Master Theses & Projects (FEAS)

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https://ontariotechu.scholaris.ca/handle/10155/687

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"Sounding out solutions: current status of ocean noise pollution and management approaches to conserve cetacean with the focus on Canadian habitats."
(2024-03-01) Nassim, Nakissa; Jawad, Dima
As human activities continue to rise, the adverse consequences of ocean noise pollution on cetaceans (Cetacean is the general noun used to describe all 90 species of whales, dolphins, and porpoises), including stranding, masking, alterations in foraging behaviors, and mating disruptions, have become a significant concern for marine conservationists. Despite international recognition, the complex nature of sound propagation in aquatic environments and diverse contributing sources have left various aspects of ocean noise pollution needing to be fully comprehended. This research project delves into the multifaceted impacts of underwater noise on cetaceans, underscoring the imperative to implement effective mitigation measures promptly. Key strategies, such as planning, resource management, raising stakeholders’ awareness and engagement, adopting noise reduction technologies, and integrating them with existing ones, establishing Marine Protected Areas (MPAs), and initiatives to decrease ship speed and reroute ship lanes, play a pivotal role in alleviating ocean noise pollution. The project emphasizes the importance of a holistic approach to address the issue, suggesting integrating noise reduction strategies into all conservation plans for seas and oceans. Recognizing the significance of engagement from both the public and private sectors, a fundamental aspect of this approach involves raising awareness to foster a shared sense of responsibility toward marine life. Furthermore, it advocates for intensified research endeavors to further our understanding of the specific impacts of noise pollution on distinct cetacean groups, enabling the refinement of mitigation strategies accordingly. This project underscores the urgent and collective need for immediate action to tackle ocean noise pollution. By adaptive management, amalgamating technological advancements, regulatory measures, and public awareness and engagement initiatives, we can promote more sustainable coexistence between human activities and the marine environment, ensuring the safeguarding and conservation of cetacean species in our oceans.
Multiscale video transformers for video class agnostic segmentation in an autonomous driving setting
(2024-08-24) Cheshmi, Leila; Siam, Mennatullah
Semantic segmentation is a key technique in the perception of autonomous driving. Traditional semantic segmentation models, however, are constrained by the need for extensive annotated datasets and struggle with unknown classes not encountered during training. On the other hand, video class-agnostic segmentation aims to segment objects without relying on their semantic category. Motion cues could be used towards that goal to account for objects outside the closed set of training classes. This project proposes an innovative approach to video class-agnostic segmentation in autonomous driving using multiscale video transformers. We enhance the Video Class Agnostic Segmentation (VCAS) dataset by integrating richer annotations and tracking data from the TAO-VOS (BDD) dataset, thereby providing a comprehensive dataset for better generalization in complex driving scenarios. Our project involves designing a novel multi-scale video transformer-based architecture that foregoes optical flow, focusing instead on learning motion implicitly to identify objects in a class-agnostic manner. This architecture utilizes the Multiscale Encoder-Decoder Video Transformer (MED-VT) framework, which processes sequential data in a multiscale approach to capture both fine and coarse-grained information. Video transformers utilize encoder and decoder components, along with attention mechanisms, to efficiently process sequential data. Our approach takes an input clip and outputs the class-agnostic segmentation of moving objects in that clip. Features extracted from the raw input clip using a convolutional backbone are treated as tokens and provided to the multiscale transformer for pixel-wise classification. Additionally, we augment the currently available video class-agnostic segmentation datasets with TAO-VOS (BDD) datasets. We also label some missing objects in TAO-VOS (BDD) datasets with a standard semantic segmentation annotation tool in a few of the sequences. The outcomes of this project include a more diverse and comprehensive dataset and a superior video class-agnostic segmentation model with improved accuracy in mean intersection over union (mIoU). Our training on datasets focused on autonomous driving scenes demonstrated a significant improvement in mIoU compared to models trained on general-purpose video object segmentation datasets.
Energy planning with hydrogen deployment strategies within interconnected infrastructures using enhanced SWITCH model
(2024-07-01) Villalobos Herra, Elena; Gaber, Hossam
The water dimension is not adequately considered in energy models when planning for hydrogen technologies. To overcome this, three novel modules have been developed for the SWITCH energy model: one that considers water drinking systems, a second module that optimizes the size/location/type of hydrogen plants, and the buildings module to integrate buildings using hydrogen-based combined heat and power systems. The modules contribute to the research community by linking the water, hydrogen and power sectors in an energy model. The modules were tested in a case study for Durham Region, using data from 2022. The main results show that the zone of Oshawa is optimal for building a hydrogen electrolysis plant, but facing drastic changes in its power and water demands. Results also show hydrogen-based combined heat and power systems would not be economically feasible unless the price of hydrogen per kilogram is less than CAD$2.13, considering the 2022 parameters.
Modeling and analysis of all-season passenger car tire using finite element analysis
(2024-06-01) Fathi, Haniyeh; El-Sayegh, Zeinab; Ren, Jing
The performance of the ground vehicle is directly affected by its tire characteristics. Tires are the main components of the vehicle that resist all forces and moments generated during contact with the ground. Therefore, it is significant to better understand the effect of all tire characteristics on the tire-road interactions such as cornering maneuvers. In this research work, a 4-groove Continental Cross Contact LX Sport tire size 235/55 R19 101H is designed and modeled using the Finite Element Analysis (FEA) Technique. All tire layers are modeled separately with the corresponding reinforcement rubber layers with detailed geometry. The tire model is then validated using static and dynamic tests at various operating conditions. To achieve the accurate performance of the passenger car tire, the tread rubber is modeled under various temperatures and analyzed using hyper-viscoelastic models. The tire-road characteristics including rolling resistance, cornering characteristics, and traction are explained and investigated.
A hybrid approach for intersection management in V2X-enabled connected vehicles
(2024-08-01) Elmoghazy, Ammar; Elgazzar, Khalid; AlWidian, Sanaa
Autonomous Vehicles (AVs) have the potential to revolutionize transportation by enhancing safety, efficiency, and convenience. However, AVs face significant challenges in complex urban environments, particularly in accurately perceiving and navigating through intersections mainly due to occlusions. This thesis addresses these challenges by integrating Vehicle-to-Everything (V2X) communication with onboard sensors to improve AV perception and decision-making capabilities. In particular, this thesis proposes a hybrid centralized-decentralized management system, which maximizes the benefits of centralized control for strategic traffic management and the responsiveness of decentralized decision-making, using edge nodes as a traffic coordinators helps reduces the computational needs on the vehicle. Such a system leverages V2X data to enhance situational awareness, optimize traffic flow, and improve overall safety and efficiency in urban environments. The methodology involves using Simultaneous Localisation and Mapping - SLAM for mapping, particle filters for localization, and waypoint generation for planning and control. The hybrid system’s performance was evaluated through simulations and real-world experiments using scaled-down vehicles equipped with advanced sensing and communication technologies. Compared to purely centralized or decentralized approaches, the hybrid system achieved up to a 14% reduction in average travel times through intersections and a 20% improvement in overall traffic flow efficiency. This thesis contributes to the development of intelligent transportation systems by demonstrating the efficacy of hybrid intersection management in enhancing AV performance in urban environments.
A multistage-constant-current, temperature-controlled, health-conscious fast charging algorithm for lithium-ion batteries
(2024-08-01) Chetri, Chandan; Williamson, Sheldon
The economical operation and wider adaptability of an electric vehicle (EV) is highly governed by the energy storage system used in the vehicle. To enhance user convenience and compete with their gasoline counterparts, EVs need fast charging methods to achieve equivalent refueling times. However, fast charging can adversely affect the health and cycle life of the battery due to excessive temperature rise resulting in accelerated degradation of the battery chemistry. Especially in subzero conditions, the chemical reactions are much slower, resulting in increased internal impedance. This leads to a higher rate of temperature rise in battery temperature and eventually faster battery degradation. This thesis proposes a closed-loop Multistage-constant-current, Temperature-controlled (MCC-TC), Health-conscious Fast Charging strategy, which modulates the charging current considering the battery temperature as feedback. The experimental validation on an automotive grade battery cell depicts lower temperature rise and rate of temperature rise following the MCC-TC charging algorithm compared to the conventional Constant-Current Constant-Voltage (CC-CV) charging algorithm.
Developing a calibrated low fidelity model and an optimized sensor network using LIVE Digital Twin for pipelines in oil and gas industries
(2024-08-01) Bondoc, Andrew E.; Barari, Ahmad
Digital Twin (DT) solutions are at the forefront of intelligent prognostics and diagnostics of physical systems. The DT is the bidirectional communication between the physical and digital environments for better scheduling, manufacturing processes, health monitoring, etc. This communication is established with an optimized sensor network. LIVE Digital Twin (LIVE DT) is a novel methodology which addresses the lack of standardised DT solutions. This thesis employs LIVE DT to develop a calibrated Low Fidelity (LF) pipeline model. Three fault cases are rapidly simulated using the LF model to circumvent the need of expensive and dangerous physical experimentation. The developed data is used to develop an optimized sensor network for intelligent vibration monitoring of the pipeline. A machine learning algorithm is trained to detect the current fault experienced by the LF model. Furthermore, the presented methods and results can be scaled and customized to produce sensor networks for any physical system.
In-depth study and analysis of lithium-ion battery states using battery degradation data from electrochemical impedance spectroscopy
(2024-07-01) Anekal, Latha; Williamson, Sheldon
This thesis focuses on the study and analysis of lithium-ion battery (LIB) degradation mechanisms throughout the battery life. The performance of LIB deteriorates due to chemical and mechanical degradation that occurs during the battery operation affecting the health of the batteries. It is critical to understand degradation mechanisms for enhancing the performance, reliability, and safety of LIBs. This thesis studies the degradation mechanisms and degradation characterization techniques for designing an equivalent circuit model (ECM) of LIB. The thesis highlights the importance of considering the degradation effects into account for different LIB state estimations including state of charge (SOC), state of health (SOH), state of temperature (SOT), and state of power (SOP). The Electrochemical impedance spectroscopy (EIS) technique is a degradation diagnostic tool. The thesis covered the EIS principles, measurement techniques, and impedance data interpretation specific to LIB. A lithium nickel cobalt aluminium oxide cylindrical (NCA) battery is subjected to charge/discharge cycles to study the battery degradation mechanisms. The impedance spectrum from EIS tests is analyzed for ECM circuit parameterization. From the experimental data the degradation parameters are studied, and it is observed that the bulk resistance (𝑅􀯦), charge transfer resistance (𝑅􀯖􀯧 ), and mass transfer resistance (𝑅􀯪) showed an increasing trend with an increase in cycle life. Growth of the solid electrolyte interface (SEI) layer is observed after the 150th cycle with the evolution of two semi-circles in the Nyquist plots emphasizing the use of second-order ECM for LIB performance estimations. The changes in the double-layer capacitance of LIB are studied through the experimental results. The changes in the temperature behaviour of LIB through aging are highlighted and attributed to the changes in the degradation parameters. Furthermore, the thesis proposes an approach for estimating SOC using an adaptive extended Kalman filter (EKF) based on the parameters obtained from EIS. By updating the ECM parameters, the proposed SOC estimation technique can maintain accuracy throughout the battery life, as the ECM parameters are continuously updated based on feedback from the EIS data.
New frontiers in population-based multi-objective feature selection
(2024-04-01) Zanjani Miyandoab, Sevil; Rahnamayan, Shahryar; Makrehchi, Masoud
Feature selection is a persistent challenge aimed at minimizing the number of features while maximizing accuracy of classification, or any other machine learning and data mining task, by mitigating the curse of dimensionality. We frame feature selection as a multi-objective binary optimization task with the objectives of enhancing accuracy and reducing the feature count. Given that feature selection, along with binary optimization problems in general, is a NP-hard problem since the size of search space increases exponentially by the increase of the number of features, so especially in high-dimensional spaces, it can be very challenging. We propose three innovative approaches to tackle large-scale multi-objective feature selection. The first technique involves an augmentation to the diversity of the population in the well-established multi-objective scheme of the genetic algorithm, NSGA-II, which is achieved through the substitution of the worst individuals with new randomly generated individuals with a limited number of features in each generation. As the second method, a binary Compact NSGA-II (CNSGA-II) algorithm has been introduced for feature selection for the first time, which represents the population as a probability distribution not only to be more memory-efficient but also to accelerate finding a better candidate solution. Additionally, we present a novel binary multi-objective coordinate search (MOCS) algorithm, which, to the best of our knowledge, is the first of its kind, demonstrating effectiveness in solving multi-objective binary optimization problems. A comparative study on the proposed methods and NSGA-II showcases the promising performance of our methods in feature selection tasks involving large-scale datasets, surpassing the renowned NSGA-II algorithm. These methods are not limited to feature selection but can be applied to various binary optimization domains, including the Knapsack problem and training Binary Deep Neural Networks.
Experimental investigations of the thermodynamic properties of Nd-C and Ce-C binary systems for TRISO fuel applications
(2024-04-01) Varga, Ryan Mathew; Atkinson, Kirk; Piro, Markus; Fitzpatrick, Bernie
TRISO fuels are proposed for modular and niche nuclear power reactor technologies, expanding the global nuclear power inventory to meet increasing energy demands through small scale applications. Expansion is dependent upon the improvements to safety through thorough understanding of fission product behaviour, studied and analyzed here using experimental techniques and comparisons to existing literature. Significant knowledge gaps exist in the thermodynamic behaviour of neodymium carbide and cerium carbide fission products that play significant roles in the qualification of TRISO fuels. Thermodynamic investigations of neodymium and cerium carbide isotopic equivalent fission products were performed to improve the knowledge base of TRISO fuels. Various crucible tests, calibrant experimentation, sample generation and sample preparation techniques, and new thermodynamic measurements have been performed. Boron nitride crucibles worked most effectively, providing useful data for comparison to existing literature. Measurements of varying molar compositions of neodymium carbide and cerium carbide provided new data to expand upon predicted phase change boundaries, with experimental phase changes exhibiting comparatively higher transition temperatures.
A hybrid compensation-based misalignment tolerant wireless power transfer system for e-mobility
(2024-04-01) Shrestha, Niranjan; Williamson, Sheldon
The thesis focuses on developing a hybrid compensation with a phase shift control strategy, aiming for misalignment tolerant constant current/constant voltage (CC/CV) charging through a wireless power transfer (WPT) system for e-mobility. The thesis proposes a hybrid multi-resonant compensation network (LCC-LCC and LCC-S) for CC/CV charging during perfect alignment, controlled by the secondary side only. Additionally, the thesis introduces a phase shift control technique in the inverter to maintain the corresponding CC and CV charging mode during the misalignment up to 100 mm between primary and secondary coils. Initially, the theoretical analysis of the proposed system is described in detail. Then, simulation results for 3.7 kW and 270 W peak load were carried out in MATLAB Simulink. Lastly, experimental testing and validation were conducted for the proposed hybrid compensated system for 270 W peak load, applicable to the E-bike. The experimental results show good consistency with theoretical and simulation analysis.
Coupled radiation transport and mobile depletion for health physics and environmental impact applications
(2024-04-01) Sawatzky, Kevin; Atkinson, Kirk D.
The next generation of nuclear reactors pose a large challenge to existing computational methods. Caribou is a MOOSE-based health physics and environmental impact code under development at Ontario Tech which aims to address some of these challenges. This work developed a discrete ordinates radiation transport solver and a radionuclide trace species transport solver in the MOOSE framework for Caribou. This radiation transport solver is compared to several benchmark problems to determine its accuracy with and without ray effect mitigation measures, finding good agreement with all the problems tested. The trace species transport solver is then verified with the method of manufactured solutions. The coupled solvers are then used to analyze the formation of ⁴¹Ar in a containment volume due to ex-core neutron fields, and the photon fields from a ¹³⁷Cs plume. The conclusions of this work indicate that these methods are a valuable addition to Caribou’s suite of capabilities.
Design and development of an LLM-based framework for crime classification and prediction
(2024-04-01) Sarzaeim, Paria; Mahmoud, Qusay H.; Azim, Akramul
Large language models (LLMs), as a subset of generative AI, have been used in different domains, such as financial, medical, legal, and agricultural applications. However, adopting LLMs for smart policing applications remains unexplored. This thesis concentrates on developing a framework based on the transformative potential of the BART, BERT, and GPT models in this domain using methods such as zero-shot prompting, few-shot prompting, and fine-tuning. As a prototype, these methods were used to comprehensively assess the performance of LLMs in crime classification and prediction based on state-of-the-art datasets from two major cities: San Francisco and Los Angeles. The main objective is to illuminate the adaptability of LLMs and their capacity to revolutionize crime analysis practices. Additionally, a comparative analysis of the aforementioned methods on the GPT model and BART with machine learning (ML) techniques is provided. The experimental results demonstrate the feasibility of integrating LLMs into smart policing systems and show that GPT models are more suitable than traditional ML models for crime prediction in most experimental scenarios.
Design and development of a framework for predicting short price jumps in cryptocurrency market
(2024-04-01) Rajaei, Mohammadjavad; Mahmoud , Qusay H.
The cryptocurrency market's volatility offers significant profit opportunities despite its risks. This thesis aims to capitalize on these opportunities by designing and developing a framework to predict whether a coin will experience growth in the next trading candle. To achieve this, we constructed a robust framework that incorporated various input features. We conducted comprehensive analyses by leveraging six machine learning models. Our methodology involves training these models on historical daily data from the Binance Exchange. Subsequently, we evaluate their performance using diverse testing datasets from January 2022 to December 2023. Demonstrating notable precision, especially with a growth rate of 1%, the model has proven effective across various scenarios, consistently yielding profits. Regarding the backward testing results, the XGBoost model combined with the trading strategy made a profit in all three test datasets of Oct 2023, Jul to Sep 2023, and LSK/USDT achieved 24%, 47%, and 98% profits, respectively. This thesis underscores the potential for leveraging well-designed machine learning models to earn significant profits, even in bearish market conditions.
Modeling and testing of an improved 8x8 scaled electric combat vehicle
(2024-04-01) Kim, Junwoo; El-Gindy, Moustafa
The current 8x8 combat vehicle pertaining to multi-wheeled vehicles indicates a lack of effort in developing advanced multi-steering electric drive vehicle. Therefore, novel steering scenarios for an 8x8 scaled electric combat vehicle that features maintaining individual wheel’s steering and speed are developed in this thesis using a scaled 8x8 compat vehicle to introduce a future steering control system for the current combat vehicles. This thesis explains the mechanical improvement of the scaled vehicle compared to the previously developed model. In addition, validation of the scaled electric vehicle model in comparison with a full-size electric drive vehicle model in TruckSim is presented to prove the advantage of using the scaled vehicle in experimental test. Furthermore, development and validation of steering strategies including traditional, fixed 3rd axle, and all-wheel steering scenarios in the scaled vehicle in terms of both experiment and simulation results are conducted to enhance the limitations imposed by conventional vehicle’s steering control system. Active Steering Controller (ASC) is developed and simulated to overcome the limitations of the complete Ackermann steering condition when the scaled vehicle is driving at a relatively high speed. The outcome of this thesis research work is a novel future electric multi-drive and multi-steer combat vehicle.
Modeling and analysis of Regional Haul Steer (RHS) truck tire model
(2024-04-01) Khosravi, Mehran; El-Gindy, Moustafa; El-Sayegh, Zeinab
In this thesis, the RHS truck tire size 315/80R22.5 was developed using Finite Element Analysis and several material properties. The tire model was then validated using static and dynamic testing, against physical measurements provided by the manufacturer. A simulation model of flooded and snow terrain was then developed using the Smoothed-Particle Hydrodynamics technique and hydrodynamic elastic-plastic material model. The tire-terrain interaction characteristics were then evaluated over flooded and snow surfaces. The interaction characteristics included the rolling resistance, cornering force, self-aligning moment, and overturning moment. The analysis was performed at different operating conditions including terrain depth, longitudinal speed, and vertical loads. In general, the results from both surfaces exhibited similar trends, even though the values were not the same. Future work involves the utilization of genetic algorithms to generate semi-empirical relationships, as well as the implementation of temperature and wear models for the RHS tire.
Autonomous UAV-UGV robot collaboration for exploration and mapping of unknown environments
(2024-04-01) Khabbaz, Noor; Nokleby, Scott
This thesis addresses the limitations of existing approaches to autonomous exploration and mapping of unknown environments that use multiple Unmanned Ground Vehicles (UGVs). An Unmanned Aerial Vehicle (UAV) is introduced into the multirobot system to overcome the challenges of relative localization and obstacle detection. A novel method is proposed for autonomously determining the UGVs’ starting poses using ArUco markers visible to the UAV, resulting in the initialization of a global merged map. A second method is developed to overcome UGV obstacle detection limitations. UAV depth camera data is processed to detect and incorporate previously unseen obstacles into the UGVs’ navigation schemes, enabling avoidance. Experimental validation demonstrates the effectiveness of both methods in enhancing system autonomy. The integration of a UAV into multi-robot systems presents a promising solution to address UGVs’ localization challenges and limited field of view to improve their functionality in hazardous environments.
Analysis of microgrid with renewable generation and energy storage system
(2024-03-01) Jadeja, Prachal; Sood, Vijay
The widespread integration of converter-fed renewable energy sources (RESs) and supported by energy storage systems (ESSs), along with their associated challenges, is causing a pressing need to reconsider the operation of distribution networks. Microgrids (MGs) appear as a practical solution to accommodate these RESs and ESSs. This thesis conducts an in-depth analysis of the design and modeling of a generic MG that integrates RESs and ESSs, incorporating a fuzzy logic controller (FLC)-based energy management system (EMS). The MG operation is evaluated in both grid-connected and islanded modes. A frequency analysis reveals that the point of common coupling (PCC) frequency is controlled within the operational range of ± 0.3 Hz, according to IEEE Standard I547. This control is achieved using a combination of battery and supercapacitor ESS in both operational modes. A comparative evaluation of Proportional Integrator (PI) v/s Proportional Resonant (PR) controllers for current controllers for the MG to reduce the amount of total harmonic distortion (THD) at PCC is conducted. Dynamic testing of MG is also conducted under various loading conditions, encompassing small-, medium-, and large-signal changes. The results prove that the PCC voltages are effectively kept within the typical range of ± 0.05 pu through the FLC-based EMS.
Comprehensive integration of safety, optimization, and regulation in ¹⁷⁷Lu-based theranostic radiopharmaceuticals: from production to dose assessment
(2024-03-01) Haghi, Pardis; Waller, Edward
In recent decades, medical radioisotopes have transformed cancer diagnosis and treatment. The advent of theranostic radionuclides, enabling both diagnostic and targeted radiotherapy with a single radionuclide, merges two nuclear medicine fields, enhancing personalized targeted radiotherapy through internal dosimetry. Among these agents, the short-lived β-emitter ¹⁷⁷Lu is a promising theranostic agent for various oncological diseases. This research covers advanced production techniques, radiation safety, transportation, Canadian regulations, and internal dose assessment in radiopharmaceutical clinical applications. The study reviews regulations for obtaining a license from the Canadian Nuclear Safety Commission for a radiopharmaceutical therapy facility. It also examines internal dose assessment in nuclear medicine, explaining each step and providing an introduction to common software codes, along with a comparison of commercial software packages. Two case studies compare software codes and internal dose assessment results, emphasizing the ongoing challenge of determining accurate organ-absorbed doses in the RPT process. The ultimate goal is to comprehensively integrate key aspects of ¹⁷⁷Lu targeted therapy into a unified overview, bridging a crucial gap in understanding.
A Graph Neural Network for pairwise surrogate modeling in population-based algorithms with tournament selection
(2024-04-01) Gharavian, Vida; Makrehchi, Masoud; Rahnamayan, Shahryar
Optimization problems widely arise in various science and engineering fields. Optimisation involves evaluating a candidate solution, which can be computationally intensive. Machine learning-based surrogate models can contribute to learning the specific pattern among the decision variables and objective values to reduce the computation time of fitness evaluation. In this study, we have proposed a novel pairwise surrogate model to identify the superiority between candidate solutions in a pairwise comparison. We demonstrated a Graph Neural Network (GNN) to be trained on number of pairs, then utilized to compare a pair of candidate solutions. To examine the efficacy of our model, we utilized the surrogate model on CEC2017 benchmarks in different dimensions. Moreover, the result of surrogate-assisted and none-assisted form of two well-known optimization algorithms were compared. Results show that the proposed method can significantly reduce the computing cost. In the presence of higher dimensions, our model is more effective than most surrogate models for comparison-based optimizers.

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