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

Now showing 1 - 10 of 85
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    A Bi-Level Data-Driven Framework for Fault-Detection and Diagnosis of HVAC Systems feature explainability
    (Elsevier, 2022-07) Movahed, Paria; Taheri, Saman; Razban, Ali; Mechanical Engineering, School of Engineering and Technology
    Machine learning methods have lately received considerable interest for fault detection diagnostic (FDD) analysis of heating, ventilation, and air conditioning (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 have also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing accuracy of the models while this leads to a high number of false positives (misleading alarms) in the system. 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 and rooftop units 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.
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    A Control Oriented Soot Prediction Model for Diesel Engines Using an Integrated Approach
    (American Society of Mechanical Engineers, 2021-11-01) Shewale, Mahesh S.; Razban, Ali; Mechanical Engineering, School of Engineering and Technology
    Diesel engines have been used in many vehicles and power generation units since a long time due to their less fuel consumption and high trustworthiness. With reference to upcoming emission norms, various engine out emissions have proved to be causing adverse effect on human health and environment. Soot, or particulate matter is one of the major pollutants in diesel engine out emissions and causes various lung related issues. There have been efforts to reduce the amount of soot generated using after-treatment devices like diesel particulate filter (DPF) to filter out particles and get clean tailpipe emissions. These technologies increase load on the system and involves additional maintenance. Also, deposition-based soot sensors have been found to be inoperative in certain scenarios like cold start conditions. In this research work, an effort has been made to develop a phenomenological model that predicts soot mass generated in a Cummins 6.7L diesel engine. The model uses in-cylinder conditions such as pressure, bulk mean temperature, fuel mass flow rate and injector orifice diameter. The difference between soot mass formed and oxidized yields the net amount of soot generated at engine out end. Furthermore, the generated soot mass is compared with benchmark results for specific load conditions and appropriate controller is designed to minimize this tradeoff. The control parameter being used here is fuel rail pressure, which controls the lift-off length, and ultimately equivalence ratio, which predicts mass of soot, generated in formation phase. The presented method shows a prediction error ranging from 5–20%, which is significantly reduced to 2% using a PID controller. The approach presented in this research work is generic and can be operated as stand-alone system or an integrated subsystem in a higher order control architecture.
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    A cooperative degradation pathway for organic phenoxazine catholytes in aqueous redox flow batteries
    (Elsevier, 2023-03) Fang, Xiaoting; Zeng, Lifan; Li, Zhiguang; Robertson, Lily A.; Shkrob, Ilya A.; Zhang , Lu; Wei, Xioaliang; Mechanical Engineering, School of Engineering and Technology
    Redox-active organic molecules that store positive charge in aqueous redox flow cells (catholyte redoxmers) frequently exhibit poor chemical stability for reasons that are not entirely understood. While for some catholyte molecules, deprotonation in their charged state is resposible for shortening the lifetime, for well designed molecules that avoid this common fate, it is seldom known what causes their eventual decomposition as it appears to be energetically prohibitive. Here, a highly soluble (1.6 M) phenoxazine molecule with a redox potential of 0.48 V vs. Ag/AgCl has been examined in flow cells. While this molecule has highly reversible redox chemistry, during cycling the capacity fades in a matter of hours. Our analyses suggest a cooperative decomposition pathway involving disproportionation of two charged molecules followed by anion substitution and deprotonation. This example suggests that cooperative reactions can be responsible for unexpectedly low chemical instability in the catholyte redoxmers and that researchers need to be keenly aware of such reactions and methods for their mitigation.
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    Abrasive Resistant Coatings—A Review
    (MDPI, 2014-05-21) Wu, Linmin; Guo, Xingye; Zhang, Jing; Mechanical Engineering, School of Engineering and Technology
    Abrasive resistant coatings have been widely used to reduce or eliminate wear, extending the lifetime of products. Abrasive resistant coatings can also be used in certain environments unsuitable for lubrications. Moreover, abrasive resistant coatings have been employed to strengthen mechanical properties, such as hardness and toughness. Given recently rapid development in abrasive resistant coatings, this paper provides a review of major types of abrasive coatings, their wearing mechanisms, preparation methods, and properties.
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    Achieving Energy-Saving, Continuous Redox Flow Desalination with Iron Chelate Redoxmers
    (AAAS, 2023-01-10) Xie, Rongxuan; Yue, Diqing; Peng, Zhenmeng; Wie, Xiaoliang; Mechanical Engineering, School of Engineering and Technology
    Desalination of saline water is becoming an increasingly critical strategy to overcome the global challenge of drinkable water shortage, but current desalination methods are often plagued with major drawbacks of high energy consumption, high capital cost, or low desalination capacity. To address these drawbacks, we have developed a unique continuous-mode redox flow desalination approach capitalizing on the characteristics of redox flow batteries. The operation is based on shuttled redox cycles of very dilute Fe2+/Fe3+ chelate redoxmers with ultralow cell overpotentials. The air instability of Fe2+ chelate is naturally compensated for by its in situ electrochemical generation, making the desalination system capable of operations with electrolytes at any specified state of charge. Under unoptimized conditions, fast desalination rates up to 404.4 mmol·m−2·h−1 and specific energy consumptions as low as 7.9 Wh·molNaCl−1 have been successfully achieved. Interestingly, this desalination method has offered an opportunity of sustainable, distributed drinkable water supplies through direct integration with renewable energy sources such as solar power. Therefore, our redox flow desalination design has demonstrated competitive desalination performance, promising to provide an energy-saving, high-capacity, robust, cost-effective desalination solution.
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    Additive Manufacturing of Metallic Materials: A Review
    (Springer, 2017) Zhang, Yi; Wu, Linmin; Guo, Xingye; Kane, Stephen; Deng, Yifan; Jung, Yeon-Gil; Lee, Je-Hyun; Zhang, Jing; Mechanical Engineering, School of Engineering and Technology
    In this review article, the latest developments of the four most common additive manufacturing methods for metallic materials are reviewed, including powder bed fusion, direct energy deposition, binder jetting, and sheet lamination. In addition to the process principles, the microstructures and mechanical properties of AM-fabricated parts are comprehensively compared and evaluated. Finally, several future research directions are suggested.
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    Agent-Based Numerical Methods for 3D Bioprinting in Tissue Engineering
    (Elsevier, 2018) Sego, T. J.; Moldovan, Nicanor I.; Tovar, Andres; Mechanical Engineering, School of Engineering and Technology
    Additive manufacturing has contributed significantly to the development of new surgical and diagnostic aids, personalized medical devices, implants, and prostheses. Now, it aspires to the direct digital manufacturing of living tissue, organs, and body parts. This can be achieved using three-dimensional (3D) bioprinting techniques in which the printing medium consists of biomaterials and living cells. Several 3D bioprinting methods are currently available, including inkjet, extrusion, and stereolithography. An emerging approach is the creation three-dimensional cellular patterns by the use of cell spheroids. The optimal application and further development of 3D bioprinting techniques could largely benefit from computational models capable of predicting the complex behavior of the printed cellular structures on multiple scales. This book chapter summarizes the state of the art of computational models in this field, with an emphasis on agent-based approaches and cell spheroid-based 3D bioprinting.
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    All-Printed MXene–Graphene Nanosheet-Based Bimodal Sensors for Simultaneous Strain and Temperature Sensing
    (ACS, 2021-05) Saeidi-Javash, Mortaza; Du, Yipu; Zeng, Minxiang; Wyatt, Brian C.; Zhang, Bowen; Kempf, Nicholas; Anasori, Babak; Zhang, Yanliang; Mechanical Engineering, School of Engineering and Technology
    Multifunctional sensors with integrated multiple sensing capabilities have enormous potential for in situ sensing, structural health monitoring, and wearable applications. However, the fabrication of multimodal sensors typically involves complex processing steps, which limit the choices of materials and device form factors. Here, an aerosol jet printed flexible bimodal sensor is demonstrated by using graphene and Ti3C2Tx MXene nanoinks. The sensor can detect strain by measuring a change in the AC resistive voltage while simultaneously monitoring temperature by detecting the DC Seebeck voltage across the same printed device pattern. The printed bimodal sensor not only expands the sensing capability beyond conventional single-modality sensors but also provides improved spatial resolution utilizing the microscale printed patterns. The printed temperature sensor shows a competitive thermopower output of 53.6 μV/°C with ultrahigh accuracy and stability during both steady-state and transient thermal cycling tests. The printed sensor also demonstrates excellent flexibility with negligible degradations after 1000 bending cycles. The aerosol jet printing and integration of nanomaterials open many opportunities to design and manufacture multifunctional devices for a broad range of applications.
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    ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand
    (Elsevier, 2018-07) Wu, Da-Chun; Amini, Amin; Razban, Ali; Chen, Jie; Mechanical Engineering, School of Engineering and Technology
    This paper proposes an innovative algorithm for predicting short-term electrical maximum demand by using historical demand data. The ability to recognize in peak demand pattern for commercial or industrial customers would propose numerous direct and indirect benefits to the customers and utility providers in terms of demand reduction, cost control, and system stability. Prior works in electrical maximum demand forecasting have been mainly focused on seasonal effects, which is not a feasible approach for industrial manufacturing facilities in short-term load forecasting. The proposed algorithm, denoted as the Adaptive Rate of Change (ARC), determines the logarithmic rate-of-change in load profile prior to a peak by postulating the demand curve as a stochastic, mean-reverting process. The rationale behind this analysis, is that the energy efficient program requires not only demand estimation but also to warn the user of imminent maximum peak occurrence. This paper analyzes demand trend data and incorporates stochastic model and mean reverting half-life to develop an electrical maximum demand forecasting algorithm, which is statistically evaluated by cross-table and F-score for three different manufacturing facilities. The aggregate results show an overall accuracy of 0.91 and a F-score of 0.43, which indicates that the algorithm is effective predicting peak demand in predicting peak demand.
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    Ballistic-diffusive phonon heat transport across grain boundaries
    (Elsevier, 2017-09) Chen, Xiang; Li, Weixuan; Xiong, Liming; Li, Yang; Yang, Shengfang; Zheng, Zexi; McDowell, David L.; Chen, Youping; Mechanical Engineering, School of Engineering and Technology
    The propagation of a heat pulse in a single crystal and across grain boundaries (GBs) is simulated using a concurrent atomistic-continuum method furnished with a coherent phonon pulse model. With a heat pulse constructed based on a Bose-Einstein distribution of phonons, this work has reproduced the phenomenon of phonon focusing in single and polycrystalline materials. Simulation results provide visual evidence that the propagation of a heat pulse in crystalline solids with or without GBs is partially ballistic and partially diffusive, i.e., there is a co-existence of ballistic and diffusive thermal transport, with the long-wavelength phonons traveling ballistically while the short-wavelength phonons scatter with each other and travel diffusively. To gain a quantitative understanding of GB thermal resistance, the kinetic energy transmitted across GBs is monitored on the fly and the time-dependent energy transmission for each specimen is measured; the contributions of coherent and incoherent phonon transport to the energy transmission are estimated. Simulation results reveal that the presence of GBs modifies the nature of thermal transport, with the coherent long-wavelength phonons dominating the heat conduction in materials with GBs. In addition, it is found that phonon-GB interactions can result in reconstruction of GBs.