Search results

This paper aims to investigate the bond strength of metakaolin-based geopolymer mortar with cement mortar.

The mortar-mortar bond strength is assessed by slant shear and split tensile tests; pure shear strength is evaluated by Mohr’s criterion for result validation. Metakaolin-based geopolymer mortar is cast over the cured cement mortar specimen with two levels of surface roughness: smooth or grooved interface. The influence of the alkaline solution to metakaolin ratio on geopolymer bond strength is studied. Compressive strength, ultrasonic pulse velocity, permeability and flow table tests are also performed.

The paper’s findings are highlighted as follows: (1) strong mortar-mortar bond properties achieved for geopolymer mortar in all tests and conditions and validated by Mohr’s criterion and pure shear, (2) a lower alkaline solution to metakaolin ratio achieves higher bond strength to Portland cement mortar and (3) geopolymer mortar has higher compressive strength and ultrasonic pulse velocity than cement mortar at all curing ages; additionally, it is more flowable and less permeable.

The full replacement of Portland cement with metakaolin, a more sustainable cementitious material, will contribute to the decarbonization of the construction industry.

Limited research has been carried out on the bond strength of metakaolin-based geopolymer mortar to Portland cement mortar. Also, computing the pure shear using Mohr’s circle criterion of metakaolin-based geopolymer to validate the results can be considered original.

The stress state near the notch affects fatigue damage directly, but quantifying the stress field is difficult. The purpose of this study is to provide a mathematical description method of the stress field near the notch to achieve a reliable assessment of the fatigue life of notched specimens.

Firstly, the stress distribution of notched specimens of different materials and shapes under different stress levels is investigated, and a method for calculating the stress gradient impact factor is presented. Then, the newly defined stress gradient impact factor is used to describe the stress field near the notch, and an expression for the stress at any point along a specified path is developed. Furthermore, by combining the mathematical expressions for the stress field near the notch, a multiaxial fatigue life prediction model for notched shaft specimens is established based on the damage mechanics theory and closed solution method.

The stress gradient factor for notched specimens with higher stress concentration factors (V60-notch, V90-notch) varies to a certain extent when the external load and material change, but for notched specimens with relatively lower stress concentration factors (C-notch, U-notch, stepped shaft), the stress gradient factor hardly varies with the change in load and material, indicating that the shape of the notch has a greater influence on the stress gradient. It is also found that the effect of size on the stress gradient factor is not obvious for notched specimens with different shapes, there is an obvious positive correlation between the normal stress gradient factor and the normal stress concentration factor compared with the relationship between the shear stress gradient factor and the stress concentration factor. Moreover, the predicted results of the proposed model are in better agreement with the experimental results of five kinds of materials compared with the FS model, the SWT model, and the Manson–Coffin equation.

In this paper, a new stress gradient factor is defined based on the stress distribution of a smooth specimen. Then, a mathematical description of the stress field near the notch is provided, which contains the nominal stress, notch size, and stress concentration factor which is calculated by the finite element method (FEM). In addition, a multiaxial fatigue life prediction model for shaft specimens with different notch shapes is established with the newly established expressions based on the theory of damage mechanics and the closed solution method.

Sufficient sample data are the necessary condition to ensure high reliability; however, there are relatively poor fatigue test data in the engineering, which affects fatigue life's prediction accuracy. Based on this, this research intends to analyze the fatigue data with small sample characteristics, and then realize the life assessment under different stress levels.

Firstly, the Bootstrap method and the principle of fatigue life percentile consistency are used to realize sample aggregation and information fusion. Secondly, the classical outlier detection algorithm (DBSCAN) is used to check the sample data. Then, based on the stress field intensity method, the influence of the non-uniform stress field near the notch root on the fatigue life is analyzed, and the calculation methods of the fatigue damage zone radius and the weighting function are revised. Finally, combined with Weibull distribution, a framework for assessing multiaxial low-cycle fatigue life has been developed.

The experimental data of Q355(D) material verified the model and compared it with the Yao’s stress field intensity method. The results show that the predictions of the model put forward in this research are all located within the double dispersion zone, with better prediction accuracies than the Yao’s stress field intensity method.

Aiming at the fatigue test data with small sample characteristics, this research has presented a new method of notch fatigue analysis based on the stress field intensity method, which is combined with the Weibull distribution to construct a low-cycle fatigue life analysis framework, to promote the development of multiaxial fatigue from experimental studies to practical engineering applications.

This study aims to enhance the understanding of fiber-reinforced polymer (FRP) applications in partially confined concrete, with a specific focus on improving economic value and load-bearing capacity. The research addresses the need for a more comprehensive analysis of non-uniform vertical strain responses and precise stress–strain models for FRP partially confined concrete.

DIC and strain gauges were employed to gather data during axial compression tests on FRP partially confined concrete specimens. Finite element analysis using ABAQUS was utilized to model partial confinement concrete with various constraint area ratios, ranging from 0 to 1. Experimental findings and simulation results were compared to refine and validate the stress–strain model.

The experimental results revealed that specimens exhibited strain responses characterized by either hardening or softening in both vertical and horizontal directions. The finite element analysis accurately reflected the relationship between surface constraint forces and axial strains in the x, y and z axes under different constraint area ratios. A proposed stress–strain model demonstrated high predictive accuracy for FRP partially confined concrete columns.

The stress–strain curves of partially confined concrete, based on Teng's foundation model for fully confined stress–strain behavior, exhibit a high level of predictive accuracy. These findings enhance the understanding of the mechanical behavior of partially confined concrete specimens, which is crucial for designing and assessing FRP confined concrete structures.

This research introduces innovative insights into the superior convenience and efficiency of partial wrapping strategies in the rehabilitation of beam-column joints, surpassing traditional full confinement methods. The study contributes methodological innovation by refining stress–strain models specifically for partially confined concrete, addressing the limitations of existing models. The combination of experimental and simulated assessments using DIC and FEM technologies provides robust empirical evidence, advancing the understanding and optimization of FRP-concrete structure performance. This work holds significance for the broader field of concrete structure reinforcement.

This research outlines the development and characterization of advanced composite materials and their potential applications in the aerospace industry for interior applications. Advanced composites, such as carbon-fiber-reinforced polymers and ceramic matrix composites, offer significant advantages over traditional metallic materials in terms of weight reduction, stiffness and strength. These materials have been used in various aerospace applications, including aircraft, engines and thermal protection systems.

The development of design of experiment–based hybrid aluminum composites using the stir-casting technique has further enhanced the performance and cost-effectiveness of these materials. The design of the experiment was followed to fabricate hybrid composites with nano cerium oxide (nCeO₂) and graphene nanoplatelets (GNPs) as reinforcements in the Al-6061 matrix.

The Al6061 + 3% nCeO2 + 3% GNPs exhibited a high hardness of 119.6 VHN. The ultimate tensile strength and yield strength are 113.666 MPa and 73.08 MPa, respectively. A uniform distribution of reinforcement particulates was achieved with 3 Wt.% of each reinforcement in the matrix material, which is analyzed using scanning electron microscopy. Fractography revealed that brittle and ductile fractures caused the failure of the fractured specimens in the tensile test.

The manufactured aluminum composite can be applied in a range of exterior and interior structural parts like wings, wing boxes, motors, gears, engines, antennas, floor beams, etc. The fan case material of the GEnx engine (currently using carbon-fiber reinforcement plastic) for the Boeing 7E7 can be another replacement with manufactured hybrid aluminum composite, which predicts weight savings per engine of close to 120 kg.

The development of hybrid reinforcements, where two or more types of reinforcements are used in combination, is also a novel approach to improving the properties of these composites. Advanced composite materials are known for their high strength-to-weight ratio. If the newly developed composite material demonstrates superior properties, it can potentially be used to replace traditional materials in aircraft manufacturing. By reducing the weight of aircraft structures, fuel efficiency can be improved, leading to reduced operating costs and environmental impact. This allows for a more customized solution for specific application requirements and can lead to further advancements in materials science and technology.

The purpose of this study is to describe the mechanism of single-crystal high-temperature creep deformation, predict the creep life more accurately and study the creep constitutive and lifetime models with microstructure evolution.

The mechanical properties of nickel-based single-crystal superalloy are closely related to the γ' phase. Creep tests under four different temperature and stress conditions were carried out. The relationship between creep temperature, stress and life is fitted by numerical method, and the creep activation energy is obtained. The creep fracture surface, morphology and evolution of strengthening phase (γ') and matrix phase (γ) during different creep periods were observed by scanning electron microscope. With the increase of creep temperature, the rafting time is advanced. The detailed morphology and evolution of dislocations were observed by transmission electron microscope (TEM).

With the increase of creep temperature, the rafting time is advanced. The detailed morphology and evolution of dislocations were observed by TEM. Dislocations are mainly concentrated in the γ channel phase, especially at high temperature and low stress.

A creep constitutive model based on the evolution of γ' phase size and γ channel width was proposed. Compared with the experimental results, the predicted creep life is within 1.4 times error dispersion band.

This paper aims to find the best method to predict the friction coefficient of textured 45# steel by comparing different machine learning algorithms and analytical calculations.

Five machine learning algorithms, including K-nearest neighbor, random forest, support vector machine (SVM), gradient boosting decision tree (GBDT) and artificial neural network (ANN), are applied to predict friction coefficient of textured 45# steel surface under oil lubrication. The superiority of machine learning is verified by comparing it with analytical calculations and experimental results.

The results show that machine learning methods can accurately predict friction coefficient between interfaces compared to analytical calculations, in which SVM, GBDT and ANN methods show close prediction performance. When texture and working parameters both change, sliding speed plays the most important role, indicating that working parameters have more significant influence on friction coefficient than texture parameters.

This study can reduce the experimental cost and time of textured 45# steel, and provide a reference for the widespread application of machine learning in the friction field in the future.

This paper aims to research the tribological and dynamic characteristics of aeroengine hybrid ceramic bearings through wear experiments and simulation analysis.

First, the authors carried out wear experiments on Si₃N₄–GCr15 and GCr15–GCr15 friction pairs through the ball-disc wear test rig to explore the tribological properties of their materials. Second, using ANSYS/LS-DYNA simulation software, the dynamic simulation analysis of hybrid bearings was carried out under certain working conditions, and the dynamic contact stress of all-steel bearings of the same size was simulated and compared. Finally, the change of the maximum contact stress of the main bearing under the change of load and rotation speed was studied.

The results show that the Si₃N₄–GCr15 pair has better tribological performance. At the same time, under the conditions of high speed and heavy load, the simulation analysis shows that the contact stress between the ceramic ball and the raceway of the ring is smaller than the steel ball. That is, hybrid bearings have better transient mechanical properties than all-steel bearings. With the speed increasing to 12,000 r/min, the maximum stress point will shift in the inner and outer rings.

In this study, the tribological and transient mechanical properties of Si₃N₄ material were comprehensively analyzed through wear experiments and dynamic simulation analysis, which provided a reference for the design of hybrid bearings for next-generation aeroengines.

Scholars mainly propose and establish theoretical models of cumulative fatigue damage for their research fields. This review aims to select the applicable model from many fatigue damage models according to the actual situation. However, relatively few models can be generally accepted and widely used.

This review introduces the development of cumulative damage theory. Then, several typical models are selected from linear and nonlinear cumulative damage models to perform data analyses and obtain the fatigue life for the metal.

Considering the energy law and strength degradation, the nonlinear fatigue cumulative damage model can better reflect the fatigue damage under constant and multi-stage variable amplitude loading. In the following research, the complex uncertainty of the model in the fatigue damage process can be considered, as well as the combination of advanced machine learning techniques to reduce the prediction error.

This review compares the advantages and disadvantages of various mainstream cumulative damage research methods. It provides a reference for further research into the theories of cumulative fatigue damage.

The multisource uncertainties, including material dispersion, load fluctuation and geometrical tolerance, have crucial effects on fatigue performance of turbine bladed disks. In view of the aim of this paper, it is essential to develop an advanced approach to efficiently quantify their influences and evaluate the fatigue life of turbine bladed disks.

In this study, a novel combined machine learning strategy is performed to fatigue assessment of turbine bladed disks. Proposed model consists of two modeling phases in terms of response surface method (RSM) and support vector regression (SVR), namely RSM-SVR. Two different input sets obtained from basic variables were used as the inputs of RSM, then the predicted results by RSM in first phase is used as inputs of SVR model by using a group data-handling strategy. By this way, the nonlinear flexibility of SVR inputs is improved and RSM-SVR model presents the high-tendency and efficiency characteristics.

The accuracy and tendency of the RSM-SVR model, applied to the fatigue life estimation of turbine bladed disks, are validated. The results indicate that the proposed model is capable of accurately simulating the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction strategy compared to RSM and SVR for fatigue analysis of complex structures.

The results indicate that the proposed model is capable of accurately simulate the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction compared to RSM and SVRE for fatigue analysis of turbine bladed disk.