<p>Sound maintenance strategies and planning are of crucial importance for wind power systems, and especially for offshore locations. In the last decades, an increased awareness of the impact of human living on the environment has emerged in the world. The importance of developing renewable energy is today highly recognized and energy policies have been adopted towards this development. Wind energy has been the strongest growing renewable source of energy this last decade. Wind power is now developing offshore where sites are available and benefits from strong and steady wind. However, the initial investments are larger than onshore, and operation and maintenance costs may be substantially higher due to transportation costs for maintenance and accessibility constrained by the weather.</p><p>Operational costs can be significantly reduced by optimizing decisions for maintenance strategies and maintenance planning. This is especially important for offshore wind power systems to reduce the high economic risks related to the uncertainties on the accessibility and reliability of wind turbines.</p><p>This thesis proposes decision models for cost efficient maintenance planning and maintenance strategies for wind power systems. One model is proposed on the maintenance planning of service maintenance activities. Two models investigate the benefits of condition based maintenance strategies for the drive train and for the blades of wind turbines, respectively. Moreover, a model is proposed to optimize the inspection interval for the blade. Maintenance strategies for small components are also presented with simple models for component redundancy and age replacement.</p><p>The models are tested in case studies and sensitivity analyses are performed for parameters of interests. The results show that maintenance costs can be significantly reduced through optimizing the maintenance strategies and the maintenance planning.</p>

This dissertation provides several techniques for solving a class of nonconvex optimization problems that arise in the thermal analysis of electronic chip packages. The topic is of interest because in systems containing delicate electronic components both performance and reliability are impacted by thermal behavior. A modeling paradigm, called Compact Thermal Modeling (CTM), has been demonstrated to show promise for accurately estimating steady state thermal behavior without resorting to computationally intensive finite element models or expensive direct experimentation. The CTM is a network model that gives rise to a nonconvex optimization problem. A solution to this nonconvex optimization problem provides a reasonably accurate characterization of the steady state temperature profile the chip will attain under arbitrary boundary conditions, which allows the system designer to model the application of a wide range of thermal design strategies with useful accuracy at reasonable computational cost. This thesis explores several approaches to solving the optimization problem. We present a heuristic technique that is an adaptation of the classical coordinate search method that has been adapted to run efficiently by exploiting the algebraic structure of the problem. Further, the heuristic is able to avoid stalling in poor local optima by using a partitioning scheme that follows from an examination of special structure in the problem's feasible region. We next present several exact approaches using a globally optimal method based on the Reformulation Linearization Technique (RLT). This approach generates and then solves convex relaxations of the original problem, tightening the approximations within a branch and bound framework. We then explore several approaches to improving the performance of the RLT technique by introducing variable substitutions and valid inequalities, which tighten the convex relaxations. Computational results, conclusions, and recommendations for further research are also provided.

Green technologies have recently gained interest for many reasons. Economic factors in conjunction with an increased social desire to reduce our environmental impact on the Earth have created a desire for more environmentally friendly technologies, especially automotive technologies such as the electric car. While public interest in electric vehicles is growing, there are a number of challenges which must first be addressed before their widespread adoption is possible. Cost, longevity, and range are all important factors which need to be addressed for electric vehicles to compete directly with their gasoline counterparts. By more efficiently using the energy stored within the battery pack, some of these issues can be addressed. This study focuses on the thermal management systems for electric vehicles and the application of design optimization in the early design phase considering the pack in its entirety. A liquid cooling system is considered for a current generation electric vehicle, with time dependent heat generation rates within the battery cells based on vehicle operating conditions. Identifying the most efficient distribution of cooling within the battery pack to achieve uniform temperature is the objective of optimization. Simulations were performed on a complete battery pack model, featuring 288 battery cells and 144 cooling plates. Anisotropic material properties and non-uniform heat generation rates are included as well as energy demands based on a representative vehicle drive cycle. Results have shown that through design optimization, the standard deviation of temperature within the battery cells can be improved by as much as 80% when compared to a conventional design. The standard deviation of temperature saw improvement from an average of 0.2828 K for a conventional design to 0.05318 K after optimization. These results are specific to the given battery pack construction, battery cell, and cooling type. The method of modeling and analysis can be extended to many battery geometries and cooling technologies in the future. Application of design optimization to the problem of thermal iii management system design can yield significant improvements to battery pack thermal management, and thereby incrementally improve the efficiency of electrified vehicles. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2013-10-30 10:49:28.639

Fjärrvärmebranschen i Sverige har sedan mitten av 1990-talet varit i en stagnationsfas, där mängden producerad och förbrukad energi i stort sett varit konstant, trots stora investeringar i fjärrvärmenätet. Samtidigt har den höga andelen återvinning gjort att det råder brist på avfallsbränsle till energiåtervinning i Sverige. I kombination med hög konkurrens om avfallsbränslet har detta medfört att aktörerna på marknaden tvingats se sig om efter nya vägar att tillfredsställa behovet av bränsle, vilket lett till att det idag importeras stora mängder avfall för energiåtervinning från utlandet. Vid Vattenfalls fjärrvärmeverk i Uppsala har det mellan åren 2006-2013 återvunnits i snitt drygt 360 000 ton avfall per år. En stor del av detta hämtas in från lokala kunder, både kommuner och privata aktörer, men man har på senare tid även behövt börja importera avfall för att tillfredsställa energibehovet. Vattenfall i Uppsala har valt att framför allt rikta blickarna mot de brittiska öarna, varifrån avfall levereras med fartyg till hamnen i Hargshamn. Anläggningen består av tre förbränningsugnar, block 1, 4 och 5, där block 1 och 4 har en gemensam bunker där avfall förvaras innan förbränning, och den nyare block 5 har en egen bunker. I detta arbete har ett planeringsverktyg skapats i Microsoft Excel. Planeringsverktyget, Fuel Logistics Optimal Planner (FLOP), använder linjärprogrammering för att generera en optimal avfallsblandning som givet ugnarnas effekt maximerar anläggningens intäkter från kunderna. Det ger även svar på frågorna i vilken bunker en given kund ska tippa sitt avfall en given vecka, ger en optimal lagernivå för lagret i Hargshamn, samt information om under vilka veckor båtar med avfall bör anlända hamnen. FLOP stöttar logistikansvarige vid anläggningen i arbetet att skapa en veckoplanering som ligger till grund för hur mycket avfall som ska beställas från de individuella kunderna under nästkommande vecka. En jämförelse mot planeringen och utfallet för 2013 visar att FLOP genererar 2,97 % högre intäkter än den tidigare planeringen, och 0,17 % högre intäkter gentemot det verkliga utfallet för året. Detta trots att en ugn, block 3, togs ur bruk under året och således inte används i FLOP. Under 2013 stod block 3 för ungefär 3,4 % av all förbränning som skedde vid anläggningen. / The first district heating-system in Sweden was implemented in the city of Karlstad in 1948 and the favorable environment for this technology lead to a quick expansion that lasted all the way to the mid 1990’s. Since then, however, the industry has stagnated due to climate change, an increase in energy efficient buildings and market saturation. This has led to the need for new strategies for the parties involved with district heating. In Sweden, many of the incinerators used for district heating use waste fuel as the main fuel source. The increased recycling of mainly household waste and the high competition on the waste incineration market has forced the affected parties to look abroad for waste fuel. Vattenfall’s waste incineration plant in Uppsala uses waste fuel and peat as main fuel for the incinerators, and between the years 2006-2013 the average annual amount of waste fuel incinerated amounted to around 360 000 metric tons. There are three incinerators, block 1, 4 and 5, connected to two bunkers storing waste fuel. Blocks 1 and 4 get their fuel from one bunker, and the newer block 5 has its own bunker attached. From the bunkers, the fuel is distributed to the incinerators by an overhead crane. The fuel is brought in from local customers at the customer’s expense for energy recovery. On top of this, Vattenfall also owns a storage facility in Hargshamn, to which it imports waste fuel from customers predominantly from the British Isles. Vattenfall then transports this waste fuel to the incineration plant whenever there is a shortage of fuel from local customers. Today, the logistics manager at the facility receives a yearly plan of all the local, contracted customers with information on how much waste each individual customer should deliver each month of the year. Every week, the logistics manager then breaks down this plan into a weekly plan, before sending out an order to each customer detailing how much waste they are expected to deliver during the subsequent week. The customers then deliver the specified amount of waste and tip it into either of the two bunkers at the facility. If one bunker is being utilized more than the other, the operators of the overhead cranes can signal to the drivers of the waste fuel trucks not to use that bunker for the time being. It is also up to the operators to make sure they feed the incinerators with an appropriate mix of fuel to keep the incinerators operating at a suitable rate. In this work, we have created a planning tool, Fuel Logistics Optimal Planner (FLOP), using Microsoft Excel and the OpenSolver add-in to yield an optimal fuel mix in the respective bunkers in regard to maximizing the overall revenue from the customers. FLOP also presents the user with an optimal storage level of waste fuel at the storage facility in Hargshamn, and informs the logistics manager about which weeks new shipments of waste fuel should arrive at the warehouse. A linear programming model was created to answer these questions. The model is based on the blending problem to get the optimal waste fuel mix to the bunkers, but it has also been influenced by the inventory management problem to make sure the storage level in Hargshamn is optimal. Backtesting FLOP against the planned and actual revenue of 2013 shows that FLOP increases the planned revenue by 2.97 % and surpasses the actual revenue by 0.17 %. During parts of 2013 a fourth incinerator, block 3, was operative at the plant, responsible for about 3.4 % of the total weight of waste incinerated. This incinerator has been omitted in the comparison.

Approved for public release; distribution is unlimited / Surgical technicians at Naval hospitals provide a host of services related to surgical procedures that include handing instruments to surgeons, assisting operating room nurses, prepping and cleaning operating rooms, and administrative duties. At the Naval Medical Center San Diego (NMCSD), there are 83 surgical technicians that must be scheduled for these duties. The three military and one civilian hospital interviewed for this thesis manually schedule these duties. Weaknesses of these manual schedules exposed during interviews at these hospitals include assignment inequities and the time needed to create them. This thesis reports on an optimization based and spreadsheet implemented tool developed to schedule surgical technicians for both daily and weekly duties at a Naval hospital. We demonstrate the tool for the surgical technician department at NMCSD. The schedulers at NMCSD verify the utility of the developed tool and cite a drastic reduction in the time required to generate timely, equitable, and accurate schedules. The study also investigates historical operating room usage data and makes suggestions for improving scheduling practices based on these data. / Commander (Select), United States Navy

The objective of the project was to develop models and methods suitable for computer implementation. In particular, this work has been devoted to generation scheduling of a power system with a sizeable amount. of hydro energy. Optimal operation planning of hydrothermal power systems aims at minimizing incurred production costs while supplying customer de­mand. The planning horizon may vary from one day to several years and the associated planning problems are categorized as short term, seasonal and long term operation planning. The topic of this thesis is short term operation planning. In this planning, it is necessary to use detailed models of the different parts of the power system. These include models of cas­caded reservoirs in a multi-river system as we11 as a representa­tion of the nonlinear generating characteristics of the hydro plants. he thermal generating units are modelled using linear production cost curves and by recognizing various technical constraints asso­ciated with the operation of these plants. Effects on the optimal operating strategy caused by interregional transmission capacity limitations are also accounted for by the model. These constraints are modelled to main1y affect the hydro plant operation. / <p>QC 20161207</p>

To achieve “provisioning elasticity”, the cloud needs to manage its available resources on demand. A-priori, static, VM provisioning introduces no runtime overhead but fails to handle unanticipated changes in resource demands. Dynamic provisioning addresses this problem but introduces runtime overhead. To avoid sub-optimal provisioning my PhD thesis adopts a hybrid approach that combines static and dynamic provisioning. The idea is to adapt an initial static placement of VMs in response to evolving load characteristics. My work is focused on broadening the applicability of clouds by looking at how the infrastructure can be more effectively used to support historically atypical applications (e.g. those that are interactive in nature with tighter QoS constraints). To accomplish this I have developed a family of related algorithms that collectively improve resource sharing on physical machines to permit load variation to be better addressed and to lessen the probability of VM interference due to resource contention. The family includes three core dynamic provisioning algorithms. The first algorithm provides for the short-term, controlled sharing of resources between co-hosted VMs, the second identifies pairs (and by extrapolation larger groups) of VMs that are predicted to be "compatible" in terms of the resources they need. This allows the cloud provider to do co-location to make the first algorithm more effective. The final, third, algorithm deals with under-utilized physical machines by re-packing the VMs on those machines while also considering their compatibility. This final algorithm both addresses the possibility of the second algorithm creating underutilized machines as a result of pairing and migration and also handles underutilization arising from “holes” left by the termination of short-duration VMs (another form of atypical VM application). I have also created a surprisingly simple static provisioning algorithm that considers compatibility to minimize VM interference that can be used before my dynamic algorithms. My evaluation is primarily simulation-based though I have also implemented the core algorithms on a small test-bed system to ensure correctness. The results obtained from my simulation experiments suggest that hybrid static and dynamic provisioning approaches are both feasible and should be effective supporting a broad range of applications in cloud environments. / February 2017

Coronary heart disease is a leading cause of death among Americans for which coronary artery bypass graft (CABG) surgery is a standard surgical treatment. The success of CABG surgery is impaired by the compliance mismatch between vascular grafts and native vessels. Tissue engineered vascular grafts (TEVGs) have the potential to be compliance matched and thereby reduce the risk of graft failure. Glutaraldehyde (GLUT) vapor-crosslinked gelatin/fibrinogen constructs were fabricated and mechanically tested in a previous study by our research group at 2, 8, and 24 hours of GLUT vapor exposure. Constructs electrospun with tropoelastin in addition to gelatin and fibrinogen fibers were also fabricated and tested for the same amounts of GLUT vapor exposure. The current study details a computational method that was developed to predict the material properties of our constructs for crosslinking times between 2 and 24 hours by interpolation and regression of the 2, 8, and 24 hour crosslinking time data. Matlab and Abaqus were used to determine the optimal combination of fabrication parameters to produce compliance matched constructs. The validity of the method was first tested on a 16 hour crosslinked gelatin/fibrinogen construct of 130μm thickness. The predicted compliance was 0.00059 mmHg-1 while the experimentally determined compliance was 0.00065 mmHg-1, a relative difference of 9.2%. Prior data in our laboratory has shown the compliance of the left anterior descending porcine coronary (LADC) artery to be 0.00071 ± 0.0003 mmHg-1. The optimization algorithm predicts that a 258μm thick construct that is GLUT vapor crosslinked for 8.1 hours would match LADC compliance. The algorithm was expanded to predict the compliance of constructs consisting of alternating layers of tropoelastin/gelatin/fibrinogen and gelatin/fibrinogen. A four layered graft was designed and fabricated using this optimization routine. The layered construct was found to have a compliance of 0.00051 mmHg-1 while the predicted compliance was 0.00061 mmHg-1, a difference of 16%. This is a promising method for matching the compliance of our TEVGs with the native tissue of various specimens.

Master of Science / Department of Electrical and Computer Engineering / Anil Pahwa / Due to the ongoing changes in the structure of the electricity markets, distribution networks have developed an appealing potential for housing distributed generation (DG). In order to make the most out of the present distribution network, this project report verifies the results and method developed in a paper (Optimal Allocation of Embedded Generation on Distribution Networks) by A. Kean and M. O’Malley, which discusses an efficient way of incorporating DG in the current power system. The methodology under consideration elaborates on how certain constraints should be adopted that will lead toward optimally sizing and placing DG in the network under examination. Along with that, the effect of voltage rise and short circuit current are observed which shows that a certain allocation to some buses will cause a sudden rise in voltage and short circuit levels throughout the network. Furthermore, the adopted methodology with its relative constraints is solved using linear programming. Linear programming provides a more accurate allocation than its heuristic counterparts when it comes to embedding DG in smaller networks. The adopted methodology is then applied to a section of the Irish rural distribution network and the results pinpoint that appropriate placement of the DG will pave the way toward higher levels of penetration. The results obtained showed the same pattern as those recorded in the aforementioned source paper, there were only minor differences that are the result of using different software’s than those that were used by the authors of the paper.

This thesis presents two methods for initial design propeller optimization using constrained non- linear programming. The process uses the Nelder-Mead simplex algorithm. The Wageningen B-series optimal propeller selection is presented along with the combined annular momentum theory and blade element theory optimization. Both techniques require preliminary hull and engine design characteristics, but do not necessitate extensive background knowledge of pro- pellers and their calculations. A comparison of the two methods shows the combined annular momentum theory and blade element theory optimization produces the more e cient propeller. The optimization programs were designed with a graphic user interface implemented in the programming language Python.