Mechanical and Energy Engineering Works
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Item Robust controller design for rotary inverted pendulum using H∞ and 𝝁-synthesis techniques(Wiley, 2021) Pramanik, Sourav; Anwar, Sohel; Mechanical and Energy Engineering, Purdue School of Engineering and TechnologyAn 𝐻∞ controller is designed to control a rotary inverted pendulum in its upright equilibrium position. The key contribution of this work is a robust controller architecture design to accommodate for uncertainty in actuator model. Robust stability and performance for a given degree of actuator and measurement uncertainty is achieved using the well established techniques of 𝜇‐synthesis and 𝐻∞ robust control methods. The design focuses on stabilizing an Inverted Pendulum in an upright position within a tolerable desired angle margin (𝛼). A dynamic plant is designed based on already established theories and published papers. It is observed that the plant is completely observable for the pendulum angle and the motor arm link angle. These two signals are also measurable via encoders and is used as an input for the controller. The output of the controller is voltage actuation which drives the motor to stabilize the pendulum in an upright position (= 0 with +/− 10 deg tolerance). A Robust Stability analysis is done along with Robust Performance, to study the stability and performance margins under modelled uncertainties. As a comparative study, a rudimentary pole placement method is also analyzed.Item Second-order reliability methods: a review and comparative study(Springer Nature, 2021) Hu, Zhangli; Mansour, Rami; Olsson, Mårten; Du, Xiaoping; Mechanical and Energy Engineering, Purdue School of Engineering and TechnologySecond-order reliability methods are commonly used for the computation of reliability, defined as the probability of satisfying an intended function in the presence of uncertainties. These methods can achieve highly accurate reliability predictions owing to a second-order approximation of the limit-state function around the Most Probable Point of failure. Although numerous formulations have been developed, the lack of full-scale comparative studies has led to a dubiety regarding the selection of a suitable method for a specific reliability analysis problem. In this study, the performance of commonly used second-order reliability methods is assessed based on the problem scale, curvatures at the Most Probable Point of failure, first-order reliability index, and limit-state contour. The assessment is based on three performance metrics: capability, accuracy, and robustness. The capability is a measure of the ability of a method to compute feasible probabilities, i.e., probabilities between 0 and 1. The accuracy and robustness are quantified based on the mean and standard deviation of relative errors with respect to exact reliabilities, respectively. This study not only provides a review of classical and novel second-order reliability methods, but also gives an insight on the selection of an appropriate reliability method for a given engineering application.Item A Novel Framework of Developing a Predictive Model for Powder Bed Fusion Process(Mary Ann Liebert, 2024) Marrey, Mallikharjun; Malekipour, Ehsan; El-Mounayri, Hazim; Faierson, Eric J.; Agarwal, Mangilal; Mechanical and Energy Engineering, Purdue School of Engineering and TechnologyThe powder bed fusion (PBF) process is a metal additive manufacturing process, which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for predictive modeling of a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for studying the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters that is, laser specifications and mechanical properties, and how to obtain an optimum range of volumetric energy density for producing parts with high density (>99%), as well as better ultimate mechanical properties. In this article, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage (i.e., around 10%), which are used for process parameter selection in accordance with user or manufacturer part performance requirements. These models are based on techniques such as support vector regression, random forest regression, and neural network. It is shown that the intelligent selection of process parameters using these models can achieve a high density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.Item Stabilizing Ti3C2Tx MXene flakes in air by removing confined water(National Academy of Sciences, 2024) Fang, Hui; Thakur, Anupma; Zahmatkeshsaredorahi, Amirhossein; Fang, Zhenyao; Rad, Vahid; Shamsabadi, Ahmad A.; Pereyra, Claudia; Soroush, Masoud; Rappe, Andrew M.; Xu, Xiaoji G.; Anasori, Babak; Fakhraai, Zahra; Mechanical and Energy Engineering, Purdue School of Engineering and TechnologyMXenes have demonstrated potential for various applications owing to their tunable surface chemistry and metallic conductivity. However, high temperatures can accelerate MXene film oxidation in air. Understanding the mechanisms of MXene oxidation at elevated temperatures, which is still limited, is critical in improving their thermal stability for high-temperature applications. Here, we demonstrate that Ti[Formula: see text]C[Formula: see text]T[Formula: see text] MXene monoflakes have exceptional thermal stability at temperatures up to 600[Formula: see text]C in air, while multiflakes readily oxidize in air at 300[Formula: see text]C. Density functional theory calculations indicate that confined water between Ti[Formula: see text]C[Formula: see text]T[Formula: see text] flakes has higher removal energy than surface water and can thus persist to higher temperatures, leading to oxidation. We demonstrate that the amount of confined water correlates with the degree of oxidation in stacked flakes. Confined water can be fully removed by vacuum annealing Ti[Formula: see text]C[Formula: see text]T[Formula: see text] films at 600[Formula: see text]C, resulting in substantial stability improvement in multiflake films (can withstand 600[Formula: see text]C in air). These findings provide fundamental insights into the kinetics of confined water and its role in Ti[Formula: see text]C[Formula: see text]T[Formula: see text] oxidation. This work enables the use of stable monoflake MXenes in high-temperature applications and provides guidelines for proper vacuum annealing of multiflake films to enhance their stability.Item Oxidation Behavior of NiCoCrAlY Coatings Deposited by Vacuum Plasma Spraying and High-Velocity Oxygen Fuel Processes(MDPI, 2023-02) Kim, Junseong; Pyeon, Janghyeok; Kim, Bong-Gu; Khadaa, Tserendorj; Choi, Hyeryang; Zhe, Lu; Dube, Tejesh; Zhang, Jing; Yang, Byung-il; Jung, Yeon-gil; Yang, SeungCheol; Mechanical and Energy Engineering, Purdue School of Engineering and TechnologyTo reduce the formation of detrimental complex oxides, bond coatings in the thermal barrier coatings for gas turbines are typically fabricated using vacuum plasma spraying (VPS) or the high-velocity oxygen fuel (HVOF) process. Herein, VPS and HVOF processes were applied using NiCoCrAlY + HfSi-based powder to assess the oxidation behavior of the bond coatings for both coating processes. Each coated sample was subjected to 50 cyclic heat treatments at 950 °C for 23 h and cooling for 1 h at 20 °C with nitrogen gas, and the weight change during the heat treatment was measured to evaluate the oxidation behavior. After the oxidation test, the coating layer was analyzed with X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive spectroscopy (EDS). The VPS coating exhibited faster weight gain than the HVOF coating because the alumina particles generated during the initial formation of the HVOF coating inhibited oxidation and diffusion. The VPS coating formed a dense and thick thermal growth oxide (TGO) layer until the middle of the oxidation test and remained stable until the end of the evaluation. However, the HVOF coating demonstrated rapid weight loss during the final 20 cycles. Alumina within the bond coat suppressed the diffusion of internal elements and prevented the Al from being supplied to the surface. The isolation of the Al accelerated the growth of spinel TGO due to the oxidation of Ni, Co, and Cr near the surface. The as-coated VPS coating showed higher hardness and lower interfacial bonding strength than the HVOF did. Diffusion induced by heat treatment after the furnace cyclic test (FCT) led to a similar internal hardness and bonding strengths in both coating layers. To improve the quality of the HVOF process, the densification of the coating layer, suppression of internal oxide formation, and formation of a dense and uniform alumina layer on the surface must be additionally implemented.Item Engineering uniformity in mass production of MWCNTs/epoxy nanofibers using a lateral belt-driven multi-nozzle electrospinning technique to enhance the mechanical properties of CFRPs(Elsevier, 2023-01) Liyanage, Asel Ananda Habarakada; Biswas, Pias Kumar; Dalir, Hamid; Agarwal, Mangilal; Mechanical and Energy Engineering, Purdue School of Engineering and TechnologyElectrospinning is one of the most diverse, cost-effective, and ecologically renowned methods for generating continuous nanofibers, but electrospinning of thermosetting polymers like epoxy and their mass volume productions have been a major challenge in recent years. This study proposes a fabrication method by addressing electrospun MWCNTS/epoxy (CNT/epoxy) nanofiber's volume processing, reproducibility, and accuracy issues in an effective manner. Lateral belt-driven (LBD) multi-nozzle electrospinning is here newly adopted to enhance the mechanical properties of the carbon fiber-reinforced polymer (CFRP) laminates. With the LBD approach, the electric field can be uninterruptedly distributed to deposit a uniform layer of CNT/epoxy scaffolds over the entire width of CFRP prepreg. This study optimizes the distance between two nozzles, the motion of the lateral belt, and the electrospinning period. The laminates made of coated CFRP show enhanced mechanical properties when compared to pristine CFRPs. Under high-stress conditions, these CFRP laminates' interlaminar shear strength (ILSS) and fatigue performance demonstrated 29% and 27% improvements, respectively. This study, which demonstrated the success of using an LBD multi-nozzle system, has enormous opportunities to produce thinner and continuous fibers with more concentrated collections at a faster rate, which is critical for commercial applications.Item Operando study of mechanical integrity of high-volume expansion Li-ion battery anode materials coated by Al2O3(IOP, 2023-06) Zhou, Xinwei; Stan, Liliana; Hou, Dewen; Jin, Yang; Xiong, Hui; Zhu, Likun; Liu, Yuzi; Mechanical and Energy Engineering, Purdue School of Engineering and TechnologyGroup IV elements and their oxides, such as Si, Ge, Sn and SiO have much higher theoretical capacity than commercial graphite anode. However, these materials undergo large volume change during cycling, resulting in severe structural degradation and capacity fading. Al2O3 coating is considered an approach to improve the mechanical stability of high-capacity anode materials. To understand the effect of Al2O3 coating directly, we monitored the morphology change of coated/uncoated Sn particles during cycling using operando focused ion beam–scanning electron microscopy. The results indicate that the Al2O3 coating provides local protection and reduces crack formation at the early stage of volume expansion. The 3 nm Al2O3 coating layer provides better protection than the 10 and 30 nm coating layer. Nevertheless, the Al2O3 coating is unable to prevent the pulverization at the later stage of cycling because of large volume expansion.Item Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting(Elsevier, 2023-05) Hosseini, Paniz; Taheri, Saman; Akhavan, Javid; Razban, Ali; Mechanical and Energy Engineering, Purdue School of Engineering and TechnologyThe growing usage of decentralized renewable energy sources has made accurate estimation of their aggregated generation crucial for maintaining grid flexibility and reliability. However, the majority of distributed photovoltaic (PV) systems are behind-the-meter (BTM) and invisible to utilities, leading to three challenges in obtaining an accurate forecast of their aggregated output. Firstly, traditional centralized prediction algorithms used in previous studies may not be appropriate due to privacy concerns. There is therefore a need for decentralized forecasting methods, such as federated learning (FL), to protect privacy. Secondly, there has been no comparison between localized, centralized, and decentralized forecasting methods for BTM PV production, and the trade-off between prediction accuracy and privacy has not been explored. Lastly, the computational time of data-driven prediction algorithms has not been examined. This article presents a FL power forecasting method for PVs, which uses federated learning as a decentralized collaborative modeling approach to train a single model on data from multiple BTM sites. The machine learning network used to design this FL-based BTM PV forecasting model is a multi-layered perceptron, which ensures privacy and security of the data. Comparing the suggested FL forecasting model to non-private centralized and entirely private localized models revealed that it has a high level of accuracy, with an RMSE that is 18.17% lower than localized models and 9.9% higher than centralized models.Item Enhancing Energy Management System using Internet-of-things Sensors and Energy Simulation -A Case Study of Industrial Ventilation(Taylor & Francis, 2023-01) Wu, Da-Chun; Movahed, Paria; Razban, Ali; Chen, Jie; Mechanical and Energy Engineering, Purdue School of Engineering and TechnologyThere is a need for energy conservation in order to achieve the 2030 Sustainable Development Goals. Many studies have demonstrated that delivering real-time assessment and energy saving goals are some of the most effective practices to reduce energy consumption and can be achieved by utilizing energy management. However, there remains a sizable gap in the development of the energy management and energy-efficiency evaluation for complicated manufacturing processes. With the current development of new technologies such as Internet of Things, big-data analytics, and machine learning, improvements can be made to further enhance the effectiveness of the energy management system. In this study, a real-time monitoring system that utilizes IoT sensors, cloud-based big data, and grey-box modeling is proposed. This paper describes the ongoing study of using the proposed energy management system in an electroplating manufacturer to reduce its ventilation energy consumption.Item A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems(Elsevier, 2023-06) Movahed, Paria; Taheri, Saman; Razban, Ali; Mechanical and Energy Engineering, Purdue School of Engineering and TechnologyLong-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. To avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. 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). 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. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.