Mechanical & Energy Engineering Department Theses and Dissertations

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Energy Optimization of Heating, Ventilation, and Air Conditioning Systems
(2024-05) Taheri, Saman; Razban, Ali; Chen, Jie; Du, Xiaoping; Chien, Stanley Yung-Ping
The energy consumption in the building sector is responsible for over 36% of the total energy consumption across the globe. Of all the energy-consumer devices within a building, heating, ventilation, and air conditioning (HVAC) systems account for over 50% of the total energy consumed. This makes HVAC systems a source of preventable and unexplored energy waste that can be tackled by incorporating intelligent operations. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. In this regard, the opening chapter delves into the evolving landscape of the HVAC industry. It explores how rapid advancements in technology, growing concerns about climate change, and the ever-present need for energy efficiency are driving innovation. The chapter highlights the shift from static to dynamic HVAC systems, where buildings become sensor rich networks enabling advanced control strategies like Model Predictive Control (MPC) and Fault Detection and Diagnosis (FDD). we first provide a comprehensive review of the literature concerning the application of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, time step, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. Next, a thorough, real-world case study for the design and implementation of a generalized data-collection and control architecture for HVAC systems in an educational building is proposed. The proposed MPC method adds a supervisory control layer on top of the current BMS by delivering temperature setpoints to the legacy controller. This means that the technique may be used to a variety of current HVAC systems in different commercial buildings. In addition, the utilization of remote web services to host the cloud-based architecture significantly minimizes the amount of technical expertise generally necessary to create such systems. In addition, we provide significant lessons learned from the installation process and we list indicative prices, therefore minimizing uncertainty for other researchers and promoting the use of comparable solutions. Chapter two focuses on Fault Detection and Diagnosis (FDD), a critical component of maintaining optimal HVAC performance and minimizing energy waste. HVAC systems are susceptible to malfunctions over time, leading to increased energy consumption and higher maintenance costs. FDD techniques play a vital role in identifying and diagnosing these faults early on, allowing for timely repairs and preventing further deterioration. This chapter introduces a novel bi-level machine learning framework for diagnosing faults in air handling units. This framework addresses key challenges associated with FDD. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). By proposing this framework, we address three persistent challenges in this field: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment. It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. Chapter three tackles the practical implementation of Model Predictive Control (MPC) in a real-world commercial building setting. It details the development, implementation, and cost analysis of a universally applicable cloud-based MPC framework for HVAC control systems. This chapter offers valuable insights into the feasibility and effectiveness of MPC in achieving energy efficiency goals while maintaining occupant comfort. The chapter delves into the hardware and software components used for data acquisition and MPC implemen tation. It emphasizes the use of cloud-based microservices to ensure seamless integration with existing building management systems, promoting wider adoption of this advanced control strategy. Three innovative control strategies are presented and evaluated in this chapter. The chapter presents compelling evidence for the effectiveness of these strategies, showcasing significant energy savings of up to 19.21%. Chapter four focuses on Occupancy-based Demand Controlled Ventilation (DCV) as a means to optimize indoor air quality (IAQ) while minimizing energy consumption. This chapter highlights the growing importance of IAQ in the wake of the COVID-19 pandemic and its impact on occupant health and well-being. Current ventilation standards often rely on static occupancy assumptions, which can lead to over-ventilation during unoccupied pe riods and wasted energy. This chapter proposes a dynamic occupant behavior model using machine learning algorithms to predict CO2 concentrations within buildings. The chapter investigates the performance of various machine learning algorithms, ultimately identify ing a Multilayer Perceptron (MLP) as the most effective in predicting CO2 levels under dynamic occupancy conditions. This model allows for real-time modulation of ventilation rates, ensuring adequate IAQ while minimizing energy consumption. The concluding chapter presents experimental findings on the effectiveness of adaptive Variable Frequency Drive (VFD) control strategies in optimizing HVAC energy consump tion. Variable Frequency Drives allow for adjusting the speed of electric motors, including those powering HVAC fans. This chapter explores the potential of using real-time occu pancy predictions to optimize VFD operation. The proposed control strategy demonstrates impressive energy savings, achieving a 51.4% reduction in HVAC fan energy consumption while adhering to ASHRAE IAQ standards. This chapter paves the way for occupant-centric ventilation strategies that prioritize both human health and energy efficiency. These results underscore the potential of predictive control systems to transform building operations to ward greater sustainability and efficiency. The chapter acknowledges the need for further validation through extended monitoring and analysis. In summary, this thesis contributes significantly to the advancement of smart building technologies by proposing practical frameworks for implementing advanced control strategies in HVAC systems. The findings presented here offer valuable insights for building designers, engineers, facility managers, and policymakers interested in creating sustainable, energy efficient, and occupant-centric buildings. The developed frameworks have the potential to be applied across a wide range of building types and climatic conditions, promoting broader adoption of smart building technologies and contributing to a more sustainable built envi ronment.
Design Requirements of Human-Driven, Hybrid, and Autonomous Trucks for Collision-Avoidance in Platooning
(2024-05) Shanker, Shreyas; Nalim, M. Razi; Anwar, Sohel; Tovar, Andres
The trucking industry faces many challenges, the most pressing of them being the rising costs to run the fleets. This is mainly caused by driver shortage, low driver retention and high wages for the drivers as well as rising fuel costs. Autonomous trucks promise to solve these issues by eliminating this bottleneck in the industry and bringing some relief to logistics companies and fleet owners. A prelude to fully autonomous trucks is expected to be seen as part of a hybrid platoon where a human driver would lead one or more autonomous trucks close behind them thus enabling higher tonnage to be transported by one driver. This enables early autonomous software to be tested and phased onto highways in a more controlled manner since present software can maintain set distances behind vehicles and respect lane markers already. Platooning also enables significant fuel savings from reduced aerodynamic drag on all vehicles at close distances. Since vehicle functionality is largely built around the driver, the removal of this piece affords the opportunity to rethink parts of the design to suit the needs of the future more favorably. Based on the prevalent literature as well as simulation of platooning scenarios under various vehicle and environmental conditions, the thesis will analyze the development of autonomous vehicles with a focus on the opportunities to rethink conventional design constraints of a truck and to design one that is better suited to the functions it will be carrying out autonomously and in the context of technologies that are in development and would be available in the future with a special emphasis on platooning scenarios. In this thesis, a MATLAB model was used to simulate a 2-vehicle platoon where the lead truck is a conventional class 8 vehicle while the key parameters of the following truck was tested in various road conditions to minimize Inter Vehicular Distance (IVD) and maximize fuel savings while ensuring safety. The study was able to conclude that an alternative design to autonomous trucks would result in maximum benefits from synergistic technologies like platooning and battery powered trucks. The results showed the most benefits from a reduction in perception-reaction time and communication technology followed by strategic configuration of vehicles in a platoon by Gross vehicle weight (GVW). Also, the need to account for coefficient of friction due to non-ideal environmental conditions with an adjustment in IVD is observed.
Benchmarking Tool Development for Commercial Buildings' Energy Consumption Using Machine Learning
(2024-05) Hosseini, Paniz; Razban, Ali; Chen, Jie; Goodman, David
This thesis investigates approaches to classify and anticipate the energy consumption of commercial office buildings using external and performance benchmarking to reduce the energy consumption. External benchmarking in the context of building energy consumption considers the influence of climate zones that significantly impact a building's energy needs. Performance benchmarking recognizes that different types of commercial buildings have distinct energy consumption patterns. Benchmarks are established separately for each building type to provide relevant comparisons. The first part of this thesis is about providing a benchmarking baseline for buildings to show their consumption levels. This involves simulating the buildings based on standards and developing a model based on real-time results. Software tools like Open Studio and Energy Plus were utilized to simulate buildings representative of different-sized structures to organize the benchmark energy consumption baseline. These simulations accounted for two opposing climate zones—one cool and humid and one hot and dry. To ensure the authenticity of the simulation, details, which are the building envelope, operational hours, and HVAC systems, were matched with ASHRAE standards. Secondly, the neural network machine learning model is needed to predict the consumption of the buildings based on the trend data came out of simulation part, by training a comprehensive set of environmental characteristics, including ambient temperature, relative humidity, solar radiation, wind speed, and the specific HVAC (Heating, Ventilation, and Air Conditioning) load data for both heating and cooling of the building. The model's exceptional accuracy rating of 99.54% attained across all, which comes from the accuracy of training, validation, and test about 99.6%, 99.12%, and 99.42%, respectively, and shows the accuracy of the predicted energy consumption of the building. The validation check test confirms that the achieved accuracy represents the optimal performance of the model. A parametric study is done to show the dependency of energy consumption on the input, including the weather data and size of the building, which comes from the output data of machine learning, revealing the reliability of the trained model. Establishing a Graphic User Interface (GUI) enhances accessibility and interaction for users. In this thesis, we have successfully developed a tool that predicts the energy consumption of office buildings with an impressive accuracy of 99.54%. Our investigation shows that temperature, humidity, solar radiation, wind speed, and the building's size have varying impacts on energy use. Wind speed is the least influential component for low-rise buildings but can have a more substantial effect on high-rise structures.
Lithium Storage Mechanisms and Electrochemical Behavior of Molybdenum Disulfide
(2024-05) Li, Xintong; Zhu, Likun; Hosop, Shin; Wei, Xiaoliang
This study investigates the electrochemical behavior of molybdenum disulfide (MoS2) when utilized as an anode material in Li-ion batteries, particularly focusing on the intriguing phenomenon of extra capacity observed beyond theoretical expectations and the unique discharge curve of the first cycle. Employing a robust suite of advanced characterization methods such as in situ and ex situ X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM), the research unravels the complex structural and chemical evolution of MoS2 throughout its cycling process. A pivotal discovery of the research is the identification of a distinct lithium intercalation mechanism in MoS2, which leads to the formation of reversible LixMoS2. These phases play a crucial role in contributing to the extra capacity observed in the MoS2 electrode. Additionally, density functional theory (DFT) calculations have been utilized to explore the potential for overlithiation within MoS2, suggesting that Li5MoS2 could be the most energetically favorable phase during the lithiation-delithiation process. This study also explores the energetics of a Li-rich phase forming on the surface of Li4MoS2, indicating that this configuration is energetically advantageous and could contribute further to the extra capacity. The incorporation of reduced graphene oxide (RGO) as a conductive additive in MoS2 electrodes, demonstrating that RGO notably improves the electrochemical performance, rate capability, and durability of the electrodes. These findings are supported by experimental observations and are crucial for advancing the understanding of MoS2 as a high-capacity anode material. The implications of this research are significant, offering a pathway to optimize the design and composition of electrode materials to exceed traditional performance and longevity limits in Li-ion batteries.
Facility Assessment of Indoor Air Quality Using Machine Learning
(2024-05) Wright, Jared Austin; Razban, Ali; Chen, Jie; Du, Xiaoping
The goal of this thesis is to develop a method of evaluating long-term IAQ performance of an industrial facility and use machine-learning to model the relationship between critical air pollutants and the facility’s HVAC systems and processes. The facility under study for this thesis is an electroplating manufacturer. The air pollutants at this facility that were studied were particulate matter, total-volatile organic compounds, and carbon-dioxide. Upon sensor installation, seven “zones” were identified to isolate areas of the plant for measurement and analysis. A statistical review of the long-term data highlighted how this facility performed in terms of compliance. Their gaseous pollutants were well within regulation. Particulate matter, however, was found to be a pressing issue. PM10 was outside of compliance more than 15% of the time in five out of seven of the zones of study. Some zones were out of compliance up to 80% of the total collection period. The six pollutants that met these criteria were deemed critical and moved on to machine learning modeling. Our model of best fit for each pollutant used a gaussian process regression model, which fits best for non-linear rightly skewed datasets. The performance of each of our models was deemed significant. Every model had at least a regression coefficient of 0.935 and above for both validation and testing. The maximum average error was 12.64 ug.m^3, which is less than 10% of the average PM10 concentration. Through our modeling, we were able to study how HVAC and production played a role in particulate matter presence for each zone. Exhaust systems of the west side of the plant were found to be insufficient at removing particulates from their facility. Overall, the methods developed in this thesis project were able to meet the goal of analyzing IAQ compliance, modeling critical pollutants using machine learning, and identifying a relationship between these pollutants and an industrial facility’s HVAC and production systems.
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.

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