Mechanical and Energy Engineering Works

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    Developing a method to calculate leaks in a compressed air line using time series pressure measurements
    (Elsevier, 2022-07) Jafarian, Alireza; Taheri, Saman; Daniel, Ebin; Razban, Ali; Mechanical Engineering, School of Engineering and Technology
    Compressed air is a powerful source of stored energy and is use in a variety of applications varying from painting to pressing in industrial manufacturing. One of the common problems in this system is air leakage. Air leaks forming within the compressed air piping network, act as continuous consumers and reduce the pressure within the pipes. Therefore, the air compressors will have to work harder to compensate for the losses in the pressure and preventing inefficiently of pneumatic devices. This will all cumulatively impact the manufacturer considerably when it comes to energy consumption and profits. There are multiple methods of air leak detection and accounting. The methods are usually conducted in non-production hours, the time that main air consumption within the piping is air leaks. In this paper, a model that includes both the production and non-production hours when accounting for the leaks is presented. It is observed that there is 50.33% increase in the energy losses, and 82.90% increase in the demand losses that are estimated when the effects of the air leaks are observed continuously and in real time. A real time monitoring system can provide an in-depth understanding of the compressed air system and its efficiency. The main goal of this paper is to find a nonintrusive way to calculate the amount of air as well as energy lost due to these leaks using time series pressure measurements.
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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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    Carbon and cellulose based nanofillers reinforcement to strengthen carbon fiber-epoxy composites: Processing, characterizations, and applications
    (Frontiers, 2023-01-10) Biswas, Pias Kumar; Omole, Oluwaseun; Peterson, Garrett; Cumbo, Eric; Agarwal, Mangilal; Dalir, Hamid; Mechanical Engineering, School of Engineering and Technology
    Since the inception of carbon fiber reinforced polymer (CFRP) composites, different nanofillers have been investigated to strengthen their mechanical and physical properties. To date, the majority of research has focused on enhancing fiber/matrix interface characteristics and/or optimizing nanofiller dispersion within the matrix, both of which improve the performance of carbon fiber-epoxy composite structures. Nanofillers can be dispersed into the polymer matrix by different techniques or nanofillers are chemically bonded to fiber, polymer, or both via multiple reaction steps. However, a few studies were conducted showing the effects of different nanofillers on the performance of carbon fiber-epoxy composites. Here a critical study has been done to explore different carbon and cellulose-based nanofillers which are used to enhance the mechanical and physical properties of carbon fiber-epoxy composites. After giving a short history of carbon fiber production, the synthesis of carbon nanotubes (CNTs), graphene, cellulose-based nanofillers (cellulose nanocrystals and nanofibers), their dispersion in the polymer matrix, and chemical/physical bonding with the fiber or polymer have been extensively described here along with their processing techniques, characterizations, and applications in various fields.
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    Energy Management using Industrial Internet of Things (IIoT) and ISO 50001
    (Indiana Chamber of Commerce, 2022-07-26) Razban, Ali; Mechanical Engineering, School of Engineering and Technology
    The manufacturers and businesses are under increasing pressure to reduce their energy consumption due to increase in energy cost and growing concern regarding global warming effect on our environment. Energy management is one of the fastest and most cost-effective ways to save money, cut greenhouse gas pollution and help businesses/ manufacturers to improve their energy efficiency. Proper energy management program reduces energy consumption, improve energy efficiency, reduces utility bills and improves profit. This can be achieved by having a proper energy management program in place which would not only improves the energy efficiency and it would also make the efficiency sustainable. “ISO 50001” can help the businesses/ manufacturers to reduce their energy consumption and achieve continual operational improvement. Measuring and considering the full array of benefits provided by energy efficiency is crucial to ensuring that all cost-effective efficiency resources are captured. New emerging technologies, such as Industrial Internet of Things (IIoT), propel the advancement of production process monitoring into real-time. An area where IIoT plays a major role is in the monitoring of energy consumption. Smart meters and sensors, which form the backbone of IIoT technology, provide awareness of energy consumption patterns by collecting real-time energy consumption data. As the data size amasses, a new word was coined – Industrial Big Data (IBD). With the help from IIoT and cheap data storage, IBD become available in almost every aspect of production process, including in energy management.
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    Electrically conductive 3D printed Ti3C2Tx MXene-PEG composite constructs for cardiac tissue engineering
    (Elsevier, 2022) Basara, Gozde; Saeidi-Javash, Mortaza; Ren, Xiang; Bahcecioglu, Gokhan; Wyatt, Brian C.; Anasori, Babak; Zhang, Yanliang; Zorlutuna, Pinar; Mechanical and Energy Engineering, School of Engineering and Technology
    Tissue engineered cardiac patches have great potential as a therapeutic treatment for myocardial infarction (MI). However, for successful integration with the native tissue and proper function of the cells comprising the patch, it is crucial for these patches to mimic the ordered structure of the native extracellular matrix and the electroconductivity of the human heart. In this study, a new composite construct that can provide both conductive and topographical cues for human induced pluripotent stem cell derived cardiomyocytes (iCMs) is developed for cardiac tissue engineering applications. The constructs are fabricated by 3D printing conductive titanium carbide (Ti3C2Tx) MXene in pre-designed patterns on polyethylene glycol (PEG) hydrogels, using aerosol jet printing, at a cell-level resolution and then seeded with iCMs and cultured for one week with no signs of cytotoxicity. The results presented in this work illustrate the vital role of 3D-printed Ti3C2Tx MXene on aligning iCMs with a significant increase in MYH7, SERCA2, and TNNT2 expressions, and with an improved synchronous beating as well as conduction velocity. This study demonstrates that 3D printed Ti3C2Tx MXene can potentially be used to create physiologically relevant cardiac patches for the treatment of MI.
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    THE MASS PRODUCTION OF MWCNTS/EPOXY SCAFFOLDS USING LATERAL BELT-DRIVEN MULTI-NOZZLE ELECTROSPINNING SETUP TO ENHANCE PHYSICAL AND MECHANICAL PROPERTIES OF CFRP
    (EPFL Lausanne, Composite Construction Laboratory, 2022-06) Ananda Habarakada Liyanage, Asel; Biswas, Pias Kumar; Agarwal, Mangilal; Dalir, Hamid
    Electrospinning is the process of ejecting a polymer melt or solution through a nozzle in the presence of a high-voltage electric field, which causes it to coalesce into a continuous filament with various shapes from a submicron to nanometer diameter. This process has gained vast attention because of its versatility, low cost, and ease of processing, leading to a massive demand for translating electrospinning experiments out of the laboratory into commercialized production. This manuscript represents an approach to mass-producing MWCNTs/Epoxy scaffolds using Lateral Belt Driven (LBD) multi-nozzle electrospinning technique to enhance CFRP's physical and mechanical properties. We mitigated the non-uniform electric field distribution challenges with the LBD approach, which helped obtain a single layer and welluniformed coverage of the electrospun MWCNTs/Epoxy scaffolds onto CFRP sheets. Interlaminar shear strength (ILSS) and fatigue performance under high-stress conditions improved by 29% and 27%, respectively. The energy of barely visible impact damage (BVID) improved by up to 45%.
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    DESIGN AND FABRICATION OF MULTI-FUNCTIONAL ENERGY STORAGE COMPOSITES INTEGRATING ULTRATHIN LITHIUM-ION BATTERY WITH ENHANCED ELECTRO-MECHANICAL PERFORMANCE
    (EPFL Lausanne, Composite Construction Laboratory, 2022-06) Biswas, Pias Kumar; Jadhav, Mayur; Ananda Habarakada Liyanage, Asel; Dalir, Hamid; Agarwal, Mangilal
    Exponential advancement in the automotive and aerospace industry promotes the need for Multifunctional Energy Storage Composites (MESCs) to minimize the dependence on fossil fuels and reduce structural weight. This study proposes and evaluates a multi-functional carbon fiber reinforced polymer (CFRP) composite with an embedded lithium-ion polymer battery, demonstrating a structural integrity concept. Here electrospun epoxy-multiwalled carbon nanotubes (epoxy-MWCNT) nanofibers were incorporated precisely on the uncured CFRP surface to enhance adequate interfacial bonding and adhesion between the layers after curing. The mechanical and physical properties of modified CFRP have been evidenced to possess higher mechanical strength than the traditional CFRP composite. Commercial ultra-thin lithium-ion battery with higher energy density has been uniquely integrated into the core of the CFRP composite structure. Comparison with conventional CFRP composite and electro-mechanical testing ensured that the electrochemical property of the embedded battery was preserved in loading/unloading conditions, and the mechanical strength of the composite structure was not compromised.
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    Nonlinear Multi-Fidelity Bayesian Optimization: An Application in the Design of Blast Mitigating Structures
    (SAE International, 2022-03-29) Valladares, Homero; Tovar, Andres; Mechanical Engineering, School of Engineering and Technology
    A common scenario in engineering design is the availability of several black-box functions that describe an event with different levels of accuracy and evaluation cost. Solely employing the highest fidelity, often the most expensive, black-box function leads to lengthy and costly design cycles. Multi-fidelity modeling improves the efficiency of the design cycle by combining information from a small set of observations of the high-fidelity function and large sets of observations of the low-fidelity, fast-to-evaluate functions. In the context of Bayesian optimization, the most popular multi-fidelity model is the auto-regressive (AR) model, also known as the co-kriging surrogate. The main building block of the AR model is a weighted sum of two Gaussian processes (GPs). Therefore, the AR model is well suited to exploit information generated by sources that present strong linear correlations. Recently, the non-linear auto-regressive Gaussian process (NARGP) model has appeared as an alternative to integrate information generated by non-linearly correlated black-box functions. The performance of the NARGP model in structural optimization has remained largely unexplored. This investigation presents a Bayesian optimization approach that implements the NARGP model as the multi-fidelity surrogate model. The optimization strategy is utilized in the design sandwich composite armors for blast mitigation. The armors are made of four layers: steel, carbon fiber reinforced polymer (CFRP), aluminum honeycomb (HC), and CFRP. The optimization problem has three design variables, which are the thickness of the CFRP and aluminum HC layers. Two objectives are minimized: the armor’s penetrations and the reaction force at the armor’s supports. The black-box functions are two finite element models with different levels of fidelity. The low-fidelity model assumes elastic behavior of the sandwich composite. The high-fidelity model considers the nonlinear behavior of each layer of the armor. The results show that the proposed non-linear multi-fidelity Bayesian optimization approach produces a more stable expansion of the Pareto front than an optimization strategy that employs the AR model. This outcome suggests that the NARGP model is an appealing alternative in design problems with a limited number of function evaluations of the high-fidelity source.
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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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    Multi-Objective Bayesian Optimization of Lithium-Ion Battery Cells for Electric Vehicle Operational Scenarios
    (MDPI AG, 2022-05-31) Gaonkar, Ashwin; Valladares, Homero; Tovar, Andres; Zhu, Likun; El-Mounayri , Hazim; Mechanical Engineering, School of Engineering and Technology
    The development of lithium-ion batteries (LIBs) based on current practice allows an energy density increase estimated at 10% per year. However, the required power for portable electronic devices is predicted to increase at a much faster rate, namely 20% per year. Similarly, the global electric vehicle battery capacity is expected to increase from around 170 GWh per year today to 1.5 TWh per year in 2030—this is an increase of 125% per year. Without a breakthrough in battery design technology, it will be difficult to keep up with their increasing energy demand. The objective of this investigation is to develop a design methodology to accelerate the LIB development through the integration of electro-chemical numerical simulations and machine learning algorithms. In this work, the Gaussian process (GP) regression model is used as a fast approximation of numerical simulation (conducted using Simcenter Battery Design Studio®). The GP regression models are systematically updated through a multi-objective Bayesian optimization algorithm, which enables the exploration of innovative designs as well as the determination of optimal configurations. The results reported in this work include optimal thickness and porosities of LIB electrodes for several practical charge–discharge scenarios which maximize energy density and minimize capacity fade.