Service Life Performance Research Articles

Application of metal oxide-based materials for improving the performance of lithium-ion batteries

With the continuous development of electric vehicles, battery technology has also been developed rapidly. Lithium-ion batteries (LIBs) with a variety of advantages become the primary choice of the current battery industry. The global demand for lithium element also shows a rapidly rising trend, however, the global supply of lithium is in a relatively urgent stage. By improving the service life and efficiency of LIBs has become a hot research topic. One of the research directions is to improve the performance of battery by improving the structure of electrode materials, and metal oxide materials have become an important part of improving the performance of electrode materials by virtue of their low cost, high structural stability, high service life and high energy density. This research will introduce manganese-based oxide cathode materials, chromium oxide cathode materials, nanostructured copper oxide anode materials, and carbon/metal oxide composite anode materials. These four types of materials can improve the service life and electrochemical performance of LIBs by improving the structural stability of the materials and increasing the flow rate of lithium ions at the electrode.

A Calculation Method of Bearing Balls Rotational Vectors Based on Binocular Vision Three-Dimensional Coordinates Measurement

The rotational speed vectors of the bearing balls affect their service life and running performance. Observing the actual rotational speed of the ball is a prerequisite for revealing its true motion law and conducting sliding behavior simulation analysis. To address the need for accuracy and real-time measurement of spin angular velocity, which is also under high-frequency and high-speed ball motion conditions, a new measurement method of ball rotation vectors based on a binocular vision system is proposed. Firstly, marker points are laid on the balls, and their three-dimensional (3D) coordinates in the camera coordinate system are calculated in real time using the triangulation principle. Secondly, based on the 3D coordinates before and after the movement of the marker point and the trajectory of the ball, the mathematical model of the spin motion of the ball was established. Finally, based on the ball spin motion model, the three-dimensional vision measurement technology was first applied to the measurement of the bearing ball rotation vector through formula derivation, achieving the analysis of bearing ball rolling and sliding characteristics. Experimental results demonstrate that the visual measurement system with the frame rate of 100 FPS (frames per second) yields a measurement error within ±0.2% over a speed range from 5 to 50 RPM (revolutions per minute), and the maximum measurement errors of spin angular velocity and linear velocity are 0.25°/s and 0.028 mm/s, respectively. The experimental results show that this method has good accuracy and stability in measuring the rotation vector of the ball, providing a reference for bearing balls' rotational speed monitoring and the analysis of the sliding behavior of bearing balls.

Advanced Micro/Nanocapsules for Self-Healing Coatings

The concept of intelligence has many applications, such as in coatings and cyber security. Smart coatings have the ability to sense and/or respond to external stimuli and generally interact with their environment. Self-healing coatings represent a significant advance in improving material durability and performance using microcapsules and nanocontainers loaded with self-healing agents, catalysts, corrosion inhibitors, and water-repellents. These smart coatings can repair damage on their own and restore mechanical properties without external intervention and are inspired by biological systems. Properties that are affected by either momentary or continuous external stimuli in smart coatings include corrosion, fouling, fungal, self-healing, piezoelectric, and microbiological properties. These coating properties can be obtained via combinations of either organic or inorganic polymer phases, additives, and pigments. In this article, a review of the advancements in micro/nanocapsules for self-healing coatings is reported from the aspect of extrinsic self-healing ability. The concept of extrinsic self-healing coatings is based on the use of capsules or multichannel vascular systems loaded with healing agents/inhibitors. The result is that self-healing coatings exhibit improved properties compared to traditional coatings. Self-healing anticorrosive coating not only enhances passive barrier function but also realizes active defense. As a result, there is a significant improvement in the service life and overall performance of the coating. Future research should be devoted to refining self-healing mechanisms and developing cost-effective solutions for a wide range of industrial applications.

Insulator defect detection algorithm based on YOLOv8-ISDD

Abstract Insulators are widely used in transmission lines due to their good insulation performance and pollution resistance. The damage to insulators will affect their service life and insulation performance, which brings potential safety hazards to transmission lines. With the replacement of manual inspection with UAV inspection, image detection technology based on deep learning is gradually combined with UAV inspection. Usually, the insulator defect target accounts for a small proportion of the acquired image. Therefore, the manuscript introduces an enhanced algorithm for detecting flaws on the surface of insulators, designated as YOLOv8-ISDD. In the backbone network, the YOLOv8-ISDD algorithm introduces reparameterized convolution (Repconv) to enhance the network’s capability in extracting features of defects; to fuse the features of different resolutions, PANet in the neck network is replaced by Bi-FPN for integrating features. Within the head network, the multi-scale detection head output is used to increase the diminutive object identification stratum and boost the algorithm’s capability to discern lesser targets. Experiments show that the mAP@0.5 of the YOLOv8-ISDD algorithm is 88.9%, which is 6% higher than that of the original YOLOv8 algorithm, indicating that YOLOv8-ISDD can detect insulator defects more accurately.

The Optimization and Control of the Engagement Pressure for a Helicopter Dry Clutch

The engagement quality of a helicopter dry clutch has a significant impact on the service life and overall flight performance of the helicopter. The engagement oil pressure is an important factor affecting the clutch engagement quality. Firstly, a nonlinear input–output dynamic model for the dry clutch is developed to investigate the optimization and control of dry clutch engagement pressure in this paper. Secondly, to efficiently obtain the optimal pressure curve, an optimal method combining the developed dynamic model with the state feedback gain of a linear quadratic optimization regulator (LQR) solver is proposed. Thirdly, considering that hydraulic actuators may struggle with tracking certain pressure curves, a hydraulic actuator for accurately tracking pressure curves based on fuzzy PID is proposed. The simulation results indicate that the developed hydraulic actuator exhibits an excellent tracking performance. Moreover, compared with linear and segmented pressure curves, the optimal pressure curve derived from the proposed method significantly reduces jerk, friction work, and engagement duration, resulting in improved helicopter dry clutch engagement quality.

Machine learning-based corrosion rate prediction of steel embedded in soil

Predicting the corrosion rate for soil-buried steel is significant for assessing the service-life performance of structures in soil environments. However, due to the large amount of variables involved, existing corrosion prediction models have limited accuracy for complex soil environment. The present study employs three machine learning (ML) algorithms, i.e., random forest, support vector regression, and multilayer perception, to predict the corrosion current density of soil-buried steel. Steel specimens were embedded in soil samples collected from different regions of the Wisconsin state. Variables including exposure time, moisture content, pH, electrical resistivity, chloride, sulfate content, and mean total organic carbon were measured through laboratory tests and were used as input variables for the model. The current density of steel was measured through polarization technique, and was employed as the output of the model. Of the various ML algorithms, the random forest (RF) model demonstrates the highest predictability (with an RMSE value of 0.01095 A/m2 and an R2 value of 0.987). In light of the feature selection method, the electrical resistivity is identified as the most significant feature. The combination of three features (resistivity, exposure time, and mean total organic carbon) is the optimal scenario for predicting the corrosion current density of soil-buried steel.

Investigation of multiple cyclic pressure loading on the service life and sorption performance of a composite sorbent

Investigation of multiple cyclic pressure loading on the service life and sorption performance of a composite sorbent

Effect of Electroplastic-Assisted Grinding on Surface Quality of Ductile Iron

Ductile iron is a heterogeneous material. The presence of spherical graphite and a hard and brittle structure makes the surface of the workpiece easily form pits and crack defects under harsh grinding conditions, which seriously affects the service life and service performance of the workpiece. The new assisted grinding process based on the electroplastic effect is expected to avoid the surface defects of ductile iron. By comparing the surface roughness and microstructure of conventional grinding and electroplastic-assisted grinding, the superiority of electroplastic-assisted grinding surface quality is confirmed. Further discussion is presented on the impact of grinding parameters on the workpiece’s surface quality under the same electrical parameters. The results show that the sensitivity of surface roughness to grinding parameters from strong to weak is grinding wheel speed, feed speed and grinding depth. The optimal combination of grinding parameters is determined as a grinding wheel speed of 30 m/s, a feed speed of 0.5 m/min and a grinding depth of 10 μm.

Probabilistic model for fatigue damage estimation of wind turbines with hidden markov model and neural network

The growing demand for sustainable and affordable energy sources has led to rapid development in wind energy systems. Ensuring the long-term performance and reliability of wind turbines requires accurate estimation of fatigue damage under diverse environmental conditions. By incorporating probabilistic methods, this paper presents a comprehensive framework for long-term fatigue damage evaluation for wind turbines with high accuracy and efficiency. The proposed framework adopts the multivariate kernel density estimation to construct the high-dimensional joint probability distribution of environmental variables. A hidden Markov model serves as the driver to build the connection between historical records and future events. The short-term fatigue damage is estimated by the deep neural network based on future environmental parameters. The long-term fatigue damage is then achieved by accumulating the short-term fatigue damage evaluations with sufficient event instances. To demonstrate the applicability and effectiveness of the proposed framework, a case study is conducted with a wind turbine in the North Sea region. The fatigue damage analysis results highlight the necessity of regular inspections and maintenance for wind turbines to ensure their long-term service life and optimal performance. The framework is capable of considering a wide range of environmental factors and accounting for the inherent uncertainties and stochastic nature, providing a robust and comprehensive approach for estimating fatigue damage of wind turbines as well as other structures under long-term environmental conditions.

Influence of iridium-tantalum interlayers on the properties of ruthenium-iridium-tin metal oxide anodes

The intermediate layer could not only delay the passivation of the titanium matrix and prolong the service life of the metal oxide anode but also directly affected the crystallization behavior and crystal structure of its surface catalytic layer, thus affecting the electrocatalytic activity of the anode. This paper’s thermal decomposition method prepared the ruthenium iridium tin metal oxide anodes with different layers of the iridium-tantalum interlayer. XRD and SEM were used to characterize the prepared electrode. Cyclic voltammetry, linear sweep voltammetry, accelerated life test et al were used to analyze the electrode performance. The results showed that the electrochemical roughness increased, the volt-ampere capacity increased, the charge transfer resistance decreased, and the electrocatalytic activity increased after adding the iridium-tantalum intermediate layer. The anode stability was greatly improved, and the accelerated life was prolonged. Among them, the greatest changes were observed when one iridium-tantalum interlayer was added, with the electrochemical roughness increasing from 283.33 % to 616.67 %, the volt-ampere charge increasing from 25.11 mC·cm−2 to 47.24 mC·cm−2, the charge transfer resistance decreasing from 1.754 Ω·cm2 to 0.684 Ω·cm2, and the enhanced electrolysis lifetime extending from 288 h to 504 h. Both the anode’s service life and electrocatalytic performance were greatly increased.

Study on the Performance Optimization of Plant-Growing Ecological Concrete

The response surface regression model of plant-growing ecological concrete is established based on the factors of the water–binder ratio, fly ash content, and design porosity, with 28-day compressive strength, connectivity porosity, and pH value as response variables. Based on optimizing the mix proportion with the regression model, different dosages of acetic acid are used as excitation agents to increase the compressive strength and reduce the alkalinity of plant-growing ecological concrete to enhance its service life and vegetation performance. The results show that the compressive strength of plant-growing ecological concrete with a water–binder ratio of 0.3, a fly ash content of 26%, and a design porosity of 22% was 10.32 MPa, the connectivity porosity was 20.00%, and the pH value was 11. After the addition of acetic acid at 0.4% of the mass of the cementitious material, the compressive strength increased by 40.29%, and the pH value decreased by 6.33%. This study proposes a cost-effective means and provides data support for the engineering application of plant-growing ecological concrete.

Fabrication and evaluation of high-entropy alloy reinforced Fe bond diamond tool

Metal-bond diamond tools find extensive use in the grinding of hard and brittle materials. The mechanical properties and wear resistance of the metal bond are crucial for the grinding performance and life of diamond tools. However, Fe bonds exhibit insufficient hardness, strength, and wear resistance, and existing reinforcement methods are difficult to simultaneously improve the self-sharpness and wear resistance of diamond tools, leading to a trade-off between grinding performance and life. In this paper, a novel AlCoCrFeNi HEA particles reinforced Fe (HEA/Fe) alloy was proposed as the bond for diamond tool to address the aforementioned issue. The work considers materials science fundamentals related to the element diffusion mechanism and particle reinforcement mechanism in the design of the HEA/Fe bond. Through the mutual element diffusion between the HEA and Fe matrix, expanded HEA regions are formed, which not only enhance the strength and overall wear resistance of the HEA/Fe bond but also generate a composite structure with varying hardness. The distinct mechanical properties of composite structure form wear-resistant and non-wear-resistant areas within the diamond tool, effectively balancing the challenging trade-off between wear resistance and self-sharpness. This allows HEA/Fe bond diamond tools to achieve both prolonged service life and high grinding performance. The results reveal that the HEA/Fe bond diamond tool exhibits a lower grinding force, a superior surface finish, and a nearly equivalent grinding ratio compared to the commercial Fe-based alloy bond diamond tool in grinding of sapphire.

Integrating Fiber Sensing for Spatially Resolved Temperature Measurement in Fuel Cells

Fiber optic sensors integrated into fuel cell stacks have the potential to significantly enhance the temperature control and health monitoring of fuel cells. Inhomogeneous loading, both within individual cells and across different cells in a stack, leads to the formation of local hotspots that accelerate aging and degrade performance. This study investigates the behavior and feasibility of incorporating polyimide-coated optical fiber sensors into bipolar plates for precise and spatially resolved temperature monitoring. The sensor is successfully integrated into a single cell of a fuel cell stack and positioned on the bipolar plate in direct contact with the membrane electrode assembly. Pre-tests are conducted to thoroughly evaluate the technical properties of the fiber in relation to specific cell requirements. Additionally, a physical prototype featuring the sensor is developed and employed to validate its effectiveness under realistic operating conditions. The temperature measurement obtained via the fiber exhibits a continuous profile throughout the entire length, covering both the active area and distributor region of the cell. Throughout the entire 60 min test period, the measuring system provides continuous and uninterrupted temperature measurements, encompassing the start of the stack, the heating phase, the subsequent stable operating point, and the cooling phase. However, some technical challenges have been identified, as mechanical pressure exerted on the fiber influences the measured temperature. While this work demonstrates promising results, further advancements are necessary to address inhomogeneous loading within fuel cells and hotspot mitigation. The precise monitoring of temperature distribution enables early detection of potential damage, facilitating timely interventions to improve the service life and overall performance of fuel cells.

On site data gathering by a collaborative network to assess durability, reliability, service life, and maintenance performance

On site data gathering by a collaborative network to assess durability, reliability, service life, and maintenance performance

Temperature rise simulation and thermal strain study of ring main unit

The internal structure of the Ring Main Unit is compact, the gap between the components is limited, and the heat dissipation performance is poor. Working in a high temperature environment for a long time will lead to structural deformation and aging, which will affect the service life and insulation performance, and may even lead to electrical accidents. In order to better solve such problems, this study uses the finite element multi-physics coupling simulation model. According to the characteristics of the Ring Main Unit, the internal components and the body are simplified, the calculation amount is reduced and the simulation accuracy is improved. In ANSYS Workbench, through the coupling calculation of steady-state heat and transient heat, the temperature distribution of the Ring Main Unit circuit under the condition of external temperature is obtained. The temperature change and distribution are further used for the thermal stress and strain analysis of the structural field to obtain the thermal strain distribution map of the conductive circuit under the influence of temperature, which provides theoretical support for the internal structure design of the new Ring Main Unit.

Reduction of hydrogen absorption into materials of membrane electrode assemblies in hydrogen generators

Objectives. To investigate the possibility of preventing hydrogen absorption into the functional structural materials of hydrogen-generating membrane electrode assemblies based on porous nickel, carbon black, and reduced graphene oxide with platinum–nickel and palladium–nickel nanoparticles.Methods. The hydrogen absorption into materials of membrane electrode assemblies of alkaline electrolyzers was evaluated using an electrolyzer with variable temperature, reagent feed rate, and gas content.Results. The study established the need to use reduced graphene oxide, in order to reduce hydrogen absorption and degradation of hydrogen-generating membrane electrode assemblies.Conclusions. The service life test results and performance of the designed variants of prototypes of membrane electrode assemblies with nanostructured electrodes based on reduced graphene oxide, preventing hydrogen absorption into functional materials and their degradation, demonstrated the creation of hydrogen generators with high energy efficiency shows potential.

Machine learning prediction of corrosion rate of steel in carbonated cementitious mortars

Corrosion rate (i.e., corrosion current density), a crucial kinetic parameter for predicting and modeling service-life performance of reinforced concrete structures, can be estimated using empirical and physics-based models. However, existing corrosion rate predictive models have limited applicability and lowered accuracy to complex scenarios when the material composition and corrosion conditions are substantially varied. In this work, machine learning approach is explored to predict the corrosion rate of steel based on a comprehensive experimental dataset. To consider the wide variation of binder composition and corrosion environment in marine concrete, the experiment involves a broad range of mixture design parameters and relative humidity levels. The result shows that electrical resistivity is the most relevant factor to corrosion rate, and its relationship with corrosion rate is highly dependent on the chloride-to-hydroxide concentration ratio ([Cl−]/[OH−]). Of the various machine learning algorithms tested, support vector regression demonstrates the highest predictability for corrosion rate. Using the feature selection method, electrical resistivity, pore solution composition ([Cl−]/[OH−]), cement proportion, and corrosion potential are identified as the major related features for corrosion rate prediction of steel in carbonated cementitious mortars. The results demonstrate that machine learning is a promising tool for predicting the corrosion rate of steel embedded in cementitious mortars.

Non-Contact Measurement of Pregnant Sows’ Backfat Thickness Based on a Hybrid CNN-ViT Model

Backfat thickness (BF) is closely related to the service life and reproductive performance of sows. The dynamic monitoring of sows’ BF is a critical part of the production process in large-scale pig farms. This study proposed the application of a hybrid CNN-ViT (Vision Transformer, ViT) model for measuring sows’ BF to address the problems of high measurement intensity caused by the traditional contact measurement of sows’ BF and the low efficiency of existing non-contact models for measuring sows’ BF. The CNN-ViT introduced depth-separable convolution and lightweight self-attention, mainly consisting of a Pre-local Unit (PLU), a Lightweight ViT (LViT) and an Inverted Residual Unit (IRU). This model could extract local and global features of images, making it more suitable for small datasets. The model was tested on 106 pregnant sows with seven randomly divided datasets. The results showed that the CNN-ViT had a Mean Absolute Error (MAE) of 0.83 mm, a Root Mean Square Error (RMSE) of 1.05 mm, a Mean Absolute Percentage Error (MAPE) of 4.87% and a coefficient of determination (R-Square, R2) of 0.74. Compared to LviT-IRU, PLU-IRU and PLU-LviT, the CNN-ViT’s MAE decreased by more than 12%, RMSE decreased by more than 15%, MAPE decreased by more than 15% and R² improved by more than 17%. Compared to the Resnet50 and ViT, the CNN-ViT’s MAE decreased by more than 7%, RMSE decreased by more than 13%, MAPE decreased by more than 7% and R2 improved by more than 15%. The method could better meet the demand for the non-contact automatic measurement of pregnant sows’ BF in actual production and provide technical support for the intelligent management of pregnant sows.

A Lifetime Prediction Method of Pressured Gas Polyethylene Pipes by Strain-Hardening Modulus and Tensile Test

In recent years, polyethylene (PE) pipes have been widely utilized for urban natural gas transmission. However, as the use of high-density polyethylene (HDPE) pipes increases, their service life and long-term performance assessment have become one of the most significant issues to be addressed. There has been a ton of studies on PE pipe life prediction techniques both domestically and internationally, but very little has been carried out on PE pipe life prediction in actual gas working environments with varying acid and alkaline levels. This experiment accelerates the aging of PE pipes using acid and alkaline corrosion immersion experiments to determine the lifespan of PE pipes. This study aims to investigate the performance changes of HDPE under strong, weak, and neutral corrosion conditions using corrosion solutions with PH values of 1, 5, and 8, to propose the impact of corrosion caused by various acids and alkalies on the HDPE aging life for natural gas, and to develop a mathematical model between the aging life of polyethylene and the PH values of acid and alkali corrosion solutions. The studies involved soaking and corroding HDPE pipes with various acidity and alkalinity chemicals to speed up the aging process, and then the tensile test was used to determine the mechanical characteristics of the aged PE pipes. Based on our findings, the empirical equation between acidity and service life of PE pipes is obtained by the mathematical fitting method, and a life prediction model of buried city gas HDPE pipes is proposed. The actual life of the aged pipes is determined by the relationship between strain-hardening (SH) modulus and aging time. The findings demonstrate that the service life of PE pipes changes with different levels of acidity and alkalinity: 1.872 days, 1060.507 days, and 1128.58 days following corrosive solution-accelerated aging with solution acidities of PH1, PH5, and PH8, respectively. The life prediction method applies to various plastic pipes in comparable environments as well as HDPE city gas pipes that are subject to acid and alkali corrosion forces.

Curing Mechanisms of Polymeric Nano-Copolymer Subgrade.

The mechanical properties of the subgrade have a significant impact on the service life and pavement performance of the superstructure of pavement. By adding admixtures and via other means to strengthen the adhesion between soil particles, the strength and stiffness of the soil can be improved to ensure the long-term stability of pavement structures. In this study, a mixture of polymer particles and nanomaterials was used as a curing agent to examine the curing mechanism and mechanical properties of subgrade soil. Using microscopic experiments, the strengthening mechanism of solidified soil was analyzed with scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), Fourier infrared spectroscopy (FTIR), and X-ray diffraction (XDR). The results showed that with the addition of the curing agent, small cementing substances on the surface of soil minerals filled the pores between minerals. At the same time, with an increase in the curing age, the colloidal particles in the soil increased, and some of them formed large aggregate structures that gradually covered the surface of the soil particles and minerals. By enhancing the cohesiveness and integrity between different particles, the overall structure of the soil became denser. Through pH tests, it was found that the age had a certain effect on the pH of solidified soil, but the effect was not obvious. Through the comparative analysis of elements in plain soil and solidified soil, it was found that no new chemical elements were produced in the solidified soil, indicating that the curing agent does not have negative impacts on the environment.