Dosimetric Evaluation of Metal Artefact Reduction using Metal Artefact Reduction (MAR) Algorithm and Dual-energy Computed Tomography (CT) Method

Purpose: Computed Tomography (CT) is one of the standard diagnostic imaging modalities for the evaluation of a patient’s medical condition. In comparison to other imaging modalities such as Magnetic Resonance Imaging (MRI), CT is a fast acquisition imaging device with higher spatial resolution and higher contrast-to-noise ratio (CNR) for bony structures. CT images are presented through a gray scale of independent values in Hounsfield units (HU). High HU-valued materials represent higher density. High density materials, such as metal, tend to erroneously increase the HU values around it due to reconstruction software limitations. This problem of increased HU values due to metal presence is referred to as metal artefacts. Hip prostheses, dental fillings, aneurysm clips, and spinal clips are a few examples of metal objects that are of clinical relevance. These implants create artefacts such as beam hardening and photon starvation that distort CT images and degrade image quality. This is of great significance because the distortions may cause improper evaluation of images and inaccurate dose calculation in the treatment planning system. Different algorithms are being developed to reduce these artefacts for better image quality for both diagnostic and therapeutic purposes. However, very limited information is available about the effect of artefact correction on dose calculation accuracy. This research study evaluates the dosimetric effect of metal artefact reduction algorithms on severe artefacts on CT images. This study uses Gemstone Spectral Imaging (GSI)-based MAR algorithm, projection-based Metal Artefact Reduction (MAR) algorithm, and the Dual-Energy method.

Materials and Methods: The Gemstone Spectral Imaging (GSI)-based and SMART Metal Artefact Reduction (MAR) algorithms are metal artefact reduction protocols embedded in two different CT scanner models by General Electric (GE), and the Dual-Energy Imaging Method was developed at Duke University. All three approaches were applied in this research for dosimetric evaluation on CT images with severe metal artefacts. The first part of the research used a water phantom with four iodine syringes. Two sets of plans, multi-arc plans and single-arc plans, using the Volumetric Modulated Arc therapy (VMAT) technique were designed to avoid or minimize influences from high-density objects. The second part of the research used projection-based MAR Algorithm and the Dual-Energy Method. Calculated Doses (Mean, Minimum, and Maximum Doses) to the planning treatment volume (PTV) were compared and homogeneity index (HI) calculated.

Results: (1) Without the GSI-based MAR application, a percent error between mean dose and the absolute dose ranging from 3.4-5.7% per fraction was observed. In contrast, the error was decreased to a range of 0.09-2.3% per fraction with the GSI-based MAR algorithm. There was a percent difference ranging from 1.7-4.2% per fraction between with and without using the GSI-based MAR algorithm. (2) A range of 0.1-3.2% difference was observed for the maximum dose values, 1.5-10.4% for minimum dose difference, and 1.4-1.7% difference on the mean doses. Homogeneity indexes (HI) ranging from 0.068-0.065 for dual-energy method and 0.063-0.141 with projection-based MAR algorithm were also calculated.

Conclusion: (1) Percent error without using the GSI-based MAR algorithm may deviate as high as 5.7%. This error invalidates the goal of Radiation Therapy to provide a more precise treatment. Thus, GSI-based MAR algorithm was desirable due to its better dose calculation accuracy. (2) Based on direct numerical observation, there was no apparent deviation between the mean doses of different techniques but deviation was evident on the maximum and minimum doses. The HI for the dual-energy method almost achieved the desirable null values. In conclusion, the Dual-Energy method gave better dose calculation accuracy to the planning treatment volume (PTV) for images with metal artefacts than with or without GE MAR Algorithm.

BIDIRECTIONAL AC-DC CONVERTERS USING ONE CYCLE CONTROL (OCC) FOR ELECTRIC VEHICLES

The electric vehicle (EV) market has seen a rapid growth in the recent past. With an increase in the number of electric vehicles on road, there is an increase in the number of high capacity battery banks interfacing the grid. The battery bank of an EV, besides being the fuel tank, is also a huge energy storage unit. Presently, it is used only when the vehicle is being driven and remains idle for rest of the time, rendering it underutilized. Whereas on the other hand, there is a need of large energy storage units in the grid to filter out the fluctuations of supply and demand during a day. EVs can help bridge this gap. The EV battery bank can be used to store the excess energy from the grid to vehicle (G2V) or supply stored energy from the vehicle to grid (V2G ), when required. To let power flow happen, in both directions, a bidirectional AC-DC converter is required. This thesis concentrates on the bidirectional AC-DC converters which have a control on power flow in all four quadrants for the application of EV battery interfacing with the grid. This thesis presents a bidirectional interleaved full bridge converter topology. This helps in increasing the power processing and current handling capability of the converter which makes it suitable for the purpose of EVs. Further, the benefit of using the interleaved topology is that it increases the power density of the converter. This ensures optimization of space usage with the same power handling capacity. The proposed interleaved converter consists of two full bridges. The corresponding gate pulses of each switch, in one cell, are phase shifted by 180 degrees from those of the other cell. The proposed converter control is based on the one-cycle controller. To meet the challenge of new requirements of reactive power handling capabilities for grid connected converters, posed by the utilities, the controller is modified to make it suitable to process the reactive power. A fictitious current derived from the grid voltage is introduced in the controller, which controls the converter performance. The current references are generated using the second order generalized integrators (SOGI) and phase locked loop (PLL). A digital implementation of the proposed control ii scheme is developed and implemented using DSP hardware. The simulated and experimental results, based on the converter topology and control technique discussed here, are presented to show the performance of the proposed theory.

The Economic Case for Electric Mining Equipment and Technical Considerations Relating to their Implementation

Underground hardrock mining can be very energy intensive and in large part this can be attributed to the power consumption of underground ventilation systems. In general, the power consumed by a mine’s ventilation system and its overall scale are closely related to the amount of diesel power in operation. This is because diesel exhaust is a major source of underground air pollution, including diesel particulate matter (DPM), NO2 and heat, and because regulations tie air volumes to diesel engines. Furthermore, assuming the size of airways remains constant, the power consumption of the main system increases exponentially with the volume of air supplied to the mine. Therefore large diesel fleets lead to increased energy consumption and can also necessitate large capital expenditures on ventilation infrastructure in order to manage power requirements. Meeting ventilation requirements for equipment in a heading can result in a similar scenario with the biggest pieces leading to higher energy consumption and potentially necessitating larger ventilation tubing and taller drifts. Depending on the climate where the mine is located, large volumes of air can have a third impact on ventilation costs if heating or cooling the air is necessary. Annual heating and cooling costs, as well as the cost of the associated infrastructure, are directly related to the volume of air sent underground. This thesis considers electric mining equipment as a means for reducing the intensity and cost of energy consumption at underground, hardrock mines. Potentially, electric equipment could greatly reduce the volume of air needed to ventilate an entire mine as well as individual headings because they do not emit many of the contaminants found in diesel exhaust and because regulations do not connect air volumes to electric motors. Because of the exponential relationship between power consumption and air volumes, this could greatly reduce the amount of power required for mine ventilation as well as the capital cost of ventilation infrastructure. As heating and cooling costs are also directly linked to air volumes, the cost and energy intensity of heating and cooling the air would also be significantly reduced. A further incentive is that powering equipment from the grid is substantially cheaper than fuelling them with diesel and can also produce far fewer GHGs. Therefore, by eliminating diesel from the underground workers will enjoy safer working conditions and operators and society at large will gain from a smaller impact on the environment. Despite their significant potential, in order to produce a credible economic assessment of electric mining equipment their impact on underground systems must be understood and considered in their evaluation. Accordingly, a good deal of this thesis reviews technical considerations related to the use of electric mining equipment, especially ones that impact the economics of their implementation. The goal of this thesis will then be to present the economic potential of implementing the equipment, as well as to outline the key inputs which are necessary to support an evaluation and to provide a model and an approach which can be used by others if the relevant information is available and acceptable assumptions can be made.

Digital geometric-sequence control technique for bidirectional dual active bridge DC-DC converters used in future electric vehicles

Bidirectional DC-DC converters are widely used in different applications such as energy storage systems, Electric Vehicles (EVs), UPS, etc. In particular, future EVs require bidirectional power flow in order to integrate energy storage units into smart grids. These bidirectional power converters provide Grid to Vehicle (V2G)/ Vehicle to Grid (G2V) power flow capability for future EVs. Generally, there are two control loops used for bidirectional DC-DC converters: The inner current loop and The outer loop. The control of DAB converters used in EVs are proved to be challenging due to the wide range of operating conditions and non-linear behavior of the converter. In this thesis, the precise mathematical model of the converter is derived and non-linear control schemes are proposed for the control system of bidirectional DC-DC converters based on the derived model. The proposed inner current control technique is developed based on a novel Geometric-Sequence Control (GSC) approach. The proposed control technique offers significantly improved performance as compared to one for conventional control approaches. The proposed technique utilizes a simple control algorithm which saves on the computational resources. Therefore, it has higher reliability, which is essential in this application. Although, the proposed control technique is based on the mathematical model of the converter, its robustness against parameter uncertainties is proven. Three different control modes for charging the traction batteries in EVs are investigated in this thesis: the voltage mode control, the current mode control, and the power mode control. The outer loop control is determined by each of the three control modes. The structure of the outer control loop provides the current reference for the inner current loop. Comprehensive computer simulations have been conducted in order to evaluate the performance of the proposed control methods. In addition, the proposed control have been verified on a 3.3 kW experimental prototype. Simulation and experimental results show the superior performance of the proposed control techniques over the conventional ones.

Electron acoustic soliton energy of the Kadomtsev-Petviashvili equation in the Earth’s magnetotail region at critical ion density

The nonlinear properties of small amplitude electron-acoustic solitary waves (EAWs) in a homogeneous system of unmagnetized collisionless plasma consisted of a cold electron fluid and isothermal ions with two different temperatures obeying Boltzmann type distributions have been investigated. A reductive perturbation method was employed to obtain the Kadomstev-Petviashvili (KP) equation. At the critical ion density, the KP equation is not appropriate for describing the system. Hence, a new set of stretched coordinates
is considered to derive the modified KP equation. Moreover, the solitary solution, soliton energy and the associated electric field at the critical ion density were computed. The present investigation can be of relevance to the electrostatic solitary structures observed in various space plasma environments, such as Earth’s magnetotail region.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Optimal day-ahead scheduling of integrated urban energy systems

An optimal day-ahead scheduling method (ODSM) for the integrated urban energy system (IUES) is introduced, which considers the reconfigurable capability of an electric distribution network. The hourly topology of a distribution network, a natural gas network, the energy centers including the combined heat and power (CHP) units, different energy conversion devices and demand responsive loads (DRLs), are optimized to minimize the day-ahead operation cost of the IUES. The hourly reconfigurable capability of the electric distribution network utilizing remotely controlled switches (RCSs) is explored and discussed. The operational constraints from the unbalanced three-phase electric distribution network, the natural gas network, and the energy centers are considered. The interactions between the electric distribution network and the natural gas network take place through conversion of energy among different energy vectors in the energy centers. An energy conversion analysis model for the energy center was developed based on the energy hub model. A hybrid optimization method based on genetic algorithm (GA) and a nonlinear interior point method (IPM) is utilized to solve the ODSM model. Numerical studies demonstrate that the proposed ODSM is able to provide the IUES with an effective and economical day-ahead scheduling scheme and reduce the operational cost of the IUES.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Catalysing the Irish Energy Transition: Capacities and Challenges

The transition to a “low carbon, climate resilient and environmentally sustainable economy by the end of the 
year 2050” has been conceptualised as the “national transition objective” in the Irish Climate Action and Low Carbon Development Bill, passed in late 2015. This has raised a myriad of questions over how this can be operationalised and resourced and whether it can maintain political momentum. This paper assesses the utility of framings informed by the transitions (MLP) and technological innovation systems perspectives in contributing to transformative societal processes, by examining their application in an Irish case study on policy and technology. Through a qualitative exploration of the broader societal and policy context of the energy sector and a more detailed examination of the innovation systems of selected niche technologies (bioenergy and electric vehicles), the Irish case study sought to identify potential catalysts for a sustainability transition in the energy sector in Ireland: where these exist, how these are being built or enabled, and barriers to change. Following a discussion on the theoretical approaches used, a description will be given of how these were applied in the conducting of the research on transition in Ireland case study and the key findings which emerged. A critical reflection will then be made on the utility of these perspectives (as applied) to contribute to broader processes of societal transformation in Ireland.

A methodology for building energy modelling and calibration in warm climates

PEDRINI, Aldomar; WESTPHAL, F. S.; LAMBERT, R.. A methodology for building energy modelling and calibration in warm climates. Building And Environment, Australia, n. 37, p.903-912, 2002. Disponível em: . Acesso em: 04 out. 2010.

Electric Vehicles as Grid Resources

Thesis (Ph.D.)--University of Washington, 2016-08

Energy efficient measures to reduce electrical energy use in commercial buildings in New York City

GEA Consulting Engineers, acting as the design engineers, was hired by the owner, East Village 207 Residential LLC2 for energy modeling for compliance with LEED NC V3 -- This report details the results of the energy simulation done with the 100% construction documents -- This report only refers to entities within the LEED3 project boundary -- The project consists of a new eight-story high-end residential condominium building with 81 units, as shown in illustration 1, and approximately 117,905 GSF, equivalent to 10,953.73 m2, is located at 211 E 13th Street in New York, NY -- The residential portion of the building will function 24-7 -- The design goal is to utilize energy efficient measures to reduce electrical energy use and aims to achieve LEED certification -- LEED EA Credit 14 requires a building to demonstrate a percentage improvement in the proposed building performance compared with the baseline building -- The Credit rewards 1 point for achieving 12% reduction in energy costs -- Additionally, the Credit rewards another point for each subsequent reduction of 2% in the building’s energy cost

An iterative analytical design framework for the optimal designing of an off-grid renewable energy based hybrid smart micro-grid

Creative ways of utilising renewable energy sources in electricity generation especially in remote areas and particularly in countries depending on imported energy, while increasing energy security and reducing cost of such isolated off-grid systems, is becoming an urgently needed necessity for the effective strategic planning of Energy Systems. The aim of this research project was to design and implement a new decision support framework for the optimal design of hybrid micro grids considering different types of different technologies, where the design objective is to minimize the total cost of the hybrid micro grid while at the same time satisfying the required electric demand. Results of a comprehensive literature review, of existing analytical, decision support tools and literature on HPS, has identified the gaps and the necessary conceptual parts of an analytical decision support framework. As a result this research proposes and reports an Iterative Analytical Design Framework (IADF) and its implementation for the optimal design of an Off-grid renewable energy based hybrid smart micro-grid (OGREH-SμG) with intra and inter-grid (μG2μG & μG2G) synchronization capabilities and a novel storage technique. The modelling design and simulations were based on simulations conducted using HOMER Energy and MatLab/SIMULINK, Energy Planning and Design software platforms. The design, experimental proof of concept, verification and simulation of a new storage concept incorporating Hydrogen Peroxide (H2O2) fuel cell is also reported. The implementation of the smart components consisting Raspberry Pi that is devised and programmed for the semi-smart energy management framework (a novel control strategy, including synchronization capabilities) of the OGREH-SμG are also detailed and reported. The hybrid μG was designed and implemented as a case study for the Bayir/Jordan area. This research has provided an alternative decision support tool to solve Renewable Energy Integration for the optimal number, type and size of components to configure the hybrid μG. In addition this research has formulated and reported a linear cost function to mathematically verify computer based simulations and fine tune the solutions in the iterative framework and concluded that such solutions converge to a correct optimal approximation when considering the properties of the problem. As a result of this investigation it has been demonstrated that, the implemented and reported OGREH-SμG design incorporates wind and sun powered generation complemented with batteries, two fuel cell units and a diesel generator is a unique approach to Utilizing indigenous renewable energy with a capability of being able to synchronize with other μ-grids is the most effective and optimal way of electrifying developing countries with fewer resources in a sustainable way, with minimum impact on the environment while also achieving reductions in GHG. The dissertation concludes with suggested extensions to this work in the future.

A methodology for building energy modelling and calibration in warm climates

PEDRINI, Aldomar; WESTPHAL, F. S.; LAMBERT, R.. A methodology for building energy modelling and calibration in warm climates. Building And Environment, Australia, n. 37, p.903-912, 2002. Disponível em: . Acesso em: 04 out. 2010.

Renewable energy scenarios for South Karelia – non-industrial energy demands

Tässä diplomityössä tarkastellaan täysin uusiutuvaa energiajärjestelmää Etelä-Karjalan maakunnan alueella, mikä onkin jo tällä hetkellä Suomen uusiutuvin maakunta. Diplomityössä tarkastellaan julkisen sektorin, liikenteen ja rakennusten energian kulutusta mutta teollisuuden energiankäyttö jätetään tarkastelun ulkopuolelle. Työssä tutustutaan tämän hetken Etelä-Karjalan energiajärjestelmään ja sen perusteella tehdään referenssi-skenaario. Tulevaisuuden skenaariot tehdään vuosille 2030 ja 2050. Tulevaisuuden skenaarioissa muutos keskittyy järjestelmän sähköistymiseen ja uusiutuvien tuotantomuotojen integroimiseen järjestelmään. Sähköistyminen kasvattaa sähkönkulutusta, joka pyritään kattamaan uusiutuvilla tuotantomuodoilla, lähinnä tuuli- ja aurinkovoimalla. Liikennesektori rajataan kumipyöräliikenteeseen ja sen muutos tulee olemaan haastavin ja aikaa vievin. Muutokseen pyritään liikennepolttoaineiden tuotannolla maakunnassa sekä sähköautoilulla. Uusiutuva energiajärjestelmä tarvitsee tuotannon ja kysynnän joustoa sekä älyä järjestelmältä. Työssä tarkastellaan myös järjestelmän kustannuksia sekä työllisyysvaikutuksia.