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Model Parameter Identification of Supercapacitors Using Metaheuristic Gradient Based Optimization
No full textA Master of Science thesis in Mechatronics Engineering by Ahmad Hussein Yasin entitled, “Model Parameter Identification of Supercapacitors Using Metaheuristic Gradient Based Optimization”, submitted in November 2023. Thesis advisor is Dr. Rached Dhaouadi and thesis co-advisor Dr. Shayok Mukhopadhyay. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Energy storage plays an essential role in both conventional and renewable energy systems, serving as a backup power source and maintaining grid stability between load-demand cycles. The effective control of energy transfer between the storage systems and the power source is of the utmost importance. Supercapacitors are notable within the realm of storage alternatives due to their suitability for high-power density applications. These technologies find applications in several domains, such as in the use of regenerative braking systems in electric vehicles and the utilization of burst mode power sources. This study investigates the parameterization of the Zubieta model, which is an electrical circuit model employed for supercapacitors. This is carried out through the utilization of a hybrid metaheuristic gradient-based optimization (MGBO) methodology. The Zubieta model is composed of three RC branches and an additional self-discharge branch, which necessitates the identification of seven parameters. The research compares the modified MGBO (M-MGBO) approach with particle swarm optimization (PSO) and two PSO variations. One approach combines Particle Swarm Optimization (PSO) and (M-MGBO), while the other incorporates a Local Escaping Operator (LCEO) to enhance the creation of positions and prevent convergence to local minima. The evaluation of performance encompassed the assessment of convergence rate, accuracy, and convergence time. The study's findings indicate that the hybrid PSO-MGBO and PSO-LCEO versions outperformed the conventional PSO approach, showing an average enhancement percentage of 51% and 94%, respectively. Additionally, both variants demonstrated a comparable level of effectiveness to the M-MGBO technique. These variations offer an effective approach for estimating the parameters of the Zubieta model, which has implications for the design and implementation of energy storage systems utilizing supercapacitors. This study highlights the potential of hybrid optimization strategies in improving the precision and effectiveness of supercapacitor model parameterization.College of EngineeringMultidisciplinary ProgramsMaster of Science in Mechatronics Engineering (MSMTR
Cold Thermal Storage System Design and Implementation On A Compressed Air Energy Storage System
No full textA Master of Science thesis in Mechanical Engineering by Rashid Ahmed Jasim Al-Rashid entitled, “Cold Thermal Storage System Design and Implementation on a Compressed Air Energy Storage System”, submitted in May 2023. Thesis advisor is Dr. Mehmet Orhan and thesis co-advisor is Dr. Abdul Hai Al-Alami. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Mechanical EngineeringMaster of Science in Mechanical Engineering (MSME
Artificial Intelligence to Enhance the Drilling of Composites
No full textA Master of Science thesis in Engineering Systems Management by Ibrahim Al Alami entitled, “Artificial Intelligence to Enhance the Drilling of Composites”, submitted in December 2023. Thesis advisor is Dr. Noha Hussein. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Advances in the study of fibre reinforced polymers have led to a huge interest in applying them to multiple fields as an alternative to more costly materials such as their metallic counterparts. However, if the machining of fibre reinforced polymers is done incorrectly this will lead to many defects. Such problems might lead to the underutilization of the fibre reinforced polymers; therefore, optimizing the drilling process is necessary to eliminate the defects. Drilled composite panels must be free of defects for them to succeed in their structural applications. Therefore, the objective of this study is to enhance the drilling process of composites by developing a machine learning mathematical model which will be able to predict the failure behaviour considering the delamination area and fibre pullout area as the response variables in terms of a set of process parameters. The proposed methodology consists of several steps to assess the quality of the drilled hole. Firstly, the composite material selection discusses the process of selecting a specific composite material taking into consideration the material’s properties. Secondly, the experimental setup describes how the experiments were conducted and what machines and tools were used in the process. Thirdly, different inspection techniques are proposed to monitor the quality of a drilled hole during the drilling process and after. Lastly, the modelling of the response variable in terms of the process parameters and the process monitoring variable. Based on a specific sample thickness and tool diameter for the composite panel the machine learning model developed was able to provide the optimum feed rate and spindle speed values needed to attain the minimum delamination area and fibre pullout area. In addition, the in-process monitoring identified a threshold value for the delamination area in terms of the force exertion.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM
Neuro-Linguistic Programming in the ESL Classroom in the UAE
No full textA Master of Arts thesis in Teaching English to Speakers of Other Languages (TESOL) by Natallia Prakharenka entitled, “Neuro-Linguistic Programming in the ESL Classroom in the UAE”, submitted in November 2023. Thesis advisor is Dr. Phillip McCarthy. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Neuro-Linguistic Programming (NLP) is a practical approach to language learning and teaching that focuses on understanding the thought and behavior patterns underlying communication and skill acquisition (Day, 2008). Despite the global success of NLP in various contexts, its implementation in the UAE remains understudied (Hejase, 2015). NLP has formed a unique theoretical nomenclature and introduced such concepts as pillars of NLP, sensory acuity, and a map of reality. This research explores whether participants unconsciously employ NLP techniques in their daily lives and academic pursuits and whether the pillars are a valid construct. The data for the study is based on a Likert scale questionnaire administered to 163 undergraduate students at a major university in the Gulf region. The statistical procedure of factor analysis is employed to analyze the survey results. The findings of this research contribute to the existing literature on NLP in education, particularly the discussion of such key constructs as NLP pillars, facilitating cross-cultural comparisons and inspiring further investigations into implementing NLP strategies in ESL classrooms.College of Arts and SciencesDepartment of EnglishMaster of Arts in Teaching English to Speakers of Other Languages (MA TESOL
Structural and Durability Performance of 3D Concrete Printing
No full textA Master of Science thesis in Civil Engineering by Abdalla Ghoneim entitled, “Structural and Durability Performance of 3D Concrete Printing”, submitted in July 2023. Thesis advisor is Dr. Adil Tamimi. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).3D concrete printing, 3DCP, is considered the latest digital technology in the construction industry, it has proven its potential in a wide range of disciplines. Recently, there has been growing interest in exploring 3D concrete printing as a novel construction method. This technique offers several advantages, including reduced waste, decreased labor-force requirements, and the ability to create complex geometries. However, utilizing concrete for 3D printing presents its own set of challenges. The material needs to maintain a flowable consistency to facilitate extrusion, while ensuring that the printed layers maintain their shape and quickly develop sufficient strength to support their own weight and subsequent layers. Unfortunately, there is a lack of guidance available for designing concrete mixes specifically tailored for 3D printing. Furthermore, there is limited understanding of how 3D printed concrete performs under varying environmental conditions. This study will assess the durability of 3DCP through water permeability, water absorption, ISAT and RCPT by testing a total of 48 specimens. The findings of the durability study revealed that the inclusion of fibers in the mix improved the stability of the printed shapes. Printed samples were less durable than cast ones due to their high porosity ratio and lack of compacting. However, samples with fibers showed higher permeability and absorption ratios compared to non-fiber reinforced samples, due to the inconsistency of the fiber distribution. Furthermore, the experimental program addressed fresh and hard state properties through flow table, vicat needle, compressive and flexural strengths using 45 different samples. The results of the flow percentage of the mix without fibers was 86%, however, the percentage of the mix containing fibers was less by 10%. The results of the Vicat needle test of the mix with fibers showed higher shape stability parameters. The compressive strength of the mix was almost similar for samples without fibers compared to cubes with fibers, with strengths of 49 and 50 MPa respectively. The failure state in flexure of the samples with no fibers was instant, whereas in comparison, the samples with fibers showed a gradual increase in strength as the beam deflects, which shows an elastic behavior.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
A Low Power Frequency Synthesizer Design for Wireless Power Transfer Applications
No full textA Master of Science thesis in Electrical Engineering by Maryam Rashed Obaid AlSuwaidi entitled, “A Low Power Frequency Synthesizer Design for Wireless Power Transfer Applications”, submitted in April 2023. Thesis advisor is Dr. Lutfi Albasha and thesis co-advisor is Dr. Hasan Mir. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Wireless power transfer (WPT) is an essential technology for the Internet of Things (IoT) as it enables the deployment of untethered and self-sustaining devices, which eliminates the need for wired power connections. With the increasing number of IoT devices, the demand for wireless charging is also growing, as it can simplify the deployment and maintenance of these devices, reduce the complexity and costs associated with their installation and operation, and allow for the creation of a truly autonomous and ubiquitous network of interconnected devices. A recently developed technique that implements Frequency Diverse Antenna (FDA) beamforming that can localize the power transmission to a desired location has been proposed, yet not verified using actual hardware. The design of a transmitter chip dedicated for FDA-based WPT is of great importance as it will prove the applicability of the FDA technique and will be a critical step towards the realization of the full potential of FDA-based WPT transmitters for IoT and various applications. One of the main blocks of a transmitter chip is the phase-locked loop (PLL), also known as the frequency synthesizer, which provides a clean carrier signal to be used for the up-conversion process. In this context, this thesis aims to design an energy-efficient frequency synthesizer, which can generate a carrier signal of 5.8 GHz, to be implemented in the FDA-based WPT transmitter chip. The total frequency synthesizer system was designed and simulated using Cadence Virtuoso on TSMC 65-nm CMOS technology, where it was able to lock to the desired frequency in 7.7 μsec with a total power consumption of 8.04 mW from a 1 V supply and a VCO phase noise of -114.6 dBc/Hz at 1 MHz offset. The novelty of this work lies in the modularity of the designed PLL structure where it allows for the reusability of all the designed circuits, except the VCO, to be used to generate different frequencies up to 29 GHz. In addition, enhanced performance of the phase frequency detector and divide-by 2 circuits where achieved. This proposed system is considered a future-proof design for future WPT transmitter chips designed at different frequencies.College of EngineeringDepartment of Electrical EngineeringMaster of Science in Electrical Engineering (MSEE
Detecting and Predicting Archaeological Sites Using Remote Sensing and Machine Learning—Application to the Saruq Al-Hadid Site, Dubai, UAE
No full textIn this paper, the feasibility of satellite remote sensing in detecting and predicting locations of buried objects in the archaeological site of Saruq Al-Hadid, United Arab Emirates (UAE) was investigated. Satellite-borne synthetic aperture radar (SAR) is proposed as the main technology for this initial investigation. In fact, SAR is the only satellite-based technology able to detect buried artefacts from space, and it is expected that fine-resolution images of ALOS/PALSAR-2 (L-band SAR) would be able to detect large features (>1 m) that might be buried in the subsurface (<2 m) under optimum conditions, i.e., dry and bare soil. SAR data were complemented with very high-resolution Worldview-3 multispectral images (0.31 m panchromatic, 1.24 m VNIR) to obtain a visual assessment of the study area and its land cover features. An integrated approach, featuring the application of advanced image processing techniques and geospatial analysis using machine learning, was adopted to characterise the site while automating the process and investigating its applicability. Results from SAR feature extraction and geospatial analyses showed detection of the areas on the site that were already under excavation and predicted new, hitherto unexplored archaeological areas. The validation of these results was performed using previous archaeological works as well as geological and geomorphological field surveys. The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. The validated results can provide guidance for future on-site archaeological work. The pilot process developed in this work can therefore be applied to similar arid environments for the detection of archaeological features and guidance of on-site investigations
A Decision Support Model for Pre-qualifying Construction Contractors using 2S-ECI
No full textA Master of Science thesis in Construction Management by Faisal Ayman Mustafa Alhendi entitled, “A Decision Support Model for Pre-qualifying Construction Contractors using 2S-ECI”, submitted in November 2023. Thesis advisor is Dr. Sameh El-Sayegh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The construction industry is distinguished by its complexity and the requirement for innovation and efficiency. The Two-Stage Early Contractor Involvement (2S-ECI) delivery approach has attracted international attention for its potential to address industry difficulties. This thesis examines the applicability of 2S-ECI in the United Arab Emirates (UAE) and develops a customized prequalification method for first-stage contractors’ selection to ensure its successful implementation. The primary goals of this research are twofold: first, to identify and assess the benefits and challenges of using the 2S-ECI delivery method in the unique context of the UAE construction industry; and second, to categorize and adapt 2S-ECI prequalification criteria to create a decision support model specifically tailored for evaluating 2S-ECI contractors operating in the UAE. In order to achieve the first goal, the benefits and challenges were identified through literature review. This is followed by a survey to determine their importance using Likert scale. The results revealed that the main benefits include incorporating contractor input during the design process, accelerating the project schedule, and improving the mitigation of risks. Additionally, the results revealed that the main challenges include unavailability of a clear first-stage contractor prequalification assessment, lack of ECI experience, and that 2S-ECI might not be suitable for all types of projects. To accomplish the second goal, sixteen prequalification criteria were identified through literature review, followed by a second survey using the Analytical Hierarchy Process (AHP) to determine the weights of the selected prequalification criteria. The results indicate that the main criteria include demonstrated experience in previous projects involving 2S-ECI, proven expertise and proficiency in the specific project type, and positive references and a history of satisfied past clients. A tailored decision-support model is proposed for evaluating and selecting 2S-ECI contractors in the UAE. The methodology ensures that the contractors selected for the first stage of the 2S-ECI process are best prepared to satisfy the specific demands and requirements of construction projects in the UAE. The findings of this study have the potential to transform the UAE construction industry.College of EngineeringMultidisciplinary ProgramsMaster of Science in Construction Management (MSCM
Reconfiguring European industry for net-zero: a qualitative review of hydrogen and carbon capture utilization and storage benefits and implementation challenges
No full textThis research study presents a dynamic discrete optimization model for the treatment of municipal solid waste (MSW) with sustainability as an essential research objective. The optimization model screens capacity selections for MSW technologies over distributed sites with consideration of pretreatment biodrying technologies for waste calorific value enhancement. The choices for these MSW technologies and the network operation are described by binary and continuous variables, respectively. The MSW network economic seeks the maximization of the net present value (NPV). Key environmental impacts of MSW technology use as well as the impacts of MSW transportation are described by a minimization optimization problem. The social target of the MSW network considers the maximization of job creation. When applying the model to a case study, sustainability objective functions showed conflict in the results. Pareto optimal solutions are found from the multi-objective optimization model and a compromised solution is suggested for the considered case study