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Closed-Loop Control of Surface Preparation for Metallizing Fiber-Reinforced Polymer Composite
Get PDFTo improve the surface properties of fiber-reinforced polymer composites, one method is to employ thermal spray to apply a coating on the composite. For this purpose, it uses a metal mesh serving as an anchor between the composite and the coating to increase adhesion. However, the metal mesh is covered by resin, and getting an acceptable coating is only possible through an optimum exposure of the metal mesh by sand blasting prior to coating. Therefore, this study aims to develop a closed-loop control system to inspect and blast the parts properly. Specifically, this approach takes the top-view images from a microscope as the inputs. A convolutional neural network (CNN) is trained to correlate these images with the corresponding exposure levels of the metal mesh, measured by a destructive method. Then, this trained CNN model can estimate the exposure level by only analyzing the top-view images. Finally, it serves as a feedback mechanism to guide the subsequent sandblasting operations. The state-of-the-art has only examined the sand-blasted composites manually, requiring expertise and experience. This method automates the inspection process efficiently with an inexpensive portable digital microscope. The experimental results show that the method can distinguish the status of surface preparation successfully, and it is practical in closed-loop control. This study also has applications to various fields of manufacturing for defect detection and closed-loop control
Traduction sensible et située en sciences humaines et sociales: Shame Before Others de Sara Ahmed (2014)
Get PDFJe propose la traduction de l’anglais vers le français du chapitre « Shame Before Others », tiré de l’ouvrage The Cultural Politics of Emotion (2014 [2004]) écrit par Sara Ahmed. La chercheure y analyse les relations entre corps, langage et émotions à travers le prisme du politique, du social et du pouvoir. Dans le chapitre choisi, Sara Ahmed observe la construction de l’identité nationale australienne au travers du partage supposé du sentiment de honte. L’autrice dissèque les discours politiques au moyen de la théorie des actes de langage d’Austin; elle use de la déconstruction derridienne pour extirper les racines de la honte. En ce sens, la pensée de l’autrice s’inscrit dans le discours féministe tel que défini par Barbara Godard : « Feminist discourse works upon language, upon the dominant discourse, in a radical interrogation of meaning. » (1990, p. 46). Je traduis ce texte en tant que traductrice et chercheure féministe selon la théorie des savoirs situés de Donna Haraway. Je mets en pratique deux concepts clés de cette théorie, l’encorporation des savoirs et la connexion partielle, et montre la pertinence d’un tel positionnement dans le cadre de la traduction d’un texte de sciences humaines et sociales à visée féministe. Ainsi, j’adjoins « les sens à la levée du sens » (Samoyault, 2020, p. 181)
Sustainable Pattern Library: Facilitating Designer's Shift to Sustainable Design Practices
Get PDFAs the demand for digital products continues to grow, prevailing design methods that largely ignore ecological or societal well-being have become an urgent problem over the last two decades. These methods of creating have led to digital products that exploit human vulnerabilities, weaken our collective view of reality and decouple the digital from the physical. This reality, blended with the energy-intensive infrastructure underpinning the internet, has resulted in an ecological footprint of the internet that rivals some of the world's highest-polluting countries. Looking at prominent design practices through the lens of their societal and ecological impacts over the last twenty years, we can see how current design processes serve to “defuture” us by damaging our planet and societal wellbeing.
Perpetuating this issue, is the fact that there are few resources for designers to aid them in alternative ways of creation. This dearth of resources that could help designers understand how to create sustainable digital products, led to the investigation of pattern libraries and ultimately the research question of “how might design patterns, in the form of a pattern library, persuade designers to shift to more sustainable UX practices?”
To develop the pattern library, a collection of research was consulted, to translate the theoretical knowledge into a set of interface patterns that can lead to more sustainable outcomes when implemented. The goal of this pattern library was to develop a tangible resource that can practically assist designers in adopting more sustainable methods of creation. This library was then tested to understand how well it achieves these goals and where the areas for improvement exist to ultimately create a tool that designers across all industries can turn to. These patterns demand that the powerful technologies that we use daily are designed for humans – and the planet we depend upon
Development of Waste Tire Gasification Process to Methanol with Comparative Lifecycle Assessment
Get PDFThis thesis presents an innovative approach to the escalating global issue of waste tire accumulation, focusing on the design and development of a novel waste tire (WT) electrified gasification process for methanol production. The proposed solution addresses the environmental impacts of existing recycling methods and offers a greener alternative. The study involves a comparative analysis of two primary processes: the conventional waste tire (WT-Conventional) and the proposed waste tire electrified (WT-Electrified) pathways. These processes are designed and simulated using AspenPlus software to ensure accuracy and feasibility.
A comprehensive techno-economic analysis is conducted for both processes, providing an in-depth understanding of their economic viability. Furthermore, this research expands its scope by implementing a Life Cycle Assessment (LCA) performed through OpenLCA software. The LCA results not only facilitate a comparison between different electricity generation sources but also benchmark the proposed pathway against other conventional routes for methanol production.
The research findings reveal promising results for the WT-Electrified process. Key performance indicators such as thermal efficiency, CO2 emissions, and economic analysis demonstrate its potential superiority over the conventional counterpart. This thesis underscores the potential of the WT-Electrified gasification process as an environmentally-friendly and economically viable solution for waste tire management. It provides a solid foundation for further exploration and refinement, paving the way towards a more sustainable future
Modeling of Current–Voltage Characteristics of Thin-film Solar Cells Incorporating Bulk and Surface Recombination: application to perovskite solar cells
No full textPhotovoltaic solar cell is one of the most important renewable energy sources, which can supply
required energy to various electronic devices as well as enormous energy to power grids. Among
various photovoltaic devices, thin-film solar cells provide high power conversion efficiency at
lower production cost. Though they provide reasonably high efficiency, there is possibility to
improve the efficiency further through properly understanding the efficiency limiting factors. To
achieve this goal, a physics-based compact analytical model for studying the carrier distribution
and resultant photocurrent alongside with the current-voltage (J-V) characteristics of bulk
heterojunction (BHJ) perovskite solar cells (PSC) has been proposed in this thesis by considering
exponential photon absorption profile, bulk and surface recombination, and carrier drift &
diffusion in the photon absorption layer.
By solving the continuity equation for both electrons and holes in the perovskite layer, it is possible
to construct an analytical formula for the position-dependent carrier concentration and associated
external voltage-dependent photocurrent. The position dependent total conduction current (sum of
the drift and diffusion currents of both holes and electrons) under steady-state is found to be space
invariant which is exactly equal to the total photocurrent calculated using the Shockley-Ramo's
theorem. The calculation of the total load current considers the actual solar spectrum, photocurrent
and voltage-dependent forward dark current. The mathematical model is fitted with experimental
results of various perovskite solar cells and useful physical transport parameters are extracted by
comparing the model calculations with the published experimental data. The effects of
recombination on the photocurrent and overall efficiency are analysed quantitatively. The charge
carrier transport parameters, especially the surface recombination velocity, have very significant
effects on the current-voltage characteristics and power conversion efficiency
Cerebrovascular Pathology Segmentation Using Weakly Supervised Deep Learning Methods
Get PDFIntracranial hemorrhage (ICH) and unruptured intracranial aneurysm (UIA) are two important cerebrovascular diseases that require prompt and precise diagnosis for effective treatment and improved survival rates. While deep learning (DL) techniques have emerged as the leading approach for medical image analysis and processing, the most commonly employed supervised learning often requires large, high-quality annotated datasets that can be costly to obtain, particularly for pixel/voxel-wise image segmentation. To address this challenge and meet the need in cerebrovascular care, we proposed and validated three novel weakly supervised segmentation methods for ICH using categorical labels and for UIA with coarse image segmentation. For ICH, we first introduced a framework to segment the lesion based on a hierarchical combination of self-attention maps obtained from a Swin transformer, which was trained only for ICH detection, achieving a Dice score of 0.407. Subsequently, by employing novel head-wise gradient-weighing of self-attention maps in the same setup, we further improved the mean Dice score to 0.444 for ICH segmentation. Our method that only relies on categorical labels showed comparable performance against popular fully supervised methods, such as UNet and Swin-UNETR. Finally, for UIA segmentation, we achieved a Dice score of 0.68 and a 95% Hausdorff distance of ~0.95 mm by proposing a new 3D focal modulation UNet, called FocalSegNet. This novel DL architecture was trained with coarse manual segmentation, providing an initial segmentation of aneurysms, which was then refined using dense conditional random field (CRF) post-processing. Our proposed methods explored new avenues using weak labels to mitigate a key bottleneck in medical DL with excellent performance and showcased their promising potential in addressing challenging medical image segmentation tasks
Automatic Generation of Residential Thermal Network Models for Predictive Control from Smart Thermostat Data
Get PDFModel Predictive Control (MPC) can help a building achieve specific objectives, such as reducing operating cost, minimizing energy consumption, or implementing demand response measures. As its name indicates, MPC relies on an accurate building model. However, determining the model structure and level of detail can be challenging. An extensive analysis on a building-by-building basis is typically required, involving significant time and cost. The task of creating a model remains a critical hurdle for the large-scale uptake of MPC.
This thesis contributes a systematic method to generate control-oriented residential building thermal models with focus on day-ahead predictions for MPC. The method relies on data from smart thermostats, since their widespread adoption provides a unique opportunity to develop advanced control strategies. The method presented here can be categorized into two main approaches: single-zone and multi-zone models.
When detailed data are available for each room, multi-zone models may provide better estimates of comfort and flexibility. Québec presents an excellent opportunity for testing multi-zone models because of its widespread utilization of decentralized electric baseboards that allow for individual room control. This research introduces a novel automatic method for multi-zone model generation and selection. The methodology starts with a very simple model and iteratively increases the complexity of the model until the model quality cannot increase further. It is then applied on data from an unoccupied experimental house in Shawinigan, Québec. The resulting 13th-order model can accurately predict all 9 zone temperatures 24 hours in advance, with a Root Mean Squared Error of less than 0.5 °C and its parameters reflect the layout of the house, previously unknown to the methodology.
The method was applied in a real-time MPC framework to the experimental house during demand response events and compared to MPC using low-order models and a “business-as-usual” (BAU) reference approach. The MPC employing the multi-zone model modeled the building thermal mass separately and managed to leverage it better to preheat more before demand response events compared to the low-order models. The MPC controller with the multi-zone model reduced electricity costs by 55% compared to the BAU scenario; it also outperformed the 40% cost reduction achieved by MPC controllers based on low-order models.
On the other hand, a single thermal zone representation can produce sufficiently accurate predictions when coupled with (uncertain) weather and occupancy forecasts. Second-order single-zone models of 7,800 houses in Ontario and Québec were used to investigate the most suitable data length, data interval and calibration horizon of building models for use in an MPC framework. Overall, models with a calibration horizon of 24 hours, data length of 7 days and time interval of 15 minutes provided the best balance between accuracy and computational resources. The models were then used to assess the large-scale deployment of MPC strategies under existing time-of-use tariffs and dynamic pricing. Results showed that the adoption of MPC can reduce the daily electricity cost on average by 16% in Ontario and by 31% in Québec, respectively.
Lastly, this thesis used smart thermostat data to model and characterize 60,000 homes across North America (the resulting model parameters have been made publicly available, enabling building archetypes and building-to-building knowledge transfer). The results showed that just modeling the indoor air temperature of the building may not suffice. Instead, single-zone models need additional states (e.g., for effective temperature of the exterior and/or interior building materials) for accurate predictions. The building time constants were computed as a means to assess building thermal storage ability
Design and Simulation of Vehicle-to-Load System with Nissan Leaf
Get PDFThe fact that the Global Warming problem poses a more significant threat to our society every day has pushed us to use energy more efficiently and cleanly. Using power more efficiently in every aspect of our lives can pave the way for achieving the goal of reducing global gas emissions and saving the planet. This can be done by applying various approaches. One of the most effective ways is to use electric car technology, which is one of the best ways not to cause more carbon emissions and to increase energy efficiency and savings.
This study aims to design and simulate a vehicle-to-load system using an electric vehicle, Nissan Leaf, to power emergency independent loads of the Future Building Laboratory (FBL). The FBL is a solar research house at the Loyola Campus of Concordia University, Montréal, Canada. This research facility is built to investigate numerous renewable energy systems that can help achieve the net-zero energy goal for a typical detached single-family dwelling in Québec. It has integrated renewable energy sources such as solar, solar-thermal, and wind, allowing the opportunity to test different power management scenarios.
In this research, the vehicle-to-load system of the FBL and Nissan Leaf is designed and simulated in MATLAB software, considering the house's rated load and the real-life system's exact ratings. The design reflects the actual characteristics of the load, EV battery, and power electronic elements in interaction. The simulation is a straightforward model of the actual system.
The last step is to validate the simulation results. The simulation model was tested experimentally at the PEER group laboratory at Concordia University, using the available converters, devices, and a real-time DSP microcontroller. Various experiments are conducted to observe the system's performance in real conditions. All time-domain and frequency-domain results match the ones obtained via simulation.
Methods for enabling the discharging feature of EVs that utilize CHAdeMO are studied and explored. The structure of the CHAdeMO connector and charging sequence are explained. Possible integration methods for Vehicle-to-Home are also explored
Defining targets and limits to urban sprawl: Are proposed greenbelt scenarios sufficient to achieve these benchmarks for Montreal by 2070?
Get PDFIncreasing awareness of the negative effects of urban sprawl has ignited a significant debate on this issue in Montreal and has emerged as a serious concern. Rapid increase in urban sprawl between 1951 and 2016 within the Montreal Census Metropolitan Area (CMA) highlights the urgency of addressing this challenge. Efforts to protect Montreal's forests, agricultural lands, and other open areas from further urban sprawl have become increasingly important. This study assesses several greenbelt scenarios as potential strategies to control urban sprawl. To explore potential future pathways and provide guidance for future planning, this study proposes targets, limits, and warning values to urban sprawl as a reference framework. Various urban development scenarios for the Montreal CMA and its Census Subdivisions (CSDs) until 2070 are developed and evaluated. Scenarios 1 to 3 are evaluated as unsustainable, scenario 4 represents a transitional range toward sustainability, scenario 5 is somewhat sustainable, and scenario 6 is sustainable. The Montreal CMA is surrounded by valuable natural areas, including agricultural lands which provide an opportunity to establish a greenbelt around built-up-areas. This study assesses four greenbelt scenarios to evaluate their potential for curbing urban sprawl. At the CMA level, the analysis reveals that while greenbelt scenarios significantly reduce sprawl compared to the current trend, they remain inefficient to achieve the limit to urban sprawl in Montreal. None of the proposed greenbelt scenarios reaches the desirable limits or targets and fall beyond the warning values. However, at the CSD level, the greenbelt scenarios significantly affect certain areas, with Gore projected to meet its target and several other CSDs falling within the range between the limit and the warning value, demonstrating effectiveness at curbing urban sprawl. This research demonstrates the potential of greenbelts to positively influence urban development patterns towards sustainability, even if the current proposal does not fully achieve the defined targets and limits. Further improvement and adaptation of these strategies may lead to more sustainable urban development outcomes in the long term. This study introduces a quantitative reference framework for evaluating the effectiveness of potential growth management strategies in the Montreal CMA and its CSDs. The findings offer a valuable perspective on the potential future of urban sprawl and allow for a comparison of various planning alternatives
Sustainable Energy Management System for AIoT Solutions Using Multivariate and Multi-step Battery State of Charge Forecasting
Get PDFThe convergence of Artificial Intelligence (AI) with Internet of Things (IoT) technologies, often referred to as AIoT, is transforming aspects of modern life, such as smart cities. This transformation, however, brings with it challenges, including energy management. In addressing this issue while upholding responsible AI principles, it is important to prioritize the sustainability of AIoT solutions by a promising approach which is using renewable energy sources. While renewable energy offers numerous advantages, its intermittent nature necessitates effective power management systems. Developing a power management system serving as a decision-making platform for AIoT-driven solutions is the goal of this study. This platform contains two critical components: accurate forecasts of battery "State of Charge" (SoC), and the implementation of appropriate control strategies. These strategies include adjusting energy consumption patterns to ensure stable and reliable system operation. This study focuses on accurate battery SoC forecasting, to this end, an experiment has been designed, and a data logging system has been developed to produce suitable data since publicly available datasets do not align with the specific characteristics and requirements of the research. The SoC forecasting in this study has been addressed as a multivariate and multi-step time series forecasting problem, where various machine learning and deep learning models including Decision Tree (DT), Random Forest (RF), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory (Bi-LSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU) were benchmarked. Extensive evaluations have been conducted for different forecasting horizons on datasets with varying time intervals. It is concluded that the Bi-GRU model outperformed other models across datasets with varying time intervals and forecast horizons according to Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) evaluation metrics