Mechanical & Energy Engineering Department Theses and Dissertations

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High Performance Thermal Barrier Coatings on Additively Manufactured Nickel Base Superalloy Substrates
(2023-08) Dube, Tejesh Charles; Zhang, Jing; Jones, Alan S.; Koo, Dan Daehyun; Yang, Shengfeng
Thermal barrier coatings (TBCs) made of low-thermal-conductivity ceramic topcoat, metallic bond coat and metallic substrate, have been extensively used in gas turbine engines for thermal protection. Recently, additive manufacturing (AM) or 3D printing techniques have emerged as promising manufacturing techniques to fabricate engine components. The motivation of the thesis is that currently, application of TBCs on AM’ed metallic substrate is still in its infancy, which hinders the realization of its full potential. The goal of this thesis is to understand the processing-structure-property relationship in thermal barrier coating deposited on AM’ed superalloys. The APS method is used to deposit 7YSZ as the topcoat and NiCrAlY as the bond coat on TruForm 718 substrates fabricated using the direct metal laser sintering (DMLS) method. For comparison, another TBC system with the same topcoat and bond coat is deposited using APS on wrought 718 substrates. For thermomechanical property characterizations, thermal cycling, thermal shock (TS) and jet engine thermal shock (JETS) tests are performed for both TBC systems to evaluate thermal durability. Microhardness and elastic modulus at each layer and respective interfaces are also evaluated for both systems. Additionally, the microstructure and elemental composition are thoroughly studied to understand the cause for better performance of one system over the other. Both TBC systems showed similar performance during the thermal cycling and JETS test but TBC systems with AM substrates showed enhanced thermal durability especially in the case of the more aggressive thermal shock test. The TBC sample with AM substrate failed after 105 thermal shock cycles whereas the one with wrought substrate endured a maximum of 85 cycles after which it suffered topcoat delamination. The AM substrates also demonstrated an overall higher microhardness and elastic modulus except for post thermal cycling condition where it slightly underperformed. This study successfully demonstrated the use of AM built substrates for an improved TBC system and validated the enhanced thermal durability and mechanical properties of such a system. A modified YSZ TBC architecture with an intermediate Ti3C2 MXene layer is proposed to improve the interfacial adhesion at the topcoat/bond coat interface to improve the thermal durability of YSZ TBC systems. First principles calculations are conducted to study the interfacial adhesion energy in the modified and conventional YSZ TBC systems. The results show enhanced adhesion at the bond coat/MXene interface. At the topcoat/MXene interface, the adhesion energy is similar to the adhesion energy between the topcoat and bond coat in a conventional YSZ TBC system. An alternative route is proposed for the fabrication of YSZ TBC on nickel base superalloy substrates by using the SPS technology. SPS offers a one-step fabrication process with faster production time and reduced production cost since all the layers of the TBC system are fabricated simultaneously. Two different TBC systems are processed using the same heating protocol. The first system is a conventional TBC system with 8YSZ topcoat, NiCoCrAlY bond coat and nickel base superalloy substrate. The second system is similar to the first but with an addition of Ti3C2 MXene layer between the topcoat and the bond coat. Based on the first principles study, addition of Ti3C2 layer enhances the adhesion strength of the topcoat/bond coat interface, an area which is highly susceptible to spallation. Further tests such as thermal cycling and thermal shock along with the evaluation of mechanical properties would be carried out for these samples in future studies to support our hypothesis.
Characterization and Simulated Analysis of Carbon Fiber with Nanomaterials and Additive Manufacturing
(2023-12) Omole, Oluwaseun; Dalir, Hamid; Agarwal, Magilal; Tovar, Andres
Due to the vast increase and versatility of Additive Manufacturing and 3D-printing, in this study, the mechanical behavior of implementing both continuous and short carbon fiber within Nylon and investigated for its effectiveness within additively manufactured prints. Here, 0.1wt% of pure nylon was combined with carbon nanotubes through both dry and heat mixing to determine the best method and used to create printable filaments. Compression, tensile and short beam shear (SBS) samples were created and tested to determine maximum deformation and were simulated using ANSYS and its ACP Pre tool. SEM imaging was used to analyze CNT integration within the nylon filament, as well as the fractography of tested samples. Experimental testing shows that compressive strength increased by 28%, and the average SBS samples increased by 8% with minimal impacts on the tensile strength. The simulated results for Nylon/CF tensile samples were compared to experimental results and showed that lower amounts of carbon fiber samples tend to have lower errors.
Optimization and Characterization of Metal Oxide Nanosensors for the Analysis of Volatile Organic Compound Profiles in Breath Samples
(2023-08) Maciel Gutierrez, Mariana; Agarwal, Mangilal; Dalir, Hamid; Nalim, Mohamed Razi
Volatile organic compounds (VOCs) are byproducts of metabolic processes that can be uniquely dysregulated by various medical conditions and are expressed in biological samples. Therefore, VOCs expressed in breath, urine and other sample types may be utilized for noninvasive, rapid, and accurate diagnostics in a point-of-care setting. Currently, the most common methods for VOC detection include gas chromatography-mass spectrometry (GC-MS) and electronic noses (E-noses) that integrate nanosensors. Both methods present important advantages and challenges that allow their implementation for different applications. While GC-MS can be used to directly identify VOCs in complex matrices, it is a non-portable and high-cost instrument. On the other hand, E-noses are portable and user-friendly VOC detectors, but they do not allow for direct VOC identification or quantification. Among different VOC rich sample types, breath offers the advantage of being a virtually limitless source of endogenous biomarkers that can be implemented for noninvasive VOC detection. The presented thesis focuses on the optimization of the operating parameters (heater and sensor voltages) of a metal oxide (MOX) sensor and breath sampling techniques (sensor casing, breath fractionation, and exhalation volume) for their implementation in exhaled VOC analysis. In parallel, an in-house feature extraction algorithm was developed and implemented for the optimization of a MOX sensor composed of a tin oxide (SnO2) sensing layer. The optimized sensor parameters (heater voltage equal to 2 V and sensor voltage equal to 0.8 V) and breath sampling protocol (24 L of whole breath analyzed using the in-house sensor casing design) were tested with exhaled breath samples from distinct volunteers which could be successfully separated with 100% accuracy. The sensor response also showed a high degree of intrasubject reproducibility (RSD < 6%). Additionally, the sensor performance was further validated under ambient conditions, and sensor degradation was studied over the course of 3 months. Finally, sensor response to synthetic VOC profiles and individual VOC standards was explored. Optimized SnO2 sensors distinguished between VOC mixtures regardless of variations in relative humidity (RH) levels. Furthermore, the characteristic sensor response to VOC standards indicates that the sensors are most sensitive toward isopropanol by a factor of 1.15 in 45% RH and a factor of 3.58 in 85% RH relative to isoprene. To translate the potential of MOX sensors to point-of-care biomedical applications, there first exists the need to establish a reference of sensor baseline signals corresponding to exhaled breath samples from healthy individuals. SnO2 sensors and breath sampling methods were implemented for the collection of individual samples from 109 relatively healthy volunteers. 10 of these volunteers provided 9 additional samples over the course of six months. In parallel, exhaled breath samples were also analyzed by GC-MS to comprehensively profile VOCs present in the samples. The results from these experiments not only aid in the identification of the healthy breath signal baseline but also allow the exploration of VOC reproducibility over time. High variation between samples from distinct volunteers was observed, but samples longitudinally collected across volunteers could not be distinguished, alluding to the existence of a universal range of sensor signals that could describe the composition of exhaled breath from healthy subjects. Finally, results were compared with relevant confounding variables to better understand how VOCs are impacted by an array of factors that are not directly correlated to disease diagnosis. Sensor signals were significantly elevated in breath samples from male volunteers compared to samples from female subjects (p-value = 0.044). Interestingly, isoprene signals resulting from the GC-MS analysis were also higher in male subjects relative to females. No other relationships were identified between sensor signals and the confounding variables of interest. Future work would require a deeper understanding of sensor degradation and life cycle, along with sensor testing using a broader range of individual VOC standards and more complex VOC profiles. Additionally, further comparison between sensor signal and GC-MS signal of relevant VOC biomarkers present in breath would be beneficial. Nonetheless, the presented be leveraged in future investigations aiming to identify biomarkers for different medical conditions. Finally, the findings disclosed in the deposited thesis suggest the ability of a SnO2 nanosensor array to be implemented for breath analysis, providing a noninvasive, easy to use, and reliable diagnostic device in a point-of-care setting.
Enhancing Mechanical Engineering Education Through a Virtual Instructor in an Ai-Driven Virtual Reality Fatigue Test Lab
(2023-08) Yahyaeian, Amir Abbas; Jones, Alan; Zhang, Jing; Du, Xiaoping
This thesis demonstrates the combination of virtual reality (VR) and artificial intelligence (AI) specifically exploring the practical application of Natural Language Processing (NLP) and GPT-based models in educational VR laboratories. The objective is to design a comprehensive learning environment where users can independently engage in laboratory experiments, deriving similar educational outcomes as they would from a traditional, physical laboratory setup, particularly within the realms of Science, Technology, Engineering, and Mathematics (STEM) disciplines. Using machine learning techniques and authentic virtual reality simulating educational experiments, we propose an advanced learning platform—Virtual Reality Instructional Laboratory Environment (VRILE). A key feature of the VRILE is an AI-powered instructor capable of not only guiding the learners through the tasks but also responding intelligently to their actions in real-time. The AI constituent of the VRILE uses the GPT-2 model for text generation in the field of Natural Language Processing (NLP). To ensure the generated instructions were contextually relevant and meaningful to lab participants, the model was trained on a dataset derived from an augmentation over user interactions within the VR environment. By pushing the boundaries of how AI can be utilized in educational VR environments, this research paves the way for broader adoption across other domains of engineering education. Furthermore, it provides a solid foundation for future research in this interdisciplinary field. It marks a significant stride in the integration of technology and education, encouraging more ventures into this promising frontier.
Modeling Acute Care Utilization for Insomnia Patients
(2023-08) Zhu, Zitong; Fang, Shiaofen; Ben Miled, Zina; Xia, Yuni; Zheng, Jiangyu
Machine learning (ML) models can help improve health care services. However, they need to be practical to gain wide adoption. A methodology is proposed in this study to evaluate the utility of different data modalities and cohort segmentation strategies when designing these models. The methodology is used to compare models that predict emergency department (ED) and inpatient hospital (IH) visits. The data modalities include socio-demographics, diagnosis and medications and cohort segmentation is based on age group and disease severity. The proposed methodology is applied to models developed using a cohort of insomnia patients and a cohort of general non- insomnia patients under different data modalities and segmentation strategies. All models are evaluated using the traditional intra-cohort testing. In addition, to establish the need for disease- specific segmentation, transfer testing is recommended where the same insomnia test patients used for intra-cohort testing are submitted to the general-patient model. The results indicate that using both diagnosis and medications as a source of data does not generally improve model performance and may increase its overhead. For insomnia patients, the best ED and IH models using both data modalities or either one of the modalities achieved an area under the receiver operating curve (AUC) of 0.71 and 78, respectively. Our results also show that an insomnia-specific model is not necessary when predicting future ED visits but may have merit when predicting IH visits. As such, we recommend the evaluation of disease-specific models using transfer testing. Based on these initial findings, a language model was pretrained using diagnosis codes. This model can be used for the prediction of future ED and IH visits for insomnia and non-insomnia patients.
Research and Development of Electric Micro-Bus Vehicle Chassis
(2022-12) Coovert, Benjamin; Tovar, Andres; Nematollahi, Khosrow; El-Mounayri, Hazim
In this project, a chassis concept has been developed for a small electric vehicle ’minibus’. The vehicle is intended to be used as a transport between agricultural locations in Ethiopia to cities where the products can be sold. The objective is to develop a chassis that can house several different modular structures for the purposes of transporting refrigerated goods, a mobile power grid, or people. Literature studies have been conducted on current electric vehicle markets, battery markets, chassis materials, and optimal cross-sections. The battery housings have also been analyzed from an environmental perspective to account for conditions in Ethiopia. Based on this, it was found that a four-wheeled ’minibus’ design including space for approximately fourteen custom batteries is optimal. It is essential to keep in mind that this project has been carried out both on a conceptual level within the framework of a degree project as well as a production project for use in Ethiopian rural areas. This master thesis project aims to provide a solid benchmark for further development and research within the subject.
Fundamental Investigation of Direct Recycling Using Chemically Delithiated Cathode
(2022-12) Bhuyan, Md Sajibul Alam; Shin, Hosop; Zhu, Likun; Wei, Xiaoliang
Recycling valuable cathode material from end-of-life (EOL) Li-ion batteries (LIBs) is essential to preserve raw material depletion and environmental sustainability. Direct recycling reclaims the cathode material without jeopardizing its original functional structures and maximizing return values from spent LIBs compared to other regeneration processes. This work employed two chemically delithiated lithium cobalt oxide (LCO) cathodes at different states of health (SOH), which are analogous to the spent cathodes but free of any impurities, to investigate the effectiveness of cathode regeneration. The material and electrochemical properties of both delithiated SOHs were systematically examined and compared to pristine LCO cathode. Further, those model materials were regenerated by a hydrothermal-based approach. The direct cathode regeneration of both low and high SOH cathode samples restored their reversible capacity and cycle performance comparable to pristine LCO cathode. However, the inferior performance observed in higher current density (2C) rate was not comparable to pristine LCO. In addition, the higher resistance of regenerated cathodes is attributed to lower high-rate performance, which was pointed out as the key challenge of the cathode recycling process. This study provides valuable knowledge about the effectiveness of cathode regeneration by investigating how the disordered, lithium-deficient cathode at different SOH from spent EOL batteries are rejuvenated without changing any material and electrochemical functional properties.
Calibration and Validation of a High-Fidelity Discrete Element Method (DEM) based Soil Model using Physical Terramechanical Experiments
(2022-08) Ghike, Omkar Ravindra; El-Mounayri, Hazim; Tovar, Andres; Zhang, Jing
A procedure for calibrating a discrete element (DE) computational soil model for various moisture contents using a conventional Asperity-Spring friction modeling technique is presented in this thesis. The procedure is based on the outcomes of two physical soil experiments: (1) Compression and (2) unconfined shear strength at various levels of normal stress and normal pre-stress. The Compression test is used to calibrate the DE soil plastic strain and elastic strain as a function of Compressive stress. To calibrate the DE inter-particle friction coefficient and adhesion stress as a function of soil plastic strain, the unconfined shear test is used. This thesis describes the experimental test devices and test procedures used to perform the physical terramechanical experiments. The calibration procedure for the DE soil model is demonstrated in this thesis using two types of soil: sand-silt (2NS Sand) and silt-clay(Fine Grain Soil) over 5 different moisture contents: 0%, 4%, 8%, 12%, and 16%. The DE based models response are then validated by comparing them to experimental pressure-sinkage results for circular disks and cones for those two types of soil over 5 different moisture contents. The Mean Absolute Percentage Error (MAPE) during the compression calibration was 26.9% whereas during the unconfined shear calibration, the MAPE was calculated to be 11.38%. Hence, the overall MAPE was calculated to be 19.34% for the entire calibration phase.
Fabrication and Characterization of Lithium-ion Battery Electrode Filaments Used for Fused Deposition Modeling 3D Printing
(2022-08) Kindomba, Eli; Zhang, Jing; Zhu, Likun; Schubert, Peter
Lithium-Ion Batteries (Li-ion batteries or LIBs) have been extensively used in a wide variety of industrial applications and consumer electronics. Additive Manufacturing (AM) or 3D printing (3DP) techniques have evolved to allow the fabrication of complex structures of various compositions in a wide range of applications. The objective of the thesis is to investigate the application of 3DP to fabricate a LIB, using a modified process from the literature [1]. The ultimate goal is to improve the electrochemical performances of LIBs while maintaining design flexibility with a 3D printed 3D architecture. In this research, both the cathode and anode in the form of specifically formulated slurry were extruded into filaments using a high-temperature pellet-based extruder. Specifically, filament composites made of graphite and Polylactic Acid (PLA) were fabricated and tested to produce anodes. Investigations on two other types of PLA-based filament composites respectively made of Lithium Manganese Oxide (LMO) and Lithium Nickel Manganese Cobalt Oxide (NMC) were also conducted to produce cathodes. Several filaments with various materials ratios were formulated in order to optimize printability and battery capacities. Finally, flat battery electrode disks similar to conventional electrodes were fabricated using the fused deposition modeling (FDM) process and assembled in half-cells and full cells. Finally, the electrochemical properties of half cells and full cells were characterized. Additionally, in parallel to the experiment, a 1-D finite element (FE) model was developed to understand the electrochemical performance of the anode half-cells made of graphite. Moreover, a simplified machine learning (ML) model through the Gaussian Process Regression was used to predict the voltage of a certain half-cell based on input parameters such as charge and discharge capacity. The results of this research showed that 3D printing technology is capable to fabricate LIBs. For the 3D printed LIB, cells have improved electrochemical properties by increasing the material content of active materials (i.e., graphite, LMO, and NMC) within the PLA matrix, along with incorporating a plasticizer material. The FE model of graphite anode showed a similar trend of discharge curve as the experiment. Finally, the ML model demonstrated a reasonably good prediction of charge and discharge voltages.
Deep Learning Based Crop Row Detection
(2022-05) Doha, Rashed Mohammad; Anwar, Sohel; Al Hasan, Mohammad; Li, Lingxi
Detecting crop rows from video frames in real time is a fundamental challenge in the field of precision agriculture. Deep learning based semantic segmentation method, namely U-net, although successful in many tasks related to precision agriculture, performs poorly for solving this task. The reasons include paucity of large scale labeled datasets in this domain, diversity in crops, and the diversity of appearance of the same crops at various stages of their growth. In this work, we discuss the development of a practical real-life crop row detection system in collaboration with an agricultural sprayer company. Our proposed method takes the output of semantic segmentation using U-net, and then apply a clustering based probabilistic temporal calibration which can adapt to different fields and crops without the need for retraining the network. Experimental results validate that our method can be used for both refining the results of the U-net to reduce errors and also for frame interpolation of the input video stream. Upon the availability of more labeled data, we switched our approach from a semi-supervised model to a fully supervised end-to-end crop row detection model using a Feature Pyramid Network or FPN. Central to the FPN is a pyramid pooling module that extracts features from the input image at multiple resolutions. This results in the network’s ability to use both local and global features in classifying pixels to be crop rows. After training the FPN on the labeled dataset, our method obtained a mean IoU or Jaccard Index score of over 70% as reported on the test set. We trained our method on only a subset of the corn dataset and tested its performance on multiple variations of weed pressure and crop growth stages to verify that the performance does translate over the variations and is consistent across the entire dataset.

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