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arXiv 2020年

作者： Wright, Andrew J. Wang, Qingyang Hu, Chongze Yeh, Yi-Ting Chen, Renkun Luo, Jian Department of NanoEngineering United States Department of Mechanical & Aerospace Engineering Program of Materials Science and Engineering University of California San Diego San diegoCA92093 United States

Improved thermomechanical properties have been reported for various high-entropy oxides containing typically five metal cations. This study further investigates a series of duodenary (11 metals + oxygen) high-entropy oxides by mixing different fractions of a five-cation fluorite-structured niobate and a seven-cation pyrochlore (both containing Yb) with matching lattice constants. Nine compositions of duodenary high-entropy oxides have been examined. All of them exhibit single high-entropy phases of either disordered fluorite or ordered pyrochlore structure. An order-disorder transition (ODT) is evident with changing composition, accompanied by a reduction in thermal conductivity (k). In comparison with the ODT criteria developed from ternary oxides, these duodenary oxides are more prone to disorder, but the ODT is still controlled by similar factors (but at different thresholds). Interestingly, there are abrupt increases in Young’s modulus (E) at low mixing concentrations near both endmembers. The E/k ratios are increased, in comparison with both endmembers. This study suggests a new route to tailor high-entropy ceramics via controlling cation ordering vs. disordering. © 2020, CC BY.

关键词： Thermal conductivity

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SHUYU Robot:An Automatic Rapid Temperature Screening System

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Chinese Journal of mechanical engineering 2020年第2期33卷 1-4页

作者： Zhao Gong Songwen Jiang Qizhi Meng Yanlei Ye Peng Li Fugui Xie Huichan Zhao Chunzhe Lv Xiaojie Wang Xinjun Liu The State Key Laboratory of Tribology&Tsinghua University(DME)‑Siemens Joint Research Center for Advanced Robotics Department of Mechanical Engineering(DME)Tsinghua UniversityBeijing 100084China Beijing Key Lab of Precision/Ultra‑precision Manufacturing Equipments and Control Tsinghua UniversityBeijing 100084China Yantai Tsingke+Robot Joint Research Institute Co. Ltd.Yantai 264000ShandongChina The First Affiliated Hospital of Tsinghua University Beijing 100016China

The health of people around the world and the global economy are under substantial threat from the outbreak of pandemics[1].Controlling pandemics is extremely challenging,with preventing the spread of pathogens the most important and critical *** all preventative actions,body temperature screening is undoubtedly highly necessary and effective[2].

关键词： Robot doubt critical

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Measurement and analysis of thermal conductivity of ceramic particle beds for solar thermal energy storage

arXiv

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arXiv 2021年

作者： Chung, Ka Man Zeng, Jian Adapa, Sarath Reddy Feng, Tianshi Bagepalli, Malavika V. Loutzenhiser, Peter G. Albrecht, Kevin J. Ho, Clifford K. Chen, Renkun Material Science and Engineering Program University of California San Diego San DiegoCA92093 United States Department of Mechanical and Aerospace Engineering University of California San Diego San DiegoCA92093 United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332-0405 United States Concentrating Solar Technologies Department Sandia National Laboratories 1515 Eubank Blvd. SE AlbuquerqueNM87123 United States

A systematic study was performed to measure the effective thermal conductivity of ceramic particle beds, a promising heat transfer and thermal energy storage media for concentrating solar power (CSP). The thermal conductivity of the ceramic particle beds was measured using a transient hot-wire (THW) method within a temperature range of room temperature to 700 oC, the target operating temperature of the next-generation CSP systems. Two different types of ceramic particles were examined: (1) CARBOBEAD HSP 40/70 and (2) CARBOBEAD CP 40/100 with the average particle sizes of ~ 400 μm and ~280 μm, respectively, and thermal conductivities ranging from ~0.25 W m-1 K-1 to ~0.50 W m-1 K-1 from 20 oC to 700 oC in both air and N2 gas. The gaseous pressure dependence of the thermal conductivity of the ceramic particle beds was also studied in the N2 environment to differentiate the contributions from gas conduction, solid conduction, and radiation. Calculations using the Zehner, Bauer, and Schlünder (ZBS) model showed good agreements with the measurements. Based on the model, it is concluded that the effective thermal conductivity of the packed particle beds is dominated by the gas conduction while the solid conduction and radiation contributes to about 20% of the effective thermal conductivity at high temperature. Copyright © 2021, The Authors. All rights reserved.

关键词： Heat storage

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Electric Field Emission in GdNiO3 Microflower

SSRN

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SSRN 2024年

作者： Karmakar, Subrata Parey, Vanshree Mistari, Chetan D. Hamid, Md Abdul Emu, Injamamul Hoque Halim, Md Abdul Sanyal, Gopal Chakraborty, Brahmananda More, M.A. Droopad, Ravi Haque, Ariful Ingram School of Engineering Texas State University San MarcosTX78666 United States Institute of Chemical Research of Catalonia The Barcelona Institute of Science and Technology Av. Països Catalans 16 Tarragona43007 Spain Department of Physics Savitribai Phule Pune University Maharashtra Pune411007 India Mechanical Metallurgy Division Bhabha Atomic Research Centre Trombay Mumbai400085 India High Pressure and Synchrotron Radiation Physics Division Bhabha Atomic Research Centre Trombay Mumbai400085 India Homi Bhabha National Institute Anushaktinagar Mumbai400094 India Materials Science Engineering & Commercialization Program Texas State University San MarcosTX78666 United States

GdNiO3 microflowers were synthesized by hydrothermal method with unit sizes of about 2.2-3.5 μm and petal sizes of ~75-100 nm. The X-ray diffraction pattern (XRD) and Raman spectra reveal an orthorhombic crystal structure with the space group Pbnm and various Raman active vibrational modes i.e., Ag, B1g, B2g associated with rare earth nickelates. The total density of states (TDOS) was calculated by density functional theory (DFT) study using a hybrid functional (HSE06) and the volume of TDOS near the Fermi level confirms the semiconducting nature of GdNiO3 with band gap ~0.688 eV. The local bulk work function (φ) was estimated theoretically at ~5.35 eV from DFT calculations which is one of the essential parameters for the field enhancement factor (β). Field emission current density (J) vs. applied electric field (E) divulges a low turn-on field (Eto)~4.6 V/μm@1 μA/cm2 and a threshold field (Eth)~5.2 V/μm@10 μA/cm2 respectively. The Fowler-Nordheim (F-N) model was introduced to calculate β ~1637 and long-term field emission current stability was observed experimentally > 4 h at the preset values of current 2 μA and 10 μA, respectively. The easy preparedness, low turn-on field, and long-term current stability without significant decay suggest this material to be an efficient electron field emitting material in the rare-earth nickelates series. © 2024, The Authors. All rights reserved.

关键词： Rare earths

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Design of an ICE Cube infrastructure capable of performing metal extraction from a substrate through biomining in microgravity conditions, using bacteria of the genus Bacillus 71

Design of an ICE Cube infrastructure capable of performing m...

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71st International Astronautical Congress, IAC 2020

作者： Campos, Graciela L. Silesky, Catalina P. Gutierrez, Erick C. Cobos, Patricia Q. Department of Mechanical Engineering University of Costa Rica Montes de Oca San Jose2060 Costa Rica Faculty of Microbiology University of Costa Rica Montes de Oca San Jose2060 Costa Rica Department of Electrical Engineering University of Costa Rica Montes de Oca San Jose2060 Costa Rica Postgraduate Program in Electrical Engineering University of Costa Rica Montes de Oca San José2060 Costa Rica

Creating sustainable mining processes to use in space exploration will help obtain affordable, clean energy and innovative means of obtaining materials in situ, eliminating their transportation from Earth. This research focuses on the design of a biomining experiment for the International Space Station through a 1U ICE (International Commercial Experiment) Cube that sustains a metal extraction mission using microfluidic chips. The studied Bacillus species have a fermentative metabolism capable of adapting to perform anaerobic respiration using metals such as iron and manganese as an external electron acceptor, reducing the metal to form soluble ions and releasing CO2 in the process that can be quantified by CO2 sensors in the microfluidic chip, and correlated with the biomining rate produced by the bacteria to indicate its viability. Results show that the designed temperature control system was capable of maintaining the microfluidic chips at a 20 °C temperature difference with respect to room temperature and with high disturbance rejection performance and robustness. The comparative analysis of the CO2 particles transmission probability reaching the outlet for sensor reading for different microfluidic chip channel geometries, indicated that the combined geometry had the most efficient distribution response with more than 60% of the particles reaching the sensor in the first 22.5 seconds after release. Finally, the analysis of the preliminary experiments results carried out with the bacteria showed that Bacillus subtilis ATCC 6633 does not show capacity to carry out manganese biomining under the study conditions, however a possible process of iron biomining from basalt by this bacterium is observed mainly under anaerobic conditions, although it is also observed in aerobiosis. The upgrade in efficiency of this biomining technique has the potential of facilitating space exploration missions while also improving life on Earth through applications in recycling and mater

关键词： Microfluidic chips

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Interaction between Nanoplastics and Pectin, a Water-Soluble Polysaccharide, in the Presence of Fe(Iii) Ion

SSRN

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SSRN 2022年

作者： Moriwaki, Hiroshi Komori, Naoya Akiyama, Yoshitake Shinshu University Department of Applied Biology Faculty of Textile Science and Technology 3-15-1 Tokida Ueda386-8567 Japan Shinshu University Research Initiative for Supra-Materials Interdisciplinary Cluster for Cutting Edge Research 3-15-1 Tokida Ueda386-8567 Japan Shinshu University Department of Mechanical Engineering and Robotics Faculty of Textile Science and Technology 3-15-1 Tokida Ueda386-8567 Japan

Recently, the potential adverse effects of nanoplastics on aquatic organisms have been frequently reported. It is important to understand the behavior of nanoplastics in the water environment. In this report, the interaction between nanoplastics and pectin, a water-soluble polysaccharide, has been studied. The liquid suspension of a polystyrene nanoplastic (10 mg L-1) was mixed with pectin (15 mg L-1) and Fe(III) (0.10 mM). The aggregation and sedimentation of the nanoplastics were then observed. The removal in percent of the nanoplastics in the supernatant after storage for 2 days was 97.3%. These results indicate that nanoplastics in water can be separated by the environmentally friendly biopolymer and provide information about the behavior of nanoplastics in the water environment. © 2022, The Authors. All rights reserved.

关键词： Iron compounds

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Erratum: Bok's equi-volume principle: Translation, historical context, and a modern perspective

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Brain Multiphysics 2024年 7卷

作者： Jack Consolini Nagehan Demirci Andrew Fulwider Jeffrey J. Hutsler Maria A. Holland Department of Aerospace and Mechanical Engineering University of Notre Dame Notre Dame IN 46556 USA Bioengineering Graduate Program University of Notre Dame Notre Dame IN 46556 USA Department of Chemical and Biomolecular Engineering University of Notre Dame Notre Dame IN 46556 USA Department of German and Russian Languages and Literatures University of Notre Dame Notre Dame IN 46556 USA Program in Neuroscience University of Nevada Reno NV 89557 USA

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Automatic petrol and diesel engine sound identification based on machine learning approaches 1

Automatic petrol and diesel engine sound identification base...

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1st International Conference on Automotive, Manufacturing, and mechanical engineering, IC-AMME 2018

作者： Frederick, Halim Winda, Astuti Solihin, Mahmud Iwan Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia Faculty of Engineering Technology and Built Environment UCSI University Cheras Kuala Lumpur56000 Malaysia

Petrol and diesel engine have a significantly different way to convert chemical energy into mechanical energy. In this work, the intelligent system approach is used to automatically identify the type of engine based on the sound of the engine. The combination of signal processing and machine learning technique for automatic petrol and diesel engine sound identification is presented in this work. After a signal preprocessing step of the engine sound, a Fast Fourier Transform (FFT)-based frequency characteristic modelling technique is applied as the feature extraction method. The resulting features extracted from the sound signal, in the form of frequency in the FFT matrix, are used as the inputs for the machine learning, the Support Vector Machine (SVM), step of the proposed approach. The experiment of FFT with SVM-based diesel and petrol engine sound identification has been carried out. The results show that the proposed approach produces a good accuracy in the relatively short training time. Experimental results show the training and testing accuracy of 100 % and 100 % respectively. They confirm the effectiveness of the proposed intelligent automatic diesel and petrol engine sound identification based on Fast Fourier Transform (FFT) and Support Vector Machines (SVMs). © 2019 The Authors, published by EDP Sciences.

关键词： Support vector machines

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T ensor-decomposition-based A P riori S urrogate (TAPS) modeling for ultra large-scale simulations

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Computer Methods in Applied Mechanics and engineering 2025年 444卷

作者： Jiachen Guo Gino Domel Chanwook Park Hantao Zhang Ozgur Can Gumus Ye Lu Gregory J. Wagner Dong Qian Jian Cao Thomas J.R. Hughes Wing Kam Liu Theoretical and Applied Mechanics Program Northwestern University 2145 Sheridan Road Evanston 60201 IL USA Department of Mechanical Engineering Northwestern University 2145 Sheridan Road Evanston 60201 IL USA Department of Mechanical Engineering University of Maryland Baltimore County 1000 Hilltop Circle Baltimore 21250 MD USA Department of Mechanical Engineering The university of Texas at Dallas 800 W. Campbell Road Richardson 75080 TX USA Co-Founders of HIDENN-AI LLC 1801 Maple Ave Evanston 60201 IL USA Institute for Computational Engineering and Sciences The University of Texas at Austin 201 East 24th Street Stop C0200 Austin 78712 TX USA

A data-free predictive scientific AI model, termed Tensor-decomposition-based A Priori Surrogate (TAPS), is proposed for tackling ultra large-scale engineering simulations with significant speedup, memory savings, and storage gain. TAPS does not require any training data and can effectively obtain surrogate models for high-dimensional parametric problems with equivalently zetta-scale ( 1 0 21 ) degrees of freedom (DoFs) using a single GPU. TAPS achieves this by directly obtaining reduced-order models through solving the weak form of the governing equations with multiple independent variables such as spatial coordinates, parameters, and time. The paper first introduces an AI-enhanced finite element-type interpolation function called convolution hierarchical deep-learning neural network (C-HiDeNN) with tensor decomposition (TD). Subsequently, the generalized space-parameter-time Galerkin weak form and the corresponding matrix form are derived. Through the choice of TAPS hyperparameters, different convergence rates can be achieved. To show the capabilities of this framework, TAPS is then used to simulate a large-scale additive manufacturing process and achieves around 1,370x speedup, 14.8x memory savings, and 955x storage gain compared to the finite difference method with 3.46 billion spatial DoFs. As a result, the TAPS framework opens a new avenue for many challenging ultra large-scale engineering problems, such as additive manufacturing and integrated circuit design, among others.

关键词： Additive manufacturing Data-free model Generalized Galerkin formulation for parametric PDEs Hierarchical neural network finite element interpolation Large-scale simulation Predictive scientific AI

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MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

arXiv

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arXiv 2022年

作者： Li, Lei Wu, Fuping Wang, Sihan Luo, Xinzhe Martín-Isla, Carlos Zhai, Shuwei Zhang, Jianpeng Liu, Yanfei Zhang, Zhen Ankenbrand, Markus J. Jiang, Haochuan Zhang, Xiaoran Wang, Linhong Arega, Tewodros Weldebirhan Altunok, Elif Zhao, Zhou Li, Feiyan Ma, Jun Yang, Xiaoping Puybareau, Elodie Oksuz, Ilkay Bricq, Stephanie Li, Weisheng Punithakumar, Kumaradevan Tsaftaris, Sotirios A. Schreiber, Laura M. Yang, Mingjing Liu, Guocai Xia, Yong Wang, Guotai Escalera, Sergio Zhuang, Xiahai School of Data Science Fudan University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Departament de Matemàtiques & Informàtica Universitat de Barcelona Barcelona Spain Computer Vision Center Universitat Autònoma de Barcelona Spain School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Northwestern Polytechnical University Xi'An China College of Electrical and Information Engineering Hunan University Changsha China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha China College of Physics and Information Engineering Fuzhou University Fuzhou China Comprehensive Heart Failure Center Wuerzburg University Hospitals Wuerzburg Germany School of Engineering University of Edinburgh Edinburgh United Kingdom School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Radiology and Diagnostic Imaging University of Alberta Edmonton Canada Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecomm-Unications Chongqing China ImViA Laboratory Université Bourgogne Franche-Comté Dijon France Computer Engineering Department Istanbul Technical University Istanbul Turkey Le Kremlin-Bicêtre France Department of Mathematics Nanjing University of Science and Technology Nanjing China Department of Mathematics Nanjing University Nanjing China

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (***/zhuangxiahai/0/myops20/). © 2022, CC BY-NC-SA.

关键词： Magnetic resonance imaging

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