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arXiv 2021年

作者： Berger, Julien Colinart, Thibaut Loiola, Bruna R. Orlande, Helcio R.B. Umr 7356 Cnrs La Rochelle Université Cnrs La Rochelle17000 France Univ. Bretagne Sud Umr Cnrs 6027 Irdl Lorient56100 France Military Institute of Engineering Mechanical Engineering Department Rio de Janeiro22290-270 Brazil POLI/COPPE Mechanical Engineering Graduate Program Federal University of Rio de Janeiro Brazil

The sorption curve is an essential feature for the modelling of heat and mass transfer in porous building materials. Several models have been proposed in the literature to represent the amount of moisture content in the material according to the water activity (or capillary pressure) level. These models are based on analytical expressions and few parameters that need to be estimated by inverse analysis. This article investigates the reliability of eight models through the accuracy of the estimated parameters. For this, experimental data for a wood fibre material are generated with special attention to the stop criterion to capture long time kinetic constants. Among five sets of measurements, the best estimate is computed. The reliability of the models is then discussed. After proving the theoretical identifiability of the unknown parameters for each model, the primary identifiability is analysed. It evaluates whether the parameters influence on the model output is sufficient to proceed the parameter estimation with accuracy. For this, a continuous derivative-based approach is adopted. Seven models have a low primary identifiability for at least one parameter. Indeed, when estimating the unknown parameters using the experimental observations, the parameters with low primary identifiability exhibit large uncertainties. Finally, an Approximation Bayesian Computation algorithm is used to simultaneously select the best model and estimate the parameters that best represent the experimental data. The thermodynamic and Feng-Xing models, together with a proposed model in this work, were the best ones selected by this algorithm. © 2021, CC BY-NC-SA.

关键词： Parameter estimation

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Machine-learning identification of hemodynamics in coronary arteries in the presence of stenosis

arXiv

引用

arXiv 2021年

作者： Farajtabar, Mohammad Momeni, Mohit Biglarian, Mohit Miansari, Morteza Micro+Nanosystems & Applied Biophysics Laboratory Department of Mechanical Engineering Babol Noshirvani University of Technology P.O. Box 484 Babol Iran Department of Advanced Robotics Italian Institute of Technology Genoa Italy Department of Mechanical Engineering Babol Noshirvani University of Technology Babol Noshirvani University of Technology P.O. Box 484 Babol Iran Department of Cancer Medicine Cell Science Research Center Royan Institute for Stem Cell Biology and Technology ACECR Isar 11 Babol47138-18983 Iran Department of Mechanical Engineering Sharif University of Technology Tehran Iran

Prediction of the blood flow characteristics is of utmost importance for understanding the behavior of the blood arterial network, especially in the presence of vascular diseases such as stenosis. Computational fluid dynamics (CFD) has provided a powerful and efficient tool to determine these characteristics including the pressure and velocity fields within the network. Despite numerous studies in the field, the extremely high computational cost of CFD has led the researchers to develop new platforms including Machine Learning approaches that instead provide faster analyses at a much lower cost. In this study, we put forth a Deep Neural Network framework to predict flow behavior in a coronary arterial network with different properties in the presence of any abnormality like stenosis. To this end, an artificial neural network (ANN) model is trained using synthetic data so that it can predict the pressure and velocity within the arterial network. The data required to train the neural network were obtained from the CFD analysis of several geometries of arteries with specific features in ABAQUS software. Blood pressure drop caused by stenosis, which is one of the most important factors in the diagnosis of heart diseases, can be predicted using our proposed model knowing the geometrical and flow boundary conditions of any section of the coronary arteries. The efficiency of the model was verified using three real geometries of LAD's vessels. The proposed approach precisely predicts the hemodynamic behavior of the blood flow. The average accuracy of the pressure prediction was 98.7% and the average velocity magnitude accuracy was 93.2%. The key contribution of the proposed framework is that it can predict the hemodynamics of the blood flow in any segment of the arterial network knowing only geometrical features and velocity boundary conditions of the segment which are accessible using non-invasive methods. According to the results of testing the model on three patient-spec

关键词： Forecasting

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Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples

arXiv

引用

arXiv 2023年

作者： Song, Andrew H. Williams, Mane Williamson, Drew F.K. Jaume, Guillaume Zhang, Andrew Chen, Bowen Serafin, Robert Liu, Jonathan T.C. Baras, Alex Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Department of Pathology The Ohio State University ColumbusOH United States

Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation1,2, with concomitant risks of sampling bias and misdiagnosis3–6. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities7–18. However, these tools have had little translation to clinical practice as manual evaluation of such large data by pathologists is impractical and there is a lack of computational platforms that can efficiently process the 3D images and provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy12–14 or microcomputed tomography15,16 and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting that there is value in capturing larger extents of spatially heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, M

关键词： Pathology

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Evaluating the Environmental Justice Dimensions of Odor in Denver, Colorado

arXiv

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arXiv 2024年

作者： deSouza, Priyanka N. Rees, Amanda Oscilowicz, Emilia Lawlor, Brendan Obermann, William Dickinson, Katherine McKenzie, Lisa M. Magzamen, Sheryl Miller, Shelly Bell, Michelle L. Department of Urban and Regional Planning University of Colorado Denver DenverCO80202 United States CU Population Center University of Colorado Boulder BoulderCO80302 United States Denver Department of Public Health and Environment DenverCO80202 United States Department of Environmental and Occupational Health Colorado School of Public Health University of Colorado Anschutz AuroraCO80045 United States Department of Environmental and Radiological Health Sciences Colorado State University Fort CollinsCO United States Department of Epidemiology Colorado School of Public Health Colorado State University Fort CollinsCO United States Dept. of Mechanical Engineering Program of Environmental Engineering Univ. of Colorado Boulder BoulderCO80309 United States School of the Environment Yale University New HavenCT06520-8354 United States

Background Odors are a documented environmental justice challenge in Denver County, Colorado, USA. Complaints are an important modality through which residents express their concerns. Objective We investigated disparities in environmental justice related-variables based on home and workplace census block groups (race/ethnicity, education-levels, renter-occupied housing, median income and median home values, gentrification) based on locations of odor complaints as well as that of potentially malodorous facilities. We report key themes identified in complaints. Methods We obtained odor complaints for 2014-2023, and the locations of facilities required to submit odor management plans as of 2023 from the Denver department of Public Health and Environment. We downloaded residential census block group-level socioeconomic data from the 2016-2020 American Community Survey;and workplace based socioeconomic data from the Longitudinal Employer-Household Dynamics dataset for 2020. We categorized neighborhoods as gentrified or not based on a typology produced by the Urban Displacement Project. We assessed exposure to potentially malodorous facilities and complaints within each census block group and investigated exposure disparities by comparing distributions of environmental justice-related variables based on if a facility or a complaint has been made, and census block group-level odor intensity categories. We used unsupervised machine learning to identify themes from the odor complaints. Results Less privileged census block groups were significantly disproportionately burdened with potentially malodorous facilities. Importantly, our study also reveals disparities in the location of facilities, not just traditional residence/-based environmental justice related variables, but in workplace/-based factors, as well. We did not observe similar disparities for odor complaints. However, complaints were significantly higher in gentrified neighborhoods. Specific facilities were mention

关键词： Location

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The development of mobile learning media using computer-aided manufacturing simulation in computer numerical control lathe

引用

AIP Conference Proceedings 2023年第1期2671卷

作者： Dwi Rahdiyanta Bernadus Sentot Wijanarka Kurnia Sandy Rini Agustiningsih Henny Pratiwi 1Department of Mechanical Engineering Education Universitas Negeri Yogyakarta Indonesia 2Graduate Program Universitas Negeri Yogyakarta Indonesia

This research aims to produce products in the form of lathe computer numerical control (CNC) learning media in non-conventional machining engineering subjects at Vocational High School 2 Klaten. This research is a Research and Development research based on ADDIE development model (Analysis, Design, Development, Implementation, Evaluation). The product was tested on 67 students majoring in Metal Fabrication and Manufacturing engineering. Data is collected using questionnaires and data analysis using descriptive analysis techniques. The results obtained that android-based learning media developed showed positive results, including from material experts got a percentage of 91.1%, media experts 94.5%, from teachers 94.7% and student responses 77.9%. With these results it can be concluded that the learning media is feasible to be used for the learning of CNC machining lathes.

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Characterizing the mechanical response of metallic glasses to uniaxial tension using a spring network model

arXiv

引用

arXiv 2023年

作者： Nawano, Aya Schroers, Jan Shattuck, Mark D. O’Hern, Corey S. Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department The City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States

Metallic glasses are frequently used as structural materials. Therefore, it is important to develop methods to predict their mechanical response as a function of the microstructure prior to loading. We develop a novel coarse-grained spring network model, which describes the mechanical response of metallic glasses using an equivalent series network of springs, which can break and re-form to mimic atomic rearrangements during deformation. To validate the spring network model, we perform numerical simulations of quasistatic, uniaxial tensile deformation of Lennard-Jones and embedded atom method (EAM) potentials for Cu50Zr50 metallic glasses in the absence of large-scale shear band formation. We consider samples prepared using a wide range of cooling rates and with different amounts of crystalline order. We show that both the Lennard-Jones and EAM models possess qualitatively similar stress σ versus strain γ curves. By specifying five parameters in the spring network model (ultimate strength, strain at ultimate strength, slopes of σ(γ) at γ = 0 and at large strain, and strain at fracture where σ = 0), we can accurately describe the form of the stress-strain curves during uniaxial tension for the computational studies of Cu50Zr50, as well as recent experimental studies of several Zr-based metallic glasses. For the computational studies of Cu50Zr50, we find that the yield strain distribution is shifted to larger strains for slowly cooled glasses compared to rapidly cooled glasses. In addition, the average number of new springs and their rate of formation decreases with decreasing cooling rate. These effects offset each other at large strains, causing the stress-strain curve to become independent of the sample preparation protocol in this regime. In future studies, we will extract the parameters that define the spring network model directly from atomic rearrangements that occur during uniaxial deformation. © 2023, CC BY-NC-ND.

关键词： Metallic glass

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Osteoporotic bone augmentation utilizing curved pattern of PMMA injection: A combined finite element and optimization investigation

Osteoporotic bone augmentation utilizing curved pattern of P...

引用

ASME 2019 International mechanical engineering Congress and Exposition, IMECE 2019

作者： Farvardin, Amirhossein Armand, Mehran Laboratory for Computational Sensing and Robotics Department of Mechanical Engineering Johns Hopkins University BaltimoreMD United States Laboratory for Computational Sensing and Robotics Department of Mechanical Engineering Johns Hopkins University Johns Hopkins University Applied Physics Lab LaurelMD United States

ISBN: (纸本)9780791859407

A potential effective treatment for prevention of osteoporotic hip fractures is augmentation of the mechanical properties of the femur by injecting it with Polymethyl-Methacrylate (PMMA). We have previously developed a preoperative planning workstation to optimize the pattern of cement injection. In this planning paradigm, injections occur on a straight line limiting the overall match between the optimal and injected volumes of the cement. In addition, new advancements in drilling techniques has made it possible to plan and drill the bone based on a curved trajectory. In this study, we introduced a methodology to find the optimal drill path for PMMA injection. With the aid of Finite Element (FE) and hydrodynamic simulations, the effectiveness of the proposed approach was evaluated. Results showed that with an average injection of 7.2 ml, the proposed method can increase the yield load of the femur by 69%. Future works involve experimental validation of this method in cadaveric studies. Copyright © 2019 ASME.

关键词： Polymethyl methacrylates

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Understanding the Durability of Nanocatalysts in Energy Conversion by Advanced Electron Microscopy

引用

Microscopy and Microanalysis 2024年第SUPPLEMENT_1期30卷

作者： Ferreira, Paulo INL – International Iberian Nanotechnology Laboratory Braga Portugal Materials Science and Engineering Program The University of Texas at Austin Austin Texas United States Mechanical Engineering Department and IDMEC Instituto Superior Técnico University of Lisbon Lisboa Portugal

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Characterization of the Root Surface of a Friction Stir Welded Uns S32760 Super Duplex Stainless Steel: Insights into its Microstructure and Corrosion Resistance

SSRN

引用

SSRN 2023年

作者： Vacchi, G.S. de Carvalho, Willian S. Meinhardt, C.P. Bergmann, Luciano A. Amancio-Filho, Sergio T. Rovere, C.A.D. Munir Rachid Corrosion Laboratory Department of Materials Engineering Federal University of São Carlos Rodovia Washington Luis Km 235 SP São Carlos13565-905 Brazil Federal University of São Carlos Graduate Program in Materials Science and Engineering Brazil Graz University of Technology – TU Graz Institute of Materials Science Joining and Forming BMK Endowed Professorship for Aviation Kopernikusgasse 24/1 Graz8010 Austria Federal University of Pampa Department of Mechanical Engineering Avenida Tiarajú 810 RS Alegrete Brazil Helmholtz-Zentrum Hereon Institute of Materials Mechanics Solid State Materials Processing Max-Planck-Straße 1 Geesthacht Germany

The root surface of a friction stir welded super duplex stainless steel (UNS S32760) was thoroughly characterized in this study. The findings revealed a refined grain structure and a conspicuous increase of 50 HV0.3 in the Root's hardness. Interestingly, the volume fraction of the ferrite phase remained largely unchanged. Rapid cooling in the welding process led to the Cr2N precipitation at the ferrite/ferrite grain boundaries in the root area, significantly increasing sensitization, reducing localized corrosion resistance under high potentials in a neutral pH saline environment, and adversely affecting root passivity in a hydrochloric acid environment compared to the base metal. © 2023, The Authors. All rights reserved.

关键词： Friction stir welding

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Analysis of ejecta ignition and velocity from 18650 Li-ion battery cells during thermal runaway

引用

Journal of Power Sources 2025年 653卷

作者： Vinny Gupta Matthew J. Dunn Andrew R.W. Macfarlane Tony Xiao Aamir Farooq Assaad R. Masri Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo N2L 3G1 Canada School of Aerospace Mechanical and Mechatronics Engineering The University of Sydney Sydney 2006 Australia CCRC King Abdullah University of Science and Technology Thuwal 23955-6900 Kingdom of Saudi Arabia Mechanical Engineering Program PSE. King Abdullah University of Science and Technology Thuwal 23955-6900 Kingdom of Saudi Arabia

Lithium-ion batteries are increasingly deployed across a range of energy storage applications, heightening the need to understand failure mechanisms such as thermal runaway. This study presents a new experimental platform focusing on optical accessibility to quantify the transient ejection and ignition dynamics from a single 18650-format cell with an NMC-811 cathode. High-speed optical diagnostics, including luminosity imaging, spectral emission, photodetection, and sound measurements, were employed to characterise the spatiotemporal evolution of ejected fragments and flaming ignition. Light and sound data are temporally correlated, demonstrating that ignition is followed by flame propagation that occurs rapidly and intermittently, with significant temporal variability despite consistent thermal runaway initiation temperatures. A gaseous ignition mode is identified where solid fragments ejected from the battery coated with metallic lithium ignite pyrophorically, acting as an ignition kernel for the flammable gaseous effluent. Atomic emission of neutral lithium (Li I) at 611 nm and 671 nm is detected during ignition and flaming ignition and is hypothesised to form via lithium plating or thermal decomposition of LiC 6 during thermal runaway. Atomic emission of sodium and potassium is also found, all of which is superimposed on the grey body distribution from luminous ejecta. In addition, the high-speed images are cross-correlated to provide time-resolved velocity fields and their statistical distributions of ejected particles during the entire thermal runaway event. Axial velocities possess a log-normal distribution, with peak velocities as large as 35 m s −1 . This work sets the platform for subsequent studies employing high-speed optical diagnostics to deliver a quantitative database to enable modelling of Li-ion battery thermal runaway and to understand flame-induced heating of adjacent cells in a battery module.

关键词： Lithium-ion battery Thermal runaway Ignition Venting Battery safety

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