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2024 International Conference on electrical Machines, ICEM 2024

作者： Lee, Matthew Jianu, Ofelia A. Kar, Narayan C. Mechanical Automotive and Materials Engineering University of Windsor Windsor Canada Electrical and Computer Engineering University of Windsor Windsor Canada

ISBN: (纸本)9798350370607

This paper presents the fluid flow and local heat transfer coefficient associated with a rotating liquid hollow rotor shaft cooling system for electric traction motors. Computational fluid dynamics (CFD) is utilized to simulate the fluid flow and heat transfer phenomena to obtain the local heat transfer coefficient. A comparison is conducted between a stationary and a rotating hollow rotor shaft cooling system using 50/50 ethylene glycol water mixture. The shaft rotation is found to alter the fluid flow through the development of vortices and a secondary flow due to the Coriolis and centrifugal effect. The alteration of the fluid flow is found to enhance the fluid turbulence, significantly increasing the local heat transfer coefficient when compared to the non-rotating case. This results in a significant decrease in the maximum operating temperature of the shaft surface. It is concluded that the local heat transfer coefficients change significantly along the length of the hollow shaft resulting in non-uniform temperature distributions. This can allow for the identification of regions of low thermal performance within the cooling system. © 2024 IEEE.

关键词： Cooling systems

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Koopman-based Deep Learning for Nonlinear System Estimation 63

Koopman-based Deep Learning for Nonlinear System Estimation

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Sun, Zexin Chen, Mingyu Baillieul, John Boston University Division of Systems Engineering United States Boston University Department of Electrical and Computer Engineering United States Boston University Division of Systems Engineering Departments of Mechanical Engineering Electrical and Computer Engineering BostonMA02215 United States

ISBN: (纸本)9798350316339

Nonlinear differential equations are encountered as models of fluid flow, spiking neurons, and many other systems of interest in the real world. Common features of these systems are that their behaviors are difficult to describe exactly and invariably unmodeled dynamics present challenges in making precise predictions. In this paper, we present a novel data-driven linear estimator based on Koopman operator theory to extract meaningful finite-dimensional representations of complex non-linear systems. The Koopman model is used together with deep reinforcement networks that learn the optimal stepwise actions to predict future states of nonlinear systems. Our estimator is also adaptive to a diffeomorphic transformation of the estimated nonlinear system, which enables it to compute optimal state estimates without re-learning. © 2024 IEEE.

关键词： Deep reinforcement learning

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Direct ink writing of gradient shear-stiffening elastomer for enhanced toughness and impact resistance

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Journal of Industrial and engineering Chemistry 2025年 149卷 593-603页

作者： Gong, Liping Zhao, Chunyu Lu, Hongda Zhang, Qingtian Chen, Zexin Yang, Junjie Yang, Jinyu Sun, Shuaishuai Du, Haiping Li, Weihua School of Mechanical Materials Mechatronic and Biomedical Engineering University of Wollongong Wollongong2522 Australia CAS Key Laboratory of Mechanical Behaviour and Design of Materials Department of Modern Mechanics University of Science and Technology of China Hefei230027 China School of Electrical Computer and Telecommunication Engineering University of Wollongong Wollongong2522 Australia

Shear-stiffening materials have garnered significant attention due to their inherent ability to rapidly respond to changes in strain rate. However, conventional studies on these materials exhibit limited mechanical performance and insufficient structural design, hindering applications in material science and smart mechatronics. Inspired by the gradient structure of a squid beak, this study uses a direct-ink-writing (DIW) technology to develop a novel gradient shear-stiffening elastomer (GSSE). Comprehensive characterizations were conducted to optimize the printability of the shear-stiffening elastomer under various testing conditions, including rheology, compression, and tension. Additionally, the toughness and impact resistance of the elastomer were systematically investigated. It is demonstrated that the biomimetic GSSE exhibits enhanced flexibility, efficient energy dissipation, and outstanding impact resistance. Consequently, this work advances the field of shear-stiffening elastomers for diverse applications by leveraging the gradient structure design strategy to achieve superior mechanical performance under complex loading conditions. © 2025 The Author(s)

关键词： Strain rate

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A large-area bionic skin for high-temperature energy harvesting applications

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Nano Research 2023年第7期16卷 10245-10255页

作者： Zhaojun Liu Bian Tian Yao Li Jiaming Lei Zhongkai Zhang Jiangjiang Liu Qijing Lin Chengkuo Lee Zhuangde Jiang State Key Laboratory for Mechanical Manufacturing Systems Engineering School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Department of Electrical&Computer Engineering National University of SingaporeSingapore 117576Singapore Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing Yantai 265503China

For the large amount of waste heat wasted in daily life and industrial production,we propose a new type of flexible thermoelectric generators(F-TEGs)which can be used as a large area bionic skin to achieve energy harvesting of thermal *** reference to biological structures such as pinecone,succulent,and feathers,we have designed and fabricated a biomimetic flexible TEG that can be applied in a wide temperature range which has the highest temperature energy harvesting capability *** laminated free structure of the bionic F-TEG dramatically increases the efficiency and density of energy *** F-TEGs(single TEG only 101.2 mg in weight),without an additional heat sink,demonstrates the highest output voltage density of 286.1 mV/cm^(2)and the maximum power density is 66.5 mW/m^(2) at a temperature difference of nearly 1000℃.The flexible characteristics of F-TEGs make it possible to collect the diffused thermal energy by flexible attachment to the outer walls of high-temperature pipes and vessels of different diameters and *** work shows a new design and application concept for flexible thermal energy collectors,which fills the gap of flexible energy harvesting in high-temperature environment.

关键词： bionic structure high-temperature flexible thermoelectric generators(TEGs)

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Shuffled multi-evolutionary algorithm with linear population size reduction

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Soft Computing 2024年第21期28卷 12671-12688页

作者： Ahandani, Morteza Alinia Kharrati, Hamed Rahimi, Afshin Department of Electrical Engineering Langarud Branch Islamic Azad University Langarud Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Mechanical Automotive and Materials Engineering University of Windsor WindsorONN9B 3P4 Canada

The evolutionary algorithms with shuffling concept divide a population into several groups and then each group try to evolve its members in an independent evolutionary process. In an attempt to increase and diversify search moves and preventing the premature convergence of such algorithms, this paper proposes a meta-heuristic technique by employing several evolutionary processes for different groups. In this strategy, different groups can be evolved using a randomly selected evolutionary process. In accordance with this purpose and to provide an ability of jumping out of local optima, which can be useful for finding global optima in complex optimization problems, four different evolutionary strategies inspired from shuffled frog leaping (SFL), shuffled complex evolution (SCE), shuffled differential evolution (SCE) and opposition-based learning (OBL) are incorporated into a new structure called the shuffled multi-evolutionary algorithm (SMEA). The SMEA is extended with linear population size reduction (LPSR) known as L-SMEA, which continually decreases the population size and number of groups according to a linear function. The SMEA is evaluated on CEC2014 and CEC2017 benchmark functions. The obtained results are compared with SFL, SCE and SDE and other state-of-the-art algorithms proposed in the literatures. Performing the statistical analysis of the obtained results demonstrate that the proposed L-SMEA is superior to other three algorithms including SFL, SCE and SDE and some other state-of-the-art algorithms. Also, the performed experiments indicate the contribution of employing multiple evolutionary strategies beside of linear population size reduction leads to achieving a more effective and robust algorithm than those used single evolutionary strategy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Evolutionary algorithms

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Electronic structure prediction of multimillion atom systems through uncertainty quantification enabled transfer learning

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npj Computational materials 2024年第1期10卷 1423-1437页

作者： Shashank Pathrudkar Ponkrshnan Thiagarajan Shivang Agarwal Amartya S.Banerjee Susanta Ghosh Department of Mechanical Engineering-Engineering Mechanics Michigan Technological UniversityHoughtonMIUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA90095USA Department of Materials Science and Engineering University of CaliforniaLos AngelesCA90095USA Faculty member of the Center for Data Sciences Michigan Technological UniversityHoughtonMIUSA

The ground state electron density—obtainable using Kohn-Sham Density Functional Theory(KSDFT)simulations—contains a wealth of material information,making its prediction via machine learning(ML)models ***,the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation,making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system ***,we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data,while comprehensively sampling systemconfigurations using *** ML models are less reliant on heuristics,and being based on Bayesian neural networks,enable uncertainty *** show that our models incur significantly lower data generation costs while allowing confident—and when verifiable,accurate—predictions for a wide variety of bulk systems well beyond training,including systems with defects,different alloy compositions,and at multi-million-atom ***,such predictions can be carried out using only modest computational resources.

关键词： alloy prediction transfer

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Guaranteed Privacy-Preserving H∞-Optimal Interval Observer Design for Bounded-Error LTI Systems 63

Guaranteed Privacy-Preserving H∞-Optimal Interval Observer ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Khajenejad, Mohammad Martinez, Sonia The University of Tulsa Departments of Mechanical Engineering and Electrical and Computer Engineering TulsaOK United States The University of California San Diego Department of Mechanical and Aerospace Engineering San DiegoCA United States

ISBN: (纸本)9798350316339

This paper furthers current research into the notion of guaranteed privacy, which provides a deterministic characterization of the privacy of output signals of a dynamical system or mechanism. Unlike stochastic differential privacy, guaranteed privacy offers strict bounds on the proximity between the ranges of two sets of estimated data. Our approach relies on synthesizing an interval observer for a perturbed linear time-invariant (LTI) bounded-error system. The design procedure incorporates a bounded noise perturbation factor computation and observer gains synthesis. Consequently, the observer simultaneously provides guaranteed private and stable interval-valued estimates for a desired variable. We demonstrate the optimality of our design by minimizing the H∞ norm of the observer error system. Furthermore, we assess the accuracy of our proposed mechanism by quantifying the loss incurred when considering guaranteed privacy specifications. Finally, we illustrate the outperformance of the proposed approach to differential privacy through simulations. © 2024 IEEE.

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Enhanced battery life prediction with reduced data demand via semi-supervised representation learning

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Journal of Energy Chemistry 2025年第2期101卷 524-534,I0011页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chi Yung Chung School of Mechanical MaterialsMechatronic and Biomedical EngineeringUniversity of WollongongNSW 2522Australia Department of Electrical and Electronic Engineering and Research Centre for Grid Modernisation The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China Photonics Research Institute The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia

Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for *** of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for ***,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL *** approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL *** approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge *** method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional *** also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder *** projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled *** approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.

关键词： Lithium-ion batteries Battery degradation Remaining useful life Semi-supervised learning

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Quantification of Si, Al, Ti and O Composition in Si/Al Oxide Based Synaptic Resistor Circuits

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 240-241页

作者： Xu, Mingjie Gao, Dawei Zheng, Jian-Guo Chen, Yong Irvine Materials Research Institute University of California Irvine CA United States Departments of Mechanical and Aerospace Engineering Materials Science and Engineering Electrical and Computer Engineering California NanoSystems Institute University of California Los Angeles CA United States

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Q-RBSA:high-resolution 3D EBSD map generation using an efficient quaternion transformer network

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npj Computational materials 2024年第1期10卷 2978-2987页

作者： Devendra K.Jangid Neal R.Brodnik McLean P.Echlin Chandrakanth Gudavalli Connor Levenson Tresa M.Pollock Samantha H.Daly B.S.Manjunath Electrical and Computer Engineering University California Santa BarbaraSanta Barbara 93106-5050 CAUSA Mechanical Engineering University California Santa BarbaraSanta Barbara 93106-5050 CAUSA Materials Department University California Santa BarbaraSanta Barbara 93106-5050 CAUSA

Gathering 3D material microstructural information is time-consuming,expensive,and *** of 3D data has been accelerated by developments in serial sectioning instrument capabilities;however,for crystallographic information,the electron backscatter diffraction(EBSD)imaging modality remains rate *** propose a physics-based efficient deep learning framework to reduce the time and cost of collecting 3D EBSD *** framework uses a quaternion residual block self-attention network(QRBSA)to generate high-resolution 3D EBSD maps from sparsely sectioned EBSD *** QRBSA,quaternion-valued convolution effectively learns local relations in orientation space,while self-attention in the quaternion domain captures long-range *** apply our framework to 3D data collected from commercially relevant titanium alloys,showing both qualitatively and quantitatively that our method can predict missing samples(EBSD information between sparsely sectioned mapping points)as compared to high-resolution ground truth 3D EBSD maps.

关键词： EBSD network resolution

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