Browsing by Author "Dai, Xianming"
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Item Battery Thermal Management System for Electric Vehicles: Design, Optimization, and Control(2021-12-01T06:00:00.000Z) Liu, Yuanzhi; Zhang, Jie; Du, Dingzhu; Fahimi, Babak; Koeln, Justin; Dai, XianmingWe are witnessing a fast-growing demand in vehicle electrification nowadays due to the widespread environmental consciousness, stringent emission regulations, and carbon neutrality implementation. As one of the most promising energy storage and electrification solutions, lithium-ion battery has been widely employed for electric vehicles (EVs) due to its excellent properties like high energy density, low maintenance, and long cycle life. However, there still exist multiple critical challenges in using lithium-ion battery at large scale as the major power source, such as reliability issues, safety concerns, and especially the range anxiety. Several promising solutions have been explored in the EV industry to mitigate the drawback of range anxiety, such as larger capacity with high energy density and ultra-fast charging. All these approaches challenge the temperature sensitive battery system as a side effect by bringing in extra overburdened waste heat. Given these concerns, battery thermal management system (BTMS) plays an indispensable role in maintaining the maximum temperature and temperature uniformity for EVs. This dissertation proposes a novel J-type air-based cooling structure via re-designing conventional U- and Z- type structures. Aiming to further improve the thermal performance, a surrogate-based optimization framework with two-stage cluster-based resampling is developed for BTMS structural optimization. Compared with the U- and Z- type, the novel J-type structure is proved with significant advancements. Based on the optimized J-type configuration, an operation mode switching module is designed to mitigate the temperature unbalance by controlling the opening degree of two outlet valves. Tested by an integrated driving cycle, results reveal that the J-type structure with its appropriate control strategy is a promising solution for light-duty EVs using an air cooling technology. Improving the energy efficiency is another potential approach to mitigate range anxiety. In this dissertation, a model predictive control (MPC)-based energy management strategy is developed to simultaneously control the BTMS, the air conditioning system, and the regenerative power. A vehicle velocity forecasting framework is integrated with the MPC-based energy management to further improve the energy efficiency. Deep learning and image-based traffic light detection techniques have been leveraged for velocity forecasting. Results show that the proposed energy management method has significantly improved the overall EV energy efficiency.Item Coarsening Droplet: Meniscus-mediated Spontaneous Droplet Climbing and Its Applications(2021-12-01T06:00:00.000Z) Guo, Zongqi; Dai, Xianming; Lee, Gil; You, Seung; Leonardi, Stefano; Qin, Zhenpeng; Xiong, GuopingInspired by nature, fundamental investigations of droplet directional movement grow explosively in recent years, aiming to generate energy, solve water shortage, and protect the environment. To move the droplets, strategies were developed based on changing the property of the droplet/substrate, and the substrate geometry. However, it is challenging to remove submicrometer droplets from the surface, which limits the potential applications of water harvesting. In this dissertation, I present a new spontaneous droplet movement on the hydrophilic slippery liquidinfused porous surface (SLIPS), named coarsening droplet. The coarsening effect can rapidly remove droplets with a diameter less than 20 μm. As a mechanism, low interfacial tension of hydrophilic SLIPS enables droplet climbing, while a high oil surface tension provides the driven force. By applying the coarsening droplet for water harvesting, it re-evaluated the classical condensation model since 1973, which neglected droplet disappearance (e.g., removed from the surface) that can enhance the heat transfer. Our new model elucidates the comprehensive heat transfer process, giving rise to a clear guideline of surface design for dropwise condensation. To further apply our new condensation theory of rapid droplet removal, I present a vapor-liquid separation surface to further enhance the water harvesting. By separating the water vapor with condensed droplets, the surface is always fresh to new water nucleation and has a higher droplet disappearance frequency with smaller droplets. In this dissertation, the coarsening droplet is focused on the fundamental study to show the importance of droplet removal. As SLIPS is not durable for water harvesting due to the loss of lubricants, I present a quasi-liquid surface (QLS) by tethering flexible polymer on various solid substrates to solve the durability issue of SLIPS. QLS shows excellent durability during water harvesting experiments, which could be further applied to industrial applications. In this dissertation, Chapter 1, I introduced the fundamental and current progress of droplet directional removal. In Chapter 2, I reported the coarsening droplet and investigate the mechanism of the coarsening droplet as surface tension force. In Chapter 3, I apply the coarsening droplet for water harvesting. The self-propelled coarsening droplet on hydrophilic SLIPS shows rapid removal of condensed submicrometer droplets regardless of surface orientations, showing a promising approach in water harvesting. In Chapter 4, I re-evaluate the condensation model on a hydrophilic slippery liquid-infused surface. I propose a modified condensation model by considering the droplet coverage ratio and removal frequency, which can precisely predict the heat flux. In Chapter 5 I further enhance the water harvesting by achieving vapor-liquid separation on T-shape structures. In Chapter 6, a quasi-liquid surface (QLS) is investigated to solve the durability issue of SLIPS. In Chapter 7, I summarized all my contributions and novelties.Item Designing Air-Independent Slippery Rough Surfaces for Condensation(Elsevier Ltd, 2019-06-19) Sirohia, Gaurav Kumar; Dai, Xianming; 0000-0001-5050-2867 (Dai, X); 308247739 (Dai, X); Sirohia, Gaurav Kumar; Dai, XianmingEnhancing condensation heat transfer is significant for power generation, heat exchangers, water harvesting, and air-conditioning. While superhydrophobic surfaces (SHS) are widely studied for condensation, this type of surface suffers from several weaknesses: (1) the hydrophobic surface chemistry does not favor nucleation, (2) the air lubricant has poor thermal conductivity, and (3) the air pocket may be displaced at an elevated humidity or subcooling. Patterned SHS can enhance vapor nucleation in the hydrophilic domains, but the superhydrophobic domains still rely on the air lubricant, resulting in the same weakness as SHS. Recently, the liquid infused surfaces have been developed by replacing the air lubricant with liquid lubricant, leading to more robust lubrication for liquid repellency. However, the original design of liquid infused surfaces shows a flat lubricant-water interface, which cannot provide a large contact area for heat transfer. Here, we successfully designed and manufactured the air-independent slippery rough surfaces (SRS) by conformal liquid lubrication on the rough solid surfaces. The surface chemistry of the SRS is governed by the liquid lubricant, not the solid textures, and the roughness is determined by the lubricated microtextures. Droplets are highly mobile on this air-independent slippery rough surface in the absence of air lubricant. Our comprehensive models provide rational design and optimization for the air-independent slippery rough surface that is highly desired in condensation heat transfer. © 2019 Elsevier LtdItem Hydrophilic Directional Slippery Rough Surfaces for Water Harvesting(Amer Assoc for the Advancement of Science) Dai, Xianming; Sun, Nan; Nielsen, Steven O.; Stogin, Birgitt Boschitsch; Wang, Jing; Yang, Shikuan; Wong, Tak-Sing; 0000-0001-5050-2867 (Dai, X); 308247739 (Dai, X); Dai, Xianming; Nielsen, Steven O.Multifunctional surfaces that are favorable for both droplet nucleation and removal are highly desirable for water harvesting applications but are rare. Inspired by the unique functions of pitcher plants and rice leaves, we present a hydrophilic directional slippery rough surface (SRS) that is capable of rapidly nucleating and removing water droplets. Our surfaces consist of nanotextured directional microgrooves in which the nanotextures alone are infused with hydrophilic liquid lubricant. We have shown through molecular dynamics simulations that the physical origin of the efficient droplet nucleation is attributed to the hydrophilic surface functional groups, whereas the rapid droplet removal is due to the significantly reduced droplet pinning of the directional surface structures and slippery interface. We have further demonstrated that the SRS, owing to its large surface area, hydrophilic slippery interface, and directional liquid repellency, outperforms conventional liquid-repellent surfaces in water harvesting applications.Item Plasmonic Nanoparticles Enabled Rapid and Ultrasensitive Infectious Disease Diagnostics(December 2022) Liu, Yaning 1991-; Huynh, Dung T.; Qin, Zhenpeng; Kahn, Jeffrey; Gassensmith, Jeremiah J.; Dai, Xianming; Lu, HongbingIn vitro diagnosis of respiratory infectious diseases is of paramount importance as evidenced by the current COVID-19 pandemic. Standard diagnostic methods, such as polymerase chain reaction (PCR) and enzyme-linked immunoassay (ELISA), provide specific and sensitive detection yet cause delayed sample-to-answer time. In contrast, rapid methods such as lateral flow assays (LFAs), have compromised sensitivity and specificity. To address these issues, herein, we have developed new plasmonic nanoparticle-based techniques for rapid and ultrasensitive diagnostics of infectious diseases, including respiratory syncytial virus (RSV) and severe acute respiratory syndrome (SARS)-associated coronavirus 2 (SARS-CoV-2). First, we studied gold nanourchins for colorimetric detection of RSV with a one-step sample-to- answer homogeneous immunoassay. We found that nanourchins have improved plasmonic coupling and virus targeting properties. We further integrated this rapid detection method onto a smartphone-based spectrometer and realized a sensitive diagnosis of the intact virus at room temperature within 30 minutes. Second, we developed plasmonic sensing of loop-mediated isothermal amplification (termed as Plasmonic LAMP). We engineered gold and silver (Au-Ag) alloy nanoshells with strong extinction in the visible wavelengths for SARS-CoV-2 detection. It also provides an additional sequence identification enabled by the plasmonic recognition of the LAMP products, thus improving detection specificity and sensitivity over the conventional LAMP. Third, we utilize the unique photothermal effects of plasmonic gold nanoparticles to substantially lower the detection limit of conventional plasmonic coupling assay by innovative DIgitAl plasMONic nanobubble Detection (DIAMOND). Taking RSV as a model target, we endeavor to build a pump-probe two laser system to generate and detect plasmonic nanobubbles (PNB) synchronically. Upon digital counting of PNB signals, we achieved rapid and ultrasensitive diagnostics of intact viruses at the single molecular level, representing viral infections in the early phase. Last, we built a simplified digital photoacoustic (dPA) detection module based on the DIAMOND platform. The plasmonic nanoparticles generate acoustic waves at much lower pulse energy with a single pulse laser stimulation than the vapor nanobubble phenomenon. Also, the dPA detection approach only requires a low-cost ultrasound transducer in the setup, which significantly reduces the complexity of the device and is practical for both laboratory and POC testing. We integrated dPA detection technique in a benchtop device and realize RSV detection at high performance (i.e., a single copy equivalent detection) and high specificity. Collectively, our work provides new capabilities for rapid, sensitive, and specific detection of infectious and other diseases.Item Symmetry Index Analysis for Inter-turn Short Circuit Fault Detection in Electrical Machines(May 2023) Khoshlessan, Mahshid 1989-; Fahimi, Babak; Akin, Bilal; Bhatia, Dinesh; Gardner, Matthew; Dai, XianmingElectric motors are a pivotal part of the ongoing shift in the transportation industry towards electrification. Nonetheless, electric motors are subject to faults. Among all faults, interturn short-circuit can be the most damaging and drastically shorten the motor’s life. It is admissible that if a motor is healthy, the distribution of the magnetic field will be symmetric around the motor. Therefore, it will be demonstrated that by capturing the magnetic signature from the end winding of the motor and processing this data, the symmetry index can be used as proof of the health of an electric machine. This thesis uses the concept of symmetry index analysis to present a fault diagnosis method to study the effects of faults specifically inter-turn short-circuits on two different types of electrical machines with two different winding arrangements. First, the study was conducted on an induction machine (IM) with a distributed winding, where the winding is customdesigned in such a way that inter-turn short circuits of 1%, 5%, and 10% can be manually applied to phase A winding. The second motor under this study is a switched reluctance motor (SRM) with concentrated winding, where 6% (1 turn out of 16 turns in phase A) and 43% (7-turn inter-turn short circuit of 16 turn in phase A) can be applied to the motor’s phase A winding. In order to collect the data, two integrated sensor boards are designed and installed at the proximity of the end winding, where the magnetic data can be captured and processed. It will be visually demonstrated that when the motor is healthy, the magnetic data will be distributed in a linearly symmetric manner. Finally, A series of machine learning (ML) methods will be applied and compared to this data to classify them. These machine learning methods are Decision trees (DT), Support Vector Machine (SVM), Gradient Boosting (GB), Random Forest (RF), and Logistic Regression (LR). Two key factors to compare the methods which are the accuracy and the execution time that the data will fit to the machine learning model are compared between these methods and then are reported using bar diagrams.