Browsing by Author "Mechanical and Energy Engineering, Purdue School of Engineering and Technology"

Now showing 1 - 10 of 39
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    2D rare-earth metal carbides (MXenes) Mo2NdC2T2 electronic structure and magnetic properties: A DFT + U study
    (AIP, 2022) Yao, Shukai; Anasori, Babak; Strachan, Alejandro; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    2D rare-earth metal carbides (MXenes) are attractive due to their novel electronic and magnetic properties and their potential as scalable 2D magnets. In this study, we used density functional theory with the Hubbard U correction to characterize the structure, termination, and magnetism in an out-of-plane ordered rare-earth containing M3C2Tx MXene, Mo2NdC2T2 (T = O or OH). We investigated the effect of the U parameter on the stability and magnetism of two possible termination sites: the hollow sites aligned with the inner Nd atoms (Nd-hollow sites) and those aligned with the closest C atoms (C-hollow sites). We found that increasing UMo stabilized the Nd hollow sites, which minimized electrostatic repulsion between C and O atoms. Using UMo = 3.0 eV and UNd = 5.6 eV, obtained via the linear response method, we found that the energetically preferred termination site was C-hollow in Mo2NdC2O2 and Nd-hollow in Mo2NdC2(OH)2. Regardless of termination and the Hubbard U value, we found Mo2NdC2O2 and Mo2NdC2(OH)2 to be magnetic. The C-hollow termination resulted in ferromagnetic states for all Hubbard U tested with no magnetic moment in Mo. In the case of Nd-hollow, Mo became magnetic for UMo ≥ 4 eV. The difference of Mo magnetism in Nd-hollow and C-hollow was explained by crystal field splitting of the Mo d orbital caused by a distorted ligand.
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    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 Technology
    Long-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.
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    A Comparison of the Ligation Torque Expression of a Ribbonwise Bracket–Archwire Combination and a Conventional Combination: A Primary Study
    (Hindawi, 2022-09-28) Lin, Bin; Jiang, Feifei; Chen, Jie; Liang, Jiaxing; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    Objective: To assess the effect of the third-order mechanics of a new ribbonwise bracket-archwire combination using an orthodontic torque simulator. Material and Methods. An orthodontic torque simulator was used to measure the third-order moment of a maxillary central incisor as it changed from a neutral position to a 40° rotation in 1° increment. A new ribbonwise bracket (Xinya, China) was compared with a conventional ligation bracket (American Orthodontic, U.S.A.). The effects of different archwire sizes (i.e., 0.017″ × 0.025″ and 0.019″ × 0.025″) and materials (i.e., nickel-titanium, titanium-molybdenum alloy, and stainless steel) were analyzed. Paired sample t-tests were conducted to compare the moments between the two bracket types corresponding to each of the archwires. The effects of the stiffness of the bracket-archwire complexes were also assessed. Results: Statistically significant differences (P=0.05) between the moments from the two brackets were found. The ribbonwise bracket-archwire complex generated larger moments when the rotation angle was lower than 30°. The ribbonwise brackets produced moments that could reach a threshold of 5 Nmm more quickly as the angle was increased. The higher the stiffness of the complex, the larger the moment. Conclusion: The ribbonwise bracket-archwire complex reached the moment threshold limits earlier than the conventional complex. When the rotation angle is less than 30°, the ribbonwise bracket-archwire complex generated a greater torque moment in comparison with the conventional complex.
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    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 Technology
    The 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.
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    Achieving SEI preformed graphite in flow cell to mitigate initial lithium loss
    (Elsevier, 2022) Yu, Yikang; Yang, Zhenzhen; Xie, Jian; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    The irreversible lithium loss due to the formation of solid electrolyte interphase (SEI) in the initial cycle on the graphite anode greatly reduces the overall cell energy density of lithium ion batteries, that is, the lost Li ions from forming SEI lead to the decrease of Li ions for the intercalation. The method of cathode prelithiation has been widely explored to compensate this lithium loss. However, these cathode additives with high lithium contents inevitably lower the loading of the cathode active materials. Here we report a novel approach to solve this challenge, a facile graphite prelithiation method by preforming SEI layers on the surface of graphite powders (Pre-SEI graphite) utilizing a specially designed flow cell. The Li accommodation in the graphite anode can be controlled by the operating time and current density in the flow cell for the electrochemical SEI formation. As a result, we demonstrate a 10% initial Columbic efficiency increase of the LiFePO4 electrode in a full cell configuration using the Pre-SEI graphite, compared with the pristine graphite anode. The electrochemical preformation of SEI on the graphite powders offers a complete solution to offset initial lithium loss without a sacrifice of active cathode material loading.
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    An Aqueous Media Based Approach for the Preparation of a Biosensor Platform Composed of Graphene Oxide and Pt-Black
    (Elsevier, 2012) Shi, Jin; Zhang, Hangyu; Snyder, Alexandra; Wang, Mei-xian; Xie, Jian; Porterfield, D. Marshall; Stanciu, Lia A.; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    The combination of Pt nanoparticles and graphene was more effective in enhancing biosensing than either nanomaterial alone according to previous reports. Based on the structural similarities between water soluble graphene oxide (GrO(x)) and graphene, we report the fabrication of an aqueous media based GrO(x)/Pt-black nanocomposite for biosensing enhancement. In this approach GrO(x) acted as a nanoscale molecular template for the electrodeposition of Pt-black, an amorphously nanopatterned isoform of platinum metal. Scanning electron microscopy (SEM) images and energy-dispersive X-ray spectroscopy (EDS) showed that Pt-black was growing along GrO(x). The effective surface area and electrocatalytic activity towards H(2)O(2) oxidation of GrO(x)/Pt-black microelectrodes were significantly higher than for Pt-black microelectrodes. When used to prepare a bio-nanocomposite based on protein functionalization with the enzyme glucose oxidase (GOx), the GrO(x)/Pt-black microbiosensors exhibited improved sensitivity over the Pt-black microbiosensors. This suggested that the GrO(x)/Pt-black nanocomposite facilitated an increase in electron transfer, and/or minimized mass transport limitations as compared to Pt-black used alone. Glucose microbiosensors based on GrO(x)/Pt-black exhibited high sensitivity (465.9 ± 48.0 nA/mM), a low detection limit of 1 μM, a linear response range of 1 μM-2mM, and response time of ≈ 4s. Additionally the sensor was stable and highly selective over potential interferents.
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    Atomically dispersed iron sites with a nitrogen–carbon coating as highly active and durable oxygen reduction catalysts for fuel cells
    (Springer Nature, 2022) Liu, Shengwen; Li, Chenzhao; Zachman, Michael J.; Zeng, Yachao; Yu, Haoran; Li, Boyang; Wang, Maoyu; Braaten, Jonathan; Liu, Jiawei; Meyer, Harry M., III; Lucero, Marcos; Kropf, A. Jeremy; Alp, Esen E.; Gong, Qing; Shi, Qiurong; Feng, Zhenxing; Xu, Hui; Wang, Guofeng; Myers, Deborah J.; Xie, Jian; Cullen, David A.; Litster, Shawn; Wu, Gang; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    Nitrogen-coordinated single atom iron sites (FeN4) embedded in carbon (Fe–N–C) are the most active platinum group metal-free oxygen reduction catalysts for proton-exchange membrane fuel cells. However, current Fe–N–C catalysts lack sufficient long-term durability and are not yet viable for practical applications. Here we report a highly durable and active Fe–N–C catalyst synthesized using heat treatment with ammonia chloride followed by high-temperature deposition of a thin layer of nitrogen-doped carbon on the catalyst surface. We propose that catalyst stability is improved by converting defect-rich pyrrolic N-coordinated FeN4 sites into highly stable pyridinic N-coordinated FeN4 sites. The stability enhancement is demonstrated in membrane electrode assemblies using accelerated stress testing and a long-term steady-state test (>300 h at 0.67 V), approaching a typical Pt/C cathode (0.1 mgPt cm−2). The encouraging stability improvement represents a critical step in developing viable Fe–N–C catalysts to overcome the cost barriers of hydrogen fuel cells for numerous applications.
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    Computation of conductive thermal distribution using non-homogenous graph theory for real-time applications in metal PBF process
    (Elsevier, 2022-09) Malekipour, Ehsan; El-Mounayri, Hazim; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    The Powder Bed Fusion (PBF) process is inherently a thermal process with complex thermal interactions between different printed zones as well as different layers. There exist only a few methods such as finite element analysis (FEA), finite element differences (FDM), graph theory (GT), Goldak’s FEA, and Rosenthal equation, which are able to predict thermal temperature distribution throughout a printed layer (2D) or part (3D). All these approaches suffer from inherent limitations including the applied boundary conditions and computational time. A rapid and reliable method to compute thermal distribution throughout a printed part is pivotal to supporting real-time closed-loop monitoring and control, enabling thermal simulation software with rapid and precise prediction, and advancing current research on thermal-related abnormalities such as residual stress and distortion. The literature shows that the conventional graph theory is the fastest approach that generates relatively precise results in a fraction of the computational time of other techniques; however, the lack of a solution to the non-homogeneous governing thermal equation through GT has hampered this method in terms of thermal load resolution, accuracy in highly rapid process such as PBF, and scope of application. In this paper, we describe the characteristics that make GT a superior approach for real-time computation of thermal field compared to other similar approaches such as FDM. Also, we develop a solution to the non-homogeneous term of the thermal conduction equation by using GT. This solution represents a breakthrough for the development of precise real-time closed-loop monitoring and control systems by providing a precise numerical solution to the thermal conduction equation in a fraction of time compared with previous traditional methods such as FEA and FDM. Ongoing work includes the development of an intelligent monitoring and control system that leverages this solution in order to optimize scan strategy real-time in metal PBF.
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    Effects of donor-specific microvascular anatomy on hemodynamic perfusion in human choriocapillaris
    (Springer Nature, 2023-12-19) An, Senyou; Yu, Huidan; Islam, MD Mahfuzul; Zhang, Xiaoyu; Zhan, Yuting; Olivieri, Joseph J.; Ambati, Jayakrishna; Yao, Jun; Gelfand, Bradley D.; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    Evidence from histopathology and clinical imaging suggest that choroidal anatomy and hemodynamic perfusion are among the earliest changes in retinal diseases such as age-related macular degeneration (AMD). However, how inner choroidal anatomy affects hemodynamic perfusion is not well understood. Therefore, we sought to understand the influences of choroidal microvascular architecture on the spatial distribution of hemodynamic parameters in choriocapillaris from human donor eyes using image-based computational hemodynamic (ICH) simulations. We subjected image-based inner choroid reconstructions from eight human donor eyes to ICH simulation using a kinetic-based volumetric lattice Boltzmann method to compute hemodynamic distributions of velocity, pressure, and endothelial shear stress. Here, we demonstrate that anatomic parameters, including arteriolar and venular arrangements and intercapillary pillar density and distribution exert profound influences on inner choroidal hemodynamic characteristics. Reductions in capillary, arteriolar, and venular density not only reduce the overall blood velocity within choriocapillaris, but also substantially increase its spatial heterogeneity. These first-ever findings improve understanding of how choroidal anatomy affects hemodynamics and may contribute to pathogenesis of retinal diseases such as AMD.
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    Effects of Ink Formulation on the Structure and Performance of PGM-Free Catalyst Layer in PEMFCs
    (IOP, 2021) Li, Chenzhao; Liu, Shengwen; Zeng, Yachao; Liu, Yadong; Wu, Gang; Cullen, David A.; Xie, Jian; Mechanical and Energy Engineering, Purdue School of Engineering and Technology
    Platinum group metal (PGM) catalysts are the major electrocatalysts for oxygen reduction reaction (ORR) in the polymer electrolyte membrane fuel cells (PEMFCs). The cost becomes unaffordable if the PEMFC is in massive application. The PGM-Free catalyst shows very promising activity in rotation disk electrode (RDE) testing. The half-wave potential could reach 0.91 V versus standard hydrogen electrode (SHE). However, in a membrane electrode assembly (MEA), the performance of PGM-Free catalysts is not good enough to replace the PGM catalysts. Since the PGM-free catalysts are so different from the PGM catalysts in terms of catalytic activity, stability, surface conditions, particle size, etc., the fabrication of PGM-Free catalyst MEA cannot simply copy the method of making PGM MEA. We proposed a novel method of fabricating PGM-Free catalyst MEA, so that the intrinsic catalyst activity from RDE can be translated into MEA performance. The method is based on the catalyst coated membrane (CCM) method using optimized ionomer to carbon (I/C) ratio and solvent mixture of catalyst ink. Using this method, the PGM-free catalyst MEA achieved the current density 44.9 mA cm-2 at 0.9 V iR-free in H2/O2 and 150 mA cm-2 at 0.8 V in H2/air, which surpassed the performance targets of US Department of Energy (DOE)for PGM-Free catalyst MEA. The property (solvent composition, dispersion of catalyst and ionomer in an ink), structure (pore structure) and the MEA performance have been characterized using, mercury intrusion porosimetry (MIP), MEA testing. A property-structure-performance relationship has been established.