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Impact of different descriptions for diffusion, heat transfer, and capillary pressure on a physics-based proton-exchange membrane fuel cell model

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International Journal of Hydrogen Energy 2025年 133卷 386-401页

作者： Carneiro, Leonardo F. Costa Junior, Esly F. de P. Andrade, Samuel T. Matencio, Tulio Postgraduate Program in Chemical Engineering PPGEQ-UFMG Brazil Postgraduate Program in Mechanical Engineering PPGMEC-UFMG Brazil Department of Chemistry DEQ/ICEX-UFMG UFMG Brazil

A physics-based, two-phase, and non-isothermal proton-exchange membrane fuel cell model is developed and used to evaluate the impact of three common modeling assumptions concerning mass and heat transport in a single-cell system. Firstly, using Fick's law to describe the diffusive transport caused significant differences in the concentration profiles when compared to the Stefan-Maxwell equation, resulting in considerable deviations in the polarization curve. Moreover, the description of thermal effects is found to be crucial to obtaining accurate water molar fraction profiles for the cathode, even when heat removal is effective. Finally, while the impact related to the capillary pressure description in the polarization curve is insignificant when ohmic losses dominate, as they considerably affect the liquid water and oxygen profiles, they may be crucial to obtain precise values at high current densities when water removal is less efficient. These results should provide valuable insights for the development of better models. © 2025 Hydrogen Energy Publications LLC

关键词： Maxwell equations

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A Systematic Evaluation of Clustering Algorithms Against Expert-Derived Clustering

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Operations Research Forum 2025年第2期6卷 1-24页

作者： Mikrou, Ioannis Sapidis, Nickolas S. Department of Mechanical Engineering University of Western Macedonia ZEP Kozani 50100 Greece Postgraduate Masters Program “Quality Management and Technology” Hellenic Open University Par. Aristotelous 18 Patras 26335 Greece

Clustering algorithms have long sought to replicate human expertise in data clustering. This research utilizes six mechatronic products and a team of domain experts to assess how closely the results of six clustering algorithms align with expert-generated outcomes. This comparison of clustering results includes validation indices, the analysis of component migrations between the generated clusters, and optical inspection. The mechatronic products were selected for their complex nature, which provides a challenging context for the clustering analysis. The algorithms used in this research are Ward’s method (WARD), partitioning around medoids (PAM), K-means (K-MEANS), divisive analysis (DIANA), density-based spatial clustering of applications with noise (DBSCAN), and clustering by fast search and find of density peaks (DPC), and the validation indices employed to evaluate the clustering results are the silhouette coefficient (SC) and the composed density between and within clusters (CDbw). © The Author(s) 2025.

关键词： Clustering performance DSM models Human expert benchmarks Machine learning Mechatronics

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Self SOC estimation for second-life lithium-ion batteries

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IEEE Access 2025年 13卷 75037-75046页

作者： Gotz, J. Viana, E.R. Galvão, J.R. Corrêa, F.C. Borsato, M. Badin, A.A. UTFPR Av. Sete de Setembro PR Curitiba3165 Brazil Department of Computer Science PR Curitiba Brazil Department of Physics UTFPR Av. Sete de Setembro PR Curitiba3165 Brazil PR Ponta Grossa Brazil Postgraduate Program in Mechanical and Materials Engineering PR Curitiba Brazil

Lithium-ion batteries (LIB) are the mainstream technology for energy storage in several industrial segments, such as mobility and stationary systems for solar, wind, or other alternative energy source. This technology has a long lifetime, low self-discharge, high capacity and density, and can store energy longer. Despite that, LIB is suitable to supply power for electric mobility if its state of health (SOH) is higher than 80%. Then, an alternative for batteries with SOH below that is recycling or second-life batteries (SLB). The first is expensive and complex;only some companies retain the technology. Still, the SLB can be an excellent solution to maintaining LIBs in operation for slow vehicles or stationary systems. However, SLB requires intelligent battery management systems (BMS) because the packs are composed of cells with different characteristics, which makes the operation more difficult. This work presents a system consisting of two Machine Learning (ML) layers to automatically estimate the state of charge (SOC) of SLB independent of the battery's capacity or age. In the first phase, a Random Forest (RF) model was built and trained to discover the curve capacity of different SLB characteristics and capacities. After the capacity curve selection, in the second phase, a new RF model was built and trained for each capacity curve to make SOC inferences of the batteries. The discharge data curve of one hundred batteries was used for the development, whereas eighteen were used for the training and eighty-two for tests. The results indicated a root square mean error (RSME) below 45 mAh for the capacity estimation (phase 1), and an RSME below 0.87% was found in the second phase for the SOC estimation. Finally, the capacity and SOC models have been inserted in a Raspberry system to measure the main parameters of SLB (voltage and current) and make inferences in real time independent of the cell's age. The results showed an RSME below 100 mAh for the first layer and be

关键词： Lithium-ion batteries

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Reliability analysis of hospital infusion pumps: a case study

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BioMedical engineering Online 2025年第1期24卷 1-21页

作者： Silva, Mayla dos S. Nunes, Maria Alzira de A. Rosa, Suélia de Siqueira R. F. Piratelli-Filho, Antônio Postgraduate Program in Integrity of Engineering Materials University of Brasilia St. Leste Projeção A - Gama Leste Distrito Federal Brasilia72.444-240 Brazil Mechanical Engineering Department University of Brasilia Campus Universitário Darcy Ribeiro Asa Norte Distrito Federal Brasilia70.910-900 Brazil Postgraduate Program in Biomedical Engineering University of Brasilia St. Leste Projeção A - Gama Leste Distrito Federal Brasilia72.444-240 Brazil Meinig School of Biomedical Engineering Cornell University Weill Hall IthacaNY14853 United States Asa Sul Distrito Federal Brasilia70200-660 Brazil

Background: Infusion pumps (IPs) are medical devices used for the continuous and precise delivery of medications or nutrients. Their use has expanded and is now widespread in emergency rooms, ICUs, pediatrics, and other hospital departments. Failures in IPs can lead to adverse events, compromising patient health. In addition to the risks to patients, IPs are the medical devices most frequently associated with reports of adverse events in Brazil, highlighting the need to monitor their operational conditions to minimize failures during use. Results: Thus, the objective of this research is to analyze the reliability of infusion pumps (IPs) in a Brazilian hospital using an internal database from Clinical engineering software. Probability distributions for repair time and time between failures were modeled, and parameters such as reliability and availability were calculated, with a focus on investigating hospital departments with recurring failures. Conclusion: In evaluating the operating equipment, a lack of detail in failure notes and service order openings was observed, which can hinder maintenance planning. The longest repair times were recorded in the ICU (Neurology), which houses the majority of IPs. Graphical analysis and testing demonstrated that the Weibull distribution effectively models both time between failures and repair time. The IP A model showed better results in terms of availability and reliability, thereby improving the security of the IPs. © The Author(s) 2025.

关键词： Weibull distribution

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An End-to-End Framework for Neonatal Asphyxia Detection through Video Analysis

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IEEE Access 2025年 13卷 74689-74704页

作者： Kurnianingsih Nobukawa, Sou Widyawati, Melyana Nurul Pramana, Cipta Aji, Nurseno Bayu Thohari, Afandi Nur Aziz Hendrawati, Dwiana Sato-Shimokawara, Eri Kubota, Naoyuki Department of Electrical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Central Java Semarang50275 Indonesia Department of Computer Science Chiba Institute of Technology 2-17-1 Tsudanuma Chiba Narashino275-0016 Japan Department of Postgraduate Program Poltekkes Kemenkes Semarang Jl. Tirto Agung Pedalangan Kecamatan Banyumanik Central Java Semarang50268 Indonesia Department of Obstetrics and Gynecology KRMT Wongsonegoro Hospital Jl. Tembalang Central Java Semarang50272 Indonesia Department of Mechanical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Central Java Semarang50275 Indonesia Graduate School of Systems Design Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo191-0065 Japan

Identifying asphyxia using computer vision in real-world settings poses challenges due to varying video quality, diverse lighting conditions, and subtle color changes in the newborn's skin. This study presents an end-to-end framework for automated neonatal asphyxia detection using time series video analysis and makes three key contributions. First, the proposed framework integrates YOLOv8-based instance segmentation with advanced feature extraction across multiple color spaces and texture analysis to detect neonatal asphyxia through the multi-modal analysis of skin features in video streams. Second, we introduce a new quality-aware temporal analysis framework that includes adaptive quality assessment for evaluating frames in real time, multi-stage feature stability tracking across temporal windows, hysteresis-based decision logic for ensuring temporal consistency, and LightGBM classification with comprehensive feature engineering to assess severity. Third, we provide a curated time series video dataset of 12,973 frames from 45 neonates, of which some were healthy, and some had asphyxia of varying severity. The findings show that the YOLOv8-based instance segmentation achieved a mean average precision (mAP@0.5) of 0.925 for accurate skin region isolation, and the LightGBM classifier outperformed traditional models with an accuracy of 0.998 and an F1-score of 0.998. The system maintains real-time processing at 30 FPS for normal and mild asphyxia cases with a minor reduction to 20 FPS in more challenging scenarios and exhibits robust temporal stability across severity levels, with consistency scores above 0.90. This framework has the potential to enhance neonatal care through continuous monitoring and timely intervention. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Deep learning

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ANN-based analysis of thin film Maxwell fluid dynamics with electro-osmotic and nonlinear thermal effects

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Alexandria engineering Journal 2025年 127卷 392-410页

作者： Irfan Saif Ud Din Imran Siddique Zohaib Zahid Muhammad Nadeem S. Islam Department of Mathematics University of Management and Technology Lahore 54770 Pakistan Department of Mathematics University of Sargodha Sargodha 40100 Pakistan Mathematics in Applied Sciences and Engineering Research Group Scientific Research Center Al-Ayen University Nasiriyah 64001 Iraq Federal University of Technology Paran UTFPR Mechanical Engineering Department DAMEC Postgraduate Program in Mechanical and Materials Engineering PPGEM Research Center for Rheology and Non-Newtonian Fluids CERNN R. Deputado Heitor Alencar Furtado 5000 - Bloco N - Ecoville Curitiba PR 81280-340 Brazil Department of Mechanical Engineering Prince Mohammad Bin Fahd University P.O Box 1664 Al Khobar 31952 Saudi Arabia

An intelligent Levenberg-Marquardt Technique (LMT) with artificial neural network (ANN) backpropagation (BP) has been used to analyze the thermal heat and mass transfer of unsteady magnetohydrodynamics (MHD) thin film Maxwell fluid flow in a porous inclined sheet with an emphasis on the influence of electro-osmosis. The activation energy, chemical reaction, mixed convection, melting heat, joule heating, nonlinear thermal radiation, variable thermal conductivity and thermal source/sink effect are taken into account for transport expressions. Appropriate similarity transformations were used to translate partial differential equations (PDEs) into ordinary differential equations (ODEs). After that, the built-in MATLAB BVP4C method was used for a data set assessed using the LMT-ANN strategy to solve these ODEs. The physical significance of the designed parameters is thoroughly discussed in both tabular and graphical form. The observed R -squared value is 1, and the mean square error up to 10 − 15 demonstrates the LMT-ANN's precise and accurate computing capability. The model’s validity is also confirmed by the strong agreement between the obtained predicted findings and numerical results, which shows a high degree of accuracy within the range of 10 − 8 to 10 − 11 . It was revealed that radiative heat considerably increases surface heat energy through accumulation, improving heat transfer qualities, whereas fluid temperature is raised by Joule dissipation, variable thermal conductivity, and heat source. Electro-osmosis and magnetic fields reduce fluid velocity by generating opposing forces that resist the flow. This problem works best in microscale fluid transport systems and drilling operations, where magnetic and electro-osmotic control are crucial. These systems include micro-electromechanical systems, lab-on-a-chip devices, porous geological formations, and thin film coating technologies.

关键词： Maxwell fluid Magnetic effect Electro-osmosis effect Variable thermal conductivity Heat source Chemical reaction

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Production of cobalt- and manganese-based oxide nanofibers via solution blow spinning: Structural properties and high-performance oxygen evolution reaction

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Applied Surface Science 2025年 703卷

作者： Pereira, Thiago O. Raimundo, Rafael A. Alves, Ricardo F. Morales, Marco A. Simões, Thiago A. Medeiros, Eliton S. Macedo, Daniel A. Dutra, Ricardo P.S. Materials Science and Engineering Postgraduate Program Federal University of Paraíba PB João Pessoa58051-900 Brazil TEMA – Centre for Mechanical Technology and Automation Department of Mechanical Engineering University of Aveiro Aveiro3810-193 Portugal Department of Mechanical Engineering Federal University of Paraíba PB João Pessoa58051-900 Brazil Department of Theoretical and Experimental Physics Federal University of Rio Grande do Norte Natal59078-970 Brazil Cabo de Santo Agostinho PE 54518-430 Brazil Department of Materials Engineering Federal University of Paraíba PB João Pessoa58051-900 Brazil

Cobalt- and manganese-based oxides (Co3O4, MnCo2O4, and CoMn2O4) with nanofiber-like morphology were successfully synthesized via solution blow spinning (SBS) and evaluated as electrocatalysts for the oxygen evolution reaction (OER). The structural, morphological, and surface properties of the prepared materials were investigated through x-ray diffraction (XRD), field emission scanning electron microscope (FESEM), Fourier-transform infrared (FTIR), Raman spectroscopy, x-ray photoelectron spectra (XPS), and magnetic measurements. The magnetic characterization of MnCo2O4, CoMn2O4, and Co3O4 confirmed their phase purity and expected magnetic behavior. MnCo2O4 exhibited ferromagnetic ordering (θ = +145 K) with Hc = 8200 Oe and Ms = 11.9 emu/g at 5 K. CoMn2O4 showed multiple transitions at 178 K, 169 K, and 81 K, suggesting ferrimagnetic ordering. Co3O4 displayed antiferromagnetic behavior with a Néel temperature of 37 K. These results corroborate with the literature, validating the synthesis and characterization. X-ray photoelectron spectroscopy (XPS) analysis confirmed the oxidation states of Co and Mn and the presence of oxygen vacancies in CoMn2O4, MnCo2O4, and Co3O4. The Oads/Olats ratio indicated a higher concentration of oxygen vacancies in CoMn2O4 (1.21), while MnCo2O4 exhibited the highest Mn4+ content (Mn4+/Mn3+ = 0.76). The Co3+/Co2+ ratio was higher in MnCo2O4 (1.94), suggesting enhanced oxidation stability. The electrochemical characterization for OER in 1 M KOH electrolyte of the CoMn2O4, MnCo2O4, and Co3O4 samples displayed overpotentials of 330, 348, and 375 mV, respectively, to achieve a current density of 10 mA cm−2. Cyclic voltammetry and electrochemical impedance spectroscopy further corroborate these findings. The electrodes exhibited great stability maintaining their performance for 20 h. A detailed surface chemistry analysis showed that oxygen vacancy-rich samples exhibited enhanced charge transfer and generated Mn catalytic sites that facilitated

关键词： Fourier transform infrared spectroscopy

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Green synthesis of Ni, Co-based oxides for water oxidation applications

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Materials Science in Semiconductor Processing 2025年 195卷

作者： Andrade, Verônica de M. Lopes, Sávio M. Raimundo, Rafael A. Alves, Ricardo F. Araújo, Allan J.M. Vieira, Pamala S. da Silva, Fausthon F. Morales, Marco A. Macedo, Daniel A. Fagg, Duncan P. Souza, Glageane da S. Academic Unit of Biology and Chemistry Federal University of Campina Grande PB Cuité58175-000 Brazil Materials Science and Engineering Graduate Program UFPB João Pessoa58051-900 Brazil TEMA - Centre for Mechanical Technology and Automation Department of Mechanical Engineering University of Aveiro Aveiro3810-193 Portugal Federal University of Paraíba Department of Mechanical Engineering PB João Pessoa58051-900 Brazil LASI - Intelligent Systems Associate Laboratory Guimarães4800-058 Portugal Materials Science and Engineering Postgraduate Program Federal University of Rio Grande do Norte Natal59078-970 Brazil Department of Chemistry Federal University of Paraíba Paraíba João Pessoa58051-900 Brazil Department of Theoretical and Experimental Physics Federal University of Rio Grande do Norte RN Natal59078-970 Brazil

This work reports new Ni,Co-oxides based nanoparticles as electrocatalysts for the Oxygen Evolution Reaction (OER), considering the growing demand for renewable energy, as well as the search for sustainable alternatives in the production of nanoparticle materials. Ni,Co-based nanoparticles were obtained via proteic sol-gel method, with Agar-Agar from red seaweed (Rhodophyta) as polymerizing agent, and characterized by using X-ray diffraction techniques (including Rietveld refinement), Field emission scanning electron microscopy and vibrational spectroscopy (infrared and Raman). All structural analyses indicated that the crystalline phases NiO, Ni0.9Co0.1O and (Ni,Co)O-Co3O4 were obtained. Electrochemical techniques were applied to investigate the OER electrocatalytic performance, with the (Ni,Co)O-Co3O4 electrocatalyst showing the lowest overpotential (356 mV vs. RHE at J = 10 mA cm−2) and a Turnover Frequency (TOF) of 9.97 × 10−3 mol O2 s−1 at just 500 mV vs. RHE. Furthermore, all prepared electrocatalysts showed excellent chemical stability up to 15 h, as indicated in chronopotentiometry data. © 2025 Elsevier Ltd

关键词： Rietveld refinement

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PRECIPITATION AND RECRYSTALLIZATION DURING PROCESSING OF A SUPERPLASTIC AL-10MG-0.1ZR ALLOY

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MATERIALS SCIENCE AND engineering A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING 1993年第1-2期166卷 135-143页

作者： MCNELLEY, TR CROOKS, R KALU, PN ROGERS, SA Materials Engineering Program Department of Mechanical Engineering Naval Postgraduate School Monterey CA 93943 USA

Microstructural evolution during thermomechanical processing (TMP) of a superplastic Al-10Mg-0.1Zr (weight per cent) alloy was studied. The TMP was carried out at 300-degrees-C and consisted of 12 rolling passes corresponding to a total strain of 2.5. Superplastic elongations at 300-degrees-C exceeded 1 000% for an optimum processing schedule involving a 30 min interpass anneal (IPA) time. Precipitation of the intermetallic beta-phase (Al8Mg5) was complete at an accumulated strain of 1.5 and the precipitate particle size and size distribution were found to be nearly constant for the remainder of the TMP. Particle stimulated nucleation (PSN) of recrystallization associated with prolonged IPA times accounted for the grain refinement necessary to support superplastic deformation. A model for PSN is applied to the data.

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Understanding the effect of preparation technique and compaction pressure on the microstructure, sinterability and mechanical properties of the Cu-20 wt% WC composite prepared with recycled WC

Journal of Alloys and Compounds Communications

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Journal of Alloys and Compounds Communications 2025年 6卷

作者： Rafael A. Raimundo Franciné A. Costa Marco A. Morales Daniel A. Macedo Angelus G.P. Silva Uílame U. Gomes Federal University of Paraíba Department of Materials Science and Engineering João Pessoa PB 58051-900 Brazil TEMA – Centre for Mechanical Technology and Automation Department of Mechanical Engineering University of Aveiro Aveiro 3810-193 Portugal Mechanical Engineering Postgraduate Program UFRN Natal 59078-970 Brazil Department of Theoretical and Experimental Physics UFRN Natal 59078-970 Brazil Materials Science and Engineering Postgraduate Program UENF Campos dos Goytacazes 28015-620 Brazil Materials Science and Engineering Postgraduate Program UFRN Natal 59078-970 Brazil

The study investigated the impact of different preparation techniques (mechanical Mixing - MM and High Energy Milling - HEM) and compaction pressures on the microstructure, sinterability and mechanical properties of the Cu-20 %WC composite using recycled WC. The results indicated that HEM promotes greater homogeneity, dispersion and refinement of the WC phase in copper, resulting in denser and more uniform microstructures after sintering. Although higher pressures increased the density of green compacts, they reduced densification during sintering, especially for powders prepared by MM, due to the segregation of the Cu and WC phases. On the other hand, compacts sintered with HEM powders prsented higher Vickers microhardness and better magnetic properties, attributed to the uniform distribution of WC particles and the increase in the interaction between the phases. Furthermore, the study highlights the influence of metallic impurities (Fe, Co and Ni) on the magnetic properties of the composite materials.

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