016, and IMPACT2002+ tools in SimaPro V9, with the results showing the CBMeOH system to be the most environmentally option in Ontario, Finland, and China, and the CCPP system as the most environmentally friendly option in the USA and Mexico. / Dissertation / Doctor of Philosophy (PhD)

Rund 70 % der Energieaufnahme von elektrischen Antriebssystemen wird von quasistationären Antrieben in Transportanlagen hervorgerufen. Eine gesteigerte Energieeffizienz führt neben reduzierten Energiekosten zu weiteren Nutzeffekten, wie z.B. einem verringerten Kühlaufwand, einem kleinerem Bauraum und einer höheren Lebensdauer der Komponenten. Zur Steigerung der Energieeffizienz dieser Transportanlagen werden in dieser Arbeit Methoden zur Auslegung der zugehörigen Antriebssysteme unter besonderer Berücksichtigung des Förderprozesses erarbeitet. Als repräsentative Transportanwendungen werden Pumpen- und Förderbandanlagen untersucht. Dabei wird das gesamte elektromechanische System analysiert, um das volle Energiesparpotenzial zu erfassen – beginnend mit dem Lastprofil des transportierten Förderguts bis hin zur elektrischen Energieversorgung. Hierzu werden alle Systemkomponenten modelliert, um die Verluste in verschiedenen Betriebspunkten im Volllast- und Teillastbereich abzuschätzen. Darauf aufbauend erfolgt die Erarbeitung praktikabler Projektierungshinweise und prozessspezifischer Auswahldiagramme, welche eine schnelle Vorauswahl der energieeffizientesten Antriebstopologie ermöglichen. Da die Verlustmodelle auf frei zugänglichen Datenblattangaben basieren, können die vorgestellten Methoden und Auswahlkriterien bereits in einem frühen Projektierungsstadium zur Auslegung einer energieeffizienten Transportanlage angewandt werden.

L’imagerie basée sur la diffraction des rayons X est une technique non-invasive puissante pour l’identification et caractérisation de matériaux différents. Comparée aux techniques traditionnelles utilisant la transmission des rayons X, elle permet d’extraire des informations beaucoup plus caractéristiques pour le matériau inspecté, comme les positions des pics de Bragg pour des matériaux cristallins et le facteur de forme moléculaire pour les matériaux amorphes. Le potentiel de cette méthode a été reconnu par de nombreuses équipes de recherche et de nombreuses applications comme l’inspection de bagage, le contrôle non-destructif, la détection de drogue et la caractérisation de tissus biologiques ont été proposées. La méthode par dispersion d’énergie (EDXRD) est particulièrement adaptée à ce type d’application car elle permet l’utilisation d’un tube à rayons X conventionnel, l’acquisition du spectre entier en une fois et des architectures parallélisées pour l’inspection d’un objet entier en un temps raisonnable. L’objectif de ce travail est d’optimiser toute la chaîne de caractérisation. L’optimisation comprend deux aspects : l’optimisation du système d’acquisition et du traitement des données. La dernière concerne particulièrement la correction des spectres de diffraction dégradés par le processus d’acquisition. Des méthodes de reconstruction sont proposées et validées sur des spectres simulés et expérimentaux. L’optimisation du système est réalisée en utilisant des facteurs de mérite comme l’efficacité quantique de détection (DQE), le rapport contraste sur bruit (CNR) et les courbes de caractéristiques opérationnelles de réception (ROC).La première application choisie, c’est l’imagerie du sein basée sur la diffraction qui a pour but de distinguer des tissus cancéreux des tissus sains. Deux configurations de collimation sans multiplexage combinant EDXRD et ADXRD sont proposées suite au processus d’optimisation. Une étude de simulation du système entier et d’un fantôme de sein a été réalisée afin de déterminer la dose requise pour la détection d’un petit carcinome de 4 mm. La deuxième application concerne la détection de matériaux illicites pendant le contrôle de sécurité. L’intérêt possible d’un système de collimation multiplexé a été étudié. / X-ray diffraction imaging is a powerful noninvasive technique to identify or characterize different materials. Compared to traditional techniques using X-ray transmission, it allows to extract more material characteristic information, such as the Bragg peak positions for crystalline materials as well as the molecular form factor for amorphous materials. The potential of this technique has been recognized by many researchers and numerous applications such as luggage inspection, nondestructive testing, drug detection and biological tissue characterization have been proposed.The method of energy dispersive X-ray diffraction (EDXRD) is particularly suited for this type of applications as it allows the use of a conventional X-ray tube, the acquisition of the whole spectrum at the same time and parallelized architectures to inspect an entire object in a reasonable time. The purpose of the present work is to optimize the whole material characterization chain. Optimization comprises two aspects: optimization of the acquisition system and of data processing. The last one concerns especially the correction of diffraction pattern degraded by acquisition process. Reconstruction methods are proposed and validated on simulated and experimental spectra. System optimization is realized using figures of merit such as detective quantum efficiency (DQE), contrast to noise ratio (CNR) and receiver operating characteristic (ROC) curves.The first chosen application is XRD based breast imaging which aims to distinguish cancerous tissues from healthy tissues. Two non-multiplexed collimation configurations combining EDXRD and ADXRD are proposed after optimization procedure. A simulation study of the whole system and a breast phantom was realized to determine the required dose to detect a 4 mm carcinoma nodule. The second application concerns detection of illicit materials during security check. The possible benefit of a multiplexed collimation system was examined.