Mechatronics Systems Engineering - Theses, Dissertations, and other Required Graduate Degree Essays

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Thermal performance of heat and water recovery systems: Role of condensing heat exchanger material

Date created: 
2019-09-11
Abstract: 

There is enormous potential for recovering a significant amount of latent heat at temperatures below 100°C from flue gas of combustion-based heating systems due to the presence of water vapor in their exhaust streams. However, condensation of acids along with water vapor in heat and water recovery systems makes a highly corrosive environment, which is a major challenge and a determining factor in selecting suitable materials for condensing heat exchangers. Despite the low cost and great corrosion-resistant properties of plastics, their relatively low thermal conductivities are not ideal for thermal management systems. it is still uncertain how significantly increasing thermal conductivity of the heat exchanger’s material affects thermal performance of the heat recovery systems. The present study aims to shed light on the effect of the thermal conductivity of a condensing heat exchanger’s material on the thermal performance of the unit. For this purpose, an analytical model is developed to predict the thermal performance of condensing heat exchangers, designed for recovering heat and water from wet flue gas. Further, to validate the model, a custom-designed condensing heat exchanger with replaceable tubes is designed in our lab and tested with 304 stainless-steel tubes and FEP plastic tubes under different inlet conditions. For the range of inlet conditions considered in this study, results show that there is a threshold for the thermal conductivity of the material, at which increasing the conductivity any further does not affect the condensation efficiency notably. It is worthy of note that this threshold, with respect to thermal conductivity of commonly used materials for such heat exchangers, has relatively low magnitude (e.g.~10-15 W"∙" m-1"∙" K-1 for stainless steel). This finding is significantly important as it unlocks the potential of using materials such as plastics and polymers with thermally conductive additives for latent heat recovery from flue gas.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Majid Bahrami
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Textile-based electromagnetic soft strain sensors for fast frequency movement and their application in wearable devices measuring multi-axial hip joint angles during running

Date created: 
2020-03-05
Abstract: 

Wearable in situ multi-axis motion tracking with inductive sensors and machine learning is presented. The production, characterization, and use of a modular and size adjustable inductive sensor for kinematic motion tracking are introduced. The sensor was highly stable and able to track high frequency (>15Hz) and high strain rates (>450%/s). Four sensors were used to fabricate a pair of motion capture shorts. A random forest machine learning algorithm was used to predict the sagittal, transverse, and frontal hip joint angle using the raw signals from the sport shorts strain sensors during running with a cohort of 12 participants against a gold standard optical motion capture system to an accuracy as high as R2 = 0.98 and an RMSE of 2° in all three planes. This present study provides an alternative strain sensor to those typically used (piezoresistive/capacitive) for soft wearable motion capture devices with distinct advantages that could find applications in smart wearable devices, robotics, or direct integration into textiles.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Carlo Menon
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Head impact kinematics and location measurement utilizing multiple inertial sensors

Date created: 
2019-09-24
Abstract: 

Impacts that cause high accelerations of the head are a major cause of mTBIs (mild traumatic brain injuries) or concussions. Repetitive head impacts can also potentially cause long term damage to the brain and cognitive abilities. Recently, due to increasing public awareness, wearable technologies and devices targeted towards measuring head impact kinematics during sport are gaining popularity. However, existing devices come with limitations that prevent on-the-field usage in one way or another. In this work, we devised methods to address the problem of accurately measuring impact kinematics and impact location, while also addressing the limitations of existing devices. We developed novel calibration and impact measurement algorithms that allowed us to design a complete impact measurement device; while also reducing the number of sensors and scale. We also proposed a wearable device prototype that can eventually be developed into a low-cost finished product for on-the-field impact measurement. We tested the accuracy of the device and algorithms by comparing the impact linear and rotational acceleration, rotational velocity, and impact location estimate with an industrial-grade IMU and a Hybrid-III dummy Head. Results showed that the device has great potential for relatively low-cost sports applications and can help in establishing a link between impacts and resulting brain injuries in the future.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Farid Golnaraghi
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Self-tuning electromagnetic vibration systems

Date created: 
2019-05-06
Abstract: 

This thesis presents the efforts made toward making industrial mechanical vibration systems smarter. This objective is accomplished in two steps. The first step is realization of mechanical vibration actuators that can mimic the behavior of mechanical dampers and springs with variable and controllable damping and stiffness. The second step includes the design and implementation of algorithms that can find the optimum damping and stiffness in different operating conditions. First, electromagnetic actuators are selected for force generation. It is shown that creating a parallel RL circuit with variable parameters in the shunt of an electromagnetic actuator results in variable damping and stiffness behavior by the actuator. It is shown that this circuit configuration can be realized using a power electronics converter connected to a power source. Next, automatic control methods are developed for adding a self-tuning loop to the system including an electromagnetic actuator. To this end, the sliding mode extremum seeking controller was utilized to make the system self-tuning in a model-free control architecture. The concept is applied to two major problems in vibration systems: vibration energy harvesting and vibration absorption, which is also known as tuned mass damping. In the former application, single variable and multi-variable sliding mode extremum seeking controllers are used for controlling the damping and stiffness of the actuator to maximize the harvested power. In the latter case, the same controller is used with the objective of minimizing the unwanted oscillations in a host structure. Analytical methods, computer simulations, and experimental results are provided to support the proposed concept and verify the theoretical findings. The results show that it is possible to achieve efficient, variable, and controllable damping and stiffness with an electromagnetic actuator comprised of a brushless DC motor and a mechanical motion conversion mechanism. It was also shown that the proposed extremum seeking controllers successfully tune the variables toward the optimum points.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Mehrdad Moallem
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) Ph.D.

Detection of sleep and wake states based on the combined use of actigraphy and ballistocardiography

Author: 
Date created: 
2019-04-30
Abstract: 

Sleep is a vital part of every humans daily circadian rhythm. People can rest and recover their body and mind and live a more active and alert life with an appropriate amount of sleep. The current gold standard method for sleep analysis is polysomnography, but due to the complexity, it is not convenient to perform it regularly and it disrupts the normal sleep environment of the patient. This thesis presents a method of integrating two alternative measurements of sleep analysis for an improved analysis. Combining the motion detection of actigraphy and the cardiac parameters of ballistocardiography, a novel algorithm was developed to analyze sleep and wake states without interfering with the natural sleep cycle of the participant. Without interfering with the natural sleep environment, this system can be implemented for continuous monitoring and be used to evaluate daily sleep patterns to assess overall sleep quality and health over time. The experimental results demonstrate the effectiveness of the novel proposed algorithm in comparison with each device used separately in improving the sleep classification.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Edward J. Park
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Fall detection algorithms using accelerometers, gyroscopes and a barometric pressure sensor

Date created: 
2018-04-19
Abstract: 

Falls commonly occur in older adults and could result in long-lies when no one is around to assist, which could result to additional emotional and physical consequences. The use of inertial sensors allows a portable and unobtrusive way to detect motion, enabling the automatic detection of falls when used with a fall detection algorithm. The wrist and trunk are two locations that are favorable for fall detection as the former provides a convenient location for the user, while the latter provides a good location for capturing the body’s general motion. The objective of this thesis is to further improve the performance of a wrist-mounted and a trunk-mounted threshold-based fall detection algorithm using inertial sensors comprised of tri-axial accelerometer, tri-axial gyroscope, and a barometric pressure sensor. The algorithms were tested using a comprehensive set of laboratory-simulated falls, activities of daily living (ADL), and near-falls. In the first study, a wrist-based fall detection algorithm for a commercially available smartwatch was proposed. The algorithm used forearm angle to filter the forearm’s downward vertical orientation that could be associated to a non-fall event’s post-activity position. Additionally, to deal with disturbance in barometric pressure data during dynamic motion, barometric pressure was used selectively in a Kalman filter. The algorithm gave 100% sensitivity, 97.2% ADL specificity, and 97.1% non-fall (i.e. including both ADLs and near-falls) specificity. In the second study, the addition of either difference in altitude or average vertical velocity to a trunk-based algorithm that uses vertical velocity + vertical acceleration + trunk-angle (base algorithm) was investigated. The experimental results show that adding either difference in altitude or average vertical velocity was able to increase the algorithm’s non-fall specificity from 91.8% to 98.0% and 99.6%, respectively.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Edward J. Park
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Towards wearable platform for accurate unconstrained trunk motion tracking using inertial and strain sensors data fusion

Author: 
Date created: 
2019-10-29
Abstract: 

The thesis focused on the development of a wearable motion tracking platform employing fiber strain sensors and inertial measurement units through a data fusion algorithm. The development of a smart sleeveless shirt for measuring the kinematic angles of the trunk in complicated 3-dimensional movements was demonstrated. Fiber strain sensors were integrated into the fabric as the sensing element of the system. Furthermore, a novel method for obtaining the kinematic data of joints based on the data from wearable sensors was proposed. More specifically, the proposed method uses the data from two gyroscopes and the smart shirt strain sensors in a combined machine learning-unscented Kalman filter (UKF) data fusion approach to track the three-dimensional movements of a joint accurately. The suggested technique thus avoids the common problems associated with extracting the movement information from accelerometer and magnetometer readings in the presence of disturbances. A study with 12 participants performing an exhaustive set of simple to complex trunk movements was conducted to investigate the performance of the developed algorithm. The results of this study demonstrated that the data fusion algorithm could significantly improve the accuracy of motion tracking in complicated 3-dimensional movements. Future work requires coherently combining both types of sensors in a wearable platform for full-body motion tracking so that the proposed algorithm can be tested in a variety of daily living activities.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Carlo Menon
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Categorization methodology for skin color images into pre-defined color scales

Date created: 
2019-09-09
Abstract: 

The ability to classify skin color based on images from bare skin is highly sought after in various industries and professions, including dermatology, cosmetics, skincare products, laser-based therapies, etc. The focus of the present research work is on designing an algorithm capable of classifying images of various skin colors utilizing the Fitzpatrick and cosmetic brand skin color scales. The images are taken from a skin area by any camera, including a smartphone camera. In this study, two different methods are employed to classify a query image into a set of reference images. Both methods introduced here are among the most commonly used approaches for comparing two image histograms and defining a Difference Index (DI). The application of the proposed algorithms is not limited to the classification of skin colors. These algorithms can be applied in painting industry for building interiors, developing apps for assisting people who are color-blind, etc.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Farid Golnaraghi
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Metamodel based optimization for dynamic blade pitch control on a vertical axis wind turbine using analytical and CFD methods

Date created: 
2019-08-19
Abstract: 

In this study, the blade pitching motion on a representative (12 kW) vertical axis wind turbine (VAWT) is optimized over a wide range of operating conditions. The pitching is referred to as active blade pitching (ABP) when it is not constrained by a predetermined motion, while the operating condition is referred to as the tip speed ratio (TSR). Computational fluid dynamics (CFD) simulations are used to estimate the instantaneous torque produced by the VAWT blades. The torque is considered the system output and is dependent on the ABP which serves as the system input. This work initially used a preliminary ABP derived using an analytic model; the VAWT was then simulated at a TSR of 2.3 with fixed blades using an analytic-ABP strategy. The simulation with the analytic-ABP generated a 33.4% increase in torque output compared to the fixed pitch strategy simulation. The analytic-ABP curve was then approximated by a function of two variables, via parameterization of the ABP. The parameters of this ABP are the optimization variables of a response surface methodology (RSM) optimization, the objective function being the CFD “black-box” simulation and the output variable being the average torque of a blade. The optimization used a three-level full factorial design (FFD) as the design of experiment (DOE) strategy in order to sample the function with an initial set of points, generate a metamodel, and search for the optimum. The ABP derived from this method, termed the FFD-ABP, was simulated; the results show that it increased the torque output by 15.5% relative to the previous analytic-ABP. A new optimization procedure is proposed in this work. It starts from the simulation results of the analytic-ABP as well as +2° and −2° offset perturbations. The optimization procedure generates an optimal ABP using a modified quadratic regression metamodel over a discretized domain; the metamodel is updated with the response of the first optimal-ABP to generate a second optimal-ABP. The procedure is repeated until the ABP converges into a narrow band. The optimal-ABP simulations resulted in a 6.5% increase in torque output with fewer function calls compared to the previous FFD-ABP. The optimization procedure was extended to several TSRs and the data used to develop a governing function and power performance charts. The governing function was based on a novel nonlinear curve fit model and it estimated the pitch based on the TSR and azimuthal angle. The maximum power operation point is increased by 13% and the torque performance at low TSR is improved.

Document type: 
Thesis
Senior supervisor: 
Krishna Vijayaraghavan
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.

Investigating cathode catalyst layer degradation in polymer electrolyte fuel cells by lab-based x-ray computed tomography

Author: 
Date created: 
2019-09-20
Abstract: 

The commercial viability of polymer electrolyte fuel cells (PEFCs) has increased rapidly over recent years with applications in public and commercial transportation, back-up power, and un-manned autonomous systems. This has come as a direct result toward increasing evidence and severity of climate change due to greenhouse gas emissions; pushing the need for government regulations to introduce stricter limits on fossil fuel combustion in new passenger cars, as well as in other light-to heavy-duty vehicles. Further cost and durability improvements in PEFCs present significant opportunities as the technology continues to be refined. PEFCs are assembled as a series of layers, each having specific functionalities to optimize the cell performance during electrochemical conversion of chemical potential energy, in the way of hydrogen and oxygen, into useable electrical power, heat, and water. These PEFC materials can undergo considerable changes during operation, and lifetime testing through critical degradation processes, which can be uniquely captured using X-ray Computed Tomography (XCT) in this complex multi-layered system. XCT provides a unique ability to delve into the innermost structures through non-destructive imaging in diverse and extensive application areas. In this thesis, a novel small-scale fuel cell fixture that mimics the performance and degradation features of a full-scale PEFC assembly is presented. By combining the 3-dimensional visualization through repeated identical location tomography using XCT scans at various temporal stages of this small-scale fixture, powerful in-situ and operando investigations of dynamic material properties are obtained. This methodology is termed as 4D CT. By means of applying accelerated stress tests focused on cathode catalyst layer degradation, unique insight into the lifetime, dynamics and interactions between the catalyst layer and surrounding components was uniquely obtained using custom developed tools and analysis methods. These new methods allow for new investigations into the temporal changes of water saturation and cathode catalyst layer morphology. It has been found that during ageing, the morphological interaction between different layers can have a considerable impact on degradation mechanisms such as crack propagation. These results uncover unique evidence around the strongly interactive nature of material degradation within a fuel cell that has previously been unobserved.

Document type: 
Thesis
File(s): 
Senior supervisor: 
Erik Kjeang
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) Ph.D.