Free radicals are a class of reactive substances produced during the operation of proton exchange membrane fuel cells (PEMFCs), which have a great impact on the durability of PEMFCs. Previous research on the fuel cell degradation mechanism mainly focused on the degradation of the membrane electrode assembly (MEA) in high Pt loading PEMFCs, especially the chemical degradation of proton exchange membrane (PEM). However, there are significant differences in the characteristics and performance of PEMFCs with low and high Pt loading especially under the high current density, which is mainly due to the oxygen transport process in cathode catalyst layers (CCLs). Currently, few relevant research has explored the impact of chemical degradation on oxygen transport in the cathode of low-Pt PEMFCs. Therefore, this work investigates the effects of free radical attack on the structure of ionomer films, the local oxygen transport process and the evolution of the ionomer coated Pt/C structure in CCLs through physicochemical characterizations, electrochemical measurements and molecular dynamic simulations. Our research found that free radical attacks decreased the electrochemical active area of CCLs, but it also temporarily improved the cell performance at high current densities. Furthermore, molecular dynamics simulations demonstrated that the ionomer exhibited higher oxygen self-diffusion and a more relaxed structure after degradation.
Electrical engineering. Electronics. Nuclear engineering, Energy industries. Energy policy. Fuel trade
High-temperature solid lubricant coatings with decent lubrication performance are essential in critical processes of metal forming and aerospace. However, their preparation is formidably challenging due to the harsh working conditions. Here, we successfully developed a solid lubricant coating via a facile and eco-friendly approach by casting a homogeneous mixture of molybdenum disulfide (MoS2) and hexagonal boron nitride (h-BN) as lubricants, silicate as the binder, and water as the solvent onto a titanium alloy substrate. This solid lubricant coating exhibited excellent and stable tribological properties with a very low coefficient of friction (COF) of 0.080 at 1,000 °C, yet in an open-air atmosphere. This superior lubrication behavior is attributed to the synergistic effect between the base lubricants h-BN and MoS2, contributing to the formation of a coating for both lubrication and lubricant protection against oxidation at 1,000 °C in an open-air environment. This work largely extends the operation temperature range of the crucial lubricant MoS2 in an open-air atmosphere and further sheds valuable light on the design of high-temperature solid lubricants via the synergistic effect between base lubricants.
A partir del año 2015, aumentó considerablemente la demanda y el precio del litio debido a su rol central en la producción de baterías. América Latina posee alrededor del 60% de las reservas de litio a nivel mundial. Los yacimientos se ubican, principalmente, en la puna que comparten Chile, Bolivia, Perú y Argentina. La posibilidad de extraer litio de los salares es promocionada por gobiernos y empresas como una oportunidad irrenunciable. A su vez, la minería de litio es presentada como clave para la solución a la crisis climática, ante la necesidad de disminuir los gases de efecto invernadero y favorecer la transición energética. En oposición, la extracción de litio es considerada por las comunidades locales como perniciosa porque involucra la pérdida de grandes volúmenes de agua, en la medida en que el litio se encuentra disuelto en los salares y de que una gran cantidad de agua dulce es utilizada durante el proceso y por la contaminación química que genera. En este trabajo se abordará la construcción discursiva y argumentativa de la extracción de litio. La hipótesis general es que la extracción de litio se presenta como inevitable en tanto no hay otras alternativas posibles frente a la crisis climática, impidiendo su discusión en términos políticos.
Titanium and its alloy scaffolds are widely utilized in clinical settings; however, their biologically inert surfaces and inherent mechanical characteristics impede osteogenesis and soft tissue integration, thereby limiting their application. Selective laser melting (SLM) was employed to fabricate scaffolds with matched cortical bone mechanical properties, achieving a composite coating of hydroxyapatite complexed with trace elements of silicon, strontium, and fluoride (mHA), along with type I collagen (Col I) and fibrinogen (Fg), thus activating the scaffold surface. Initially, we utilized the excellent adhesive properties of dopamine to co-deposit mHA and polydopamine (PDA) onto porous Ti-6Al-4V scaffolds, which was followed by immobilization of type I collagen and fibrinogen onto PDA. This bioinorganic/bioprotein composite coating, formed via PDA bonding, exhibits excellent stability. Moreover, in vitro cell experiments demonstrate excellent biocompatibility of the porous Ti-6Al-4V scaffold with composite bioactive coatings on its surface. Preosteoblasts (MC3T3-E1) and human keratinocytes (HaCaT) exhibit enhanced adhesion and proliferation activity, and the osteogenic performance of the scaffold is significantly improved. The PDA-mHA-Col I-Fg composite-coated porous titanium alloy scaffold holds significant promise in enhancing the efficacy of percutaneous bone transplantation and requires further investigation.
Prognostics and health management (PHM) is crucial for ensuring the safe operation of machinery, improving the productivity and increasing economic benefits. High-quality life-cycle data, as the basic resource in the field of PHM, are able to carry the key information which reflects the complete degradation processes of machinery. However, due to the high costs in data acquisition and insufficient development in storage and transmission technology, typical life-cycle data is extremely scarce, which limits the theoretical research and engineering application of PHM for machinery. In order to solve this dilemma, accelerated life tests of rolling element bearings are carried out by Prof. Yaguo Lei’s research group from School of Mechanical Engineering, Xi’an Jiaotong University (XJTU) and the Changxing Sumyoung Technology Co., Ltd. (SY), Zhejiang. These tests lasted for two years and the acquired datasets, i.e., XJTU-SY bearing datasets, have been publicly released for all PHM researchers. The XJTU-SY bearing datasets contain run-to-failure vibration signals of 15 rolling element bearings under three different operating conditions. These datasets have high sampling frequency, large amount of data, abundant failure types and detailed recording information. Accordingly, these datasets not only provide fresh “data blood” for PHM and promote the research of fault diagnosis and remaining useful life prediction, but also are able to help to improve intelligent maintenance decision making in industry.
Magnetorheological materials refer to field-response smart materials whose properties are controllable with a magnetic field, including fluid, grease, elastomer, and gel. The unique magnetorheological effect exhibited by these smart materials is a physical phenomenon where physics and engineering intersect and has extensive application prospects in modern machinery. In electro-mechanical systems, magnetorheological materials offer a superior design method for mechanical devices used in the fields of transmission, damping, and braking. It is important to control the magnetorheological materials for advancing the design philosophy of modern electro-mechanical devices. Hence, this paper presents a recent progressive review on the fundamentals of magnetorheological materials and numerous applications. Firstly, an introduction to the magnetorheological effect and different types of magnetorheological materials are presented in this review. Then, the individual and coupled effects of sedimentation, temperature, and magnetic field on magnetorheological materials are discussed. Finally, magnetorheological materials-based devices have been extensively reviewed, including actuator, clutch, damper, brake, pump, valve, and robot, thus aiming to provide useful information for facilitating the design of complex electro-mechanical systems.
Rolling bearings, an essential fundamental component in machinery and equipment, have been widely used. Predicting the remaining useful life (RUL) of rolling bearings helps maintain the reliability of mechanical systems. Accurate prediction of RUL requires extracting deep features in complex non‐linear vibration signals, the prediction results often vary widely. This paper proposes a RUL prediction method based on convolutional neural network (CNN), bi‐directional long‐short term memory (BiLSTM), and bootstrap method (CNN‐BiLSTM‐Bootstrap) to model the forecasting uncertainty. The first step is to extract the first prediction time (FPT) of the degradation phase of rolling bearings using an adaptive method for the 3σ intervals of rolling bearing vibration signal kurtosis. The model extracts the spatio‐temporal features through CNN and BiLSTM, and combines the bootstrap method to quantify the RUL prediction interval (PI) of rolling bearings. The comparison with existing models verified the effectiveness and generalization of the proposed model.
Tip clearance cavitation (TCC) is a type of vortex cavitation. It widely exists in axial flow hydraulic machinery and has significant negative influence on the mechanical service life and the operating stability. It is necessary to suppress the tip clearance vortices (TCV) to control the TCC in engineering applications. Based on the analysis of the advantages and disadvantages of the present various suppression strategies, a new coupling method is proposed in this study by combining the damping approach and the diversion approach. Porous medium material is used to realize the coupling effect. A 2 mm span length porous tip is installed on the solid tip surface of a hydrofoil under two gap sizes conditions (representing two types of gap flow pattern), and excellent suppression results of the TCV and TCC are obtained. The characteristics and mechanism of the clearance flow are analyzed by numerical simulation. The numerical accuracy is verified by experimental qualitative observations. The simulation results show that the temporal and spatial stability of the clearance flow field is enhanced, and the leakage velocity and the TCV strength are weakened via the combined action of damping and diversion effects. There is a difference in the damping mechanism between the two gap flow patterns. It is a comprehensive result of viscous dissipation and momentum loss in the jet pattern represented by the small gap size, and primarily, the result of momentum loss in the rolling pattern represented by the large gap size.
In various engineering fields, bearings are crucial for the operation of rotating machinery. Therefore, the early and precise detection of bearing failures is essential to prevent mechanical issues and maintain optimal machinery performance. This study proposes a fault classification framework based on multi-domain feature extraction, the least absolute shrinkage and selection operator method, long-short term memory, and the self-attention mechanism. Fifteen time-domain, five frequency-domain, and four chaotic-domain features are extracted from the raw data. To validate the model's accuracy and stability, datasets from the Hanoi University of Science and Technology (HUST), a newly published dataset, and Case Western Reserve University (CWRU) were utilized. Experimental validation using open-source bearing datasets demonstrates that the proposed framework can be effectively deployed, highlighting its potential as a fundamental pillar in the field of intelligent manufacturing. The findings show that our model achieves an F1-score of 99.903% for the test set with nine selected features across 24, encompassing all five bearing categories within the HUST dataset. Furthermore, its application to the CWRU dataset yielded comparable metrics, reaching a 98.742% F1-score with eight selected features among 24 features. The objective is to achieve successful prediction outcomes with a reduced number of parameters and to emphasize the significance of incorporating chaotic features into the process for data sets characterized by chaotic processes.
The development of effective methods for diagnosing bearing faults has received significant research attention. As the engineering environment tends to be complex, mechanical equipment in operation affected by the interference is more intricate and variable, resulting in a single sensor being increasingly unable to meet the needs of high-precision fault diagnosis of rotating machinery. In contrast, multisensors contain more comprehensive and redundant information, which can improve the reliability of fault diagnosis. In this article, a new collaborative fault diagnosis method based on multisensors is proposed, which dynamically enhances and fuses the diagnostic information of multiple sensors at the decision level to achieve more accurate and reliable results. First, a series of base models driven by statistical features and deep features in parallel are constructed to gain a series of pre-diagnosis results. Second, a new dynamically enhanced weighted voting strategy (DEWVS) is designed. Through the dual consideration of diagnostic accuracy and misclassification of the base models, the strategy constructs the diagnostic performance indicator matrix and realizes the dynamic assignment of the voting weights of each base model to enhance the effective information of each pre-diagnosis result, obtaining more reliable collaborative diagnostic results. Finally, the proposed method is evaluated by an experimental dataset of the axle box bearing of a high-speed train. The experimental results validate the necessity of multisensor collaborative diagnosis and demonstrate the superiority of the proposed method.
Arash Hashemi, Grzegorz Orzechowski, A. Mikkola
et al.
Artificial intelligence and mechanical engineering are two mature fields of science that intersect more and more often. Computer-aided mechanical analysis tools, including multibody dynamics software, are very versatile and have revolutionalized many industries. However, as shown by the literature presented in this review, combining the advantages of multibody system dynamics and machine learning creates new and exciting possibilities. For example, the multibody method can assist machine learning by providing synthetic data, while machine learning can provide fast and accurate subsystem models. The intersection of both approaches results in surrogate and hybrid modeling techniques, advanced control algorithms, and optimal design applications. A notable example is the development of autonomous systems for vehicles, robots, and mobile machinery. In our review we have found nontrivial, innovative, and even surprising applications of machine learning and multibody dynamics. This review focuses on applying neural networks, mainly deep learning, in connection with the multibody system method. Over one hundred and fifty papers are covered, and three main research areas are identified and introduced: data-driven modeling, model-based control and estimation, and data-driven control. The paper starts with a primer on machine learning and concludes with future research directions. The main goal is to provide a comprehensive and up-to-date review of existing literature to inspire further research.
Shukhrat Fayzimatov, Bakhtiyor Mardonov, Bakhtiyor Khusanov
et al.
This article addresses the pressing need for advancements in modern mechanical engineering in Uzbekistan, focusing on increasing the durability and reliability of machinery components. The study proposes the utilization of pneumatic vortex processing as an innovative solution, leveraging aerodynamic flow energy, particularly the vortex effect. Through theoretical analysis and experimental studies, various designs of devices for pneumatic vortex processing of cylindrical parts are developed and analyzed. The interaction between deforming balls and the surface being processed is thoroughly investigated, considering factors such as turbulence, surface roughness, and input pressure. Optimal parameters, including ball diameter, number of balls, and inlet pressure, are determined to achieve the desired surface quality. The study reveals the influence of input pressure and initial surface roughness on surface quality and processing force. Findings suggest that by adjusting the diameter and number of balls at a fixed input pressure, optimal combinations can be identified for different workpiece sizes and materials. These results provide valuable insights for enhancing the durability and reliability of machinery components through optimized pneumatic vortex processing techniques.
R. Kussainov, N. Kadyrbolat, R. Kurmangaliev
et al.
This study examined the effect of electrolytic plasma hardening (EPH) on the properties of 45-grade steel, which can serve as an alternative to traditional heat treatment methods used before the release of finished products. The results of the experiments showed that the mechanical and operational properties of 45-grade steel significantly improved after the application of EPH. Steel 45, widely used in mechanical engineering and agricultural machinery production due to its strength characteristics and durability, demonstrated a significant increase in performance after treatment. It was observed that the properties of 45- grade steel improved considerably after applying electrolytic plasma hardening methods, with the hardness of the samples increasing by 3.1-3.62 times, and wear resistance improving by 7 times. Moreover, the study highlighted the economic efficiency of the proposed treatment method, showing that the introduction of EPH can lead to a significant reduction in material costs, extended product life, and improved environmental performance by reducing energy consumption and emissions. The use of a non-toxic 20% sodium carbonate solution in EPH contributed to the uniform distribution of electric current in the cell and allowed for achieving optimal cooling rates of the sample.
Abstract. In the rapidly evolving landscape of manufacturing and material forming, innovative strategies are imperative for maintaining a competitive edge. Augmented Reality (AR) has emerged as a groundbreaking technology, offering new dimensions in how information is displayed and interacted with. It holds particular promise in the panel of instructional guides for complex machinery, potentially enhance traditional methods of knowledge transfer and operator training. Material forming, a key discipline within mechanical engineering, requires high-precision and skill, making it an ideal candidate for the integration of advanced instructional technologies like AR. This study aims to explore the efficiency of three distinct types of user manuals—video, paper, and augmented reality (AR)—on performance and acceptability in a material forming workshop environment. The focus will be on how AR can be specifically applied to improve task execution and understanding in material forming operations. Participants are mechanical engineering students specializing in material forming. They will engage in a series of standardized tasks related to machining processes. Performance will be gauged by metrics like task completion time and error rates, while task load will be assessed via the NASA Task Load Index (NASA-TLX) [1]. Acceptability of each manual type will be evaluated using the System Usability Scale (SUS) [2]. By comparing these various instructional formats, this research seeks to shed light on the most effective mediums for enhancing both operator performance and experience.
Edward Dodzi Amekah, Emmanuel Wendsongre Ramde, David Ato Quansah
et al.
The shift towards renewable energy sources has heightened the interest in solar photovoltaic (SPV) systems, particularly in grid-connected configurations, to enhance energy security and reduce carbon emissions. Grid-tied SPVs face power quality challenges when specific grid codes are compromised. This study investigates and upgrades an integrated 90 kWp solar plant within a distribution network, leveraging data from Ghana's Energy Self-Sufficiency for Health Facilities (EnerSHelF) project. The research explores four scenarios for SPV placement optimization using dynamic programming and the Conditional New Adaptive Foraging Tree Squirrel Search Algorithm (CNAFTSSA). A Python-based simulation identifies three scenarios, high load nodes, voltage drop nodes, and system loss nodes, as the points for placing PV for better performance. The analysis revealed 85 %, 82.88 %, and 100 % optimal SPV penetration levels for placing the SPV at high load, voltage drop, and loss nodes. System active power losses were reduced by 72.97 %, 71.52 %, and 70.15 %, and reactive power losses by 73.12 %, 71.86 %, and 68.11 %, respectively, by placing the SPV at the above three categories of nodes. The fourth scenario applies to CNAFTSSA, achieving 100 % SPV penetration and reducing active and reactive power losses by 72.33 % and 72.55 %, respectively. This approach optimizes the voltage regulation (VR) from 24.92 % to 4.16 %, outperforming the VR of PV placement at high load nodes, voltage drop nodes, and loss nodes, where the voltage regulations are 5.25 %, 9.36 %, and 9.64 %, respectively. The novel CNAFTSSA for optimal SPV placement demonstrates its effectiveness in achieving higher penetration levels and improving system losses and VR. The findings highlight the effectiveness of strategic SPV placement and offer a comprehensive methodology that can be adapted for similar power distribution systems.
Osei-Agyeman Yeboah, Nicholas Mensah Amoah, Kwadwo Antwi-Wiafe
The high price of energy due to the green energy policy will cause adjustments across the U.S. economy is predicted in the present computable general equilibrium with specific factors model. This includes energy input, especially electricity with capital and labor to produce manufacturing and service goods. 2022 labor, energy, and sector-specific capital input data on U.S. manufacturing, service, and agricultural sectors is applied to specific factors of the computable general equilibrium model. The model, which assumes constant returns, full employment, competitive pricing, and perfect labor mobility across industries hypothesizes a range of price changes due to project potential adjustments in factor prices and outputs. The U.S manufacturing sector is revealed to have a higher degree of noncompetitive pricing for energy factor inputs, but not on labor and capital as advocates for green energy tout by the new technology. The policy has virtually no significant impact on the service and agricultural sectors. The high price of green energy will cause an elastic decrease in all energy inputs. The output from energy-intensive manufacturing only rises in the long run by 4 % while service and agriculture fall. Clear winners are the owners of energy resources through their price-searching behavior. This includes the government, which owns a large share of hydrocarbon reserves.
Abstract Carbon fiber reinforced polytetrafluoroethylene (CF/PTFE) composites are known for their exceptional tribological performance when sliding against steel or cast iron in inert gas environments. Compared to experiments in humid air, about an order of magnitude lower wear rate and several times lower coefficient of friction have been reported for tests conducted in dry nitrogen and hydrogen. Moreover, trace moisture has been shown to affect the friction and wear significantly of this tribosystem, although a possible effect of oxygen cannot be ruled out due to uncertainties regarding the oxygen concentrations. While several studies have pointed out the environmental sensitivity of CF/PTFE, the understanding of the underlying mechanisms are very limited. The objective of this research is to investigate the individual and combined effect of oxygen and moisture on the tribological behavior of CF/PTFE sliding against steel. Additionally, this study aims to elucidate the underlying mechanisms that govern the environmental sensitivity of the system. Climate-controlled three-pin-on-disc experiments were conducted in nitrogen atmospheres at various concentrations of oxygen and moisture. The tribological results clearly demonstrate that both moisture and oxygen contribute to increased friction and wear. However, the adverse effect was much more pronounced for oxygen than moisture. A qualitative method was developed to estimate the tribofilm coverage on the CF/PTFE surface. Results showed strong correlation between high coverage of strongly adhered tribofilm and low wear rate. Moreover, a loosely adhered tribofilm was observed on top of the CF/PTFE surface in presence of moisture. FTIR analysis indicated that the loosely adhered tribofilm found in the moisture-enriched environment contained a significant amount of adsorbed water, which may explain the lower coefficient of friction in presence of moisture compared to oxygen. The adsorbed water in the loosely adhered tribofilm could be an indication of moisture-driven lubrication by the non-graphitic carbon in the tribofilm.