Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

An oscillating water column (OWC) wave energy converter (WEC) is a device designed to extract energy from waves at sea by using the water to move trapped air and thus drive an air turbine. Because the incident waves and the force caused by the power take-off (PTO) interact, control of the power take off (PTO) system can increase the total energy converted. A numerical model was developed to study the interaction of an OWC with the water and other structures around it. ANSYS AQWA is used here to find the effects on the water surface in and around the central column of a five-column, breakwater-mounted OWC. For open OWC structures, coupled modes were seen which lead to sensitivity to incident wave period and direction. The frequency-domain displacements of the internal water surface of the central column were turned into a force-displacement, time-domain model in MATLAB Simulink using a state space approximation. The model of the hydrodynamics was then combined with the thermodynamic and turbine equations for a Wells turbine. A baseline situation was tested for fixed turbine speed operation using a wave climate for a region off the north coast of Devon. A linear feedforward controller and a controller based on maximising turbine efficiency were tested for the system. The linear controller was optimised to find the combination of turbine speed offset and proportional constant that gave maximum energy in the most energy abundant sea state. This increased the converted energy by 31% in comparison to the fixed speed case. For the turbine efficiency control method, the increase was 36%. Energy conversion increases are therefore clearly possible using simple controllers. If increased converted energy is the only criterion for controller choice, then the turbine efficiency control is the best method, however the control action involves using very slow turbine speeds which may not be physically desirable.

This research investigates the effects that geometry and control have on the absorption characteristics of active wavemakers and looks at the feasibility of modelling these wavemakers in commercial computational fluid dynamic software. This thesis presents the hydrodynamic coefficients for four different types of wavemakers. The absorption characteristics of these wavemakers are analysed using different combinations of control impedance coefficients. The effect of combining both geometry and control is then investigated. Results, quantifying the absorption characteristics are then presented. It is shown that the amount of absorption for a given paddle differs greatly depending on the choice of control coefficients used to implement complex conjugate control. Increased absorption can be achieved over a broader bandwidth of frequencies when the geometry of the wavemaker is optimised for one specific frequency and the control impedance is optimised for an alternate frequency. In conjunction to this theoretical study, a numerical investigation is conducted in order to verify and validate two commercial computational fluid dynamic codes' suitability to model the previously discussed absorbing wavemakers. ANSYS CFX and FLOW3D are used to model a physical wavemaker. Both are rigorously verified for discretisation errors and CFX is validated against linear wavemaker theory. Results show good agreement and prediction of the free surface close to the oscillating wavemaker, but problems with wave height attenuation and excessive run times were encountered.

The AOE Accumulated Ocean Energy Inc. (AOE) wave energy converter (WEC) is a cascaded pneumatic system, in which air is successively compressed through three point absorber devices on the way to shore; this air is then used to drive an electricity generator. To better quantify the performance of this device, this thesis presents a dynamically coupled model architecture of the AOE WEC, which was developed using the finite element solver ProteusDS and MATLAB/Simulink. This model is subsequently applied for the development and implementation of control in the AOE WEC. At each control stage, comprehensive power matrix data is generated to assess power production as a function of control complexity. The nature of the AOE WEC presented a series of novel challenges, centered on the significant residency time of air within the power take-off (PTO). As a result, control implementation was broken into two stages: passive and active control. The first stage, passive control, was realized as an optimization of eight critical PTO parameters with the objective of maximizing exergy output. After only 15 generations, the genetic algorithm optimization led to an increase of 330.4% over an initial, informed estimate of the optimal design, such that the annually-averaged power output was 29.37 kW. However, a disparity in power production between low and moderate energy sea-states was identified, which informed the development of an active control strategy for the increase of power production in low energy sea-states. To this aim, a recirculation-based control strategy was developed, in which three accumulator tanks were used to selectively pressurize and de-pressurize the piston at opportune times, thereby increasing the continuity of air throughput. Under the influence of active control, sea-states with significant wave heights between 0.75 m – 1.75 m, which on average encompass 55.93% of the year at the Amphitrite Bank deployment location, saw a 16.3% increase in energy production. / Graduate / 2018-08-18

In an attempt to describe how patterns emerge in biological systems, Alan Turing proposed a mathematical model encapsulating the properties of such processes. It details a partial differential equation governing the dynamics of two or more substances, called morphogens, reacting and diffusing in a specific manner, in turn generating what has now come to be denoted as Turing patterns. In recent years, evidence has accumulated to support Turing's claim and it has been proposed that it is responsible for the dynamical characteristics of phenomena such as skin pigmentation and branching of lungs in vertebrates. The aim of this paper is to study how the choice of model parameters and reaction kinetics influence the nature of patterns generated, as well as explore how boundary control can be employed to generate pre-defined patterns and the efficiency of this procedure. To simulate the patterns, the differential equation is solved in Python by means of a spectral method using discretized space and time domains. The model parameters were then studied to try to gain insight in their effects on the patterns yielded. The boundary control was implemented in MATLAB using a difference method. The metric used for efficiency was taken to be the energy expenditure of the boundary cells. The complex dynamics of the studied systems make it difficult to draw valuable conclusions on the influence of the parameters, but the results support the expected characteristics of the models used. The efficiency of the pattern generation is deemed to be closely related to the amount of boundary control utilized.

In 1952 Alan Turing published his paper "The Chemical Basis of Morphogenesis", which described a model for how naturally occurring patterns, such as the stripes of a zebra and the spots of a leopard, can arise from a spatially homogeneous steady state through diffusion. Turing suggested that the concentration of the substances producing the patterns is determined by the reaction kinetics, how the substances interact, and diffusion.  In this project Turing's model with linear reactions kinetics was studied. The model was first solved using two different numerical methods; the finite difference method (FDM) and the finite element method (FEM) with different boundary conditions. A parameter study was then conducted, investigating the effect on the patterns of changing the parameters of the model. Lastly the controllability of the model and the least energy control was considered. The simulations were found to produce patterns provided the right parameters, as expected. From the investigation of the parameters it could be concluded that the size/tightness of the pattern and similarity of the substance concentration distributions depended on the choice of parameters. As for the controllability, a desired final state could be produced thorough simulations using control of the boundary and the energy cost of producing the pattern increased when decreasing the number of controls.

Wave energy has great potential in energy harvesting, but due to its high system cost per electricity production, it is still in the pre-commercialization stage for grid connection. A wave energy converter (WEC) system that harvests energy through wave motion consists of a wave energy converter and a power take-off (PTO). A wave energy converter, usually a floating buoy, absorbs the hydrodynamic motion from wave and generates a mechanical oscillation. A power take-off (PTO) with mechanical transmission, which harvests the electrical energy through the mechanical energy, usually includes a transmission that converts linear motions from the buoy to rotational motions, an electromagnetic generator that produces electricity from a rotational shaft, and a power electronics converter that converts the ac electric power from the generator and charges the output dc battery or the ac grid. The models of the WEC system are usually oversimplified in a multi-physics study. A PTO model as an ideal actuator with 100 % efficiency will show a different frequency response than the real tested results and can make the controller design invalid. A conventional regular-wave circuit model shows discrepancies in power and force prediction in time-domain under irregular wave conditions. A model that can bring the multiple fields together, and provides an accurate prediction from irregular wave dynamics and non-ideal PTO mechanism is needed. A methodology that converts mechanical transmission equations into a circuit model is created. The equivalent circuits of mechanical components such as one-way clutches, gears, a ball screw, mechanical couplings, and generator are derived respectively to describe the dry frictions, viscous damping, and mechanical compliances in these components. The non-ideal efficiency and force of the PTO are predicted in electrical simulations by integrating these sub-circuit models. The circuit model is simplified, and its parameters are categorized as dc and ac unknowns. Using PTO with a mechanical-motion-rectifier (MMR) gearbox as an example, the dc and ac tests on the PTO are performed sequentially to extract two sets of parameters through linear regression or nonlinear curve fitting. The simulated efficiencies of 30 – 80% match well with experimental results. The model is validated through its prediction capability over 25 test conditions on input forces, output voltages, and efficiencies, with correlation coefficients R2 value of 0.9, 0.98, and 0.981, respectively. An equivalent circuit model of fluid-body dynamics for irregular waves, applicable to real ocean conditions with frequency-dependent radiation damping, is developed. Different from PTO modeling, the time-invariant circuit is created from a fourth-order RLC equivalent circuit through transfer function approximation in the frequency domain and Brune network. The circuit-based wave energy converter (WEC) model is verified by comparing the results with the predictions of a detailed model under irregular wave conditions in the time and frequency domains based on a point absorber type of WEC with a power take-off (PTO). The results show that the developed model gives an accurate dynamic prediction for a WEC under both regular and irregular conditions. Along with the PTO model, the circuit-based W2W model is completed for control and design optimization of the WEC system. Wave energy converter systems have faced various challenges such as reciprocal wave motion, high peak-to-average power ratio, and potential wave height from hundred-year storm conditions. These could lead to an overdesigned power take-off (PTO) of the system and significantly reduce the lifetime of the power electronics converter. The power ratio between the peak and the average power of the wave power converter is around 10 – 20 times. Power optimization is necessary to reduce the over design ratio of the power electronics converter. The design guideline that optimizes the power ratings for the power converter and the generator is introduced. The methodology is developed from the W2W circuit model taking the losses of the power converter and the generator into consideration. By optimizing the power limiting and field-weakening controls, the ratio from the average output power to the rated power of the power converter is reduced to 2.4 in the maximum wave condition, and 15 in the annual wave profile. A maximum energy control algorithm on the power electronics in wave energy application is developed to increase the total energy produced from the power converter in a wave energy converter (WEC) system. A 4-D damping and power leveling maps for maximum energy are built for the algorithm. The maps are based on the irregular W2W circuit model and reliability analysis on the IGBT module. From the yearly wave mission profile, the strategy is proved to increase energy by 16 times or increase the lifetime from 3 to 18 years in exchange for 6 % of average output power than the conventional maximum power algorithm. In conclusion, this work provides a new circuit-based perspective for co-designing the multi-disciplinary WEC system. The methodologies of circuit modeling can benefit the co-design process of other mechatronic power systems, such as electric vehicle or renewable energy system. The newly invented mechanical device – the mechanical motion rectifier, is understood thouroughly via the non-ideal electrical model. The commercialization of wave energy converter is driven forward through the reduction of the levelized cost of electricity (LCoE) which is made possible by increasing the energy production and optimizing the cost per output power of the generation and power conditioning stages. / Doctor of Philosophy / Wave energy, if all been harvested along the U.S. coastline, can power around 65% of the energy consumption in U.S.. Comparing to other renewable energy sources like solar or wind, ocean wave can provide up to 90% of steady uptime. With the high energy density (2-3 kW/m2), it can produce more energy with the same amount of installation area comparing to the energy density of wind turbine (0.6 kW/m2) and solar panel(0.2 kW/m2). The predictability of wave provides advantages like planning installation, power dispatching, and maintenance activities. Although with all these advantages, wave energy converter system is still in the research stage due to its high system cost per electricity production. One of the challenges that need to be solved is the irregularity from the wave motion that leads to high instantaneous peak power into the wave energy converter, which usually reaches up to 10 - 20 times of the average power. The high peak power will not only bring high mechanical/electrical stress but also result in an overrating design of the components in the system. Another obstacle that prevents the wave energy system from moving forward is the high testing cost from the validations in wave-energy-test sites or tank-test sites. A high-fidelity multi-disciplinary system model, including hydrodynamics, mechanical dynamics, electromagnetics, and power electronics, is needed to predict the behavior of the system and reduce the cost of design validation. This work provides a unified circuit-based perspective for co-designing the multi-disciplinary wave energy system. The efficiencies and mechanical dynamics of the system are accurately predicted via the non-ideal electrical model. These methodologies of circuit modeling can also benefit the co-design process of other mechatronic power systems, such as electric vehicles or renewable energy systems. The peak of the irregular power is controlled by the power-leveling and field-weakening control, and as a result, the overdesign ratio of the power converter reduces from 11.1 to 2.4. Through proper design of the converter's control algorithm, the total produce electric energy is increased by 15 times, as well as the lifetime of the power electronics extended from 3 years to 18 years. Therefore, the commercialization of wave energy converter is driven forward through the reduction of the levelized cost of electricity (LCoE), which is made possible by optimizing the component lifetime and the output energy utilizing the developed circuit-based wave-to-wire model.